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Effects of chronic ozone exposure and estimated flux on plant growth and conductance under field conditions Wright, Elaine Frances 1995

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EFFECTS OF CHRONIC OZONE EXPOSURE AND ESTIMATED FLUX ONPLANT GROWTH AND CONDUCTANCE UNDER FIELD CONDITIONSbyELAiNE FRANCES WRIGHTB.Sc., University of Toronto, 1982M.Sc., University of British Columbia, 1988A THESIS SUBMITI’ED iN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYmTHE FACULTY OF GRADUATE STUDIES(Department of Plant Science)We accept this thesis as conformingto the required standardTHE UNWERSJTY OF BRITISH COLUMBIAAPRIL, 1995© Elaine Frances Wright, 1995In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(Signature)Department of PThe University of British ColumbiaVancouver, CanadaDateDE-6 (2188)ABSTRACTOzone is a major air pollutant in many parts of the world. In the Lower Mainland ofBritish Columbia, occurrences of levels exceeding the B.C. Level A hourly objective of 50 ppb(parts per billion; nl L1) are frequent in suburban and rural areas. Growth and yield responses ofnumerous crop and tree species to ozone at daytime hourly average concentrations up to orexceeding 200 ppb have been described by others. However, this information has largely comefrom studies using open-top field chambers and using experimental protocols involving theenrichment of the ambient air with ozone that do not simulate true field conditions.The present studies used an open-field zonal air pollution system (ZAPS) to examine theeffects of low levels of ozone on the yield and growth dynamics of locally important cultivars ofpea (Pisum sativum L. cv. Puget), potato (Solanum tuberosum L. cv. Russet Burbank), bean(Phaseolus vulgaris L. cv. Galamore) and radish (Raphanus sativus L. cvs. Cherry Belle andFrench Breakfast), and of young Douglas-fir saplings (Pseudotsuga menziesii (Mirb.) Franco),under true field conditions. The experiments were conducted in 1986 and 1988-1990, and involvedexposures to 12 randomly assigned treatments characterized by stochastically varying degrees ofenrichment of the ambient ozone levels, together with ambient air control plots. In each year, thetreatments obtained in the ZAPS had unimodal concentration distributions that fitted the Weibulldistribution, and typical season-long diurnal average concentration profiles with mid-afternoonmaxima. Since the enrichment levels were proportional to the ambient ozone level, the ranges ofconcentrations achieved in the treatments varied from day to day and from year to year.For each species and season, the exposure treatments were summarized in terms ofseasonal daytime means and various cumulative exposure indices. However, comparisons of linearregressions of different growth variables with different indices led to standardization on the use ofthe D50 index, defined as the number of days per season or per harvest interval in which an hourlymean ozone concentration of 50 ppb was exceeded in any hour between 0900 and 2100 (PDT).Significant (p <0.05) negative linear regressions of yield with increasing D50 exposurewere found for all crops. The effects on final yield were reflected in significant decreases in theiidry weights of the total plant, stems and leaves, and in leaf areas, by the time of final harvest.However, during the earlier stages of growth, the effects were less marked and the regressionsfailed to reach significance, even at p <0.10.Three experiments with radish in 1989 revealed pronounced differences in response,depending on time of year. No significant effects of ozone were observed on either Cherry Belle orFrench Breakfast cvs. in the first experiment in June, but significant growth reductions wereobserved in the July-August and August-September plantings. The second planting experiencedthe highest exposures, but since the exposures during the first and third experiments werecomparable, the different responses observed suggest the influence of other environmental factorsrelated to time of year.In general the dynamics of crop growth were adversely affected by increased exposure,reaching significance at the later harvest intervals. Absolute growth rates of all crops and cultivarswere significantly reduced (p <0.05), but relative growth rates were more variable and theirreductions only reached significance at p <0.10, although in several cases significant increaseswere observed in the early stages ofgrowth.Overall, adverse effects on the growth of all of the crops were observed even though thedaytime hourly average ozone concentrations rarely exceeded 120 ppb. Differences between theresponses of the radish cultivars indicated that cv. Cherry Belle is more sensitive than cv. FrenchBreakfast. No consistent significant effects of ozone on the growth of Douglas-fir saplingsoccurred until the end of the second season of exposure (1989). By then, reductions in secondflush growth were observed. These effects were carried over into the following year, as revealedby reduced leader growth in early 1990. Although Douglas.fir has been described as relativelyinsensitive to ozone, these results suggest that long-term detrimental effects on growth may occur.In all species the relationships between many growth variables and exposure appeared to be nonlinear. However, although there were several cases in which non-linear Weibull or gammafunctions provided improvements in fit compared to simple linear regressions, there was noconsistent pattern of improvement.iiiSince indices such as D50 merely define exposure and do not necessarily reflect uptake ofozone by foliage, estimates of ozone flux were made during the experiments with radish andDouglas-fir. Whole plant porometry was used at intervals to detennine water vapourconductances, which were then used to estimate ozone fluxes over the preceding time period. Withradish, linear regressions of various growth measures using estimated flux (FLUXSUMC) as thedependent variable generally showed poorer fits than regressions based on the D5 0 index.However, with second flush growth of Douglas-fir, better fits were obtained with FLUXSUMCthan D50, reaching significance for weight and leaf area at the final harvest in 1989. To avoid theassumption that measured conductances accurately reflected conductances during the precedinginterval of days, estimates of ozone flux were also calculated based on multiple linear regressionsof conductance and several environmental variables. These estimates of flux (FLUXSUMM)employed the meteorological variables and ozone levels recorded throughout the exposure seasons.Although 75 percent of the comparisons with linear regressions based on the D50 index showedbetter fits with FLUXSUMM, only the regression for second flush weight at harvest 5 wassignificant. The present study therefore provides only limited support for the view that the use offlux estimates rather than exposure indices results in improvements in describing growth responsesto ozone.ivTABLE OF CONTENTSABSTRACT.TABLE OF CONTENTS vLIST OF TABLES viiiLIST OF FIGURES xiiLIST OF ABBREVIATIONS xviiACKNOWLEDGMENTS xviii1. INTRODUCTION 12. LITERATURE REVIEW 52.1 Air Quality 52.2 Air Pollutant Exposure 72.3 Effects of Ozone Exposure on Crop Growth 102.3.1 EFFECTS OF OZONE ON INJURY AND YIELD 102.3.2 EFFECTS OF OZONE ON BIOMASS PARTITIONING 172.3.3 EFFECTS OF OZONE ON THE DYNAMICS OF PLANTGROWTH 192.3.4 EFFECTS OF OZONE ON PHYSIOLOGICAL PROCESSES 222.4 Effects of Ozone on Trees 242.4.1 EFFECTS OF OZONE ON YIELD AND GROWTH 242.4.2 EFFECTS OF OZONE ON BIOMASS PARTITIONING 272.4.3 EFFECTS OF OZONE ON THE DYNAMICS OF TREEGROWTH 282.4.4 EFFECTS OF OZONE ON PHYSIOLOGICALPROCESSES 292.5 Air Pollutant Uptake 332.5.1 UPTAKE OF OZONE BY VEGETATION 332.5.2 EFFECTS OF OZONE ON DIFFUSiVE CONDUCTANCE 402.5.2.1 Crops 412.5.2.2Trees 443. MATERIALS AND METHODS 473.1 Experimental Design 473.2 Field Plots and Gas Delivery System 493.3 Ozone Monitoring and Control 513.4 Data Collection and Handling 533.5 1986 Field Experiment 533.5.1 FIELD PREPARATION 533.5.2 PLANT MATERIALS 533.5.3 CROP, PEST AND RODENT MANAGEMENT 533.5.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS 543.5.4.1 Pea 543.5.4.2Potato 543.6 1988 Field Experiment 553.6.1 FIELD PREPARATION 553.6.2 PLANT MATERIALS 563.6.3 CROP AND PEST MANAGEMENT 573.6.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS 573.6.4.1 Bean 573.6.4.2 Douglas-Fir 58V3.7 1989 Field Experiments.593.7.1 FIELD PREPARATION 593.7.1.1 Radish 593.7.1.2 Douglas Fir 593.7.2 PLANT MATERIALS 593.7.3 CROP AND PEST MANAGEMENT 603.7.3.lRadish 603.7.3.2 Douglas-Fir 603.7.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS 603.7.4.1 Radish 603.7.4.2 Douglas-Fir 613.7.5 PLANT GROWTH ANALYSIS iNDICES CALCULATIONS 623.7.6 CONDUCTANCE MEASUREMENTS 633.7.6.1 Radish 633.7.6.2 Douglas-Fir 633.7.7 OZONE FLUX ESTIMATION 653.8DataAnalysis 663.8.1 EXPOSURE STATISTICS 663.8.2 EXPOSURE-RESPONSE REGRESSION ANALYSES 673.8.3 FLUX-RESPONSE REGRESSION ANALYSES 683.8.4 ANALYSIS OF VARIANCE: RADISH EXPERIMENTS 704. RESULTS 714.1 Performance of the Zonal Air Pollution System 714.2 Pea 784.2.1 AIR QUALITY 784.2.2 TEMPORAL CHANGES TN GROWTH 794.2.3 EXPOSURE-RESPONSE RELATIONSHIPS 844.2.3.1 Simple Linear Regressions 844.2.3.2 Non-linear Regression Analyses 914.3 POTATO 964.3.1 AIR QUALITY 964.3.2 TEMPORAL CHANGES TN GROWTH 974.3.3 EXPOSURE-RESPONSE RELATIONSHIPS 974.3.3.1 Simple Linear Regression Analyses 974.3.3.2 Non-Linear Regression Analyses 1024.4 Bean 1054.4.1AIRQUALITY 1054.4.2 TEMPORAL CHANGES IN GROWTH 1054.4.3 EXPOSURE-RESPONSE RELATIONSHIPS 1074.4.3.1 Simple Linear Regression Analyses 1074.4.3.2 Non-Linear Regression Analyses 1134.5 Radish 1174.5.1 AIR QUALITY and ENVIRONMENTAL PARAMETERS 1174,5.2 ANALYSIS OF VARIANCE 1184.5.3 TEMPORAL CHANGES IN GROWTH 1214.5.3.1 Cherry Belle 1214.5.3.2 French Breakfast 1244.5.4 EXPOSURE-RESPONSE RELATIONSHIPS 1264.5.4.1 Simple Linear Regression Analyses 126vi4.5.4.2 Non-Linear Regression Analyses.1354.5.5 WHOLE PLANT CONDUCTANCE 1364.5.6 FLUX-RESPONSE RELATIONSHIPS 1444.6 Douglas-Fir 1494.6.1AIRQUALITY 1494.6.2 BASELiNE DATA 1988 1504.6.2.1 EXPOSURE-RESPONSE RELATIONSH[PS 1988 1514.6.3 EXPOSURE-RESPONSE RELATIONSHIPS 1989 1524.6.3.1 Simple Linear Regression Analyses 1524.6.3.2 Non-Linear Regression Analyses 1604.6.4 WHOLE-PLANT CONDUCTANCE 1604.6.5 FLUX-RESPONSE RELATIONSHIPS 1674.6.5.1 Models Using Actual Conductance Data 1674.6.5.2 Models Using Estimated Conductance Values 1705. DISCUSSION 1735.1 Performance of the ZAPS 1735.2 Exposure Indices and Response Models 1745.3. Effects of Ozone on Plants 1755.3.1 PEA 1755.3.2 POTATO 1775.3.3 BEAN 1795.3.4 RADISH 1815.3.5 DOUGLAS-FIR 1845.3.6 COMPARISONS BETWEEN THE CROPS AND TREESSUBJECT TO OZONE STRESS 1865.4 Effect of ozone on conductance 1885.5 Flux-response relationships 1916. SUMMARY 1937. REFERENCES 1968. APPENDIX 217viiLIST OF TABLESPAGETable 1. Summary of crop growth responses to ozone exposure. 21Table 2. Summary of tree growth responses to ozone exposure. 29Table 3. Summary often studies on the physiological response ofPicea abies toozone exposure. 33Table 4. Summary of the ozone exposure periods for three growing seasons. 51Table 5. Summary of the range of seasonal ozone exposures, M 12, TD, D5OCand SUM5O for all treatments at each Pea harvest. 78Table 6. Results of the simple linear regressions of pea cv. Puget total, stem, leafweight, area and number versus D5OC, for harvest 1 through 6. 85Table 7. Results of the simple linear regressions of pea cv. Puget bud ,flower andpod weight, and pod number versus D5OC for harvest 3 through 6. 87Table 8. Results of the simple linear regressions of pea cv. Puget marketable podnumber, total seed number, seed weight/seed number, seeds in each sizecategory and pea and pod fresh weights versus D5OC for harvest 6. 89Table 9. Seed distribution and relative proportion by size category for pea cv.Puget obtained at the final harvest. 90Table 10. Results of the simple linear regressions of pea AGR, RGR and RGRpWversus D5OC for 5 harvest intervals. The sign of the coefficient (b i)and p value are presented. 90Table 11. Summary of the range of seasonal ozone exposures, M12, TD, D5OCand SUM5O for all treatments for five Potato harvests in 1986. 96Table 12. Results of the simple linear regressions of potato cv. Russet Burbanktotal weight, and tuber weight versus D5 OC for harvests 1 through 5. 101Table 13. Results of the simple linear regressions of potato cv. Russet Burbanktuber number, marketable weight per tuber and tuber fresh weight versusD5OC at the final harvest. 101Table 14. Results of the simple linear regressions on relative growth rates betweenharvests intervals for potato AGR, RGR and RGRTUW versus D5OC.The sign of the coefficient (b1) and p value are presented. 102Table 15. Summary of the range of seasonal ozone exposures, M12, TD, D5OCSUMS 0 for bean over all treatments and six harvests. 105viiiTable 16. Results of the simple linear regressions of bean cv. Galamore total,stem, and leaf weight; leaf number and area versus D5OC for harvests4through6. 110Table 17. Results of the simple linear regressions of bean cv. Galamore forLAR versus D5OC, for harvests 4 through 6. 110Table 18. Results of the simple linear regressions of bean cv. Galamore podnumber, pod weight and harvest index versus D5OC, for harvests 4through 6. 111Table 19. Results of the simple linear regressions of bean cv. Galamore podgrowth variables versus D5OC, for the final harvest. 113Table 20. Summary of the range of seasonal ozone exposures, M12, TD, D5OC andSUM5O over all treatments in each radish experiment and harvest. 117Table 21. Summary of ANOVA results: F-values for the effects of ozone onradish weight, leaf number and area, and four ratios computed on a perplant basis for each harvest. 120Table 22. Summary of relative growth rates for total and hypocotyl weight andabsolute growth rate for each experiment by harvest interval andtreatment averaged separately for the controls and ozone enrichedtreatments for cvs. Cherry Belle and French Breakfast. 125Table 23. Results of the simple linear regressions on total and hypocotyl dryweight for radish versus D5OC for each cultivar, experiment andharvest. 127Table 24. Results of the simple linear regressions on leaf dry weight and leafarea for radish versus D5OC for each cultivar, experiment and harvest. 128Table 25. Results of the simple linear regressions on harvest index and leaf arearatio for radish versus D5OC for each cultivar, experiment and harvest. 129Table 26. Results of the simple linear regressions on specific leaf area and leafweight ratio for radish versus D5OC for each cultivar, experiment andharvest. 130Table 27. Results of the simple linear regressions on hypocotyl fresh weight forradish versus D5OC for harvest 4, for two cultivars and threeexperiments. 135Table 28. Results of the simple linear regressions on growth rates betweenharvests intervals for radish AGR, RGRTW and RGRH1r versus D5OC.The sign of the coefficient (b 1 )and p value are presented. 136ixTable 29. Whole plant conductance measurements of radish plants for 2 cultivarsand two experiments. Values are means±standard errors (n=40). 137Table 30. Correlation analysis between ozone and a number of environmentalvariables measured concurrently over 4 measurement days and 2experiments. Values are the Pearson product moment correlationcoefficients and the sign indicates the type of association betweenvariables. 143Table 31. Simple linear regressions of conductance on various environmentalvariables for radish cv. Cherry Belle in two experiments. 144Table 32. Simple linear regressions of conductance on various environmentalvariables for radish cv. French Breakfast in two experiments. 145Table 33. Simple linear regressions of porometer radish total weight versus fluxindices and D5OC for 2 cultivars and 2 experiments. 146Table 34. Simple linear regressions of porometer radish hypocotyl weight versusflux indices and D5OC for 2 cultivars and 2 experiments. 147Table 35. Seasonal mean pollutant concentrations (ppb) in 1988 for eachtreatment, for Douglas fir between 178-269 Julian days. 149Table 36. Seasonal mean pollutant concentrations (ppb) in 1989 for eachtreatment, for Douglas fir between 159 - 257 Julian days. 149Table 37. Summary of the range of ozone exposures, M12, TD, D5OC andSUM5O for Douglas fir across all treatments for each harvest in 1989. 150Table 38. Baseline data for DougIasfir seedlings, planted in 1988. 151Table 39. Compilation of simple linear regression results from the seven treeharvests in the 1989 growing season of total weight and stem volumeratios versus D5OC. 153Table 40. Results of the simple linear regressions between totalweight of the second flush and leader length versus D5OC. 156Table 41. Results of the simple linear regressions between Douglas-firRGRSV versus D5OC, for 7 harvests. 158Table 42. Conductance of Douglas-fir over seven measurement periods. Valuesare means and standard errors (N=67). 161Table 43. Correlation analysis between ozone and a number of environmentalvariables measured concurrently on four days during which conductancewas measured. Values are Pearson product moment correlationxcoefficients and the sign indicates the type of association betweenvariables. 166Table 44. Simple linear regressions of mean conductance for a number of onenvironmental variables, over four measurement periods during 1989. 166Table 45. Simple linear regressions of total weight, total weight of first flushand total new weight of porometer tree variables versus D5OC andFLUXSUMC. 168Table 46. Simple linear regressions of total weight of the second flush,leaf area and leaf weight of the second flush and leader lengthversus D5OC and FLUXSUMC. 169Table 47. Simple linear regressions of total weight, ratios, treeheight and stem volume versus D5OC and FLUXSUMC. 170Table 48. Results of the simple linear regressions between tree growthvariables versus FLUXSUMM and D5OC. 172xiLIST OF FIGURESPAGEFigure 1. Schematic representation of the conceptual model of plant response toozone stress. 34Figure 2. General layout of the Zonal Air Pollution System. Numbers on the blocksrefer to the densities of orifices in the individual plots in 1986. 48Figure 3. Zonal Air Pollution System manifold layout. 50Figure 4. Modified whole plant porometer, adapted from Livingston et al. (1984)64Figure 5. Sampling locations A and B within the treatment block used for evaluatingthe horizontal ozone distributions in 1986. 72Figure 6 Horizontal ozone distributions over sampling locations A and B, 40 cm abovethe soil. The hatched areas indicate parts of the treatment plots from whichplant sampling took place. The manifold over both locations had singly spacedorifices with orifices in 4 to the right and in 3’s to the rear. 73Figure 7. Vertical profile of mean ozone concentrations averaged over 2 locations(± standard deviation; n=30), in block 2 for 1986.=> = manifold height(cm). 75Figure 8. Season-long diurnal profile of average ozone concentrations achievedfor three enriched treatments and one ambient air plot in 1989. 76Figure 9. Season-long frequency distributions of one hour mean concentrations in10 ppb ranges for all treatments in 1989. 77Figure 10. Total dry weight of pea cv. Puget harvested between June 26 and July 31in 1986. Each point represents a treatment mean of 8 sub-samples (plants).Treatments 13 and 14 are the ambient air controls 80Figure 11. Number of pods per plant of pea cv. Puget harvested between June 26and July 31 in 1986. Each point represents a treatment mean of 8sub-samples (plants). Treatments 13 and 14 are the ambient aircontrols. 81Figure 12. Pod weight of pea cv. Puget harvested between June 26 and July 31in 1986. Each point represents a treatment mean of 8 sub-samples(plants). Treatments 13 and 14 are the ambient air controls. 82Figure 13. Mean absolute growth rate of pea cv. Puget harvested between June 26and July 31 in 1986. Data used in the plots are obtained from fittedcurves of quadratic regressions of weight versus time. Each pointrepresents a treatment mean of 8 sub-samples. 83xiiFigure 14. Total dry weight of pea, cv. Puget harvested between June 26 andJuly 31 in 1986 versus D5OC, for six harvests. Each point representsa treatment mean of 8 sub-samples (plants). 86Figure 15. Pea dry weight/Seed number for pea, cv. Puget for the final harvestJuly 31, 1986 versus D50C. Each point represents a treatment meanof 8 sub-samples (plants). 88Figure 16 a-e. Relationship between a-c) total dry weight; d) pod dry weight and e) seednumber for pea, cv. Puget D5OC. Each point represents a treatment meanof 8 sub-samples (plants). The prediction lines were based on modelsdescribed in the text. 92Figure 17. Non-linear relationships between pod fresh weight (harvest 6) and D5OCfor pea cv. Puget. The prediction lines were based on models describedin the text. 95% confidence limits are shown for the linear model. 93Figure 18. Non-linear relationships between pea fresh weight (harvest 6) and D5OCfor pea cv. Puget. The prediction lines were based on models describedin the text. 95% confidence limits are shown for the linear model. 95Figure 19. Total dry weight for potato cv. Russet Burbank harvested between June 9and August 17 in 1986. Each point represents a treatment mean of 3-6sub-samples (plants). Treatments 13 and 14 are the ambient aircontrols. 98Figure 20. Absolute growth rate of potato cv. Russet Burbank harvested betweenJune 9 and August 17 in 1986 for 4 harvest intervals. Data used in thegraphs are derived from fitted curves using quadratic regressions for eachtreatment of total weight versus time. 99Figure 21. Scattergrams of total weight for potato cv. Russet Burbank harvested betweenJune 9 and August 17 in 1986 versus D5OC, for 5 harvests. Each pointrepresents a treatment mean of 3-6 sub-samples (plants). 100Figure 22 Non-linear relationships between total weight and tuber weight of potato cv.Russet Burbank (harvest 5) and D5OC. Each point represents a treatmentmean of 6 sub-samples (plants). The prediction lines were based on theWeibull models outlined in the text. 103Figure 23. Non-linear relationships between tuber fresh weight (harvest 5) andD5OC or potato cv. Russet Burbank. The prediction lines were basedon models described in the text. 95% confidence limits are shown forthe linear model. 104Figure 24. Total dry weight for bean cv. Galamore harvested between June 20 andAugust 9 in 1988. Each point represents a treatment mean of 16sub-samples (plants). Treatments 13 and 14 are the ambient airxiiicontrols. 105Figure 25. Number of pods for bean cv. Galamore harvested between June 20 andAugust 9 in 1988. Each point represents a treatment mean of 16sub-samples (plants). Treatments 13 and 14 are the ambient aircontrols. 108Figure 26. Pod dry weight for bean cv. Galamore harvested between June 20 andAugust 9 in 1988. Each point represents a treatment mean of 16sub-samples (plants). Treatments 13 and 14 are the ambient aircontrols. 109Figure 27. Scattergrams of pod dry weight for bean cv. Galamore versusD5OC, for harvests 4 through 6. Each point represents a treatmentmean of 16 sub-samples (plants). 112Figure 28a-e. Relationship between a-c) total dry weight and d-e) pod dry weight forbean cv. Galamore and ozone exposure expressed as D5OC, for lateharvests. The prediction lines were calculated from the regressionmodels described in the text. 115Figure 29a-c. Relationship between a) pod weight/pod number; b) pod fresh weight andc) marketable pod number for bean, cv. Galamore and ozone exposureexpressed as D5OC, for harvest 6. The prediction lines were calculatedfrom the regression models described in the text. 116Figure 30a-c. Mean a) air temperature; b) solar radiation and c) wind speed for eachof the three radish experiments in 1989. Meteorological data represent meanscomputed over the 29 day exposure period, by hour for each Expt. 119Figure 31. Total dry weight for radish cv. Cherry Belle experiment 2, harvestedbetween July 27 and August 9 in 1989. Each point represents atreatment mean of 5-10 sub-samples (plants). Treatments 13,14 and 15 are the ambient air controls. 122Figure 32. Total dry weight for radish cv. Cherry Belle experiment 3, harvestedbetween September 1 and September 13 in 1989. Each pointrepresents a treatment mean of 5-10 sub-samples (plants). Treatments13, 14 and 15 are the ambient air controls. 123Figure 33a-d. Scattergrams of total dry weight for radish cv. Cherry Belle versusD5OC, for harvests 1 through 4 in experiment 2. Each point representsa treatment mean of 5-10 sub-samples (plants). 131Figure 34a-d. Scattergrams of total dry weight for radish cv. Cherry Belle versusD5OC, for harvests 1 through 4 in experiment 3. Each point representsa treatment mean of 5-10 sub-samples (plants). 132Figure 35a-d. Scattergrams of hypocotyl dry weight for radish cv. Cherry Belle versusxivD5OC, for harvests 1 through 4 in experiment 2. Each point representsa treatment mean of 5-10 sub-samples (plants). 133Figure 36a-d. Scattergrams of hypocotyl dry weight for radish cv. Cherry Belle versusD5OC, for harvests 1 through 4 in experiment 3. Each point representsa treatment mean of 5-10 sub-samples (plants). 134Figure 37a-d Mean conductance versus the number of days after planting for radishcvs. Cherry elle and French Breakfast, for one ambient air control andfour ozone-enriched treatments in experiments 2 (a-b)and 3 (c-d). 138Figure 38a-d. Mean conductance versus mean hourly ozone concentration (ppb) forradish cvs. Cherry Belle and French Breakfast, for one ambient aircontrol and four ozone-enriched treatments in experiments 2 (a-b)and 3 (c-d). 139Figure 39a-d. Mean conductance versus mean hourly ozone concentration (ppb) in theprevious hour for radish cvs. Cherry Belle and French Breakfast, for oneambient air control and four ozone-enriched treatments in experiments2 (a-b) and 3 (c-d). 140Figure 40a-d. Mean conductance versus time of day (hour) for radish cv. Cherry Bellefor one ambient air control and four ozone-enriched treatments, fourmeasurement periods (a-d) in experiment 2. 141Figure 41a-c. Three-dimensional response surfaces across five harvests of total weightof the second flush of Douglas-fir versus D50 expressed as a)cumulative (1988 and 1989 exposures), b) seasonal and c) incrementalindices. The response surfaces were generated using Sygraph, withdistance weighted least squares. Outliers are highlighted- all related toTreatment 15 (one of the ambient air controls). 154Figure 42a-c. Three-dimensional response surfaces across five harvests of total weightof the second flush of Douglas-fir versus D50 expressed as a)cumulative (1988 and 1989 exposures), b) seasonal and c) incrementalindices, with the outliers noted in Figure 41 deleted. 155Figure 43a-e. Total dry weight of second flush of Douglas-fir versus D5OC in harvests4-6; a, b and c) complete data sets (n= 15) showing outliers; d and e)data sets with outliers deleted. Linear regressions are described inTable 40 and the text. Non-linear regressions are described inSection 4.6.3.2 157Figure 44a-f. Leader length of Douglas-fir versus D5OC in harvests 5 and 6 (1989)and 7 (1990); a, b and c) are complete data sets (n= 15 in harvests 5and 6; n= 14 in harvest 7), showing outliers; d, e and f) are data setswith the outliers deleted. Linear regressions are described in Table 40and the text. Non-linear regressions are described in Section 4.6.3.2. 159xvFigure 45a-b. a) Average daily conductance measurements for Douglas-fir usinga whole-plant porometer over 7 sampling dates in 1989; b) Meanhourly diurnal conductance for Douglas fir over 4 sampling dates in1989 for five treatments. 162Figure 46a-b. Mean conductance for Douglas-fir measured over four samplingdates versus ozone concentration and solar radiation in the previoustwo hours. 164Figure 47a-c. Seasonal mean ozone concentration in the previous two hours versusa) mean solar radiation, b) absolute humidity and c) soil temperature forDouglas fir. 165xviLIST OF ABBREVIATIONSASFJUD2H arc sin transformation of the ratio of final stem volume vs. initial stem volumeARSTW arc sin transformation of the ratio of total new weight-total weight/total weightBN bud numberBW bud weightCSTRs Continuously stirred reactorsEUROTC European Open-top chamber ProgrammeFD2H final stem volumeFHT final tree heightFN flower numberFW flower weightHI harvest indexHTERM leader lengthID2H initial stem volumeLAR leaf area ratioLWR leaf weight ratioLN leaf numberLA leaf areaNCLAN National Crop Loss Assessment NetworkNAR net assimilation rateNO nitric oxideNO2 nitrogen dioxideNOx oxides of nitrogen03 ozoneOTCs Open-top chambersPAD Pollutant absorbed dosePDT Pacific daylight timePGA Plant growth analysisPN pod numberPW pod weightra aerodynamic resistanceboundary layer resistanceresidual, internal or mesophyllic resistancer5 stomatal resistancert resistance to gas transferRGR relative growth rateSTD stem diameterSW stem weightTN tuber numberTUW tuber weightTW total weightTW1 total weight first flushTW2f total weight second flushTWnew total weight newUS United StatesSee Appendix 2 for a description of the exposure indices.xviiACKNOWLEDGMENTSMy research supervisor, Dr. V. C. Runeckles, gave freely of his time, energy andresources in all stages of this study. His support and encouragement during the final writing of thethesis are gratefully acknowledged and sincerely appreciated.My other committee members, Drs. G.W. Eacton, P.A. Jolliffe and M.D. Novak, alsoprovided assistance and helpful feedback, particularly during the preliminary stages of the studyand the final stages of thesis preparation. The help of Derek White, Christia Roberts and PeterGamett in the experimental set-up and maintenance of the instrumentation and equipment isacknowledged with my sincere thanks.I am also indebted to my mother for her interest, encouragement and willingness to assistin many ways. The encouragement of my sisters, Sheryl and Janice, and my father, Gordon, is alsoappreciated. The help and support provided by my childrens’ main caregiver, Lisa Sanders, duringthe preparation of the thesis is sincerely appreciated.Above all I would like to express my deepest gratitude to my husband, Philip; he has beena source of encouragement, an advisor and supportive partner throughout. My thanks also go tomy children, Alexandra, Ashley and Damon, for their understanding and patience while theirmother was otherwise pre-occupied!xviii1. INTRODUCTIONOzone, an important phytotoxic component of photochemical smog, is a major airpollutant in many parts of the world (National Research Council (US.), 1992). Previous researchhas shown that exposure to ozone results in a significant decrease in yield (Heck et al., 1982,1983), delayed flowering (Amundson et al., 1986), and alterations in biomass partitioning (Cooleyand Manning, 1987) of crops and other vegetation.Historically, studies investigating the effects of exposure to ozone on plant growth wereprimarily limited to those conducted in controlled environments. Through the efforts of theNational Crop Loss Assessment Network (NCLAN) in the United States, and more recently theEuropean Open-top Chamber Programme (EOTC), the relationships between seasonal ozoneexposures and yields of a number of important crops were developed from field experiments usingopen-top chambers (OTCs) (Heagle et al., 1988; Mathay, 1988).It has been suggested that the type of exposure methodology employed in theseexperiments with OTCs falls short of resembling actual field conditions (Younglove et al., 1994),resulting in a modification of the plant’s microclimate such that the effects on final yield areoverestimated (Grunhage and Jager, 1994). In addition to the chamber effects, the experimentalprotocols used in these studies have been criticized because the 24-h average treatment profiles didnot resemble those of ambient air; the concentration distributions were frequently bimodal ascompared to the unimodal distribution observed in ambient air; and the same relative treatmentswere applied daily, as a result of which there was no simulation of the time-series typical ofnaturally occurring exposures (Runeckles and Wright, 1988). Information regarding the effects of03 on growth and development in crops and tree seedlings under true field conditions is limited.Most of the information available on effects of 03 on vegetation concerns effects on yieldrather than on the dynamics of growth leading to the final yield, yet plants do not appear to beequally sensitive to ozone exposure at all stages of their life cycle (Krupa and Manning, 1988).Hence the pattern and timing of exposure with regards to the crops’ stage of development will1determine the effects on growth and development (Krupa and Teng, 1982; Krupa and Kickert,1987). Although there is a lack of information about the effects of pollutant exposure at differentdevelopmental stages, the concept is supported by a recent re-analysis of field data on four cropswhere the occurrence of ozone during flowering, early fruit set and pod development had thegreatest influence on yield (Younglove et al., 1994).To create a phytotoxic event, ozone in the ambient air must be taken up by the plant.Gaseous diffusion through the stomata is the primary route of ozone entry. Exposure and dose areterms frequently encountered in the air pollution literature, and are often used interchangeably.However, pollutant exposure, defined as a function of the ambient concentration and time, is notnecessarily representative of what diffuses into the leaf. The concept of “effective dose” of apollutant introduced by Runeckles (1974) refers to the ambient pollutant concentration absorbed bythe plant that is involved in causing a response. Effective dose may be estimated by the fluxdensity of the pollutant towards the plant (Grunhage et al., 1993). In spite of this distinctionbetween exposure and dose, most of the studies of effects of pollutants have focused onestablishing empirical relationships between growth and various measures of pollutant exposurebased on external concentrations. The use of exposure rather than true dose in response modelsprobably contributes to difficulties in making comparisons of plant responses across experiments(Unsworth, 1982; GrUnhage and Jager, 1994). The earliest study that attempted to use an estimateof flux density as the independent variable in ozone response regressions was that conducted onfield tobacco in Southern Ontario by Mukanimal (1965). Recently, Grunhage et al. (1993) haveused micrometeorological methods and concentration gradient measurements to estimate verticalflux densities of ozone to tobacco. When regressed against foliar injury, the use of flux densitiesresulted in improvement over the use of simple measures of exposure.Reich (1987) has pointed out that measurements of stomatal conductance assist inexplaining the differences in response between different species and exposure conditions. Thus,models incorporating stomatal conductance measurements may provide a means for extrapolatingresults for a particular crop over a wider range of enviromnental conditions and genotypes and may2assist in establishing the level of uncertainty of models based on exposure-response versus truedose-response. However, to date, few experiments have measured ozone uptake in conjunctionwith measurements on growth in the field. Furthermore, a shortcoming of this approach is the lackof information concerning internal resistance(s) to ozone, which some researchers believe to be animportant factor influencing plant response (Tingey et a!., 1973; Taylor et a!., 1982; Runeckles,1992).An alternative to the direct determination of stomatal conductance involves the use ofconventional micrometeorological measurements. This approach has been used by Leuning et al.(1979b) for field grown corn (Zea mays L.). However, the method employed could not distinguishbetween flux of ozone to the soil and flux to the vegetation, thus liniiting its use in assessing plantuptake-response relationships (Runeckles, 1992).In view of the criticisms of studies conducted using chambers, and the inadequaciesinherent in the use of measures of exposure rather than dose in establishing plant growth responserelationships to ozone, the present studies were undertaken using an open-field exposure system.For two species, radish and Douglas-fir, diffusive conductances determined by whole-plantporometry were used to estimate 03 flux densities at intervals through the growing season, and bymeans of their relationships to measurements of meteorological variables, to estimate flux densitieson a continuous basis.Research Objectives1. To investigate the impact of chronic exposure to ozone on growth of one cultivar each of pea(Pisum sativum L.), potato (Solanum tuberosum L.) and bean (Phaseolus vulgaris L.), twocultivars of radish (Raphanus sativa L.) and a single provenance of Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco), under true field conditions.2. To determine the impact of ozone on diffusive conductance of radish and Douglas-fir underfield conditions.3. To calculate ozone flux densities through the use of actual conductance and estimated3conductance measurements based on empirically derived relationships with micro-meteorologicalvariables, and compare the use of exposure versus flux in growth-response relationships.42. LITERATURE REVIEW2.1 Air QualityOzone is a major air pollutant in many parts of the world (National Research Council(US.), 1992). Elevated levels are recorded in both urban centres and rural areas. In urban areasemissions of pollutants such as oxides of nitrogen (NO) and hydrocarbons, and the resultingatmospheric chemistry cause strong diurnal variation in 03 concentrations (Angle and Sandhu,1989). Ozone (03) and nitrogen dioxide (NO2)in urban areas are linked by the following keyreactions:NO2 + sunlight —* NO +0 (1)O+0+M—*03M (2)0+NO->02N0 (3)where M = hydrocarbons (Angle and Sandhu, 1989). The steady-state concentration of ozone isthen given by; [031 = k1 [N02]/k3 [NO], where k1 and k3 are specific reaction rate constants forreactions 1 and 3 respectively (Angle and Sandhu, 1989). In the winter months the ratio ofk1/k3is about 30% less than in summer because of lower solar intensity and temperature. Stable air andshallow mixing depths in winter lead to increased nitric oxide (NO) concentration and to lowervalues of the NO2 /NO ratio. As a result the steady-state ozone concentrations are smaller inwinter (Angle and Sandhu, 1989). The formation of 03 in urban situations is enhanced by thepresence of various volatile hydrocarbons, which react with NO, and thus prevent reaction 3 fromoccurring. Because of the mechanisms involved in its formation in urban atmospheres, 03 istherefore termed a secondary pollutant.Remote sites (e.g., high elevation sites) are typically free from the effects of local andregional anthropogenic emissions that are likely to influence 03 concentrations, resulting in littlediurnal change (Wolff et al., 1987). Due to the lack of 03 scavenging species in these sites,average 03 concentrations may remain fairly high, relative to some urban centres (Peake and Fong,1990). The authors found that typical background ozone concentrations in Calgary averaged 13ppb while on Fortress Mountain the average was over 43 ppb In the Lower Mainland of British5Columbia and surrounding rural areas occurrences of 03 levels exceeding the current air qualitystandard of 80 ppb for one hour are not unusual, due to an abundant source of locally derivedprecursors and long-range transport (B.C. Enviromnent, 1989).Knowledge of the concentrations of 03 typically occurring at locations not influenced byurban emissions are of interest in the consideration and establishment of secondary air qualitystandards (Altshuller, 1987). The first step in the process of developing an air quality objective orstandard involves establishing the risk to human health and vegetation as a function of dosage ofthe pollutant (Chock, 1989). For an assessment of the relationship between ozone levels andvegetation response, accurate measurement of the pollutant and characterization of itsconcentration distribution are required (Fowler and Cape, 1982).Air pollutant concentrations are by nature random variables (Georgopoulos and Seinfeld,1982) that are frequently autocorrelated and tend to display episodic fluctuations due to changes inmeteorological conditions and pollutant emissions (Chock and Sluchak, 1986). Therefore the basicstatistical assumptions of independence and identically distributed variables for air pollution dataare not met (Georgopoulos and Seinfeld, 1982). In addition, secondary pollutants such as 03,unlike primary pollutants such as NO and NO2,may not be well described by a log-normaldistribution, because their concentrations tend to be dependent on localized chemical processes asopposed to atmospheric dispersion (Flower and Cape, 1982; Nosal, 1984). Nosal (1984) andTaylor et al. (1986) found Weibull and gamma functions best described the distribution of ambient03 concentration data. In contrast, Lefohn and Jones (1986) found that hourly ozoneconcentrations tended to display a log-normal distribution if the data collected were subject tourban emissions whereas the distribution was skewed to the right when the data were collectedfrom more remote locations. These differences in distribution have implications regarding thestatistical summary of 03 exposure dynamics used to evaluate crop response. Curran and Suggs(1986), Chock and Sluchak (1986) and Chock (1989) concluded that the use of extreme values insetting air quality standards is associated with large uncertainty and recommended the use ofsummary statistics in the middle range of the distribution.6Current air quality standards and objectives for ozone are based on a maximum one-houraverage concentration. The form of the future objectives and standards is presently under reviewin Canada, the U.S.A. and Europe. At the present time in Canada the maximum desirable andacceptable levels for ozone are 51 and 82 ppb for one hour, respectively.2.2 Air Pollutant ExposureMuch research has been directed at developing pollutant exposure-response relationshipsthat may be used to estimate the effects of differences in ambient air quality on crops and othervegetation. The main objective of this type of research has been to find a useful predictor of plantresponse which adequately describes the effect on vegetation of ambient concentrations that canultimately be utilized in the establishment of ambient air quality standards (Jager et al., 1991).Although numerous field studies have been performed to assess the effects of 03 on plants,responses have been variable. This is likely a reflection of the use of different exposure facilities,environmental and cultural conditions, exposure dynamics and intraspecific and interspecificvariation in plants (Jacobson, 1982; Taylor et al. 1982; Roberts et al., 1984). The variation inresponse of different species and the lack of predictive power in quantif,iing plant response to 03 isalso a reflection of the inadequacy of the independent variable(s) which does not necessarilyestimate the amount of the pollutant reaching reactive sites in the plant (Unsworth, 1982; Kickertand Krupa, 1991; Taylor and Hanson, 1992).Exposure is defined in terms of the pollutant concentration in the ambient air or treatmentmonitored at the plant canopy over time, or for the duration of the experiment (Lefohn andRuneckles, 1987). It is sometimes expressed as the product of concentration and time (Unsworth,1982).There has been a great deal of effort directed towards the development of meaningfulexpressions of exposure for the quantification of exposure-response relationships. Numerousexposure indices have been developed, discussed and reviewed (Krupa and Kickert, 1987; Lefohnand Runeckles, 1987; Runeckles 1987; Hogsett et al., 1988; Lee et al., 1988; Musselman et al.,71988). Much of the work on developing exposure indices came from the National Crop LossAssessment Network (NCLAN) program in the United States (Heck et al., 1988). This programutilized OTCs to expose plants to ambient air enriched with 03 either by constant amounts or inproportion to the ambient level. The factors influencing exposure-response relationships developedby this program have been extensively reviewed by Heagle et a!. (1988). The original empiricalmodels developed from NCLAN data used the season-long mean of daily 7-h means (0900-1559)to characterize 03 exposure (Heck et al., 1982, 1983). However, the use of a mean is in conflictwith the underlying non-normal distribution of air quality data (Krupa and Kickert, 1987),previously discussed in Section 2.1.In a re-analysis ofNCLAN data by Lee et al. (1988) a comparison of a large number ofdifferent indices (e.g., evaluating peak concentrations, cumulative impact of multiple peaks, andadjustments for various stages of phenological development) failed to find any single index whichperformed best with the data available for all crops under consideration. Nevertheless, based onthe relative performance of these indices they were able to demonstrate that peak concentrationswere important in eliciting a response, but inclusion of lower concentrations in the summary wasalso necessary. In addition, plant sensitivity increased between flowering and maturity and plantsresponded to cumulative impacts.In contrast to the importance of peak concentrations found by Lee et al. (1988) recentwork by Krupa et al. (1994) examining yield loss in nonfiltered OTCs has suggested thatconcentrations of 03 up to 83 ppb were critical in eliciting a response and that levels in excess ofthis were less important. The importance of concentrations in this range has also been reported byGrunhage and Jager (1994). They suggested that concentrations in excess of this occurred duringperiods where the plant was least receptive, due to high stomata! resistance in response to highvapour pressure deficit, thus reducing flux. While these results are compelling, some caution mustbe exercised as all measurements took place 3 metres above the ground rather than at the plantcanopy, which is typical of most studies reported in the literature.According to Kickert and Krupa (1991) the lack of consensus on exposure indices is to be8expected given the emphasis in the literature on the use of exposure indices which do notincorporate the time series or the stochastic nature of ambient air quality. This was investigated byKrupa and Nosal (1989) using a spectral coherence time series analysis on weekly growthincrements of alfalfa. They found that intra-season variation in growth of alfalfa was related to thepattern of 03 exposure. Current definitions of exposure also suffer from an inability toincorporate the rate of detoxification and repair during periods of respite (Marshall and Ferman,1988). This may be a particularly important consideration for long-lived species such as trees,where repair and maintenance processes may occur, modifying the long-term response.Accordingly, tree response models need to include the exposure dynamics rather than single annualor season-end values (Kickert and Krupa, 1991).The concept of “effective dose” of a gaseous pollutant was introduced by Runeckles (1974)to describe that portion of the ambient ozone concentration that is actually involved in causing aresponse in the plant. Last (1982) emphasized the concern expressed by others (Tingey and Taylor1982; Reich, 1987; Pye, 1988) that concentration needs to be modified to account for resistances togas transfer, periods of respite and periods of increased dry deposition when resistance to transferis low (Grunhage et al., 1994). This is particularly important as pollutant flux is not necessarilyproportional to concentrations in the air and peak flux does not necessarily coincide with peakconcentration (Jager et al., 1991; Grunhage et al., 1994). Atkinson and Winner (1987) have alsoindicated that it is difficult to predict pollutant uptake from single measurements of conductance,as stomatal responses to a pollutant may change with the number of exposures.In theory, dose-response relationships for 03 can only be established if the “effective” orabsorbed dose is known. Pollutant absorbed dose (PAD), first introduced by Fowler and Cape(1982) as a development of the “effective dose” concept, is defined as the amount of pollutant thatis taken up per unit leaf area, over a period of time. This was calculated as the product ofconcentration, time and stomatal or canopy conductance and represents accumulated flux(Runeckles, 1992). A number of researchers have suggested that flux (pjg m2s4)may be used todefine dose (Leuning et al., 1979b; Tingey and Taylor, 1982). Pollutant absorbed dose may9represent a suitable substitute for flux (Runeckles, 1992). One drawback of this approach is theabsence of a term accounting for internal resistance that has been shown by Taylor et al. (1982) toplay a role in uptake, exceeding stomatal resistance at higher concentrations of 03. Someresearchers believe that an internal or biochemical mechanism may be more important thanresistance to gas exchange, in determining plant response to 03 (Evans and Ting, 1974; Taylor etal., 1982).While there is some evidence demonstrating that pollutant flux is a reasonable indicator ofinjury (Mukaminal, 1965; Elkiey and Ormrod, 1981; Amiro and Gillespie, 1985) others havefound that injury did not relate well with estimates of total flux (Graham and Onnrod, 1989). Thelack of a well-defined relationship between pollutant absorbed dose based on stomatal resistance,and plant response expressed as foliar injury has also been reported by Bicak (1978) for severalplant species.In summary, work by Mukammal (1965), Mukammal et al. (1982), Adomait et al. (1987)and Gnmhage et al. (1993) have demonstrated the importance of flux in the development of dose-response as opposed to exposure-response relationships. However, for the calculation of flux,information such as stomatal conductance needs to be collected on a continuous basis and berepresentative of the plant or canopy. For field experiments, micro-meteorological observationsmay be the most appropriate techniques for obtaining this type of information (Runeckles, 1992).Flux density or air pollutant uptake by plants is discussed further in section 2.5.2.3 Effects of Ozone Exposure on Crop Growth2.3.1 EFFECTS OF OZONE ON INJURY AND YIELDHistorically, most evaluations of the effects of 03 on crops were based on the productionof foliar symptoms of injury. Various studies have since pointed out that foliar symptomdevelopment is not a reliable indicator of effects on plant growth and yield (Tingey et al., 1973;Oshima et al., 1975; Tingey and Reinert, 1975; Bennett and Runeckles 1977). One reason for thisis that compensatory responses to 03 may result in recovery from injury (Jacobson, 1982).10Since the original work on foliar response to 03, crop yield response to 03 has beenextensively documented. As indicated in Section 2.2, much of this work was through the efforts ofNCLAN (summarized in Heck et al., 1988), and now more recently EUROTC Programme(overview in Mathay, 1988), using field-based OTC experiments. The primary objective in thistype of research was to quantify the effects of 03 on yield of different crops, ultimately to be usedin evaluating the potential effects of air pollution on crop production (Heck et a!., 1984). This typeof infonnation was a necessary step in the process of setting national air quality standards for 03in the US (Heagle et al., 1988). Numerous empirical mathematical relationships describingexposure and crop response have been described, utilizing linear and non-linear regressions. Krupaand Kickert (1987) and Kickert and Krupa (1991) have reviewed a number of examples of processoriented and empirical models of plant response to gaseous air pollutants. As indicated in Section2.2; selection of the appropriate summary of exposure index is a critical step in the development ofthese relationships. Descriptions of some of the exposure indices currently employed in theliterature are outlined in Appendix 2. Briefly, they include season-long means of daily averageconcentrations between 0900 and 1559 hours (M7); 0900-2059 hours (M12) or some otheraveraging period such as the 24-h mean (M24), sums of fractions of concentrations above variousthresholds (AOTXX); the sums of absolute concentrations when these thresholds are exceeded(SUMXX) and the sums of all concentrations in a 24-h period for the season (TD). Few attemptshave been made to define true dose-response relationships. As will be discussed in Section 2.5.1,when approximations of flux (derived from meteorological measurements, or estimates of leaf orcanopy resistance and mean ozone concentrations) are used in these models as opposed to indicesof exposure, statistical improvements have been found (Mukammal, 1965; Reich, 1987; Gn.inhageet al., 1993). Despite this, the majority of the relationships described in the literature are exposure-response models (Runeckles and Chevone, 1992).The appropriate mathematical form for the response functions has been a cause for debateand has been reviewed by Krupa and Kickert (1987) and Lefohn and Runeckles (1987). Briefly,simple linear regressions have proven useful in describing the responses of a number of crops to1103 (Foster et a!., 1983b; Kress and Miller, 1983; Pell et a!., 1988), but for some species theysuffer from an inability to reflect the curvilinear nature of the responses observed (Runeckles,1992). Because of this the Weibull function was adopted by the NCLAN program to describe 03exposure-response relationships. The model and its parameters are as follows:Y a x e(jb)C (4)where Y is yield, X is an exposure index, a is the yield at zero exposure and b and c are parameterscontrolling the shape of the function (Rawlings and Cure, 1985). However, as Runeckles andChevone (1992) have pointed out, a shortcoming of this model is that it is a monotonic decreasingfunction and consequently cannot account for possible stimulations in growth caused by exposureto low levels of 03 (Bennett et al., 1974; Sanders et al., 1992c). Runeckles and Wright (1988)proposed a gamma function to overcome this limitation. This three-parameter model has the form:Y=a(X+1)Cxe> (5)where Y is yield, X is the exposure index, a is the yield at zero exposure, and b and c are modelparameters. Regardless of the different response functions and measures of exposure used bydifferent workers, the overall adverse effects of 03 on crop yields have been well demonstrated(Hecketal., 1984).While a general overview of the literature is presented in the following Sections, the focushas been on the species used in the thesis research.Skarby and Jonsson (1988) found a decrease in yield of peas and potatoes when exposed toNF+03 in OTCs for the growing season. Pell et a!. (1980) found that intermittent exposures to200 ppb for 3 hours (biweekly) resulted in reduced tuber number, tuber weight and total solids.These reductions were accompanied by an increase in reducing sugars at 120 or 140 days for cvs.Norland and Kennebec respectively. Decreases in tuber number and weight, and percent dry12matter, and increased reducing sugars with intennittent exposures of 150 ppb or 200 ppb for 3hours in the greenhouse, were also observed in Cherokee and Norchip (Pell and Pearson, 1984).Linear reductions in total weight, the number and weight of tubers when exposed to NF+03 inOTCs were observed in cv. Norchip (Pell et al., 1988). Foster et al. (1983) also found linearreductions in tuber weight, leaf, root and total weight and tuber number with 03 exposure up to44.2 ppmh. In contrast, Clarke et al. (1978) using ethylenediurea (EDU), a chemical protectantagainst 03, reported no effect of 03 on tuber yield or size in Norchip between EDU-treated anduntreated plants, even though the use of EDU reduced foliar injury in the field. However, laterexperiments on field exposed potatoes found that foliar injury was a function of cumulative oxidantexposure (Clarke et al., 1983). When foliar injury exceeded 60%, tuber yield was reduced by 25and 31% in Norland and Norchip.Significant decreases in leaf area and plant dry weight have been reported by Greitner andWinner, (1988) with exposure of radish cv. Cherry Belle to 120 ppb 03. Walmsley et al. (1980)also found a 50% decrease in dry weight of cv. Cherry Belle exposed to 170 ppb 03 for 36 days.Barnes and Pfirrmann (1992) found a significant decrease in cv. Cherry Belle dry weight withexposure to 03 (73 ppb) in a phytotron for 27 days. Reinert and Gray (1981) examined the effectof exposure duration on growth in radish. They exposed cv. Cherry Belle to concentrations of 200and 400 ppb 03 for 3-6 hours 18 days after seeding in a continuously stirred reactor (CSTR) andfound exposure length had no additional effect on growth. A 40% decrease in root dry and freshweight was associated with the highest concentration (400 ppb). Johnston et al. (1986) found thatconcentrations of 100 ppb for 3hlweek for 3 weeks had no effect on radish growth, whereas 50 ppbfor 40 h/week for 5 weeks decreased root weight by 50% (Tingey et al., 1971). Work by Sandersand Reinert (1982) on radish cv. Cherry Belle found plant response to intermittent exposures of 03(300 ppb for 3 hours) was also influenced by plant age. Radish plants appeared most sensitive to03 as the hypocotyl was beginning to enlarge at 12-18 days from seeding. Decreases in root andleaf weight were observed regardless of the age at exposure. However, the greatest declines werein age class 3 (19-23 days from seed) then age class 2 (12-16 days from seed) and the least in age13class 1 (5-9 days from seed). The authors concluded that either the radish piants exposed in week3 were more sensitive at this stage of development, or that plants exposed in weeks 1 or 2 had timeto recover.In experiments using OTCs responses of plants have been shown to vary with the level ofenrichment supplied and the pattern of delivery. Bender et al. (1990) investigated the response ofbean cv. Rintintin, grown in pots over two growing seasons, to elevated 03 concentrations appliedusing open-top chambers. Impacts on yield, were examined using non-linear regression analysesand three different characterizations of the 03 exposure (Total Dose (TD); 8-h (M8) and 24-h(M24) seasonal means). Significant treatment effects were found only for the third experimentwhere 03 concentrations ranged from 6.25-108.9 ppb (8 h mean) versus 2.85-49.8 ppb forexperiments 1 and 2. Final yield data indicated a negative effect of 03 on biomass, leaf area andpod weight when the concentration exceeded 60 ppb. No real improvement in fit of the regressionmodels was found with the use of TD as opposed to seasonal means. Kohut et al. (1988) exposedbean cv. California Light Red to a range of 03 concentrations (CF air, 5 0-120 ppb) including bothdaily and intermittent exposures. Response was assessed through 12 harvests. Significantdifferences were detected among treatments for the early harvests and at mid-season. Plantssubjected to peak 03 concentrations usually had more reduced growth than the remainingtreatments. However, no significant differences remained at the season end. It was suggested thatthe differences among treatments were due to a delay in the growth of plants exposed to peak 03treatments. Decreases in soybean yield were also found by Unsworth et al. (1984) and Heagle etal. (1986) with cv. Davis exposed to both variable and constant concentrations of 03. However,the nature of the exposure regime did not influence response.Stage of plant development at the time of exposure has also been found to affect plantresponse to 03. Blum and Heck (1980) demonstrated that bean cv. BBL-290 intermittentlyexposed to acute 03 concentrations (300 and 600 ppb) between 8-17 days after planting, showedpartial recovery of growth at the final harvest. The greatest effect of 03 on plant stages wasassociated with fruit set and development of fruit. An exception to this was pod weight which was14reduced by 600 ppb 03 regardless of growth stage at the time of exposure. Haas (1970) foundthat mature bean plants in fruit were more sensitive to injury than younger plants. However,Aniiro et al. (1984) found that primary leaves on young bean plants and trifoliate leaves on fruitingplants were both equally sensitive to similar 03 flux densities.Stimulations in plant biomass at low level exposures to 03 have been reported by anumber of researchers (Bennett et al., 1974; Sanders et al., 1 992c). Work by Sanders et al.(1992c) on bean cv. Nerina utilizing OTCs and concentrations ranging from CF air, NF air and NFplus 6, 12, 18, 25 and 30 ppb 03 found that low concentrations ofNF+6- 12 ppb (M7 between 20-32 ppb) stimulated yield. This was expressed as an increase in leaf and pod number, leaf area atthe last harvest, and a longer leaf retention of the canopy. Low concentrations had no effect on leafor stem weight or pod weight/plant, seed weight/plant or seeds/pod. In contrast, exposuresexceeding NF+18-20 ppb (M7 = 38 ppb) resulted in a reduction in number of pods/plant,seeds/pod, weight of individual seeds, seed weight/plant, leaf number, leaf area, leaf weight and anincrease in stem weight. The proportion of dry weight allocated to pods showed no response to 03at any of the harvests. In a similar experiment using cv. Lit, increases in seeds/pod and number ofpods/plant were also found at low concentrations of 03 (Sanders et al., 1992b). However, at higherconcentrations, the number of seeds/pod was unaffected, but the number of pods/plant, seedweight/plant, seed weight and total weight declined (Sanders et al., 1992b).Genotypic differences in response to 03 stress have been reported by a number ofresearchers (Brennan and Rhoads, 1976; Knudson-Butler and Tibbitts, 1979; Meiners andHeggestad, 1976; Huci and Beversdorf 1982; Reinert et al., 1984). Differences in response bysoybean cultivars to 03 stress have been noted by Muichi et al. (1988) and Temple et al. (1991).Temple et al. (1991) found a decrease in root weight, seed number, pod number, seed weight andstem weight in sensitive cultivars of soybean. Clarke et al. (1990) examined six potato cultivarsexposed to ambient air conditions. Foliar injury between 20-40% resulted in a decrease in tuberyield, although the degree of response was cultivar dependent. Clarke et al. (1978) also found thetype of response to 03 stress was cultivar specific in that Norland had smaller tubers whereas15Norchip produced fewer tubers. Heggestad (1973) and Foster et al. (1983b) have alsodemonstrated that responses of potato to 03 stress are cultivar specific. Exposure to 250 ppb 03reduced yield in Centennial Russet but not in White Rose (Foster et a!., 1983b). Mosley et al.(1978) in a field study examining the response of 59 cultivars of potato to polluted ambient airfound that later maturing varieties were generally more resistant to 03 injury. Those plants withthe highest injury had reduced yields. Reinert et al. (1972) examined the response of nine radishcultivars to acute levels of 03 delivered over 1.5 hours (350 and 700 ppb). On the basis of foliarinjury, they found cv. Cherry Belle to be the most sensitive and cv. French Breakfast to bemoderately sensitive.Some researchers have observed that exposure to 03 results in premature senescence ofplants. Kohut et al. (1986) found soybean plants senesced two weeks earlier than those in CF air.This was also observed by Unsworth et al. (1984) and Amundson et al. (1986) with cv. Hodgson.Muichi et al. (1988) and Huci and Beversdorf (1982) found that early season cultivars of soybeanand bean respectively, senesced earlier compared to late season cultivars subjected to 03 stress.Miller et al. (1989) in studies on soybean examined the effect of water stress and 03 andfound no interaction between these two variables and yield. Pell et al. (1993) have also reported alack of interaction between 03 and water stress in radish. In contrast, Heggestad et al. (1985)found a greater reduction in yield of water-stressed soybean in NF+03 air versus plants notsubjected to water-stress, relative to the CF air treatment. Temple et al. (1985) in a two-year studyon cotton found no effect of 03 on severely water-stressed cotton in the first year. However, workin the following year suggested that the interaction between the two stresses was dependent on theseverity of the drought stress. This was also found in a two-year study on alfalfa (Temple et al.,1988). The physiological response of water-stressed plants subject to 03 stress will be discussedin greater detail in Section 2.5.2.162.3.2 EFFECTS OF OZONE ON BIOMASS PARTITIONINGOzone effects on plant growth, development, and partitioning of biomass of a number ofspecies have been studied under controlled environment conditions with fewer studies having beenconducted in field settings. Alterations in biomass partitioning, have resulted in an increase inabove ground biomass at the expense of roots, for a number of species, following 03 exposure(Oshima eta!., 1978, 1979; Blum and Heck, 1980; Blum et al., 1983; Miller et al., 1988) Undersome circumstances, alteration in partitioning was found to be related to concentration (Blum et a!.,1983) or sampling date (Miller et al., 1988). Alteration in partitioning may result from increaseddemand by damaged leaves, such as increased energy requirements for maintenance and repair, atthe expense of the roots. Cooley and Manning (1987) have suggested that the relative sinkstrengths may determine how 03 will affect biomass partitioning in plants.Blum et a!. (1983) found that exposures to 150 ppb 03 (4 hId for 6 days) decreased theproportion of carbon allocated to roots in favour of the leaves. At concentrations of 100 ppb andless, the proportion of carbon allocated to roots increased. The greater negative impact of 03 onroot rather than shoot dry weight was the result of altered metabolism of photosynthetic surfacesassociated with 03 injury and a concomitant reduction in translocation of photosynthate to theroots. Tingey et al. (1971) exposed radish, cv. Cherry Belle to low concentrations (50 ppb) of 03and found a greater percent reduction in root and shoot dry weight, suggesting a possibleimpairment in leaf metabolism, or, an interference with translocation of photosynthates to the roots.This has also been found by Atkinson et al. (1988) and Walmsley et a!. (1980) and Tingey andBlum (1973). Walmsley et al. (1980) suggested that plants treated with 03 produce new, 03-tolerant leaves at the expense of the hypocotyl. This has also been observed by Johnston et al.(1986) for radish cv. Scarlet Globe following intermittent exposures to 03 at 400 ppb. Thedecrease in hypocotyl weight was attributed to greater retention of photosynthate by the foliage atthe expense of roots.Miller et al. (1988) examined the effects of chronic exposure to 03 on growth of cotton ina field situation. They found that 03 reduced total biomass, preferentially affecting the leaves and17roots. The effect of 03 on partitioning of biomass among the plant parts varied during the growingseason. Pell et al. (1993) also found that the time of year when radish plants were exposed affectedthe partitioning of biomass. No significant effects of 03 were found for rootJshoot ratio in thespring, whereas root/shoot ratio declined with exposure to 03 in the fall. Plants exposed in thespring allocated more biomass to flowering and more to the hypocotyl in the fall. Time ofexposure was also shown to influence biomass partitioning in bean cv. Pinto exposed in OTCs withconstant additions of 03 up to 60 ppb (Heck et al., 1988). Exposures early in the season resultedin a decrease in pod weight, pods/plant, root and shoot weight; but shoots and roots were similarlyaffected. Root weight rather than shoot weight was more severely impacted by late seasonexposure. Pod yields were greater in the late season exposure than in the early season.Under some circumstances, plants have partitioned less assimilate to the developing fruitthan leaves. In a greenhouse study with cotton, Oshima et al. (1979) found that 03 reduced thedry weight of all plant parts, with the largest reductions occurring in the roots and boils. Still otherstudies have shown that plants preferentially partitioned more assimilate to the fruit under 03stress. Oshima et al. (1975) found that 03 induced reductions in tomato plant biomass were notassociated with yield reductions. When yield losses occurred, fewer fruit rather than smaller fruitwere produced. The implications of a reduction in yield for plants with determinate inflorescenceswhen exposed to 03 may be more serious, as they have limited opportunities for developing newflowers and fruit (Oshima et al., 1979). As indicated in Section 2.5.1 yield reductions in potatowere associated with reductions in number and size of tubers (Clarke et al., 1983; Pell andPearson, 1984). Foster et al., (1 983a) found that 03 had less effect on partitioning of assimilate topotato tubers, than to shoots and roots.In03-stressed soybean, two stages in the alteration of assimilate partitioning have beenshown to occur (Cooley and Manning, 1987). At concentrations exceeding 50 ppb during earlygrowth, assimilate was partitioned to the leaves rather than roots. During flowering and poddevelopment assimilate was partitioned to the seeds at the expense of the leaves, stems and roots(Tingey et al., 1973; Heagle et al., 1974; Blum and Tingey 1977). Smith et a!. (1990) found that18foliar sensitivity to 03 in soybean, was greatest at the critical flowering stage and that root growthwas more sensitive than shoots. in contrast, Amundson et a!. (1986) found that although yieldswere decreased with 03 stress, no change in biomass partitioning of cv. Hodgson occurred. Initialflowering was also delayed in the 03 treatments exceeding 50 ppb They suggested that the delayin flowering due to higher 03 exposures contributed to a difference in pod number and size.2.3.3 EFFECTS OF OZONE ON THE DYNAMICS OF PLANT GROWTHGrowth analysis techniques have been employed in the air pollution literature for a limitednumber of crop species (Pell et al., 1990; Atkinson eta!., 1988; Miller et al., 1988; Hogsett et a!.,1984; Walmsley et al., 1980; Bennett et al., 1979; Oshima et al., 1978, 1979). In the case ofradish cv. Cherry Belle, examination of the various growth functions distinguished anaccommodative response to long-term, low level 03 exposures where the net assimilation rate(NAR) and relative growth rate (RGR) of the control and 03 treatments (170 ppb for 36 days)showed no significant difference by the end of the experiment (Walmsley et al., 1980). Althoughthe ozonated plants were smaller, they had adapted to the presence of 03. They were growing atthe same rate and distributing assimilate in the same way.Barnes and Pfirrmann (1992) found significant declines in specific leaf area (SLA),absolute growth rate (AGR) total weight, AGR shoot weight and AGR hypocotyl weight in radishplants exposed to 73 ppb 03 for 27 days in a phytotron. No effect was found for leaf area ratio(LAR). Pell et a!. (1990) reported a significant reduction in root and hypocotyl weight of radishcv. Cherry Belle at the highest 03 concentration used (660 ppb), whereas RGR of whole plantswas not affected by the 03 treatments, suggesting a compensation in response to the pollutant.Atldnson et a!. (1988) found that effects on growth of radish, exposed for a total of2l days usingcontinuously stirred reactors (CSTR’s) chambers, were related to the exposure concentration.Significant reductions in NAP. and increases in LAR and SLA occurred inO3stressed plantsexposed to 200 ppb. RGR at the 200 ppb treatment was 50% lower than in the control plants.The increase in LAR and SLA indicates that exposure to 03 resulted in the production of thinner19leaves.As previously discussed in Section 2.3.2, Pell et al. (1993) found that the timing ofseasonal exposure had an effect on radish response to 03 stress. There was no effect of 03exposure on RGR when plants were exposed in the spring; however when exposures took place inthe fall, significant decreases in ROR were noted. RGR was also lower in the fall than in spring.Differences in environmental variables between the two exposure periods, such as, temperature andlight intensity were suggested as factors that may modify plant response.Oshima et al. (1979) identified sensitive developmental stages in parsley through shifts inrelative growth rates. The initial decrease in RGR of the treated plants was followed by a period ofrecovery, where these plants produced more leaves than the controls at the expense of root growth.Table 1 summarizes some of the responses seen in growth variables for a number of cropspecies. Different parameters show variable responses to stress. The alternating increase anddecrease in AGR for bean (Blum and Heck, 1980) and LAR and leaf weight ratio (LWR) forcotton were found to be harvest dependent (Table 1) (Miller et al., 1988). Earlier harvests showedan increase for these variables, whereas the later harvests showed a decrease. The lack of an effectof 03 on cotton was likely due to a compensatory effect, that is, the decrease in photosyntheticefficiency (NAR) was offset by a greater leaf area (LAR) (Miller et al., 1988). The decrease inLWR and LAR in the later harvests was indicative of leaf abscission due to 03 stress, whereasearly on, more of the total biomass of these stressed plants was in leaf tissue (Miller et al., 1988).Effects on RGR in snapbean were found to be related to 03 concentration. Both increases anddecreases in RGR were noted for snapbean at 600 ppb, whereas decreases only were observed at300 ppb (Blum and Heck, 1980). It was suggested that negative RGR for the older plant stageswas a reflection of premature senescence and abscission, which affected the ozonated plants morethan the controls. Decreases in RGR of unifoliate bean leaves was found for Pinto bean exposed in20Table 1. Summary of crop growth responses to ozone exposureAGR RGR LAR NAR LWR. SLA ReferenceRaphanus sativus L. - l/D l/D NE - - Walmsley et a!., 1980- D I D I Atkinson et al., 1988D - NE - - D Barnes & PfuTmann, 1992- NE - - - - PelletaL, 1990- - - - PelIetal., 1993Phaseolus vulgaris L. - D - - - - Haas 1970l/D l/D - - - - Blum and Heck, 1980- D - - - - Arnthor, 1988Glycine max (L.) Merr - - - D - - Reich et a!., 1986D D - NE - - Muichi et al., 1988Gossypium hirsutum L. D NE TI]) D l/D I Miller et al., 1989NB. I = increase; D = decrease; NE = no effect; - = not reported.only were observed at 300 ppb (Blum and Heck, 1980). It was suggested that negative RGR forthe older plant stages was a reflection of premature senescence and abscission, which affected theozonated plants more than the controls. Decreases in RGR of unifoliate bean leaves was found forPinto bean exposed in OTCs at twice the ambient air concentrations (Amthor et al., 1987, 1988).Hans (1970) found that beans cv. Sanilac with the least amount of bronzing injury when exposedto ambient air conditions, had the highest RGR, whereas the lowest ROR and NAR was associatedwith the most bronzed bean leaves. Flower development coincided with maximum leaf areadevelopment. At full bloom, leaf area loss began to exceed the production of new leaf area. Thoseplants most injured reached full bloom one week earlier than the least injured plants, despite thefact that they coincided in maximum leaf area development and flower initiation. Hans (1970)suggested that the rate of growth influenced the severity of bronzing, and stage of growth regulatedthe time at which bronzing occurred. Bronzing occurred 10-11 days after maximum leaf areaexpansion.Mulchi et al. (1988) found that maturity grouping for soybean played a role in plantresponse to low level exposure to 03 (NF + 40 ppb). AGR and RGR decreased with 03 exposure21in all groups. However, the greatest response to 03 stress was in the earlier maturing varieties.Nevertheless, this maturity group also had the largest yield under 03 stress. Ozone was found tohave no effect on NAR. This has also been noted by Reich et al. (1986) for soybean.2.3.4 EFFECTS OF OZONE ON PHYSIOLOGICAL PROCESSESPhysiological processes such as photosynthesis, respiration and stomatal function areknown to be affected by air pollution. Variation in response of these plant processes to airpollutants, both within and between species has been reported in the literature. In part the variationin response is genetically controlled. However, environmental conditions both before and duringfumigation also play a role (Darrall, 1989; Runeckles and Chevone, 1992). The effects of 03 onsome of these processes are briefly reviewed in the following paragraphs. The effects of 03 ondiffusive conductance are discussed further in Section 2.5.2.As indicated previously, 03 can inhibit photosynthesis by causing stomatal closure whichrestricts the supply of external CO2 resulting in reduced photosynthesis. Alternatively, thepollutant may be absorbed into the leaf mesophyll where biochemical processes associated withCO2 fixation are impaired. Studies examining whether or not03-induced changes in conductanceprecede or follow changes in photosynthesis provide information about the sequence of theseevents. These issues are discussed further for crops in the following paragraphs and for trees inSection 2.4.4.Reductions in net photosynthesis as a result of exposure to low 03 concentrations (<150ppb) has been reported for several plant species (Coyne and Bingham, 1978; Reich and Amundson,1985; Reich et al., 1986).Sanders et al. (1 992a) found that net photosynthesis was lower in the NF high 03treatment versus CF air in bean cv. Lit. However, the decrease was temporary and concentrationdependent as no effect was observed in the lower 03 treatments. The transient effect of 03 onCO2 exchange rates following low level exposure has been reported for other species (Runeckles,1991). Schenone et a!. (1994) noted that net photosynthesis for bean cv. Taylo?s Horticultural in22the NF treatment was affected by growth stage, with the greatest impairment occurring during podripening. Reich et al. (1986) found a linear decrease in net photosynthesis for soybean wasassociated with a decrease in chlorophyll following exposure to 03. Pigment loss in response to03 is thought to be due to the release of pigments from chioroplast membranes that havedisintegrated (Sanders et al., 1992a). Pell et al. (1993) found that 03 induced a significantdecrease in stomatal conductance in radish, but, decreases in net CO2 assimilation occurred earlier,suggesting an additional mechanism for 03 effects on photosynthesis, beyond reduction in gasexchange. Greitner and Winner (1988) found a delay in response of photosynthesis in radishwhereby significant reductions only occurred after 25 days of exposure. This is in contrast toBarnes et al. (1992) where decreases were noted at day 14 and remained lower for the remainder ofthe experiment.Atkinson et al., (1988) reported that exposure to 03 induced proportional changes in netphotosynthesis and dry weight. Reich et al. (1986) and Amundson et al. (1987) have also reportedcoincident reductions in net photosynthesis and yield on soybean and winter wheat respectively.Amthor et al. (1988) observed an increase in respiration in bean cv. Pinto associated with03 exposure at twice ambient concentrations. An increase in respiration rate was also found byAben et al. (1990) for faba bean.2.4 Effects of Ozone on Trees2.4.1 EFFECTS OF OZONE ON YIELD AND GROWTHThe impact of 03 stress on trees has been reviewed by Pye (1988) and more recently byChappelka and Chevone (1991). Various responses have been recorded both within and betweenspecies for a range of growth parameters (Pye, 1988). Due to logistical problems associated withstudying mature trees, much of the research reported has been done on seedlings or saplings.Although seedling experiments provide information regarding the short-term effects of 03 ongrowth they may have limited applicability for assessing the impacts on mature stands which differ23for example in terms of carbon allocation and canopy structure (Pye, 1988). In addition, needlelongevity in conifers complicates any assessment of impacts from 03 exposure because of thepossible interaction between needle age, environmental factors and multiple exposures.Hogsett et al. (1989) in a one-year study using open-top chambers examined the effects of03 (exposures for 134 days averaging 71 to 670 ppb), acid fog and SO2 on five westernconiferous species and found variable responses following termination of the treatments. Heightand stem diameter were followed during exposure and post-exposure following spring/summerelongation. Height and bud elongation in Pseudotsuga menziesii showed no response duringtreatment with 03 and acid fog, whereas Tsuga heterophylla (Raf) Sarg. had a 10% decrease ingrowth rate of height and stem diameter during exposures and Thujaplicata Donn showed an 8-12% increase in height during exposures. Following bud elongation in the spring significantdecreases in needle, stem and root weight were noted for Pseudotsuga menziesii, Tsugaheterophylla and Pinus pondersosa Dougi. ex P.&C. Lawson. Thujaplicata had an increase inneedle and stem weight but a decrease in root weight. Pinus contorta Dougi. was unaffected inyear one, but decreases in all components were significant in the repeated experiment in year 2.Chappelka et al. (1985) found a linear decrease in root, stem and leaf weight in 9-week oldLiriodendron tulipfera L. exposed for 6 weeks to 100 ppb 03. In more recent work on the samespecies Chappelka et al. (1988) exposed 9-week old seedlings to 03 concentrations up to 150 ppbfor 6 weeks and found no effect on total weight or height despite a decrease in stomatalconductance. Mahoney et al. (1984) have also found no effect of low levels of 03 on height andweight of 6-week old seedlings ofL. tulipifera. Jensen and Patton (1990) exposed one-year oldseedlings for a single growing season up to 200 ppb and found that although height, leaf weight,leaf area and total new weight decreased in the first measurement period (July), no effects of 03were noted in subsequent harvests (September and October). In contrast, Duchelle et al. (1982)found a decrease in height growth of eight tree species including L. tulipifera exposed to non-filtered air in OTCs for 2 years. Reich et al. (1986) found no effect of a 9-week exposure to 03 onleaf area, total weight or leaf number in Acer saccharum Marsh. despite decreases in net24photosynthesis, height and stem diameter.Leaf longevity was affected in Populus deltoides Bartr. exposed to 60-80 ppb 03 (Reichet al., 1984) and at 125 ppb (Reich and Lassoie, 1985). Significant decreases in the number ofleaves, height, stem diameter and leaf and stem weight were also noted (Reich et al., 1984). Dryweight of the plants in 85 and 125 ppb were 10-15% lower than in 25 and 50 ppb treatments. Theeffects of 03 concentration were linear. P. deltoides has been shown to be sensitive to 03exposure by Jensen (198 ib). Accelerated senescence has also been documented for other treespecies, such as Pinus strobus (McLaughlin et al., 1982). An increase in leaf senescence has alsobeen observed for Betulapubescens Ehrh., B. verrucosa Ehrh. and Alnus incana (L.) Moenchexposed for 50 days in chambers supplemented with 03 concentrations ranging from 23-82 ppb.Linear decreases in weights of all plant parts were noted (Mortensen and Skre 1990). Lineardecreases in all plant parts with 03 exposure have also been shown for Fraxinus americana L.seedlings (Chappelka and Chevone, 1986).Townsend and Dochinger (1974) found leaf development to be a factor in susceptibility tofoliar injury ofAcer rubrum L. under 03 stress. In their experiment, the youngest (<25 mm) andthe oldest (fully expanded) leaves were the most tolerant to 03, whereas leaves >25 mm long but <90% expanded were most susceptible.Marked differences in response to a range of 03 concentrations have also been observed.Kress and Skelly (1982) found that F americana exhibited growth stimulations at 50 ppb 03 intotal weight and L. tulipfera for height, whereas decreases in total dry weight were found at 150ppb for F americana. Exposures took place over a 28 day period for 6 hours/day.Inconsistencies in response of certain plant variables measured following treatment hasbeen reported by some authors (Shafer and Heagle 1989; Edwards et a!., 1990). The latterexposed Pinus taeda L. to twice the ambient air concentration for one growing season and foundthat height and diameter growth, which showed no impact, did not accurately reflect the effects of03 on biomass increment. Significant decreases in total weight were found. In contrast, Adams eta!. (1988) found decreases in stem volume expressed as (D2H), and secondary needle weight ofF.25taeda seedlings exposed to ambient air plus 60 ppb 03 in OTCs for one growing season, buteffects on root and shoot weight were not significant. More recent work by Adams et al. (1990) onthe same seedlings exposed for three growing seasons showed no effect on height, stem diameter ortotal weight. Shafer et at. (1987) found linear decreases in stem diameter, height, and weight ofcomponent parts ofP. taeda seedlings, for 3 of four ecotypes tested, after a single 5 monthexposure to 03 (delivered at 0.5-2 times ambient air in OTCs). More recently, Shafer and Heagle(1989) reported that after 3 years exposure to 03 stem height was variable and no longersignificantly related to 03. Shoot, branch and root weight and stem diameter were suppressed by03 in 3 of the 4 ecotypes. In contrast, Reinert et al. (1988) reported that P. taeda exposed to 320ppb 03 for 12 weeks in CSTR’s chambers showed a 21% decrease in height and a 16% decrease indiameter compared with the controls. Kress et al. (1988) and McLaughlin et al. (1988) observedsimilar reductions in growth with P. taeda seedlings subjected to their highest 03 treatment in thefield. Wiselogel et al. (1991) observed a decrease in relative height and root collar diameter growthrates in 22-week old P. taeda exposed to 160 and 320 ppb 03 (8 h/day for 4 days/week) for 9weeks. Again, ecotypes differed in their response to 03 stress.A number of studies have been reported examining 03 stress effects on Picea rubens Sarg.With the exception of Lee et al. (1990) no significant effects have been noted for stem, needle, root,total new or total old weights (Taylor et al., 1986; Kohut et al., 1990; Alscher et al., 1989).Although decreases in dry weight of stem, needles and roots were reported by Wilhour and Neely(1977) on Pseudotsuga menziesii exposed to 100 ppb for 6 hours/day, 7 days/week for 22 weeks,the effects were not significant. Dobson et al. (1990) found no effect of intermittent exposures to03 on root, shoot or needle weights ofPicea abies L. and P. sitchensis L.262.4.2 EFFECTS OF OZONE ON BIOMASS PARTITIONINGExposure to 03 has been shown to affect carbohydrate partitioning in trees (Cooley andManning, 1987). Tingey et al. (1976) found that exposure to 03 resulted in an increase in theconcentrations of soluble sugars and starch in tops ofPinus ponderosa seedlings but lower levelsin roots, indicating a decline in the amount of sugar translocated. Miller et al. (1986) found thatthe decline in root growth ofF. ponderosa following exposure to 03 was associated with areduction in the concentration of soluble sugars in the phloem. Kuppers and Klump (1988) foundvirtually no effect of 03 exposure on starch accumulation in current year needles of four-year oldPicea abies, whereas starch accumulated in one-year old needles and there was reducedtranslocation of assimilates to the roots (documented as reduced starch content). Kress and Skelly(1982) in a survey of 10 eastern tree species found that 03 had a greater impact on roots ratherthan shoots. A similar response was found by Hogsett et al. (1985) for two Pinus species: rootweight was more impacted by treatment than shoot weight. Shafer and Heagle (1989) attributedthe suppression of shoot, branch and root weight to an alteration in partitioning of assimilate in P.taeda. McLaughlin et al. (1982) found a higher retention of labeled photosynthate by foliage andbranches in oxidant sensitive Pinus strobus was accompanied by a decrease in export to roots andboles. A decrease in root to shoot ratio was also found in F americana exposed to 150 ppb over a5 week period (Chappelka and Chevone, 1986). This is in contrast to Chappelka et al. (1985),Reich and Lassoie (1985) and Mortensen and Skre (1990) who found no effect on the root to shootratio of L. tuhpzfera, Populus deltoides and Betula pubsecens, B. verrucosa or Alnus incanarespectively.A short term fumigation with 03 in Pseudotsuga menziesii revealed no effect on totaluptake of14C02, However, there was a temporary decline in rootlsoil respiration (Gorissen andvan Veen, 1988). Spence et al. (1990) found a reduction in photosynthesis and total carbontransport to roots ofPinus taeda seedlings fumigated with 120 ppb for 12 weeks. The shift inallocation of photosynthate favoured stems at the expense of the roots. No differences in root27weight were found. The authors speculated that if 03 reduces translocation of sugars to roots thelong-term effects may take years to develop.2.4.3 EFFECTS OF OZONE ON THE DYNAMICS OF TREE GROWTHGrowth analysis has been used by a few researchers for assessing the impact of 03 on treespecies (Jensen 1981, 1982;1983; Chappelka and Chevone, 1986; Chappelka et al., 1985; Hogsettet al., 1985). Their work and that of others is summarized in Table 2. Jensen (1981a, i.981b)found a decrease in RGR, RGR-leaf area, NAR and LAR in Populus deltoides and Liriodendrontulipfera with 03 exposures of 100 ppb for six weeks. The decline in LAR was attributed to earlyleaf senescence or bud set. Work by Chappelka et al. (1985) on the same species at identicalexposures showed a linear increase in LAR and no effect on LWR. RGR of Fraxinus americana,P. deltoides, Pinus elliotii (Engelm.) and P. densa (Engeim.) seedlings has been shown to decreasewith exposure to 03 (Jensen 1981a; Chappelka and Chevone, 1986; Hogsett et al., 1985). In anexperiment with Acer saccharinum L., RGR, RGR-leaf area, and RGR-leaf weight were notsignificantly different from the control until exposure concentrations reached 200 ppb (Jensen,1983).Jensen and Patton (1990) examined the response ofL. tulipifera RGR over a series ofharvest intervals, throughout the experimental period. RGR increased with exposure to 03 in thefirst harvest interval and declined in the remaining harvest interval. NAR decreased with exposureto 03, and SLA and LAR were unaffected. It was suggested that the increase in RGR with thefirst harvest interval was because growth in the second harvest was unaffected by 03 whereas thedecline in RGR with the second harvest interval was a result of growth beingimpacted by 03 at the third harvest. RGR for two year old Picea sitchensis was unaffected by asingle-season long exposure to 03 where concentrations ranged from < 5-170 ppb (Lucas et al.,1988). A similar result has been noted for Picea rubens (Taylor et al., 1986; Lee et al., 1990).28Table 2. Summary of tree growth responses to ozone exposureLWR SLA References-- (Jensen ,1981a)- NE (Jensen, 1982)- D (Jensen, 1981b)-- (Jensen 1981b)NE I (Chappelkaetal., 1985)- I (Chappelka et al., 1988)- NE (Jenson and Patton, 1990)- NE (Jensen, 1982)-- (Jensen, 1983)-- (Chappelka and Chevone,1986)-- (Hogsettetal., 1985)-- (Lucas et al., 1988)-- (Taylor et al., 1986)(Lee et al., 1990)NB. I = increase; D = decrease; NE = no effect; - = not reported; D* decreased with an increase in acidity of rain,but at pH 5.6 ROR was higher in the 03 treatment than in the control2.4.4 EFFECTS OF OZONE ON PHYSIOLOGICAL PROCESSESIt is well established that 03 can cause reductions in net photosynthesis in a number of treespecies (Yang et al., 1983; Reich, 1987). Reduction in photosynthesis have been observed in bothconifers, and hardwoods (Carlson 1979; Coyne and Bingham 1982; Reich 1983; Reich et al.,1986; Reich et al., 1987; Fuhrer et al., 1990). The effects on photosynthesis have been detected inadvance of foliar symptom development (Miller et al., 1969; Barnes, 1972, Heck et a!., 1973) orchanges in stomatal conductance (Coyne and Bingham, 1981).Nevertheless, the response is not always well defined. Keller and Hasler (1987) found 03concentration influenced plant response. Levels of 50 ppb had no effect on photosynthesis, while150 ppb caused a significant decrease in photosynthesis ofPicea abies. Dobson et al. (1990)similarly found no effect of short term low level exposures at 80 ppb on photosynthesis in P. abies.However long-term exposure ofPopulus deltoides x trichocarpa to 85 or 125 ppb 03 reducedPopulus deltoidesPopulus deltoides xP. trichocarpaLirodendron tulipferaAcer saccharinumFraxinus americanaPinus elliotti and P. densaPicea sitchensisPicea rubensAGR RGR LAR NAR- D D D- D NE NE- D D D- D D DD’ J -- NE D I- ID NE I- D NE D- D- D- D- NE- NE- NE29photosynthesis, leaf chlorophyll content and increased respiration (Reich, 1983). The decrease inphotosynthesis was thought to be partly due to accelerated aging in leaves exposed to 03. This hasalso been reported by Reich and Lassoie (1984) for P. deltoides.Responses to treatment are frequently found to be species or clone specific (Yang et al.,1983). Exposure of 150 ppb in Quercus alba L. delivered over two growing seasons had no effecton photosynthesis or respiration (Foster et al., 1990). Lee et al. (1990) also found no effect onphotosynthesis in one year old Picea rubens seedlings exposed to 100 ppb 03 4 hours per day, 3days a week for 10 weeks. These results are supported by Taylor et al. (1986) and Kohut et al.(1990) and their work on Picea rubens seedlings and Schaap and Wang (1989) for Pseudotsugamenziesii. However, Steingrover and van der Beek (1993) have reported a reduction inphotosynthesis for 32-year old P. menziesii under ambient 03 conditions, with concentrations aslow as 40 ppb.The pollution-induced response of photosynthesis has also been reported to increase withneedle age and the degree of visible foliar injury. Wallin et al. (1990) found that current yearshoots ofPicea abies experienced an increase in net photosynthesis following exposure over threegrowing seasons; one-year old shoots decreased, but the effect was not significant. Netphotosynthesis significantly decreased in two-year old shoots. Ozone had no significant effect onrespiration regardless of shoot age. In contrast to Wallin et al. (1990), Fuhrer et al. (1990) foundthat exposure to 03 concentrations exceeding 50 ppb resulted in a significant decrease inphotosynthetic capacity of one year old needles ofF. abies whereas current year needles wereunaffected. A similar response was also documented by Kuppers and Kiump (1988) both for bothcurrent and 1 year old needles. Coyne and Binghain (1982) reported that photosynthesis in needlesof naturally exposed Pinus ponderosa decreased with increasing injury and that first or secondyear needles were more impacted than current year needles. The most injured needles had higheststomatal conductances.Trees have been found to display variation in sensitivity during their annual growth cycle.Before budbreak, exposure ofPicea sitchensis to 03 had no effect on net photosynthesis whereas30the same exposure following budbreak (post dormancy) resulted in a decline in photosynthesis. Incontrast, net photosynthesis increased with 03 exposure in P. abies regardless of budbreak.Eamus et al. (1990) also found that mean photosynthetic rate was higher in03-treated P. abies,both in current and previous year needles. The trees were measured prior to budbreak, 205 daysfollowing exposure to 50 ppb for two growing seasons.It has been suggested by Maier-Maercker and Koch (1992) that 03 contributes to forestdecline by causing increased transpiration during seasonal diy periods exacerbating the effects ofwater deficit. The work of Freer-Smith and Dobson (1989) on P. abies and P. sitchensis lendssupport for this argument. They found an increase in transpiration for both species followingexposure to 80 ppb for 5 hours. Keller and Hasler (1984) also found an increase in transpirationwith P. abies exposed to 03 concentrations exceeding 150 ppb, whereas transpiration decreased inAbies alba Mill. Barnes et a!. (1990) found that transpiration for P. abies exposed to 78 ppb 03increased in current year and one-year old needles. One-year old needles were more impacted thancurrent year. Skarby et a!. (1987) found that transpiration declined in ozonated shoots ofPinussylvestris L. Keller and Hasler (1987) measured transpiration in 12-year and 80-year old grafts ofP. abies 27 weeks following fumigation for 7 months and found that transpiration significantlydecreased in one clone at 150 ppb and an increased in the other. Since CO2 uptake differedsignificantly from the controls at 150 ppb in both clones they concluded that these processes werenot solely regulated through stomatal opening. Boutton and Flagler (1990) found that stomatalconductance and transpiration in Pinus echinata Mill. showed no response to long-term 03exposure. However both net photosynthesis and water use efficiency were significantly reduced.Other studies on P. taeda (Sasek and Richardson, 1989) and P. ponderosa (Coyne and Bingham,1982) have also suggested that a reduction in the capacity of the plant to fix carbon rather than animpact on the stomata! apparatus was responsible for declines in net photosynthesis. Coyne andBingham (1981) reached this conclusion after observing that decreases in photosynthetic capacitywere greater than reductions on stomatal conductance.Decreases in net photosynthesis not accompanied by declines in biomass could suggest that31repeated instantaneous measurements of net photosynthesis on single leaves do not adequatelyreflect total plant net photosynthetic rate and subsequent carbon utilization patterns and biomassaccumulation in seedlings exposed to 03 (Chappelka and Chevone, 1988). Alternatively, the lackof a subsequent decrease in biomass could suggest that these instantaneous measurements do notreflect the periods of respite where leaves may recover to pre-fuinigations levels of photosyntheticcapacity.Long-term physiological effects of 03 on trees have been documented. For exampleEamus et al. (1990) found that previous exposure ofPicea abies to 03 exceeding 50 ppb resultedin increased photosynthetic activity and stomata! conductance both on current year and one-yearold needles compared with controls maintained in CF air, 205 days after treatment. Thephysiological change indicated by Eamus et al. (1990) may be the result of reduced frost hardiness.This was also documented by others (Brown et a!., 1987; Barnes and Davison, 1988; Lucas et al.1988). Ozonated trees with decreased frost hardiness may be expected to be metabolically moreactive than trees maintained in clean air before budbreak in the spring.The significant increase in stomatal conductance of previously03-treated one-year needlesfound by Eamus et al. (1990) may be a result of a decrease in intercellular CO2 concentration dueto increased photosynthetic rates in these treated needles. It may also reflect a long lastingimpairment of stomatal regulation by 03.Table 3 summarizes the variety of reponses detected for Picea abies from a number ofrecent studies. In general the response of photosynthesis, respiration and stomatal conductance arevariable for current and one-year old needles/shoots, but show consistent decline with the two-yearold shoots for all studies. In summary, the table indicates the complex nature of response to 03,This no doubt reflects differences in age of experimental material, the exposure facility,concentration, timing and duration of exposure and physiological measurements, andmethodological differences in equipment used for these measurements.32Table 3. Sunmiary of ten studies on the physiological response ofPicea abies toozone exposureShoots/seedlings AuthorCurrent One year Two yearneedles/shootsI/NE I/D/NENE I/NENet I/DINEPhotosynthesisStomatal I/NEconductanceWater Use NE - - - 4EfficiencyTranspiration TINE T/NE T/NE - 3,4,7,8,9,10Respiration- TINE TINE NE 1,6Chlorophyll- NE lID D 5,6Starch- NE I - 1Accumulation.NB. One or more ofthe authors reported an effect in one or more of the categories listed for each variable measured.1. Kuppers and Klump, 1988; 2. Maier-Maercker and Koch, 1992; 3. Freer-Smith and Dobson, 1989; 4. Dobson et al.,1990; 5. Eamus et al., 1990; 6. Wallin et al., 1990; 7. Fuhrer et al., 1990; 8. Keller and Hasler, 1984; 9. Keller andHealer, 1987; 10. Barnes et al., 1990. I = increase; D = decrease; NE = no effect with 03 treatment; - not reported2.5 Air Pollutant Uptake2.5.1 UPTAKE OF OZONE BY VEGETATIONTo create a phytotoxic event, ozone in the ambient air must be taken up by the plant.Gaseous diffusion through the stomata is the primary route of ozone entry, and access to internalsites of reaction are necessary to elicit an effect (Runeckles, 1992). Tingey and Taylor (1982)have suggested that 03 flux is a function of physical and chemical properties of the gas and liquidphase pathways. They proposed the following conceptual model of plant response to 03 stress, inwhich they viewed plant response as a sequence of physiological and biochemical events startingwith uptake and ending in injury. The processes involved are: leaf conductance (encompasses gas-and liquid-phase processes) to 03, perturbation , homeostasis and injury. Figure 1 provides aschematic representation of these processes.D 1,2,3,4,5,6,7,9D 3,4,5,6,7,8,9,33Conductancegas phase liquid phaseFigure 1. Schematic representation of the conceptual model of plant response to 03 stress,from Tmgey and Taylor (1982).Flux of 03 in the gas-phase is dependent on the concentration gradient betweenthe ambient air and intercellular spaces within the leaf’ and the boundary layer, stomatal andintercellular conductances. However, liquid-phase processes are primarily controlled by thecapacity of metabolic pathways to react with the pollutant or its products and is referred to asresidual resistance (Tingey and Taylor, 1982; Runeckles, 1992; Taylor and Hanson, 1992).Various models have been developed to analyze or simulate gaseous pollutant uptake byplants (Bennett et al., 1973; O’Dell et al., 1977; Taylor et al., 1982; Unsworth, 1982; Wesely etal., 1982; Baldocchi et al., 1987). Many of these models rely on the analog resistance principlederived from Ohm’s law where the rate of transfer of pollutants into the leaf, flux density (F, J.g m3 s) is a function of the concentration gradient between the air (Ca lJ.g m3)and leaf interior (Cjig m3); and a series of resistance to gas transfer (it, s m4) (Unsworth, 1982; Runeckles, 1992):F= (Ca - C i)/rt (6)These resistances to gas transfer (rt) include aerodynamic (ra), stomatal (r) and mesophyllic (rr;InjuryPerturbationHorneostasis34internal or residual). C1 is usually considered to equal zero (Runeckles 1992). Residual resistanceis similar in concept to chemical resistance at the surface of the mesophyll cells described byLeuning, (1979a) and mesophyll resistance described by Wesely et al. (1978). Mesophyllresistance to water loss is assumed to be zero, on the other hand 03 has a low solubility in water,and therefore the same assumption may not be valid (Wesely et al., 1978).Another approach used to investigate pollutant uptake by plants is derived fromChamberlain (as cited in Unsworth, 1982) who developed the concept of deposition velocity (vg;ms’) which is equal to the dry deposition rate, associated with turbulent mixing above the plantcanopy (Wesely et al., 1978), as given by,vgF/Cr (7)where Cr (rig m3)is the concentration at a reference height (Unsworth, 1982). Deposition velocity= 1/resistance when the concentration difference is between a reference height and a sink where theconcentration is zero (Unsworth, 1982). In this form deposition velocity is equivalent toconductance (Runeckles, 1992).vg[ra+rs+rrl4 (8)Deposition velocity is difficult to determine, varying with time, and as a function of chemicalreactivity, physiological activity of the plants, and various meteorological variables such as windspeed and canopy surface properties (Baldocchi et al., 1987; Runeckles 1992; Fuentes et al., 1992;Grünhage et al., 1993).Although a number of reports in the literature have shown that gas-phase conductance isrelated to injury (Engle and Gabelman, 1966; Townsend and Dochinger, 1974; Runeckles andRosen, 1977; Butler and Tibbits, 1979), there is insufficient evidence to conclude that conductanceand injury are directly related (Tingey and Talyor, 1982). For this reason, although gas-phase35conductance affects the rate at which 03 diffuses into the leaf, conductance measurements withoutconsideration of other plant processes may fall short of a complete understanding ofvariation inplant response. While the importance of liquid-phase conductance has not been adequatelyassessed at this time, preliminary evidence suggests that its effect on uptake may be significant(Tingey and Taylor, 1982; Taylor et al., 1982; Amiro et al., 1985). Nevertheless, stomatalresistance plays a major role in ozone uptake, as displayed by the daily increase and decrease influx accompanying the diurnal cycle of stomatal activity irrespective of changes in ambient ozoneconcentrations (Leuning et al., 1979a as cited in Runeckles, 1992).There are three experimental approaches currently described in the literature forquantifying uptake (flux) of ozone by plants. These approaches, which include gas-exchangechamber studies, field studies and deposition studies will be described in more detail in thefollowing paragraphs.In the chamber and field studies ozone uptake is measured using a variety of techniquessuch as cuvettes, chambers and instrumentation such as steady-state porometers. The equipmentmeasures leaf-air exchanges of CO2 and water vapor on single leaves, fascicles or branches(Reich, 1983; Aniiro et al., 1984, 1985; Skarby et al., 1987). Uptake is calculated from a massbalance related to concentration differences between the inlet and outlet. Though accurateestimates of ozone flux can be made, the primary disadvantage of cuvettes or chambers are thattypically only a small portion of the plant can be fumigated at any one time, and therefore wholeplant response can only be inferred. Work by Amiro et a!. (1984) on bean has shown that thehigher the ozone flux density the less time is required for visible foliar injury to occur. Therelationship between flux density and yield was not assessed. It has been suggested that inadequateair movement in some controlled environment studies may lead to artificially high boundary layerresistance (rio) values, which inflate the level of pollutant gas concentration needed to elicit aresponse (Ashenden and Mansfield, 1977 as cited in Runeckles, 1992). Hence, this type of datamay be of limited use in establishing dose-response relationships (Rune&les, 1992).In field experiments, stomatal conductance is typically measured on individual leaves;36branches or needles, using a steady-state porometer at infrequent intervals during the study, fromwhich uptake is subsequently calculated. According to Leuning et al. (1979) ozone flux to plantscan be determined if stomatal resistance to water vapour and ozone concentration measurementsare available. One of the major disadvantages of this approach is that continuous measurementsare frequently not practical or possible, reducing the sensitivity of the estimates obtained for fluxdensity. Leuning et al. (1 979b) found that the use of a single curve for canopy resistance(developed from diffusion porometer measurements taken on two separate occasions) over thewhole season failed to reveal situations where reduced 03 flux occurred as a consequence ofovercast conditions resulting in stomatal closure. The type of exposure facility is also importantwhen attempting to extrapolate experimental observations to plant response in ambient air. Forexample, the use of open-top chambers (OTCs) may lead to an overestimation of theconcentrations required to elicit a response due to excessive modification of the plantsmicroclimate. This occurs as a result of continuous air flow into the chambers to facilitate mixingand therefore reduces their utility in extrapolating to effects on plants under ambientconditions.(Grunhage and Jager, 1994; Younglove, 1994).Reich et al. (1987) in their review on quantifring plant response to ozone, estimated ozoneuptake as the product of ozone exposure (total hours of exposure x average concentration) andmean diffusive conductance for each species. Using linear regression analysis of published datafor conifers, hardwood trees and agricultural crops, percent reduction in net photosynthesis andgrowth were better correlated with estimated uptake than with ozone dose. They suggested thatvariation in sensitivity to 03 was dependent on variation in leaf diffusive conductance. Althoughhe was able to show an improvement in the relationship’s fit using uptake rather than exposure, theassumption that each species has the same conductance regardless of concentration or exposuredynamics is not supported in the literature (for example Tingey and Taylor, 1982).Deposition has been studied utilizing micro-meteorological techniques by a number ofauthors above crop, grassland canopies and forested sites (Wesely et al., 1978; Leuning et al.,1979b; Wesely et al., 1982, van Pul et al., 1990; Fuentes et al., 1992; Grunhage et al., 1993,371994). Some researchers have taken the approach of assuming that the rate of uptake by vegetatedsurfaces is equal to the vertical flux or dry deposition rate associated with turbulent mixing abovethe plant canopy (Wesely et al., 1978). Meteorological techniques commonly used in depositionstudies are based on the theory of turbulent flow of the atmospheric boundary layer and wereoriginally used to calculate flux densities of heat, momentum, and water vapour (van Pul et al.,(1990).Van Pul (1990) compared the use of three meteorological techniques (eddy-correlation,profile or gradient and Bowen-ratio) to examine flux of 03 to a maize crop. It was concluded thatboth the eddy-correlation and Bowen-ratio techniques gave fairly reliable estimates of the dailydeposition of 03. However, as Leuning et al. (1 979b) point out, the use of the Bowen-ratioapproach is hampered by the inability to distinguish between fluxes to the soil and vegetation.Wesely et al., (1978) utilized the eddy-correlation technique to determine the deposition velocity of03 above a maize canopy. They found that 03 deposition exhibited a diurnal trend varyingbetween 0.2-0.8 cm s’ in the day with the highest peaks occurring mid-morning. Since mesophyllresistance to water loss is often set at zero, Wesely et al., (1978) assumed mesophyll resistances to03 to be small in their calculations, despite its low solubility in water. Further work over a fullcanopy soybean field found the deposition velocity of 03 to be approximately 0.8 cm s4 in the dayand 0.3 cm s1 under windy conditions at night (Wesely et al., 1982). Leuning et al. (1979b) usingthe Bowen-ratio and the canopy resistance approaches estimated that typical 03 fluxes to cornplants ranged from 0.3-0.8 .tg m2 s when the ozone concentration varied between 40-100 ppb,whereas Fuentes et al. (1992) found that ozone deposition over a deciduous forest was muchhigher, ranging from 1.0-1.7 ig m2 s’ when the ozone concentration varied between 60 and 80ppb. This is not surprising given the turbulent structure of air over a forest canopy and the largerleaf area relative to crop plants (Fuentes et al., 1992).Grunhage et al. (1993) found an improvement in the relationship between flux density(calculated using a micrometeorological approach) and leaf injury in tobacco, compared to thatbetween ozone concentration expressed as the seasonal 7-h ozone concentration and leaf injury.38Flux density was a function of ambient 03 concentration, exchange properties of the atmosphereand the sink strength of the plants. Further work by Grunhage et al. (1994a) examined theresponse of a grassland ecosystem to air pollutants from which they were able to demonstrate thatflux densities depended on the exchange properties of the atmosphere such that equalconcentrations of a gas and equal sink properties of the canopy did not necessarily result in similarflux densities.In these studies the effects of 03 deposition on the plants are not described. Anassessment of plant response when subjected to these estimates of pollutant flux is a necessaiyfollow-up. As indicated previously, Aniiro et al. (1984, 1985) examined the injury response ofbean to ozone flux densities comparable to those estimated by Leuning et al. (1 979b) and Weselyet al. (1978). Although they confIrmed that plants subject to the greatest pollutant flux had thehighest injury no assessment on growth or yield was made.Many researchers have recognized that pollutant uptake is influenced by environmentalconditions (Bennett et al., 1973; Unsworth 1980). This is supported by the work of Mukammal(1965) who successfully related the occurrence of injury in tobacco to uptake based on the ambientconcentration of 03 modified by a coefficient of evaporation (proportional to evapotranspiration).Investigations such as Mukammals point to the need for measurements of 03 uptake via thestomata, during investigations aimed at establishing dose-response relationships for vegetation.Additional work by Mukammal et al. (1982) and Adomait et al. (1987) investigating 03 injury tobean and relating it to environmental factors such as temperature and rainfall, further reinforcedthe validity of the previous work. In each case meteorological data were used as substitutes forstomatal information to provide estimates of flux.392.5.2 EFFECTS OF OZONE ON DIFFUSIVE CONDUCTANCEStomata! behaviour is of interest in air pollution studies because of its control over CO2exchange, internal plant water status and gaseous pollutant uptake. Qualitative and quantitativedifferences in stomatal behaviour among plant species, ecotypes or phenological stages may playimportant roles in determining plant performance and adaptations (Butler and Tibbetts, 1979;Olszyk and Tibbetts, 1981; Reich and Admundson, 1985). This is thought to be one of the reasonsthat different plants and cultivars show differing sensitivity to air pollutants (Engle and Gabelman,1966; Reich, 1987). However there are some differences in sensitivity that are not attributable tostomatal responses (Bicak, 1978; Elkiey et a!., 1979; Coyne and Bingham, 1982).Environmental factors have been found to act as modifiers of the effects of air pollutantson conductance and may serve in part to explain inconsistent stomatal responses to 03 (Darrall,1989). Although differences in susceptibility to 03 among plants have also been attributed toanatomical or morphological characteristics by some researchers, the role of stomata in regulatinggas exchange suggests stomata play a major role in species and cultivar differences to gaseous airpollutants. Stomatal aperture has a major influence on the rate of uptake of gaseous pollutants andthe role of the stomata in determining resistance to gaseous pollutants has been extensivelyreviewed (Unsworth and Black, 1981). Stomata can in turn, be affected by the direct effect of airpollutants on the guard cells and by indirect effects on the process of photosynthesis resulting in anincrease in intercellular CO2 concentration (Mansfield and Freer-Smith, 1984).Short-term measurements of photosynthesis, transpiration, respiration and stomata!conductance are often used as an indicator of the physiological status of plants. Rates or averagesobtained from these studies are often useful for comparing gross physiological differences betweenspecies or shoots of the same species. However, short-term measurements can vary both hourly,diurnally and seasonally and also between shoots of the same plant. As a result, short-termmeasurements provide limited information regarding the mechanisms controlling photosyntheticcapacity and transpiration (Winner and Mooney 1980; McLaughlin et al., 1982; Winner et a!.,1985). Nevertheless, simultaneous measurements of stomata! conductance and photosynthesis40during fumigation episodes provide information regarding the interrelationships of these processes.Reductions in conductance as a result of exposure to 03 may not result in a proportional decline inphotosynthesis. These issues are discussed further for crops and trees in the following sections.2.5.2.1 CropsExposure to ozone has been shown to affect stomatal movement in crops. Increases inconductance have been observed for pea (Olszyk and Tibbitts, 1981a) and temporarily in bean(Schenone et al., 1994). Decreases have been observed for a number of species, some of whichinclude bean (Butler and Tibbitts, 1979), radish (Pell et al., 1992), soybean (Reich et al., 1985).Lack of response to 03 has also been reported for radish (Atkinson et al., 1988; Pell et al. 1993).Reich (1987) suggested that sensitive species and cultivars generally have higher stomatalconductances. Consequently, it is assumed that uptake is enhanced in these plants at a particularambient concentration, more so than in resistant species. There is some evidence in the literature tosupport this view. Moldau et al. (1990) found that the decrease in stomatal conductance of beanexposed to acute levels was highly correlated with total 03 absorbed and less so with the above-leaf ambient concentration. They concluded that stomatal closure in the presence of 03 resultedfrom adsorption of 03 on to the surface of stomatal cells where derivatives of the oxidant causedchanges in cell wall properties.Chronic exposures are differentiated from acute exposures by the concentration andduration of the enrichment delivered. Acute exposures are typically short-term fumigations (lessthan one day) at concentrations in excess of 200 ppb. Extrapolation of impacts from acuteexposures to situations at lower concentrations may be unrealistic as the sequence of events thatoccurs with acute exposures is likely different from chronic exposures. Resistant cultivars of onion,tobacco and bean have been shown to close their stomata more rapidly or completely thansusceptible cultivars during fumigation with 03 (Engle and Gableman, 1966; Rich and Turner,1972; Knudson-Butler and Tibbitts, 1979; Amiro et al., 1984; Temple, 1991). This is alsosupported by work on the development of resistant lines of radish cv. Cherry Belle (Gillespie and41Winner, 1989). Resistant plants had 50% lower conductance rates in 03 treatments.Work by Sanders et al. (1992a) suggests that there is a threshold in response below whichan impact on stomatal response does not occur. Decreases in conductance in the lower ozonetreatments were not observed. Negative impacts on conductance in the non-filtered-high (NF-H)03 treatment were reversible, and these plants recovered to pre-exposure levels before the onset ofvisible injury.Muichi et al. (1988) examined the effect of maturity class on stomatal conductance insoybean (measured twice between 1000 and 1400 h) and found conductance was lower followingexposure to ozone delivered at NF+40 ppb (6hlday 5 d/week for 13 weeks). However, maturityclass was unaffected by treatment.Work by Atkinson et al. (1988) indicate the variation in response of conductancemeasurements throughout the growing period of a crop. They measured stomatal conductance onradish using a steady state porometer, on days 9, 25 and 37 after planting. At day 9, conductancein CF air was higher than at 200 ppb 03; by day 25, stomatal conductance was higher at 200 ppband lower in the control. By day 37, conductance was lower in the03-treated plants, but thedifferences were not significant. These results indicate the problem associated with makingcomparisons between treatments on the basis of short-term measurements and extrapolating toresponse over the entire exposure period of a crop.Increased stomatal resistance in response to ozone stress has been noted by Dijak andOrmrod (1982) with peas exposed in chambers to 150 ppb for 6h/day. Response was influencedby the cultivar. Mini, a sensitive cultivar, had slower stomatal closing than resistant cultivars.There was also a greater production of ethylene in03-treated plants regardless of the cultivar. Thesluggish response of stomata following exposure of primary bean leaves to 03 has also been notedby Rosen and Runeckles (1977). In contrast to Dijak and Ormrod (1982), Olszyk and Tibbitts(1981a) have reported an increase in stomatal conductance of pea cv. Alsweet exposed to 130 ppb03.There is some evidence suggesting that flux density influences stomatal response. Amiro42et al. (1985) found an increase in bean conductance to 03 at low flux densities (<4 mgm2h’)and a decrease at high flux densities. The increase was associated with an increase in transpirationand photosynthesis. The authors concluded that changes in the mesophyll or residual conductancein addition to stomatal conductance to 03 flux may be a contributing factor at high flux densities.Factors such as relative humidity and soil moisture have been shown to modify stomatalresponse to 03. Rich and Turner (1972) found that at high relative humidity stomatal resistance inPinto bean was unaffected by the presence of 03. However, at low relative humidity stomatalresistance is increased. When the plants were water stressed, stomatal resistance increased abovethat of non-stressed plants, in the presence of 03. This possible reduction in uptake associatedwith an increased resistance has been suggested as an explanation for why water-stressed plantsare more tolerant of 03 (Tingey and Hogsett, 1985). While exposure to 03 resulted in a decreasein stomatal conductance in soybean, Reich et al. (1985) found no interaction between water stressand 03 treatments. Despite this, water use efficiency declined with 03 exposure in plantsmaintained at field capacity, suggesting that stomatal conductance was reduced in response toincreased internal CO2 concentrations as a result of decreased net photosynthesis.Seasonality appeared to play a role in affecting stomatal response to 03 stress. Pell et al.(1993) found that stomatal conductance of Radish cv. Cherry Belle and Wild Type was unaffectedby exposure to 03 in the spring. However, significant decreases occurred with exposure to 03 inthe fall. This was despite the greater concentrations of ozone delivered in the spring (due to higherambient air levels) and the higher overall conductance values for this period. Differences in lightintensity and air temperature (both were lower in the fall experiment) were cited as possibleexplanations for the variation in responses measured. Seasonal differences in stomatalconductance have also been reported by Aben et al. (1990) in their work on faba bean. Exposureto 03 during early summer resulted in minor declines in stomatal conductance and no effects onphotosynthesis and respiration, whereas in the fall, stomatal conductance, photosynthesis and wateruse efficiency declined and respiration increased. The differences in response were attributed tobetter growing conditions and higher 03 uptake rates at the same 03 concentration in the fall. The43authors concluded that 03 had a direct effect on the stomata and photosynthetic system and thatthe stomata were more sensitive.2.5.2.2 TreesStomata! responses to 03 in trees are quite variable. A decrease in stomatal conductancewith 03 exposure has been observed for Pinus ponderosa. (Coyne and Bingham, 1982); Abiesalba L. (Keller and Hasler, 1984); and Picea abies (Wallin et al., 1990). Other researchers havefound no significant change in stomata! conductance following exposure to 03 for Quercus albaL.(Foster et a!., 1990), Picea abies (Dobson et al., 1990; Fuhrer et al., 1990), Pinus taeda L.(Sasek and Richardson, 1989) and Abiesfraseri (Pursh.) Poir. (Seiler et al., 1994). In otherstudies, stomata! conductance was found to be higher in03-fumigated Picea abies (Keller andHasler, 1984; Keller and Hasler, 1987; Freer-Smith and Dobson, 1989; Eamus et al., 1990), Pinussylvestris (Skarby et al, 1987) and Picea sitchensis (Freer-Smith and Dobson, 1987; Dobson et al.,1990;).Various factors have been shown to influence stomatal response or lack of response to 03exposure. These include the tree species (Reich and Lassoie, 1984; Reich and Amundson, 1985),needle age (Coyne and Bingham 1981; Reich and Lassoie, 1984; Eamus et al., 1990), time of daywhen the measurements are taken (Skarby et a!., 1987), relative humidity (Reich and Lassoie,1984; Jensen and Roberts, 1986), timing of measurements, (i.e. measurements taken concurrentlyor post treatment) (Keller and Hasler, 1987; Eamus et al., 1990), dormancy status or active growth(before or after budbreak) (Freer-Smith and Dobson, 1989), ozone concentration (Reich andLassoie, 1984; Keller and Hasler, 1987) and degree of foliar injury (Coyne and Bingham 1981).Other factors such as light intensity have also been shown to influence stomatal responseof03-stressed trees. Keller and Hasler (1987) found that stomata of a single genotype of Piceaabies were less responsive to light intensity changes after 03 exposure, suggesting that 03 mayhave a direct effect on stomata! function.Recently some work has been carried out with trees to assess the interactions between 0344and drought (Maier-Maercker and Koch, 1991; Dobson et al., 1990; Tseng et a!., 1988). There isevidence to suggest that water-stressed plants are less sensitive to 03 fumigation because 03uptake is limited by stomata! closure in response to drought (Tingey and Hogsett, 1985).However, the degree of protection offered by water stress may be counterbalanced by a reductionin CO2 fixation induced by the water stress (Temple and Benoit, 1988). Dobson et al. (1990)reported that a decline in conductance for Picea abies subjected to drought corresponded with adecrease in uptake for this species. They also found evidence of stomathl opening in response to03 in plants maintained at field capacity whereas this effect was reduced with water-stressedplants.Tseng et al. (1988) working with Abiesfraseri found no interaction between water stressand 03. Exposure to 03 for ten weeks had no significant effect on transpiration or stomata!conductance. However, an increase (not significant) in conductance was seen at concentrationsexceeding 100 ppb. In contrast, water stress resulted in a significant decrease in plant biomass,transpiration, stomata! conductance and photosynthesis. Maier-Maercker and Koch (1991, 1992)in their work on Picea abies found a deficiency in stomatal control of leaves subject to droughtstress when exposed to ambient versus filtered air. They suggested that the dysfunction is relatedto structural changes, such as partial deligrnfication in cell walls of the stomatal apparatus. If thestomata! cell wall stores more water as a result of partial delignification stomata would be lesssensitive to the drying of air. As a result, the stomata would not react as quickly, exacerbatingwater loss in the drought-stressed tree.Keller and Hasler (1984) found that Picea abies stomata declined in responsivenessfollowing exposure to 03. This was also reported by Barnes et al. (1990) for Picea abies, forPinus sylvestris by Skarby et al. (1987), and for Populus deltoides Bartr. by Reich and Lassoie(1984), who also suggested that the slow response of stomata is a result of accelerated aging. Intheir study the absolute conductance was unaffected, but the range of individual leaf conductanceswas reduced by exposure to 03 concentrations of 125 ppb.In summary, the physiological responses of plants to chronic low levels of 03 are not well45defined. Both increases, decreases, no change, or a reversal back to the status gj upon theremoval of 03 have been reported for photosynthesis, transpiration and stomatal conductance, in anumber of species. Studies investigating the response to acute exposures are more clearly defined(Darrall, 1989). Concentrations in excess of 200 ppb typically result in a loss of cellularhomeostasis with irreversible damage to the plant (Sanders et al., 1992a). Recovery after low levelexposure to 03 indicates an adaptation to stress, which may be diminished with subsequentexposures (Heath, 1988).463. MATERIALS AND METHODS3.1 Experimental DesignThe experiments were conducted in field plots in the Zonal Air Pollution System (ZAPS)located on the Totem Field of the University of British Columbia during the summers of 1986,1988 and 1989. The system, which is described below in Section 3.2, provided twelve 03enrichment treatments over three 12 x 10 m blocks (Figure 2). In 1986, a fourth block providedambient air controls and in 1988 and 1989 two additional controls were added. Block 4 waspositioned as in 1986; block 5 was situated in a moist area of the adjacent field and block 6 in adrier site (Figure 2).One drawback of working with a gas using an open-air system is that it prohibits theinclusion of a control within each block where the 03 enrichment is applied. The experimentaldesign is one with incomplete blocks. Control or ambient air blocks were situated separately fromthe blocks where gaseous treatments were to be applied. However, since the control blocks werethemselves exposed to the stochastic exposures characteristic of the ambient air, they also received03 exposures. Inter-comparison among the control blocks permitted an estimation of thehomogeneity of the growth responses.The plant species used in this experiment were one cultivar each of pea, potato and bean,two of radish and a provenance of Douglas-fir. The crops and cultivars were selected on the basisof vegetable production data which demonstrated their importance in terms of the amount ofproduction in the Lower Mainland of B.C (Data provided by M. Sweeney, B.C. Ministry ofAgriculture and Food, Abbotsford). In addition, the use of radish was desirable due to its compactnature and short life cycle. Furthermore, it has been used by a number of authors (Tingey et al.,1971; Walmsley et al., 1980; Reinert and Saunders 1982; Johnston et al., 1986) as a bioassayspecies, but not under field conditions. Douglas-fir is an important timber species of the west coastof Canada, but little is known of its response to ambient 03.Within each 3 by m experimental plot the selected species were randomly assigned to47Block 4(control)supply line\12m4+1 :1Om[ 1Block 5(control)N25mBlock 6(control)25m4’Block 225m/; Iment hut/////Block 1 Block 3Figure 2. General layout of the Zonal Air Pollution System (ZAPS). Numbers on theblocks refer to the densities of orifices in the manifolds over individual plots in 1986(See Section 3.2). Note: Control Blocks 5 and 6 were only used in 1989.48sample areas. All one metre (m) borders surrounding the sample plot areas were planted with theadjacent species, but plants were not harvested from these rows. In addition, plants adjacent togaps in plots left by earlier harvests were not utilized. The number of rows devoted to each cropwithin a sample plot area was 4 each for pea and potato in 1986. In 1988, 4 rows of bean and 13rows of tree seedlings were planted in each plot area. Standard planting densities were used for thecrops.3.2 Field Plots and Gas Delivery SystemThe Zonal Air Pollution System (ZAPS) utilized for dispensing the gaseous treatment hasbeen described previously by Wright (1988) and Runeókles et al. (1990). Briefly, the systemconsisted of a manifold of PVC pipes supported 80 cm above the ground over each treatmentblock, (Figure 3) excluding the controls. Horizontally positioned orifices spaced at 1 m intervalson alternating sides of the manifold were continuously supplied with air, which could be enrichedwith 03, through underground PVC pipes. Although the same amount of03-enriched air wassupplied to each manifold, different amounts were released over the different plot areas accordingto the density of orifices (ito 4) at each location in the manifold over each plot. The differentnumbers of orifices were randomly assigned to the different plots at the beginning of each fieldseason. In the case of radish and Douglas-fir in 1989, three separate randomizations of orificedensities (03 treatments) took place, one prior to the initiation of each radish experiment. Sincethe objective of the study was to provide 12 randomly different exposure regimes, this rerandomization of orifice densities in 1989 was compatible with providing stochasticity. These 12plots, in addition to the two plots assigned in the control block in 1986 and the three ambient airblocks in 1988 and 1989, made a total of 14 plots and 15 plots respectively.Total exposure periods for pea, potato and bean were 58, 75-76 and 43 days respectively,in 1986 and 1988. In 1989 fumigations began in the ZAPS plots on June 6 and continuedthroughout all three radish experiments (29 days each) (Table 4).49lOmE23mburiedsupply line —_____________ __________ ______________— 8 m_A4mplot areasFigure 3. Zonal Air Pollution System manifold layout. Orifices spaced at imintervals, directed inwards on the peripheral pipes and bilaterally on the interiorpipes.50Table 4. Summary of the ozone exposure periods for three growing seasons.Year Supply period Dates Julian days Days after planting1986 0700-2100PDT1 June2-Augustl7 153-228 241988 0700-2100PDT June27-September26 178-269 331989 0700-2 100 PDT June 6-September 15 157 - 257 NAPDT’ Pacific daylight timeNA Not applicableIn 1989, 03 treatments were initiated approximately 37 days following the first flush ofgrowth in Douglas-fir. The total exposure period for 1989 was 101 days. The pattern of dailysupply of 03 was the same as that for radish.In each year the 03 generator (Grace Model GS 4060) was supplied with compressedoxygen throughout the experimental period. The supply of 03 extended over a 14 hour period,0700-2100 hours, Pacific daylight time (PDT). In 1989 the exposures commenced June 6 andterminated September 15. A breakdown in the 03 generator (and a delay in the arrival ofreplacement parts) prevented an earlier commencement of 03 treatments, which had originallybeen planned for April to follow the initial harvest (prior to bud break in the tree seedlings).3.3 Ozone Monitoring and ControlAn 03 monitor, Model 1003-AH (Dasibi Environmental Corporation, Glendale, CA) wasoperated on a time-sharing basis among the 14 treatment plots for a 2-minute sampling period onceevery 30 minutes. In 1988, the Model 1003-AH 03 monitor and a Bendix Model 8002 monitor(Bendix Corp., Lewisburg, WV.), on loan to the B.C. Ministry of Environment from WashingtonState University, were operated on a time-sharing basis among the 15 treatment plots. Air fromeach of the 15 plots was drawn continuously through 1/4 inch teflon lines of equal length (40 m),each fitted with dust filters. The filters used were Duropore HVHP, 0.45 rim, 25 mm diameter51(Millipore). All filters were replaced every two weeks. The location of two additional samplinglines in blocks five and six, 25 m from block 4, necessitated lengthening all the sampling lines to 40m from the previous 30 m in 1986. Previous testing found line losses to be 0.1 ppb/m (Wright,1988). All sampling line inlets were located at bean plant canopy height, and thus variedthroughout the season (40-8 0 cm).The 03 concentration released over the treatment plots was controlled by a feedbackprogram from a Campbell Scientific Model 21-X data acquisition system as in 1986 (Wright,1988). This feedback program regulated the input voltage to the 03 generator. An algorithm for03 generation, utilizing information from the average of six readings taken over two minutes every30 minutes for the ambient air plot (control block 4), was used to adjust the output to theservomotor controlling the generator.To reduce the occurrence of sharp peaks at start-up and termination of the daily release ofsupplementary 03, a step-up and step-down procedure was programmed into the Campbell 21-Xwhich progressively modified the gain on the generator (from 0% to 100% or 100% to 0% in stepsof 25%) for the first two and last two hours of each daily enrichment period.In 1989 two 03 monitors, Model 1003 -AH (Dasibi Environmental Corporation, Glendale,CA), were operated on a time-sharing basis among the 15 treatment plots and an additionalmonitoring line was used in block two. Air from each of the 16 sampling locations was drawncontinuously through 1/4 inch teflon lines of equal length (40 m), each fitted with filters as in 1988.All filters were replaced every two weeks.All sampling line inlets were located at 40 cm above the ground. They were situated inthis position to provide the best compromise between the differing canopy heights of the radish andtree seedlings.In addition to monitoring the plots for 03, air was monitored in block 2 (Figure 2) fornitrogen oxide (NO) and nitrogen dioxide (NO2)using a Thermo Electron (Model 14D/E)Chemiluminescent NOx, NO, NO2 analyzer. Additional channels in the Campbell 2 1-X collectedwind speed, and wind direction data, relative humidity, soil and air temperature and the nitrogen52oxide and nitrogen dioxide measurements. All measurements were made every two minutes.3.4 Data Collection and HandlingDetails of the data collection and handling are applicable to all years. In brief, the signalsfrom the Campbell 21X were stored on cassette tapes and downloaded to the U.B.C. mainframecomputer for processing and analysis.35 1986 Field Experiment3.5.1 FIELD PREPARATIONThe field was treated with Roundup (Glyphosphate) to kill all perennial weeds and grasses.Following this the land was prepared by rototilling and harrowing and an all purpose fertilizer(13:16: 10) was applied at the rate of 900 kg/ha on all plots. Following incorporation of this intothe soil, the ZAPS was installed above the treatment plots.3.5.2 PLANT MATERIALSThe two plant species used in this study were one cultivar each of pea (Pisum sativum L.cv. Puget) and potato (Solanum tuberosum L. cv. Russet Burbank).Standard planting procedures were followed for each crop and are outlined in Wright(1988), Seeding took place May 9-10. The growing season for peas was 83 days and for potatoes100 days. The exposure season for peas was 58 days and 75-76 days for potatoes.CROP, PEST AND RODENT MANAGEMENTDetails of the management of these two crops outlined in Wright (1988), are brieflysummarized here. The peas were fully emerged by May 19, 10 days from seeding. Potato shootswere completely emerged by May 30, 21 days from planting. Unseasonably cool spring weatherdelayed crop emergence.Rainfall was supplemented by overhead sprinkler irrigation where necessary during thegrowing season. Undesirable insects were controlled by applications of Diazinon to the foliage at53the recommended rates. Attempts to control rodents by traps and poison bait were only moderatelysuccessful. Due to the occurrence of an infestation of Early Blight (Alternaria solani) the potatoexperiment was terminated 20 days earlier than planned.3.5.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS3.5.4.1 PeaA total of six harvests spaced at 11 day intervals was taken for pea over the 83 daygrowing season. The dates for these harvests were as follows: June 6 (28 days after planting), June17, June 18, July 9, July 20 and July 31. The timing of the final harvest was established on thebasis of pea development, considering all plots collectively. At each harvest, eight plants wererandomly selected (as determined prior to commencing the experiment) from the row and cut atground level. Two plants were left on either side of each gap.At each harvest, the following measures of plant size were obtained: stem length (SL) fromthe soil surface to the tip of the main stem, number of leaves (LN), leaf area (LA), pod number(PN), flower number (FN), bud number (BN), pod weight (PW), flower weight (FW), bud weight(BW), leaf weight (LW) and stem weight (SW). The numbers of peas in size classes less than “l”,and “1 through 6” were assessed at the final harvest. This procedure is employed by the industryto assess plant maturity and schedule harvests. Pea and pod fresh weight were recorded at the finalharvest. Pea data were recorded on a per plant basis.Dry weights were measured using a Mettler PC4400 balance after samples were dried toconstant weight in a forced air oven at 70°C. Leaf area and leaf weight included the petiole,tendrils and stipules. Leaf area was obtained using a LI-COR LI-3 100 leaf area meter.Data on pod and pea fresh weights have been presented in Wright (1988).3.5.4.2 PotatoA total of five harvests spaced at 18 day intervals was used for potato. The dates for theseharvests were: June 9 and 10 (31 days after planting), June 26 and 27, July 13 and 14, July 30 and31 and August 16 and 17.54On the basis of a randomization scheme developed before planting, three plants wereremoved in the first four harvests and six in the final harvest. One plant remaining on either side ofthe gap resulting from successive harvests was not harvested. Measurements taken at each harvestincluded height of the longest stem from the soil surthce to the main stem tip (SL), number ofleaves (LN), leaf area (LA), tuber number (TN), stem number (SN), tuber dry weight (TUW) andleaf weight (LW).Leaf areas were again measured using a LI-COR LI-3 100 leaf area meter. Leaf area andleaf dry weight included the petiole and leaf blade. Leaf area was not obtained for the finalharvest. Tubers were left in the field to mature for one week following the harvesting of the tops.Upon harvesting, excess soil was removed from the tubers, fresh weights were determined and thetubers were subsequently dried. Dry weights were measured using a Mettler PC4400 balance aftersamples were dried to constant weight in a forced air oven at 700C. The numbers of tubersmeeting the minimum size (industry standards) were assessed at the final harvest and are referredto as marketable tubers. Data on tuber fresh weight have been presented in Wright (1988).3.6 1988 Field Experiment3.6.1 FIELD PREPARATIONA composite soil sample was taken from each of the six blocks, April 7, 1988 (Figure 2).The samples were air dried and sieved using a 25 mm stainless steel sieve. The samples wereprepared for the following analyses in the Soil Science Department at U.B.C : pH, total nitrogen,available phosphorus, calcium, magnesium and potassium. Soil for pH determination was mixedinto a slurry with distilled water (ratio of 1:1), left to stand for one hour, then read using a FisherAccumet pH meter. Morgan’s extraction procedure was used for detennining available calcium,magnesium and potassium. Extracts were subsequently read using an Atomic AbsorptionSpectrophotometer. Bray’s method was utilized for determining available phosphorus. Sampleswere read on a spectrophotometer at 700 mu. To assess soils for total nitrogen, soil samples were55digested in concentrated sulphuric acid at 420°C. Total nitrogen was determined using anautoanalyzer. Results from these soil tests are presented in Appendix 1. A compromise had to bemade between the results of the soil tests and the needs of bean versus tree seedlings, for the finalapplication of fertilizer to the blocks. A mixture of 3 4-0-0, 13-16-10 and 0-0-60 was applied at arate of 300 kg/ha in a ratio of 1:6:2 respectively. This amounts to a rate of 37.3 kg/ha for N, 32kg/ha for P and 60 kg/ha for K.In late April, all sampling lines were retrieved and cleaned by blowing oxygen throughthem. All lines were then extended to a standard length of 40 m and reburied.On May 5th, fertilizer was broadcast on the plots at the rates indicated above and rototilledinto the soil. On May 9th, the plots were harrowed and the ZAPS was installed over the threetreatment blocks. The plots were then raked by hand and marked out for planting.3.6.2 PLANT MATERIALSBean seed (Phaseolus vulgaris L., cv. Galamore) was supplied by W. Brotherton SeedCo., Inc. Moses Lake, Washington, US4 through Royal City Foods Ltd.. Seeds inoculated withlegume Rhizobium (Nitragin C. Milwaukee, Wis.) were sown five cm deep in rows 50 cm apart,May 25- 26, 1988. Thinning to five cm apart took place when the cotyledons had fully emerged.Due to equipment failures, treatments did not commence until June 27th, 33 days after planting.The Douglas-fir seed source was coastal, taken at an elevation of 152 m from the BlueMountain area. Typical lengths for the 2+0-year old seedlings were 20 cm with a root collar of 3.5mm. These trees, supplied by B.C. Ministry of Forests were lifted in November 1987 andmaintained in coolers at a temperature of 0-2°C until delivery on May 24, 1988. Trees weremaintained in a cooler at approximately 4°C until planting, May 25-26.The seedlings, were planted 25 cm apart, with rows 25 cm apart. In the interest ofproviding a possible source of mycorrhizal inoculum, a handful of forest soil (A horizon and litterlayer) was placed in the rooting zone of each seedling. Soil from the A horizon was obtained fromthe U.B.C. Haney Research forest, Maple Ridge, B.C.. Soil from the litter layer was obtained56from a mixed forest near Port McNiell, Vancouver Island, courtesy of Morag McDonald (U.B.C.,Department of Forestry).3.6.3 CROP AND PEST MANAGEMENTGermination was delayed due to unseasonably cool weather. The beans were fullyemerged by June 6, 12 days from seeding. After planting, weeds were controlled by hoeing andhand weeding. Natural rainfall was supplemented by overhead sprinklers on June 25, July 2, July21, July 27 and August 6.By June 20, chlorosis was noted on bean plants in a number of the plots, particularlyblocks one (1-hole and 3-hole plots), two (1-hole and 3-hole plots) three (1-hole and 4-hole plots)and five. After consultation with Dr. B. Holl (Department of Plant Science), an iron deficiencywas suspected and beans in all plots were treated with a foliar spray of 330 Fe Sequestrene (10.0% Fe) applied at standard rates (1 lb/100 gal). In a further effort to counteract the chlorosis, 12g/row of 3 4-0-0 (rate 40 kg/ha) was applied for the bean crop in all plots, June 21. Approximately1.5 ml of 3 4-0-0 was applied to the base of each tree in all plots.No improvement was noted in the chlorotic plants by June 24. A solution of 15-15-18with trace elements was applied at the rate of 9 litres per 10 m row in all plots. This treatment wasrepeated on July 13. Tree seedlings were not treated. An improvement was noted in the conditionof all bean plants.The tree seedlings flushed twice during the 1988 growing season, in the third week of Juneand, again during the first week of August.3.6.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS3.6.4.1 BeanUsing a randomization scheme developed prior to planting, 16 plants (cut at ground level)were measured per harvest for a total of six harvests over the 76 day growing season. Two plantson either side of the remaining gap were not harvested. The dates for these harvests were as57follows: June 20-21 (prior to the commencement of 03 treatments), June 30-July 1 (3 days after03 treatments commenced), July 11-12, July 20-21, July 29 and August 9.The timing of the final harvest was established on the basis ofpod development. Thenumber of beans meeting the minimum diameter class (industry standards) was assessed at thefinal harvest and are referred to as marketable pods. This procedure is employed by the industry toassess plant maturity and schedule harvests. At each harvest, the following measures of plant sizewere obtained: number of leaves (LN), leaf area (LA), pod number (PN), flower number (FN), budnumber (BN), pod weight (PW), flower weight (FW), bud weight (BW), leaf weight (LW) andstem weight (SW). Pod fresh weight was recorded at the final harvest. Bean data were recordedon a per plant basis.Dry weights were measured using a Mettler PC4400 balance after samples were dried toconstant weight in a forced air oven at 70°C. Leaf area and leaf weight included the petiole. Leafareas were obtained using a LI-COR LI-3 100 leaf area meter.3.6.4.2 Douglas-firAs tree growth is typically variable, elucidation of any possible treatments can often beimproved by the use of covariates in subsequent analyses. Preliminary non-destructivemeasurements were taken on all trees in the experimental plots on June 16-17 (22 days followingplanting). These measurements included stem length from the soil surface to the tip of the apicalbud and stem diameter. Due to the elliptical nature of the stem, two measurements were taken atright angles to each other at the base of the stem (ground level), then averaged. Thesemeasurements were used to calculate an initial diameter, which was then squared, and multipliedby the seedling height (D2H) to give “stem volume”. These measurements were repeated onAugust 22-24, 56-58 days following the commencement of the 03 treatments. The stem diameterwas measured using Vernier calipers.One initial destructive harvest was taken in 1988. This took place on June 20 (25 daysafter planting). A total of 60 trees was harvested prior to the first flush taking place (bud break).58These trees were randomly selected from all plots, as no 03 treatments had yet commenced.Measurements taken included the following: length of the stem from the soil surface to thetip of the apical bud (SL); stem diameter (SD, measured as above), stem weight (SW) and needleweight (NW) and leaf area. These data were collected to provide baseline information on theseedlings prior to the commencement of treatments. Dry weights were measured using a MettlerPC4400 balance after samples were dried to a constant weight in a forced air oven at 70°C.3.7 1989 Field Experiments3.7.1 FIELD PREPARATION3.7.1.1 RadishPlastic pots containing standard sterilized potting mix with added fertilizer were employedin these experiments to reduce the experimental error and avoid the soil fertility problemexperienced in 1988. The pots were submerged to simulate field conditions, reduce the degree ofdiurnal heating and soil moisture loss. All plots were cleared of weeds by hand in early May,1989. Holes were dug for each of the 1,500 four-inch pots, 100 per plot, allowing each pot to besubmerged to soil level. Once the pots were in place, netting was place approximately half a metreabove the soil surface to deter birds from feeding on the newly germinated seedlings. Nets wereremoved after the appearance of the first true leaves.3.7.1.2 Douglas-firThe tree seedlings were well established in the plots during the 1988 experimental period.All weeds surrounding the tree seedlings were removed by hand in late April.3.7.2 PLANT MATERIALSTwo cultivars of radish (Raphanus sativa L. cv. Cherry Belle and cv. French Breakfast)were used in this study. Seeds were purchased from Stokes Seeds Ltd., St. Catherines, Ontario.Seeds were sown in four-inch pots containing a standard sterilized potting mix (sandy loam) withadded osmocote (14-14-14, slow release fertilizer). Each of the 100 pots (2 cultivars x 50 pots)59was randomly assigned a position within the control and ZAPS plots.The dates for commencement of the three radish experiments were June 8, July 11 andAugust 15.3.7.3 CROP AND PEST MANAGEMENT3.7.3.1 RadishPlants were thinned to one plant per pot, one week after seeding. Plants were watered asrequired throughout each experiment. All plants were watered during the evening prior to gasexchange measurements.Pests were controlled when necessary with applications of Diazinon at the recommendedrate (1 ml 5OEC/l). Slugs were particularly prevalent during the last two experiments. In anattempt to deter them from feeding on the radish leaves and hypocotyls, bait was placed in variouslocations in each plot. This method was not very successful and resulted in fewer plants beingavailable for measurement at each of the scheduled harvests.3.7.3.2 Douglas-firNeither fertilizer nor supplementary water were applied to the tree seedlings during the1989 experimental period. A patchy infestation of woolly aphid in all blocks, first noticed in lateApril, was treated with applications of Safers soap at a rate of 20 ml per litre (2%) on June 15th.Applications were made with a backpack sprayer and all foliage was sprayed to drip. A repeatapplication was not necessary.Budbreak started during the last week of April. A second flush started during the lastweek in June, with> 90% of the trees flushing by the second last week in July.3.7.4 HARVEST SCHEDULE AND PLANT MEASUREMENTS3.7.4.1 RadishHarvests were scheduled to take place 13, 17, 21, 25 and 29 days following seeding foreach experiment. At each harvest date, plants were randomly selected (selection determined prior60to commencing the experiment), dug up and washed. Hypocotyl fresh weight (final harvest only)and leaf area were then measured using a Mettler PC4400 balance and LI-COR L13000 leaf areameter respectively. Dry weights of the leaves, roots and hypocotyl were obtained using a MettlerPC4400 balance after samples were dried to constant a weight in a forced air oven at 700C.The combination of bird and slug damage in each of the experimental plots severelyreduced the number of available plants for harvest; this led to one of the harvests being eliminated,leaving four harvests per experiment instead of five. In experiment one, bird damage was so severethat a further harvest (17th day harvest) also had to be eliminated.3.7.4.2 Douglas-firAn initial destructive harvest of 26 trees per treatment was taken on April 20, 1989, priorto the first flush of the 1989 season. This was to establish a baseline for any effects that may havebeen incurred as a result of the 1988 03 treatments. The size of the trees dictated the frequency ofsubsequent harvests, which were at approximately three-week intervals following initiation of 03treatment. Needle areas were measured only on trees used for porometer measurements (Section3.7.5.2).Measurements taken at the harvest in April included the following: stem height (FHT)from the soil surface to the tip of the apical bud, stem diameter (STD, as measured in 1988), lengthof the leader (HTERM) and stem dry weight (includes branches and needles).Additional harvests took place June 22 (approximately 2 weeks after 03 treatmentscommenced), July 17, August 3, August 25, and September 15. Measurements taken at this timewere FHT and STD and HTERM. As well, trees were separated into primary (including alloriginal growth having taken place prior to bud break in April), secondary (result of bud break inApril), and tertiary growth. Dry weights of these component parts and needle area (porometertrees only) were recorded.Dry weights were made using a Mettler PC4400 balance after samples were dried to aconstant weight in a forced air oven at 700C. Leaf areas were then measured using a LI-COR61L13000 leaf area meter.A final set of non-destructive measurements was taken on July 9th, 1990. Twenty-fourtrees per treatment were measured. These measurements included FHT and STD and HTERM.3.7.5 PLANT GROWTH ANALYSIS INDICES CALCULATIONSTechniques of plant growth analysis were employed to follow changes during the growthof pea, potato, bean and radish at different 03 exposures. The classical approach was utilized tocompute growth analysis variables for these data sets (Hunt, 1978). Harvest index (HI), leaf arearatio (LAR), specific leaf area (SLA) and leaf weight ratio (LWR) were calculated from the rawdata, then averaged by treatment.Regressions relating total weight as a function of time (days) were fitted for each speciesand treatment using quadratic models (Y = a + b 1x +b2x). Estimated values were extrapolatedfrom the fitted curves. Mean absolute growth rate (AGR) values between successive harvests werecalculated from estimated values of TW as follows:AGR = [Total Weight 2-Total Weight l]/[Time 2- Time 1]As a preliminary step to calculating relative growth rate, the raw data of all response variableswere transformed to natural logarithms and the mean calculated over all plants (subsamples) foreach treatment and harvest. Regressions relating transformed weight as a function of time (days)were fitted for each species and treatment using quadratic models as described previously.Estimated values were extrapolated from the fitted curves. The mean relative growth rate (RGR)between successive harvests for pod weight, hypocotyl weight or tuber weight where applicable,and total plant weight were calculated from estimated values of ln(PW), ln(HW), ln(TUW) orln(TW) using the following formula:RGR = ln(Plant response 2 - Plant response 1)/[Time 2- Time 1]623.7.6 CONDUCTANCE MEASUREMENTS3.7.6.1 RadishConductance measurements were made at hourly intervals over a 10-h measurementperiod, 16, 20, 24 and 28 days into the fumigation period, using a Micromet whole plant porometer(described in Livingston et al., 1984). All readings commenced at 0800 h and terminated at 1700 hwhere possible. As initial trials demonstrated, a minimum of 45 minutes was necessary to samplefive treatments, for both cultivars and two plants per cultivar (subsamples), only one ambient airplot (control block 4) and four 03 enriched plots (Block 2, see Figure 2) were selected forsubsequent measurements.Conductance measurements were undertaken only in the last two experiments due toequipment failure of the Micromet porometer during Expt. one.Two plants (subsamples) per cultivar for each of 5 treatments were measured hourly,representing a total of 10 readings per plant per measurement period (10-h day). Conductance,vapour density and transpiration were calculated by the system software. The plants weredestructively harvested the following day and the data were subsequently corrected for theappropriate leaf area.3.7.6.2 Douglas-firConductance measurements were obtained using a modified Micromet porometer. By theend of the 1988 growing season the size of the tree seedlings had exceeded the chamber dimensionsof the original model. Consequently, a large (1 m length x 30 cm diameter) stainless steel chamberwas constructed together with an appropriately sized hinged base plate to seal the stem (Figure 4).The original temperature and relative humidity sensors and fan, supplemented by an additional fanwere inserted into the modified top plate. The measurements (includes assembly and conductancereadings, which took 40 s) were completed in less than two minutes per tree seedling. Conductancemeasurements were made at hourly intervals, July 6, July 21, July 25, August 2, August 19,August 23 and August 29 for a 10-h measurement period on each63fan motorFigure 4. Modified whole plant porometer (after Livingston et al., 1984). The stainless steelchamber (1 m length x 30 cm diameter) was constructed together with an appropriately sizedhinged base plate to seal the stem.to controllertop platess cylinderhingeVimclaspgasketbase platebase plate64date. Measurements made on July 21-August 2 were repeated on the same tree seedlings, as werethose on August 19 and 23. Measurements earlier than July were not possible as the porometerwas undergoing servicing. All readings commenced at 0800 h where possible and terminated at1700 h. Excessive dewfall prohibited an earlier start for porometer measurements.Two trees per treatment were measured hourly, representing a total of 10 readings per treein one day. As with the radish experiment, one ambient air plot and four 03 enriched plots wereselected for these measurements. The trees were destructively harvested on July 17, August 3,August 25 and September 15, at which point the conductance data were subsequently corrected forthe appropriate leaf area. The lag time between making the conductance measurements andobtaining the biomass data was not considered to be an issue as trees are slow to put on growth,relative to crops.3.7.7 OZONE FLUX ESTIMATIONThe 03 flux densities through the stomates (i.g m2 s’) for radish and Douglas-fir werecalculated as follows:1) The individual hourly means (average of 2 plants/subsamples) of leaf conductance valuesfor water vapour obtained for each treatment were converted to conductance for 03 bymultiplying each value by the theoretical ratio of diffusion coefficients for water vapourand 03 (0.5535).2) All hourly 03 concentration values for each measurement period and treatment wereconverted to jig m3 (ppm x 1997.84).3) The concentration (jig m3)was then multiplied by the hourly conductance values(converted to m and corrected as above) for each treatment for each 10-hmeasurement period.These computations were performed separately for each radish cultivar and Douglas-fir. Asindicated previously, conductance data were unavailable for all 03 and control treatments due tothe length of time needed to obtain measurements. The calculation of flux densities assumes that65the concentration of 03 is in the substomatal cavity is zero (see Laisk et al., 1990).3.8 Data Analysis3.8.1 EXPOSURE STATISTICSIt is clear from the literature that an assessment of impacts ofgaseous pollutants on cropsis very dependent on the choice of the index used to define the exposures received. Cumulativeindices such as SUM6O, A0T40 and various weighted indices (described in Appendix 2) have beenproposed as being generally applicable as independent variables in response models (Lefohn andRuneckles, 1987; Lee et al., 1991; Fuhrer and Achermann, 1993), because of their biologicalrelevance in contrast to seasonal means such as M7 or M12 (Musselinan et al., 1994). However,in the present studies and others based on experimentation using chamberless field exposures(Wright, 1988), analyses comparing the use of these indices with those of the DAYXX typeshowed that the DAY5O index performed well in both linear and non-linear models. Hence it wasused as the independent variable in the response models presented here.The D5OC index is based on the hourly means (derived from 2-minute means (n = 6)recorded for each treatment between 0900-205 9 h, and summed over the harvest intervals for thecrop in question. This exposure index is simple to compute and accounts for concentrations inexcess of a threshold value reported to be potentially injurious to crops (Krupa et al., 1993;Grunhage and Jager, 1994). It is a cumulative index and does not violate statistical assumptions ofnormally distributed data (such as taking a seasonal average) in its computation. It falls short inthat it is still a single summary of exposures applied, which by virtue of the pollutant in questionand the type of exposure system used here, is stochastic in nature.The impact of 03 exposure on indices of growth (AGRand RGR) was examined usingboth interval (I), and cumulative exposure indices (C), as the independent variables in regressionanalyses. “Interval” refers to the exposure between harvest intervals, for example, at harvest 2 theindependent variable is Harvest 2-Harvest 1 exposure. “Cumulative” refers to the cumulativeexposure history for that harvest, commencing with the initiation of 03 treatments. This procedure66was repeated for all data sets using D50 in the computations.3.8.2 EXPOSURE-RESPONSE REGRESSION ANALYSESAn assessment of the impact of treatments applied is to some extent dependent on thechoice of the mathematical form of the response model. As an initial approach to addressingObjective 1, simple linear regression analysis was used to establish the nature of the relationshipsbetween 03 treatment and total plant biomass, vegetative weight (for potato), leaf area, tuber, bud,flower, pod, leaf, and stem weight (where applicable), fresh weights for pea, potato and bean,radish hypocotyl (FHW), and simple ratios which included Harvest index (HI) (marketableweightltotal weight), and Leaf area ratio (LAR), Specific leaf area (SLA) (leaf area/leaf weight),Leaf weight ratio (LWR) (leaf weight/total weight), absolute growth rate (AGR) (glday) andrelative growth rate (RGR) (gig/day), at different stages during plant growth. A separate analysiswas performed for each harvest, utilizing the D5OC computed on a per harvest basis as theindependent variable. Data used in the regression analyses were treatment means (N=14 in 1986;N=15 in 1988 and 1989). Each value represented the mean of 3 to 6 plants (subsamples) forpotato; 6 to 10 plants for pea; 8 to 16 for bean and 5 to 10 for radish. If curvilinearity wasobserved in the scatterplots, non-linear regression analysis was attempted employing both Weibulland gamma models, depending on the data. Results from these analyses are presented when thecorrected coefficient of determination (r2co r) represented an improvement in fit above that foundfor linear regressions (r2).A similar approach was used to examine the incremental change between the initial (June)and mid-season (August) D2H (stem volume) for Pseudotsuga menziesii in 1988 (RGRSV). D5OCwas used as the independent variable for Julian days 178-234 (June 27 to August 22).In 1989 destructive measurements of tree seedlings throughout the growing seasonpermitted the evaluation of treatment effects on total weight (TW), final diameter squaredmultiplied by tree height (FD2H; an assessment of stem volume), total weight of new growth(TWnew), total weight of the first and second flush (TW1fand TW2frespectively). In addition,67treatment effects were evaluated for relative changes in TW and D2H by expressing these as thearc sine ratio of T’new/TW, (ASRTW) and the arc sine ratio ofFD2H - initial diameter squaredmultiplied by tree height(ID2H)/FD, (ASFJUD2H). The arcsine transformation was used tocorrect for a skewed distribution.These treatment effects were evaluated at each harvest using the D5OC index as theindependent variable, summed over the 1989 season or encompassing both the 1988 and 1989exposure period (D508889)for certain variables.Exposure-response relationships for the 1990 non-destructive measurements were assessedusing D5OC summed over the 1989 exposure season. Incremental changes in stem volume(RGR5v)were evaluated between the 1988 and 1990 measurements as well, using the sameindependent variable summed to harvest date.3.8.3 FLUX-RESPONSE REGRESSION ANALYSESTo address Objective 3, models of plant variables versus flux for the tree seedlings andboth radish cultivars, by experiment (radish only) and harvest date were computed utilizing simplelinear regression analysis. This type of comparison was only possible with the treatments whereconductance measurements were collected. Treatment means (1 ambient air plot and 4 03-enriched plots) were used in all analyses (n=5 for the five treatments). In each case the meanrepresented the average of two subsainpies. The limited number of data points prohibited the useof non-linear models.The flux index computations were based on the hourly estimates of flux calculated asdescribed in Section 3.7.7, for the 7 or 8 measurement periods for trees and radish respectively.During the interval prior to dates on which conductance was measured, conductance values for thatmeasurement date were used to compute flux estimates based upon the actual hourly 03concentrations. These hourly flux values were then summed over each daily ten-hour period forthe interval days prior to, and including, the day on which conductance was measured, to provideone index per treatment (termed “FLUXSUMC”). This same procedure was repeated for Douglas68fir.Conductance measurements were recorded during 8 days of the experimental period forradish and 7 for Douglas-fir for five treatments. Concomitant micro-meteorological measurementswere used to investigate their relationships to conductance on a diurnal basis in order to developempirical methods for estimating actual dose. Due to the large amount of scatter in the data theconductance measurements were averaged over the two plants (subsamples), and measurementperiods, by the ten measurement hours, by treatment for each radish cultivar or Douglas-fir, for usein these analyses (n=50, for the five treatments and 10 measurement hours/treatment). Themeteorological variables were averaged over the same measurement periods by hour. Theassociations between 03 and all meteorological variables were examined using correlationanalysis. Simple linear regressions were computed for all meteorological variables versus stomatalconductance for both species. Stepwise multiple regression was used to assess the relativeimportance of all meteorological variables in predicting stomatal conductance. Once a final modelwas selected, it was used to generate conductance values for the entire exposure period (within the10-h period for each day) for each experiment and all treatments, based upon the meteorologicaldata for the day. These estimated conductances were then used to compute hourly estimates of fluxthroughout the season, those sums of which yielded an exposure index based on meteorologicaldata. This index (tenned “FLUXSUMM”) was used as the independent variable in the regressionanalyses of plant variables for each harvest for all treatments (Douglas-fir and radish only).Treatment means were used in all regression analyses (n—15, for 1203-enriched and 3 ambient airtreatments). The assumption in developing the model for generating flux based on estimated ratherthan actual conductance data was that it could be generally applied to all treatments even thoughthe data from which it was developed formed a subset of these treatments.693.8.4 ANALYSIS OF VARIANCE: RADISH EXPERIMENTSComparisons of treatment means by analysis of variance for pea, potato, bean andPseudotsuga menziesii were not possible due to the lack of treatment replication.Bartlett’s test for homogeneity of variance was utilized prior to proceeding with analysis ofvariance on the radish data. An appropriate transformation was applied to the data to achievehomogeneity of variance, where necessary. Significant differences were tested using ANOVA for asplit-plot design. Each harvest was analyzed separately, (two experiments (replicates) for harvestone and three experiments (replicates) for harvests two to four), to test for statistical significanceof replicate, cultivar (sub-plot) and treatment (main plot) effects (for expected mean squares, seeAppendix 3, Table 2). Treatments were partitioned into the following orthogonal contrasts:Control versus 03, and 03 levels linear. As the treatments were not evenly spaced, coefficientswere computed as follows: Total 03 exposure (ppm) for each treatment- mean total 03 exposure(ppm) across all treatments. The coefficients were computed separately for each harvest(Appendix 3).704. RESULTS4.1 Performance of the Zonal Air Pollution SystemTo assess the performance of the ZAPS in terms of the types of horizontal 03 distributionsachieved, 16 different positions were sampled for 15-minutes each, in two independent locationswithin one treatment block, in the 1986 field season (indicated as A and B in Figure 5). Allmeasurements at each location were completed in a four hour period. Figures 6a and 6b illustratethe horizontal distributions of 03 over locations A and B respectively, at 40 cm above the soil withthe manifold located 80 cm above the soil, in the absence of vegetation. The hatched portionsrepresent parts of the harvested plots, where less variation in concentrations can be noted. Thearrow indicates the prevailing wind direction, which was predominantly from the south-east whenthe measurements were made in location A and from the south-west for location B. In both casesthe windspeed averaged 1 m/s during the measurement period with a range of 0.5-1.5 rn/s. Thiswas typical of the range in windspeed obtained for all 3 growing seasons, at the time when theambient air conditions called for enrichment. The prevailing wind probably contributed to theminor concentration gradient recorded in these two locations. Not unexpectedly, the concentrationsare somewhat more variable at the outer edges of the block, no doubt due to intrusion of ambientair (Figures 6a and 6b). This feature of the ZAPS distribution was anticipated and plants were notharvested from the buffer zone during any of the three years of field work (Figures 6a and 6b).There was no evidence of abnormally high concentrations being directly associated with thedischarge points within the sample location. Because of the prevailing wind and carry-over effects,no treatment plot would consistently receive the highest level of enrichment, nor the lowest. Overthe areas of A and B, the supplementary 03 provided by the ZAPS was therefore fairly uniformlydistributed with a mean of 19.5 for each location and coefficients of variation of 38.9% and 30.7%.This is the only information available on the horizontal distributions, and it was obtained fromplots with a density of 1-hole/rn. It was assumed that comparable distributions occurred over plotswith a greater number of release orifices.7110 mh.buried3msupply line -— 8 m4mplot areasFigure 5. Sampling locations A and B within the treatment block used for evaluating thehorizontal ozone distributions in 1986.72300p30000Figure 6a and 6b. Horizontal ozone distributions over sampling locations A and B, 40 cmabove the soil. The hatched ateas indicate parts of the treatment plot from which plantsampling took place. The manifold over both locations had singly spaced orifices, withorifices in fours to the right, and in threes to the rear.015.010,0a073A vertical 03 profile generated over the 1-hole plot in the absence of a plant canopyshowed dispersal of 03 down to the lowest point measured, 10 cm, Figure 7. This confirmed thatthe system was functioning as proposed, and provided the desired uniformity of concentrations.However, there was a certain amount of variation in the average concentrations at all heights (asindicated by the standard deviation).Average daily profiles for representative treatments with different densities of orifices in1989, are shown in Figure 8. The profiles are from data for three03-enriched treatments and asingle ambient air plot (control). The season-long 24-hour diurnal profiles resemble thoseoccurring in ambient air (T13) with a reasonably gradual enrichment taking place when thegenerator starts up at 0700-h and ends at 2100-h. In addition, the curves demonstrate theproportionality of the increased levels of 03 provided in the treatment plots. The average hourlymaxima are reached between 1600-1800 h and the background level of 03 ranged from 15-3 0 ppbin a 24-h period over the season (Figure 8).The frequency distribution data presented in Figure 9 are for all treatments in 1989. Theactual frequencies of the concentration ranges are shown together with plots of the fitted Weibullprobability density functions (PDF), obtained from:PDF(c) (?/a)(cIa$’ e)using the parameter values for the complementary distributions, after adjusting the ordinate scaleby multiplying by the total number of hourly averages and the concentration range width (10).Inspection of Figure 9 shows that the distributions for ambient air (Treatment 13, 14 and15, controls) are narrow, with the lowest a and highest values of the treatments depicted. Ingeneral, as the frequency of higher concentration ranges increases, a increases, while ) decreases,and the distributions are positively skewed, with pronounced tails to the right. Values of between3.25 and 3.6 approximate the normal distribution.740a.)0C,’a)0a.)1101009080706050403020100I I0 5 10 15 20 25 30 35Ozone concentration (ppb)Figure 7. Vertical profile of mean ozone concentrations averaged over two locationsin Block 2 for 1986 (± standard deviations, n = 30). Arrow indicates manifold height.75C40CC.)C0C.)C0NC6050403020100-K> Treatment 1111 Treatment 8V Treatment 10- • Treatment 13 (Control)0 3 6 9 12 15 18 21 24Hour of dayFigure 8. Season-long diurnal profile of average ozone concentrations achieved for threeenriched treatments and one ambient air plot in 19897600.00•3.0C0z0 50 100 150 200 250Ozone concentratfon (ppb)Figure 9. Season—long frequency distributions of one—hour mean concentrations in IOppbranges for all treatments in 1989.600Treatment 5400Treatment 650000.0a300C0Z 200100r2 = 0.998a = 61.82A = 1.53Treatment 7= 0.995a = 48.92A = 1.88Treatment 8= 0.998a = 39.41A = 2.72r2 = 0.995a = 43.08A = 1.88Treatment 9 Treatment 10 Treatment 11 Treatmentr2 = 1.000 r’ = 0.998a = 36.95 a = 38.26A = 3.33 A = 2.6412700600500j04000300C0Z 2001000700600500004000300C0Z 200100r2 = 0.991a = 52.29A = 1.73r2 = 0.999a = 37.12A = 3.16Treatment 13(Control)Treatment 14(Control)= 1.000a = 30.60A = 3.32Treatment 15(Control)= 1.000a = 31.36A = 3.11= 1.000a = 30.01A = 2.920 50 100 150 200 50 100 150 200 50 100 150 200Ozone concentration (ppb) Ozone concentration (ppb> Ozone concentration (ppb)774.2 Pea4.2.1 AIR QUALITYTable 5 presents a summary of exposure indices for the pea field data. These include therange of seasonal means (M12, ppb), total exposures (TD; ppm-h), D5OC (days) and the sum of03 concentrations when a threshold of 50 ppb was exceeded (SUM5O, ppb-h) across alltreatments, at each harvest. Note that all indices were calculated for the daily 12-h window from0900-205 9. The indices for each treatment and harvest are summarized in Appendix 4.Table 5 clearly illustrates that the magnitude of the indices is dependent on the method ofexpression. For the cumulative exposure indices the largest increases in exposures took placebetween harvests 3 and 4, where the D5OC index showed an increase from 20 to 30 and theSUM5O from 5589 to 9046 ppb-h (Table 5). These increases were coincident with flowering andpod set. The M12 index permits a comparison between extremes in treatment means at eachharvest, but does not reflect the fluctuating conditions that prevail in the field. The small increasesseen for either extreme recorded for the last three harvests reflect periods during which lowerambient air 03 levels were experienced. Treatments 8, 10, 11 and 12 had the highest number ofdays with hourly averages exceeding the higher threshold of 100 ppb throughout the season(Appendix 4, Table 1).Table 5. Summary ofthe range of seasonal ozone exposures, M12, TD,D5OC and SUM5O for all treatments at each Pea harvestHarvest Julian M12 (ppb) TD (ppm-h) D5OC (days) SUM5O (ppb-h)Day (12-h period) (24-h period) (12-h period) (12-h period)1 153-157 16-59 1-4 0-4 0-17792 153-167 16-51 4-13 0-12 0-40613 153-178 15-44 7-19 0-20 0-55894 153-189 15-47 10-27 0-30 0-90465 153-200 14-45 12-3 1 0-37 0-102936 153-211 13-42 14-36 0-41 0-12105784.2.2 TEMPORAL CHANGES IN GROWTHTrends for total weight (TV1’), pod number (PN) and pod weight (PW) over the growingseason are presented in Figures 10 through 12. For comparative purposes the control plots arerepeated on each of the three graphs. Few differences were detectable between the control andtreated plants for TW until harvest 3, 39 days after planting (d.a.p.), where some separationbetween the03-enriched plants and the controls was observed. The disparity between them wasmaintained through to the last harvest at 83 d.a.p. (Figure 10). Similar trends were noted for stem,and leaf weight and leaf area (data not presented).Leaf number (LN) increased steadily for all treatments with a leveling off taking place inthe last two harvests (data not displayed). At 30 d.a.p. the control plants had an average of 1 moreleaf than the 03 -enriched plants, while at 83 d.a.p. they averaged up to 15 more leaves. LAR andLWR declined throughout the experiment, with no consistent effect of 03 exposure (data notpresented). SEA tended to increase over time with similar responses in all treatments (data notpresented).Pods appeared after 50 d.a.p. At 72 d.a.p. the control plants showed increases in podnumber above the03-enriched plants which were maintained through 83 d.a.p. (Figure 11).Treatments 2 and 9-12 had the lowest number of pods produced 83 d.a.p. At 72 d.a.p. pod fillingin the control plants seemed to lag behind the03-enriched plants since their pod weights weresimilar in magnitude despite the greater number of pods present (Figures 11 and 12). Pod weightincreased rapidly in all treatments after harvest 5 and was highest in the controls at 83 d.a.p. andlowest in treatments 2, and 9-12.On average the control plants had greater fitted AGRS at all harvest intervals than theaverage03-enriched plant (Figure 13). However, no consistent differences between the fittedRGRS of control and03-treated plants were found, although the values declined in all treatmentsover time (data not shown).7950454035130252015l0505045403513025a 20015105028 39 50 61Age (number of days after plaetieg)— ——TRTI— U— ThT2—Es— ThT3— X— TRT4—)K---— TRT 13—0----— TRT 14— 0— TRT5— 0—-TRT6—tx— ThT7— X— TRT8—X——--— TRT 13—0----— TRT 145045403500130125- 20isto— •— TRT9— 0— TRTIO— Es— TRTII—X— TRTI2—s---—— TRT 13—0—--— TRT 1428 39 50 61 72 83Age (number of days after planting)72 8328 39 50 61 72 83Age (camber of days after planting)Figure 10. Total dry weight of pea cv. Puget harvested between June 26 and July 31, 1986. Each point representsa treatment mean of 8 aubsamples (plants). Treatments 13 and 14 are ambient air controls.go50454035302520151005045403530025201510050434035300252015100Figure 11. Number of pods per plant of pea cv. Puget harvested between June 26 and July 31 in 1986. Eachpoint represents a treatment mean of 8 sub samples (plants). Treatments 13 and 14 are the ambient air controls.0 TRTI— EJ TRT2— & TRT3—- TRT4—s---—— TRT 13—0——-- TRT 14— 0— TRTS— 1:1—-TRT6— En—-TRT7—X— TRT8—5(-——— TRT 13—0-—-- TRT 140— TRT9E— TRTIO— Er-TRTI1X TRTI2—X---— TRT 13—0--——— TRT 1428 39 50 61 72 83Age (number of days after planting)28 39 50 6) 72 83Age (number of days after planting)28 39 50 6) 72 83Age (number of days after planting)8125—G— TRT5D— TRT6& TRT7— X TRT8—X—--— TRT 13—0-——— TRT 14—0— TRT9—0— TRTIO— &— TRTIIX TRTI2—)t(--—— TRT 13—0———— TRT 142039 1510— 0— TRTI—0— TRT2—Er— TRT3—- TRT4—X-—-— TRT 13—0—----- TRT 1421 39 50 61 72 83Age (number of days after planting)39 15t28 39 50 61 72 83Age (somber of days after planting)39 151028 39 50 61 72 83Age (number of days after planting)Figure 12. Pod weight of pen cv. Puget harvested between June26 and July31 in 19g6. Each point represents atreatment mean of g sub-samples (plants). Treatments 13 and 14 are the ambient air controls.022.01.00.50.00.01.501.000.500.0>a000.01.5-0.52.0Harvest Isterval-0.5• TRT 1U TRT 2• TRT 3TRT 4Ii TRT 13U TRT 14• TRT 5O TRT 6• TRT 7TRT 8I’I TRT 130 TRT 14• TRT 90 TRT 10• TRT 11TRT 12UIII TRT 13DTR-r14-0.5 —Figure 13 Mean absolute growth rate of pea cv. Puget harvested between June 26 and July 31, 1986. Data used inthe plots are obtained from fitted curves of quadratic regressions of weight versus time. Each point represents atreatment mean of 8 subsamples (plants). Treatments 13 and 14 are ambient air controls.2 3 4Harvest Interval2.01.51.00.5liT2 3Harvest Interval4834.2.3 EXPOSURE-RESPONSE RELATIONSHIPS4.2.3.1 Simple Linear RegressionsResults of the simple linear regressions of pea, growth variables versus 03 exposure byharvest, are presented in Tables 6 through 10. Total weight, stem and leaf weight, leaf area andleafnumber decreased significantly with an increase in 03 in the later harvests (Table 6). Therelationships strengthend with increased time of exposure to 03. Figure 14 presents scattergramsof the relationship between total weight and D5OC for each harvest.A few significant positive and negative linear regressions were occasionally found for LARand LWR versus 03 exposure, during the growing season. However, their randomness acrossharvests and the fact that the relationships were associated with both positive and negativecoefficients discounted their significance (data not presented). No significant effect of 03treatments on SLA was found at any of the harvests (data not presented).Bud weight decreased significantly with increased 03 exposure in harvests 3 and 4; flowerweight decreased significantly in harvests 4 and 5 (Table 7). As bud production was on the declinein harvests 5 and 6 and flower production in harvest 6 it is not surprising that significantrelationships were not observed at those times. On the other hand, pod development was ofincreasing importance; both pod weight and pod number decreased with an increase in 03 forharvests 4 through 6 (Table 7). The relationships reached statistical significance by harvest 5 forpod number and harvest 6 for pod weight. The relationship between 03 exposure and harvestindex was positive for all six harvests, indicating that pod weight relative to total plant weight isgreater for the 03 treated than control plants, but, although the degree of fit improved withsuccessive harvests, none of these relationships was significant (data not presented).84Table 6. Results of the simple linear regressions of pea cv. Puget total, stem,leaf weight, area and number versus D5OC, for harvest 1 through 6.Harvest a b1 PTotal Weight (g)1 0.27816 -0.0123200 0.144 0.18102 1.16249 -0.0400660 0.274 0.05503 4.09355 -0.0885400 0.597 0.00124 9.76022 -0.1851510 0.688 0.00025 14.54868 -0.1409350 0.409 0.01386 32.81757 -0.4549000 0.493 0.0051Leaf Weight (g)1 0.23224 -0.0092240 0.109 0.24802 0.91492 -0.0324800 0.275 0.05403 2.87032 -0.0628100 0.595 0.00124 5.97229 -0.1145100 0.674 0.00035 6.16568 -0.0723430 0.498 0.00506 9.40916 -0.1425490 0.502 0.0046Stem Weight (g)1 0.04592 -0.0030920 0.255 0.06552 0.24757 -0.0075870 0.25 1 0.06773 1.15842 -0.0245200 0.589 0.00144 3.23276 -0.0604500 0.686 0.00035 4.67362 -0.0484300 0.461 0.00766 7.2943 1 -0. 1095500 0.487 0.0055Leaf Area (sq. cm)1 0.00423 -0.0000640 0.017 0.65902 0,02094 -0.0008770 0.318 0.03563 0.05776 -0.0012040 0.474 0.00704 0. 12937 -0.0025340 0.685 0.00035 0. 13 175 -0.0018360 0.555 0.00206 0.16525 -0.0024114 0.493 0.0051Leaf Number1 5.35526 +0.00614 0.000 0.95902 10.1204 -0.11959 0.113 0.24073 17.77487 -0.248705 0.496 0.00494 25.52365 -0.354655 0.651 0.00055 22.78411 -0.192343 0.515 0.00386 26.99083 -0.305240 0.516 0.003885PEA Harvest-i PEA Harvest - 4Figure 14. Total dry weight of pea cv. Puget harvested between June26 and July 31 in 1986 versus D5OC, forsix harvests. Each point represents a treatment mean of 8 subsamples (plants).0.40.350.30.250.20.150.10.05141210422 3 4 5I•••• ••.. .0 5 10 15 20 25 30 35D5OCPEA Harvest-50D5OCPEA Harvest -21.6 201.4 18. 161.24:0 i :0l20.8 . . I0a • •0.6•••860.440.2 20 00 3 6 9 12 15D5OCPEA Harvest-30•4.543.52.5l.50.50••••.••• •10 20 30 40D5OCPEA Harvest-65045 •40• •:0302520•••15 •.••I000 10 20 30 40 50D5OC0 5 10 15 20 25D5OC86Table 7. Results of the simple linear regressions of pea cv. Puget bud,flower and pod weight, and pod number versus D5OC for harvest3 through6.Harvest a b1 r2 pBud Weight(g) 3 0.06482 -0.0012114 0.433 0.01104 0.20360 -0.0040900 0.597 0.00125 0.05782 -0.0007460 0.084 0.3 1406 -0.00011 +0.0000088 0.139 0.1900Flower Weight (g) 4 0.17834 -0.0033110 0.321 0.03485 0.11928 -0.0018700 0.306 0.04006 0.00086 +0.0000249 0.025 0.5920Pod Weight(g) 4 0.16160 -0.0016600 0.067 0.37305 3.53228 -0.0175420 0.063 0.38806 16.03925 -0.2061800 0.490 0.0053Pod number 4 2.45568 -0.0210570 0.088 0.30205 15.90905 -0.2147760 0.537 0.00296 36.49429 -0.5855600 0.448 0.0089At the final harvest there were significant negative impacts of 03 on marketable podnumber, total seed number and on the seed numbers in categories up to size 4 (Table 8). Therewas also a significant linear increase in average weight of each seed with increasing 03concentration (Table 8) as shown by the scatter diagram in Figure 15. Seed weight is affected bymaturity. The older seeds (size classes 5-7) tend to be heavier as they were composed mostly ofstarch. The relationship between average seed weight and 03 was no doubt due to treatments 10-12 which produced the lowest number of seeds and had the greatest proportion of their seeds in sizeclass 5. Plants in these treatments had matured fhster than the other treatments.The distributions of seeds in each size category relative to the total were similar for thecontrol versus ozonated plants, even though average seed numbers in each category were870.12 I I00)0080.060 00,04° 0.020.00 I0 9 18 27 36 45D500Figure 15. Pea weightlSeed number for pea cv. Puget for the final harvest, July 31, 1986 versusD5OC. Each point represents a treatment mean of 8 subsamples (plants).88Table 8. Results of the simple linear regressions of pea cv. Puget marketable pod number,total seed number, seed weight/seed number, seeds in each size category andpea and pod fresh weights versus D5OC for harvest 6.Plant variable a r2 pMarketable Pod Number26.6340 -0.41458 0.510 0.0041Number of seeds less than size one13.6364 -0.24854 0.591 0.0013Number of seeds equal to size 136.0000 -0.67868 0.698 0.0002Number of seeds equal to size 29.8391 -0.15751 0.455 0.0081Number of seeds equal to size 318.1953 -0.21625 0.511 0.0040Number of seeds equal to size 417.3937 -0.18499 0.241 0.0744Number of seeds equal to size 59.9253 -0.03943 0.013 0.6970Number of seeds104.979 -1.52453 0.649 0.0005Seed weight per seed number (g/seed)0.0528 +0.00066 0.355 0.0247Pod fresh weight (g)78.360 -0.92300 0.481 0.0060Pea fresh weight (g)29.400 -0.3040 0.338 0.0293approximately double in the control plants (Table 9). Ozone apparently did not affect the relativeproportions of seed size categories, although it had a negative effect on total production, as shownby the regressions of pea and pod fresh weights (Table 8) (Wright, 1988).It is important to note that the timing of the final harvest was a compromise betweenmaturity of all treatments. The ozonated plants matured faster than the control plants and theirpods were ready for harvest sooner than the control pods. In reality, the control plants should havebeen harvested at a later date, more closely matching their maturity status.89Table 9. Seed distribution and relative proportion by size category for pea cv. Pugetobtained at the final harvest.Seed size category LT1 EQ1 EQ2 EQ3 EQ4 EQSOzonatedplants Seednuniber 6.9 17.7 5.5 12.1 11.6 7.9Percent of total 11.1 28.6 9.0 19.6 18.8 12.8treatedControl plants Seed number 14.6 37.8 10.6 20.3 22.6 15.8Percent of total 12.0 31.1 8.7 16.6 18.6 13.0controlWith regard to effects of 03 on growth dynamics, significant linear decreases in AGR andRGR in response to 03 stress, were found for most harvest intervals (Table 10). With theexception of the first harvest interval, 03 had a significant negative impact on AGR. Significantnegative relationships were found for ROR, in the first two harvest intervals, while for the thirdinterval, the relationship was borderline significant (Table 10). For the remaining harvest intervalsthere were no significant effects of treatments applied on RGR. No significant relationships werefound for RGRpW.The regression results presented in Table 10 used the cumulative exposure index up toharvest date. Analyses using interval indices yielded fewer significant regressions (data notpresented).Table 10. Results of the simple linear regressions of pea AGR, RGR and RGRpW versus D5OCfor 5 harvest intervals. The sign of the coefficient (b1) and p value are presented.Harvest Interval1 2 3 4 5AGR (+) 0.1579 (-) 0.0043 (-) 0.0008 (-) 0.0074 (-) 0.0151RGR (-) 0.0378 (-) 0.0397 (-) 0.0735 (-) 0.8982 (+) 0.4559RGRp (-) 0.0696 (-) 0.8267 (+) 0.6286904.2.3.2 Non-linear Regression AnalysesInspection of the data for a number of variables revealed curvilinear trends. As a result,various non-linear models were tested for improvement in fit relative to the linear regressions. Forexample, when the Weibull model was fitted to TW and D5OC data, no convergence was possiblefor harvests 1 through 3, although the following models were obtained for harvests 4 through 6:Harvest 4; TW = 9.962*exp((D50C/35.501)\1.137) r20= 0.699Harvest 5; TW = 18.193*exp((D50C/244.7l0y0.282) r20= 0.656Harvest 6; TW = 46.255*exp((D50C/38.l43yo.337) r20= 0.767These show reasonable improvements in fit over those obtained with simple linear regressions(Table 6). The models are depicted in Figure 16 a-c. No meaningful convergence was obtainedwith the gamma model for these data, because of the tendency for the relationship to increase athigher D5OC values (see below).Some improvement in fit was also obtained with the Weibull model for PW, but only inharvest 6 (Table 8):PW 23 .O96*exp((D50C/45 .778Y’0.28 1);r2cOff=0.839.The model is presented in Figure 16 d. No convergence was possible with earlier harvests usingeither the Weibull or gamma models. Likewise for seed number for the final harvest, someimprovement in fit was obtained using the Weibull model:Seed number = 142.125*exp((D50C/38.341)0.380);r2con=0.883 (vs 0.649, Table 8).The model is shown in Figure 16 e.The selection of non-linear models is of course not limited to Weibull and gammafunctions. However, in the context of exposure-response, only models which become asymptotic tozero at high exposure values are relevant, which excludes general polynomials and someconvergences of the gamma model. With this caveat in mind, Figure 17 shows the correspondingmodels for pod fresh weight:gamma: y = 112.9*exp(0.0065*D50C)*(1+D50C)A0.305; r2 = 0.87791a dHarvest=4 Harvest=515- 3000 4 50 60D500 0500b eHarvest=5 Harvest=620 1500 00 0 20 30 40 50 60 0 W 20 30 40 50 600500 0500CHarvest=6500500Figure 16a-e. Relationship between a-c) total dry weight, d) pod dry weight and e) seed number forpea, cv. Puget versus D5OC. Each point represents a treatment mean of 8 subsamples (plants). Theprediction line was calculated from the Weibull regression models outlined in the text.92‘Ca)‘Ca)I01201008060reciprocal (r2=0.645)40 exponential (r2=0.551)Weibull (r2=0.869)20gamma (r2=0.877(±0.95 confid. limits)0 linear (r2=0.481)0D5OCFigure 17. Non-linear relationships between pod fresh weight (harvest 6) and D5OC for peacv. Puget. The prediction lines were based on models described in the text. 95% confidencelimits are shown for the linear model.10 20 30 40 5093Weibull: y = l14.3*exp((D50C/54.8)A0.259; r2 = 0.869exponential: y= 84.0*exp(0.019*D50C); r2 = 0.55 1reciprocal: y = 92.8/(1+0.645*D5OC); r2 = 0.645Figure 18 shows the linear and the following non-linear models relating pea fresh weight andD5OC:gamma: y = 45.9*exp(0.150*D5OC)*(1+D5OC)A0.0386; r2 0.9 15Weibull: y = 46.4*exp((D50CI67.67)t’0.172; r2 = 0.88 1exponential: y = 31.5*exp(0.016*D50C); r2 0.401reciprocal: y = 35.3/(1+0.031*D5OC); r2 = 0.503.In both sets, the non-linear models have greater r2 values than the linear models. Thereciprocal model is better than the simple exponential, and the Weibull models are appreciablybetter still. Although the gamma models have the highest r2 values, the particular models do notmeet the criterion for becoming asymptotic to zero (as shown clearly in Figure 17) and hence arenot justified.946050.._a)a).1-420reciprocal ft2=O 503exponential fr2_040 1)H Weibull (r2=0.881)10 —gamma (r2=0.915)(±0.95 confid. limits)0 linear (r2=0.338)0 50D5OCFigure 18. Non-linear relationships between pea fresh weight (harvest 6) and D5OC for peacv. Puget. The prediction lines were based on models described in the text. 95% confidencelimits are shown for the linear model.10 20 30 40954.3 POTATO4.3.1 AIR QUALITYTable 11 presents a swnmaly of exposure indices for potato. For the cumulative exposureindex (SUM5O) the largest increases took place between harvests 2 and 3 and 4 and 5, where theindex increased by a factor of 3000-h ppb or more (Table 11). The increase from harvest 2 wascoincident with the onset of tuber formation. The ranges of M12 values declined through the cropsgrowing period, indicating that, in view of the magnitude of the increases in TD and SUM5O thatoccurred between later harvests, there were increasing numbers of low hourly averages as theseason progressed. Treatments 8, 10, 11 and 12 had the highest number of days with hourlyaverages exceeding 100 ppb by the end of the growing season (Appendix 4, Table 2). The indicesfor each treatment and harvest are summarized in Appendix 4 (Table 2).Table 11. Summary of the range of seasonal ozone exposures, M12, TD, D5OC and SUM5Ofor all treatments for five Potato harvests in 1986.Harvest Julian M12 (ppb) TD (ppm-h) D5OC (days) SUM5O (ppb-h)Day (12 h period) (24-h period) (12-h period) (12-h period)1 153-160 16-59 2-8 0-7 0-29962 153-177 15-44 7-18 0-19 0-52323 153-194 15-46 11-28 0-33 0-92934 153-210 13-41 13-35 0-40 0-1 14185 153-228 13-41 18-47 0-52 0-14676964.3.2 TEMPORAL CHANGES IN GROWTHTrends in TW over all five harvests are displayed in Figure 19. Overall, adverse effects of03 on TW were only clear by the final harvest, 100 d.a.p, with the greatest differences occurringbetween the controls and treatments 9 through 12 (Figure 19).With the exception of the first harvest interval, fitted AGRs were generally greater in thecontrol than in03-treated plants (Figure 20). With the exception of treatment 1, the control AGRsincreased more rapidly than those for 03 treatments. Unlike all other treatments AGR decreasedover time for treatment 8.4.3.3 EXPOSURE-RESPONSE RELATIONSHIPS4.3.3.1 Simple Linear Regression AnalysesResults of the simple linear regressions of potato cv. Russet Burbank growth variables ateach harvest are presented in Tables 12 and 13. In general, exposure to 03 resulted in a decreasein total plant weight. Although significant negative impacts of 03 on 1W were found in the firstharvest they subsequently failed to reach significance until the final harvest (Table 12), as shownby the scattergram depicted in Figure 21.The effects on leaf area and vegetative weight were ambiguous, with both positive andnegative trends observed, none of which was significant (data not presented). However, tuber dryweight decreased with an increase in 03 exposure from harvest 2 onwards and reached significanceat the final harvest (Table 12). Decreases in tuber number and marketable weightltuber weremarginally significant at the final harvest, and decreases in tuber fresh weights were clearlysignificant (Table 13).Although SLA, LAR, and LWR appeared to decrease in harvest one, all ratios showedincreasing trends with 03 concentration in the remaining four harvests, but, none of the linearregression models was significant (data not presented). Although HI tended to decrease withincreased exposure to 03, no significant relationships were found (data not presented).97350350300350300300250200•a 150I-10050031 48 65 82 100Age (number of days after plaadeg)250200150F-100500— 0 TRT1— LI— TRT2— Er-TRT3— X TRT4X TRT 13O TRT 140 TRT5— LI— TRT6Er TRT7—X TRTO—%——-— TRT 130 TRT 14— TRT9250— 0 TRTIO1200 — Er150 — X TRTI23(——TRTI3100—— TRT 1450031Figure 19, Total dry weight for potato cv. Rusaet Burbank harvested between June 9 and August 17 in 19g6.Each point repretents a treatment mean of 3-6 sub-samples (plants). Treatments 13 and 14 are the ambientcontrols.31 48 65 82 100Age (number of days after p1sarin)48 65 82 100Age (number of days after pisadag)98109876540-1 iZhh[ I I2Harvest Interval4I• TRT 1O TRT 2• TRT 3III TRT 4El TRT 13El TRT 14• TRT 5O TRT 6• TRT 7II TRT 8El TaT 13El TaT 14• TRT 9El TaT 10• TRT 11I]ffl TaT 12El TaT 13El TaT 1410985-o6C24(200—11098C24(200Harvest Interval4I- ui1Tiii I1 2 3 4Harvest IntervalFigure 20. Mean absolute growth rate of potato, cv. Russett Burbank between harvest intervals, harvested from June 9 andAugust 17, 1986. Data used in the graphs are derived from fitted curves from quadratic regressions of weight versus time.99Harvest I Harvest 4504540353025S 201510500 5 10 15 20 25 30 35D5OCFigure 21 Scattergrams of total weight for potato, cv. Russett Burbank harvested between June 9 and August 17 in 1986 versus D5OC, for fiveharvests. Each point represents a treatment mean of 3 to 6 subsamples (plants).5.04.0be. 3.00.0•..........2 4 6 8DSOCHarvest 20..I.. .....250200be.H 150be• 10001-500400350300250.0200150I-.1005000 5 10 15 20 25 30 35 40 45 50D5OCHarvest 5......I.... ..0[. . .• • •• •..•..0 10 20 30 40 50 60D5OC5 10 15 20D5OCHarvest 3.......1601401201008060I40200100Table 12. Results of the simple linear regressions of potato cv. Russet Burbanktotal weight, and tuber weight versus D5OC for harvests 1 through 5.Plant Harvest a b1 r2 pvariableTW (g) 1 3.5806 -0.1677900 0.296 0.04442 19.1581 +0.2625140 0.033 0.53223 78.8325 -0.3207700 0.021 0.62294 165.1709 -0.2546700 0.008 0.76575 301.7519 -2.2602000 0.486 0.0056TUW (g) 2 3.8794 +0.0053 180 0.000 0.95893 43.1728 -0.2507900 0.055 0.41784 115.7470 -0.3192800 0.033 0.53325 249.4422 -1.9930000 0.487 0.0055Table 13. Results of the simple linear regressions of potato cv. Russet Burbanktuber number, marketable weight per tuber and tuber fresh weight versusD5OC at the final harvest.Plant variable a b1 pTuber number8.7851 -0.0320 0.240 0.0750Marketable tuber weightper tuber (g/tuber) 28.7803 -0.1534 0.283 0.0503Tuber fresh weigh (g)t956.47 -6.8290 0.468 0.0070Ozone significantly reduced AGRs in the later harvest intervals (Table 14), i.e. during theperiod of tuber development. Although, RGRS tended to increase with 03 exposure in the first twoharvest intervals, and decrease thereafter, no significant linear models were found (Table 14),regardless of the exposure index. No significant relationships between 03 andRGRTUW were found (Table 14).101Table 14. Results of the simple linear regressions on relative growth rates betweenharvests intervals for potato AGR (gig), RGR (g/g/day) and RGR1-TJW versus D5OC.The sign of the coefficient (b1) and p value are presented.Variable Harvest Intervals1 2 3 4AGR (+) 0.1894 (-) 0.2687 (-) 0.0039 (-) 0.0047RGR (+) 0.1423 (+) 0.6159 (-) 0.1020 (-) 0.0781RGRpj - 0.5593 (-) 0.3314 (-) 0.9453 (+) 0.83914.3.3.2 Non-Linear Regression AnalysesAs described for pea growth in Section 4.2.3.2, non-linear regression analysis was used toaddress apparent residual curvature in several of the potato data sets. Attempts at using theWeibull and gamma models failed to reach convergence in models involving several growthmeasures and D5OC, especially in the early harvests and harvest intervals. Some improvement infit relative to the linear regressions (Table 12) was found for TW and TUW using the Weibullmodel in harvest 5:TW = 321.803*exp((D50C/125.641)0.739); r2corr = 0.518.TUW = 274.104*exp((D50C/l20.084)10.650); r2corr = 0.540.The models are shown in Figure 22.All of the non-linear models tested resulted in somewhat higher r2 values than the linearregression of tuber fresh weight and D5OC listed in Table 13. Figure 23 depicts the following nonlinear models in addition to the linear model:gamma: y = 1037.0*exp(0.0061*D50C)*(1+D50C)’0.047; r2 = 0.505Weibull: y = 1032.3*exp(D50C/140.05)’0.682; r2 = 0.5 10exponential: y = 978.9*exp(0.0089*D5OC); r2 = 0.489reciprocal: y = 1002.9/(1+0.0121*D5OC); r2 0.507,and reveals the close similarities between them, with the Weibull model providing the best fit.102Harvest=53)-Cc,)a)a)0 10 20 30 40 50 60 70 80D5OCHarvest=5350300250200150100500350300250C)200150F-10050040 50 60 70 80D500Figure 22. Non-linear relationships between total and tuber weight of potato cv. Russet Burbankin 1986 versus D5OC. Each point represents a treatment mean of 6 subsamples (plants). Theprediction lines were calculated from the Weibull models outlined in the text.0 10 20 30103exponential (r2=0.489)Weibul 1 (r2=0.5 10)gamma (r2=0.505)-(±095 confId. limits)linear (r2=0.468).—a)a)I-’a)‘Creciprocal (r2=0.507)120010008006004002000Figure 23. Non-linear relationships between tuber fresh weight (harvest 5) and D5OC forpotato cv. Russet Burbank. The prediction lines were based on models described in the text.95% confidence limits are shown for the linear model.0 10 20 30 40 50 60D5OC1044.4 Bean4.4.1 AIR QUALITYTable 15 presents a summary of the ranges of exposure indices for bean in 1988. Theexposures of bean in 1988 were markedly lower than those of pea and potato in 1986 (Tables 5and 11). Due to equipment failure no03-enrichment occurred until three days prior to harvest 2.For the cumulative exposure index (SUM5O) the largest increase in exposures took place betweenharvests 5 and 6, where the index increased by a factor of 505 ppb (Table 15). This increase wascoincident with the onset of pod filling. The seasonal means, TD, and hours and days abovethreshold concentrations for each treatment and harvest are summarized in Appendix 4 (Table 3).Table 15. Summaiy of the range of seasonal ozone exposures, M12, TD, D5OC SUM5Ofor bean over all treatments and six harvestsHarvest Julian M12 (ppb) TD (ppm-h) D5OC (days) STJM5O (ppb-h)Days (12-h period) (24-h period) (12-h period) (12-h period)1 No exposures2 178-181 20-33 1-2 0-2 0-4423 178-191 16-27 4-6 0-5 0-13314 178-201 16-24 7-9 0-7 0-14675 178-209 16-28 8-13 1-10 0-21356 178-220 16-30 10-17 1-15 7-46354.4.2 TEMPORAL CHANGES IN GROWTHTotal plant weight increased in all treatments up until the final harvest 76 d.a.p. (Figure24). Data for treatment 15 (a control plot) are not presented as they behaved as outliers in allregression analyses. Treatment 13 (another of the ambient air controls) had the highest totalweight throughout the experimental period, whereas treatment 14 (the other control)105302520.015io5030252000.01510530252000.0l50105026 36 47 56Age (number of days after planting)— 0— TRT1— 0—-TRT2— Es——TRT3— X— TRT4—*(-— TRT 13—0----— TRT 140—-TRT5— D— TRT6— Es— TRT7—)0--TRTg—)K——-- TRT 13—0-——— TRT 14— 0- TRT9— C— TRT1O— Er—-TRT1IX—-TRT12—X--—-— TRT 13—0—-— TRT 14Figure 24. Total dry weight of bean cv. Galamore harvested between June20 and August 9 in 1988. Each pointrepresents a treatment mean of 16 sub-samples (plants). Treatments 13 and 14 are ambient air controls.26 36 47 56 65 76Age (unmber of days after planting)26 36 47 56 65 76Age (unmber of days after planting)65 76106performed in a manner similar to most of the03-enriched treatments (Figure 24). The number ofleaves per plant increased rapidly between 36 and 56 d.a.p, and tended to level off after this pointto an average of 10 leaves per plant for most treatments (data not presented). The LAR showed asimilar pattern, with maxima 47 d.a.p. (data not presented) The tendency for both LN and LAR todecline after about 56 d.a.p. coincided with pod filling as shown by the increases in both podnumber and pod weight. (Figures 25 and 26). LWR declined over time with similar responses inall treatments (data not presented).In all treatments, AGR increased following the second harvest interval, and RGR increasedafter the first harvest interval leveling off at the last two intervals (data not presented). Theaverage RGR was between 0.05-0.10 gIg/day for all treatments, indicating that they were equallyefficient at increasing in biomass in all treatments.4.4.3 EXPOSURE-RESPONSE RELATIONSHIPS4.4.3.1 Simple Linear Regression AnalysesNo enrichment had occurred by harvest 1, and only three days of03-addition had beenapplied by harvest 2. In addition, the nutrient deficiency in several plots affected growth untilharvest 3, by which time the amelioration of the deficiency by fertilizer application appeared tohave been effective (see Section 3.6.3). As a consequence, data for harvests 1 through 3 have beenexcluded from the analysis.Results of the simple linear regressions of bean cv. Galamore total, stem and leaf weightsand leaf area are presented in Table 16. No significant negative linear relationships were foundbetween 03 exposure received throughout the growing season, although non-significant negativetrends were found for the final harvest. On the other hand these led in turn to the positiverelationships found between LAR and 03 exposure although the relationships only reachedsignificance for harvests 5 (Table 17). No significant relationships were found between LWR andSLA and 03 exposure (data not presented).1073530— G TRT125— D— TRT20020 Er—-TRT3-g 15 — X- TRT4—3<---—— TRT 13100 TRT 140263530— 0——TRT525— 0—-TRT6120 — Es— -TRT78 15 — )0- -TRT8—3<——— TNT 13100— TRT 14026 36 47 56Age (number of days after plunting)3530— 0——TRT925— U— TRTIO20— Er-TRT11—>0- TRTI2—3<——— TRT 1310—0—--— TNT 14026 36 47 56Age (number of dnys after plaetiug)Figure 25. Number of pods for bean cv. Galamore harvested between June 20 and August 9 in 1988. Each pointrepresents a treatment mean of 16 sub-samples (plants). Treatments 13 and 14 are ambient air controls.36 47 56 65 76Age (number of days after planting)65 7665 7610010987.0S32095753210987532026 36 47 56 65 76Age (number of days after planting)100— 0— TRTI— 0— TRT2& TRT3— X TRT4—X---—- TRT 130 TRT 14— 0— TRTS— U—-TRT6— 1r-TRT7— X TRT8—*(——-— TRT 130 TRT 14— 0—-TRT9—0— TRT1O— &— TRT11— )0- TRT12—)I& TRT 13—0— TRT 14Figure 26. Pod dry weight of bean cv. Galamore harvested between June20 and August 9 in 1988. Each pointrepresents a treatment mean of 16 sob-samples (plants). Treatments 13 and 14 are ambient air controig.26 36 47 56 65 76Age (number of days after planting)26 36 47 56 65 76Age (number of days after planting)109Table 16. Results of the simple linear regressions of bean cv. Galamore total, stem,and leaf weight; leaf number and area versus D5OC for harvests 4 through 6.Variable Harvest a b1 pTotal weight (g)4 6. 15900 0.181660 0.031 0.54795 9.99290 0.321650 0.099 0.27216 18.6086 -0.16315 0.073 0.3514Stem weight (g)4 2.53860 0.088700 0.032 0.54115 4.02968 0.229710 0.179 0. 13 166 6.29955 -0.00715 0.001 0.9295Leaf weight (g)4 3.34300 0.093100 0.034 0.52645 4.52646 0.128540 0.079 0.33176 5.04351 -0.01925 0.014 0.6912Leaf area (sq. cm)4 0.08730 0.003540 0.050 0.44055 0.10809 0.006940 0.246 0.07106 0.12187 0.000283 0.005 0.8183Leaf number4 10.6173 -0.18906 0.083 0.31655 10.4319 0.174750 0.087 0.30496 9.52000 0.019230 0.016 0.6714Table 17. Results of the simple linear regressions of bean cv. Galamore forLAR (sq. cni/g) versus D5OC, for harvests 4 through 6.Harvest a b1 p4 0.01496 +0.000067 0.071 0.35565 0.01137 +0.000241 0.345 0.02736 0.00704 +0.000058 0.112 0.2428110Unlike pea (Section 4.2.3.1) no significant relationships were found between bud andflower weight and 03 exposure (data not presented). However, consistently negative relationshipswere found between pod weight and exposure, which reached significance in harvests 4 and 6(Table 18). The scattergrams in Figure 27 illustrate the trends in pod weight. The greater podweight found for the control plants was not a result of a larger number of pods for these plants,since pod number was not significantly affected by 03 exposure (Table 18). The long-termadverse effects of 03 exposure on harvest index also reached significance by the final harvest(Table 18).No significant impact of 03 was found for marketable pod number (Table 19). However,the effect on pod fresh weight was marginally significant (p = 0.0646), at the final harvest whilemarketable pod weight, overall weight/pod and weight/marketable pod all decreased significantlywith increasing 03 (Table 19).Table 18. Results of the simple linear regressions ofbean cv. Galamore pod number,pod weight and harvest index versus D5OC, for harvests 4 through 6.Variable Harvest a pPN4 3.07714 -0.350480 0.244 0.07275 19.1124 +0.505980 0.089 0.30036 22.6693 -0.022740 0.001 0.9206PW (g)4 0.02870 -0.003890 0.295 0.04495 1.36404 -0.047240 0.129 0.20806 7.26458 -0.137090 0.371 0.0208HI4 0.00436 -0.000570 0.190 0.11935 0.13327 -0.006130 0.199 0.11026 0.39806 -0.004976 0.489 0.00541111.51.2D 0.90.60.3SS.....0 1 2 3 4 5 6 7 8D5OCFigure 27. Scattergrams of pod dry weight for bean cv. Galamore versus D5OC, for harvests 4 through 6. Eachpoint represents a treatment mean of 16 sub-samples (plants).0.050.050.040.040.030.030.020.020.010.010.002.42.11.8.... S...I II2 4 6 8 10 12D5OC00.012108420III II• I I• II II0 4050c12 16112Table 19. Results of the simple linear regressions of bean cv. Galamore pod growthvariables versus D5OC, for harvest 6Growth a b1 r2 pvariableMarketable pod number14.69780 -0.113873 0.090 0.2985Marketable pod weight (g)6.98831 -0.139440 0.408 0.0139Pod fresh weight (g)90.74426 -1.145770 0.256 0.0646Marketable pod weight!marketable pod number (g!pod)0.48453 -0.007100 0.349 0.0260Pod weight/pod number (g!pod)0.33396 -0.006348 0.433 0.0105Linear regression analyses for D5OC versus AGR, RGR and RGRp resulted in nosignificant relationships at any harvest interval (data not presented).4.4.3.2 Non-Linear Regression AnalysesExanmiation of the residuals for TW (data not presented) suggested that non-linear modelsmight provide an improvement in the relationships for different harvests. No meaningfulconvergence was achieved using the Weibull model at any harvests. However, the gamma modelprovided some improvements (in terms ofr2 values) in the relationships between TW and D5OCfor harvests four through six (cf. Table 16):Harvest 4 TW=7.201*(D50C+1)”O.3*exp((0. 27*D50C));r2coffO.080, vs. 0.03 1Harvest 5 TW2.38*(D50C+1)O352*exp((0.102*D50C));r2coffO.2 18, vs. 0.099Harvest 6 TW=3 1. 70*(D5OC+1)’cO.6l2*exp(±O. 076*D5OC)); r2COffO. 359, VS. 0.073The gamma model also resulted in some improvement in the relationships found for PWversus D5OC in harvests five and six (cf. Table 18):Harvest 5 PW=1 .422*(D50C+1)’O28*exp(..(0.039*D5 OC)); r2coff0. 143, VS. 0.129113Harvest 6 PW= 10. 82*(D50C+1)tO.456*exp(((0.042*D50C));r2COff=0.522, vs. 0.371Modest improvements in fit were found for gamma models of pod weight / pod number, pod freshweight and marketable pod weight at the final harvest in comparison with the linear models (cf.Table 19):PWPN=0.321*(D50C+1)AO.OS6*exp((0.o31*D50C)); r2COff = 0.438, vs. 0.433PFW=105 .964*(D50C+1)O.O*exp((0.018*D50C)); r2COff = 0.289, vs. 0.256MPW=9. 188*(D50C+1yO.334*exp (O.025*D50C)); = 0.534, vs. 0.408.As discussed above in connection with the fitting of non-linear models to the pea data(Section 4.2.3.2), gamma models may not be asymptotic to zero, although they may showimproved fits as compared with linear models. Over the range of exposures obtained in the beanstudy, several gamma models showed upward slopes at higher exposures. This is shown for TV!(Figures 28 a, b and c) and for PW at harvest 6 (Figure 28 e). Such upward curvature occurswhenever the exponent term has a positive sign. PW showed a consistent decline at harvest 5(Figure 28 d). Of the models shown in Figure 29, only the PW/PN ratio for harvest 6 showedconsistent declines. The upward slopes in Figures 28 a and b are in keeping with the observationthat the linear models had positive coefficients (Table 16). Hence, in spite of the improvements infit obtained with the gamma models, only those for PW at harvest 5 and PW/PN ratio at harvest 6are acceptable. The prevalence of positive linear coefficients also accounts for the inability ofmonotonically declining functions such as the Weibull to converge or provide realistic models.114Harvest 414 -1210abIHarvest 58642032de15 205 10050CHarvest 501 2 3 4 5 6 7 80500Harvest 5:/0 5 10 15 20 00600Harvest 65 10 15060020CIC30252015100 5 20Figure 28a-e. Relationship between a-c) total dry weight and d-e) pod dry weight for bean cv. Galamoreand ozone exposure expressed as D5OC, for later harvests. The models are described in the text.10 150500115aHarvest 60,50.40.10.00 5 10 15 20D5OCb1201::60 0402000 5 10 15 200500C10D500Figure 29a-c. Non-linear relationships between bean pod weight/pod number, pod fresh weightand marketable pod number versus D5OC, for the final harvest. The models are outlined in thetext.1164.5 Radish4.5.1 AIR QUALITY and ENVIRONMENTAL PARAMETERSSeasonal mean 03 concentrations (M12) ranged from 30 to 55, 27 to 84 and 22 to 54 ppbfor Expts. 1 through 3 respectively (Table 20). Although the highest TD was comparable betweenExpts. 1 and 3, for harvest 4 there was a large difference between these two experiments for theSUM5O indicating a preponderance of relatively high hourly averages during Expt. 1. However,Expt. 2 had the highest TD and SUM5O for all harvests (Table 20). The range of D5OC wassimilar for all three experiments and harvests. The indices for each experiment, treatment andharvest are sunmiarized in Appendix 4, Table 4.Table 20. Summary of the range of seasonal ozone exposures, M12, TD, D5OC andSUM5O over all treatments in each radish experiment and harvest.Expt. Harvest Julian M12 (ppb) TD (ppm-h) D5OC (days) STJM5O (ppb-h)Days (12-h period) (24-h period) (12-h period) (12-h period)1 2 159-179 30-52 12-18 1-17 464-73553 159-183 30-52 15-22 2-21 1201-92534 159-187 30-55 17-27 2-25 1201-125212 1 192-207 26-69 8-17 0-14 0-95662 192-212 27-80 11-26 1-19 51-160683 192-216 27-79 13-30 1-23 51-189454 192-220 27-84 15-37 2-27 103-233383 1 227-243 20-45 6-12 0-14 0-38012 227-247 21-48 8-17 0-18 0-54103 227-25 1 22-53 10-22 0-22 0-74424 227-255 22-54 12-27 0-26 0-9142NB. Expt. = experiment.Mean hourly temperature was highest in Expt. 2 and comparable for Expts. 1 and 3 from1171200 h onwards (Figure 30 a). The lower air temperatures experienced during the early morninghours in experiment 1 reflected the situation in June-July 1988 (Figure 30 a). Solar radiation waslowest overall in Expt. 3 and highest for Expt. 2 from 1400 onwards (Figure 30 b). The threeexperiments were distinctly different in terms of wind speed (Figure 30 c), Expt. 1 having thelowest mean wind speed during 0800 and 1600 hours. Higher wind speeds together with similarair temperatures during the latter portion ofThis period may have led to enhanced exchangebetween the plants and the atmosphere during Expt. 3 and higher flux densities of 03 in thisexperiment.4.5.2 ANALYSIS OF VARIANCEAs mentioned in Section 3.8.4 (Methods and Materials), the three experiments with radishwere originally designed as replicates, to permit hypothesis testing using ANOVA. For harvests 1and 2, variances were found to be homogeneous for all primary variates. For harvest 3 thevariances for root weight (RW) and for harvest 4 the variances for leaf weight (LW) and leaf area(LA) were heterogeneous. These data were subjected to a natural logarithmic (In) transformationto stabilize the variances.Results from the ANOVA for all radish variables for each harvest (Table 21) demonstratea significant experiment effect for most variables and significant treatment effects were found forLA and SLA in harvest 3 and TW, HW and LW in harvest 4, where the03-enriched treatmentswere on average lower for these variables. There were significant cultivar differences for LA andRW in harvest 1. This difference persisted for RW through harvest 4, at which time the cvs. alsoshowed significant differences in LW, LWR and HI, with French Breakfast showing greater leafand root growth, and accordingly lower HI than Cherry Belle (Appendix 3, Table 1).Partitioning of the treatment effect into control versus03-enriched (Oz) led to significantdifferences for TW, HDW, LW, RW and LWR in harvest 1 (Table 21) with the118a252321G) 19S0)170)15 EXPT3D EXPT213 EXPT10b800S 50000)5 4QQ0)02000)c’ EXPT3D EXPT20 0 EXPT10C2.0- 1.6S0ci)1.00.50 EXPT3m EXPT20.0 0 EXPT10Figure 30a-c. a) Mean air temperature (°C), b) solar radiation (W m2)and c) windspeed (m s)for each of the 3 radish experiments in 1989. Meterological data represent means computed overthe 29 day exposure period, by hour, for each experiment.6 12 18 24Time of day (hour)6 12 18 24Time of day (hour)6 12 18 24Time of day (hour)119Table 21. Summary of ANOVA results: F-values for the effects of ozone on radish weight, leafnumber and area, and four ratios computed on a per plant basis for each harvest.SOURCE df TW HW LW LN LA RW LAR SLA LWR HIHarvest 1Expt 1Trt 14ConvsOz 1Ozlin 1Trt*eXpt 14Cult 1Cult*Expt 1Cult*Trt 14115.7 * 133.7 * 93.8 * 115.9 * 1.6 p1.95ns 1.44ns 1.XOns 1.llns 1.23ns9.18 * 884 * 6.87 * 0.13 us 2.10 usO.Olus O.24ns 0.O8ns 0.lOus 1.4Ous1.24ns 1.O7ns 1.5Ons 1.22us 2.22nsO.OOns 0.2Ons 0.O8ns 1.65ns 31.00*13.36 * 20.81 * 10.24 * 36.79 * 0.78 ns0.65 ns 0.88 ns 0.63 us 0.81 us 0.56 ns36.6 * 821.3 * 16130.1 * 120.6 * 167.4 *1.88ns 1.2lns 1.l2ns 1.O4ns 0.79ns7.30 * 4.38 us 3.29 us 4.85 * 3.32 us0.89as 1.97ns 2.74us 0.37ns 0.5lus1.llns 2.33ns 2.3lns 1.32us 20.8Ous305.00 * 0.42 ns 0.52 us 0.30 ns 0.28 us0.03 us 81.96 * 90.02 * 16.21 * 26.37 *1.3lus 1.OOns 1.O9ns 0.88ns 1.42nsHarvest 2Expt 2Trt 14ConvsOz 1Ozlin 1Trt*expt 28Cult 1Cult*Expt 2Cult4Tr 1433.81 * 68.45 * 6.51 * 22.88 * 8.67 *0.99ns 0.86us 1.O4ns 0.75ns 0.93us1.OOns 0.75ns 1.28ns 0.43ns 0.O4ns5.22 * 4.02 ns 5.36 * 1.29 us 4.35 *3.74 * 2.33 * 5.07 * 3.26 * 5.81 *1.27ns 1.87ns 0.O7ns 10.56ns O.2Ous16.27 * 27.22 * 743 * 11.24 * 7.31 *0.62ns 0.84ns 0.73ns 1.6Ons 1.O6ns118.45 * 733 * 0.42 us 57.47 * 76.13 *1.23us 1.O7us 1.36ns 0.6lns O.47ns1.93 ns 3.80 us 3.48 us 0.83 us 0.93 ns2.3Ons 0.Olus 0.29ns 0.57ns 0.99ns1.85ns 1.26ns 1.O4ns 1.4Ons 1.62ns0.43 us 0.44ns 0.70 us 1.98 us 2.03 ns8.63 * 543 * 0.81 us 20.42 * 24.71 *1.l7us 1.O3ns 1.O8ns 0.69ns 0.82nsHarvest 3Expt 2Trt 14ConvsOz 1Ozlin 1Trt*expt 28Cult 1Cult*Expt 2Cult*Trt 1463.63 * 82.03 * 12.67 * 13.58 * 20.19 *1.33 ns 1.21 its 1.46 us 0.78 us 2.61 *0.OOns 0.67ns 2.Olns 4•44* 5.16*2.84 ns 2.76 us 1.97 ns 0.21 us 4.83 *2.79 * 2.69 * 1.79 ns 2.01 * 1.67 ns0.11 us 3.46ns 2.63us 26.72* 1.4Ons8.85 * 9.98 * 15.89 * 339 * 22.75 *1.O2ns 0.93us 1.O3us 1.S8ns 1.34us9.47 * 124.27 * 106.74 * 64.10 * 70.35 *0.62 us 1.52 us 3.01 * 1.13 ns 1.21 us2.42 us 11.20 * 8.96 * 6.41 * 7.88 *0.00 us 0.02 ns 6.93 * 1.09 ns 2.11 us1.88* 2.48* 1.l6ns 2.24us 2.29*2.49 us 3.52 us 0.06 us 7.05 us 7.21 ns16.32 * 21.28 * 7.52 * 18.36 * 21.77 *2.15 * 0.99 us 1.59 ns 1.05 its 0.76 usHarvest 4Expt 2Trt 14ConvsOz 1Ozlin 1Trt*expt 28Cult 1Cult*Expt 2Cult*Trt 14136.15 * 185.62 * 28.21 * 67.56 * 8.71 *2.61 * 2.48 * 2.55 * 0.75 ns 1.95 us13.99 * 13.88 * 10.22 * 2.40 ns 1.32 ns4.lOus 3.3Ous 4.87* 0.9lns 3.42ns5.23 * 6.15 * 2.29 * 1.30 us 0.92 us16.13 ns 4.51 us 39.25 * 15.52 us 7.62 us2.43 ns 3.36 * 8.24 * 2.32 ns 5.31 us0.99ns 0.78us 0.77ns 0.57us 0.96ns19.04* 1.O7ns 1.45ns 78.40* 68.74*1.86 us 0.98 ns 0.97 ns 2.00 ns 2.06 us5.09 * 0.69 us 0.15 us 9.04 * 8.83 *6.53 * 0.27 us 0.37 us 0.46 us 0.64 us1.09 us 1.03 us 1.01 us 2.83 * 2.34 *35.44 * 2.13 its 0.22 us 29.53 * 31.45 *5.01 * 1.77ns 1.61 us 14.02 * 14.80 *0.69us 1.O4ns 1.OOus 0.87ns 0.8OusNB. TW = Total weight (g); HW = Hypocotyl dry weight (g); LW = Leafweight (g); LN = Leafnumber; LA = Leaf area (sq. m); RW =Root weight (g); FHW = Fresh hypocotyl weight (g); LAR = Leaf area ratio (sq. mlg); SLA = Specific leaf area = (sq. m/g); Leafweightratio = (gig); HI = Harvest index (g/g; hypocotyl weight/total weight); Expt = experiment; Trt =treatinent; Cou control; Oz = 03. Notethat there was no harvest 1 in experiment 1 due a reduction in the number plants as a result ofbirds foraging, therefore the dfforexperiments =1. RW in harvest 3 and LW and LA in harvest 4 are the results from hi transfoniiatious tostabilize the variances.120average control plant being larger for all variables than the average03-enriched plant (Appendix3, Table 1). There was a significant control versus enriched 03 treatment effect for LN, LA,LAR, SLA, LWR, and HI in harvest 3 (Table 21) with the average control plant having fewerleaves, smaller LAs, LARs, SLAs and LWRs but larger HIs than the average Oz plant. Thesedifferences were maintained for LWR and HI in harvest 4 at which time significant control versusOz treatment effects were also demonstrated for TW, HDW, LW, RW (Table 21) and hypocotylfresh weight (HFW) (data not presented), with the average control plant being larger for allvariables (Appendix 3, Table 1). A significant Oz linear effect was found for RW and LW inharvest 4 (Table 21), indicating that all treatment levels within the range of those used in theexperiment are significantly different from one another in their effects. According to Little (1981),further infonnation can be extracted if one proceeds with an analysis using single degree offreedom contrasts even when non-significant F-values for treatments are found, as seen in thepresent study.A significant Cultivar x Expt. interaction was found for most variables in harvests 1through 3 and for HW, LA, LW, RW, LWR and HI in harvest 4 (Table 21).The significant experiment effect found for all but two plant variables in two of theharvests suggests that further analyses of the radish data should be performed on a per experimentbasis. The significant interactions between treatment and experiment of a number of the plantvariables measured and cultivar and experiment provides further justification for not consolidatingthe data across experiments or across cultivars (Table 21). All regressions examining the plantresponse to 03 exposure and conductance versus a number of meteorological variables weretherefore performed for each experiment and cultivar separately.4.5.3 TEMPORAL CHANGES IN GROWTH4.5.3.1 Cherry BelleTrends in TW over each harvest for two of the three experiments with cv. Cherry Belle arepresented in Figures 31 and 32. In Expt. 1, few differences in TW were discernible1213.50003.0000 — 0 - TRT I2.50000—-TRT2so—‘ -TRT32.0000— X ThT41.5000—K---— lilT 13I-.1.0000 -0- TRT 140.5000 I TRT 150.000017Experiment 2, Cherry Belle3.50003.0000 — - TRT52.5000— 0—- TRT6S——-TRT74 2.0000 )& TRTS1.5000lilT 131.0000 —0—— lET 14o.sooo I TIlT 150.0000173.50003.00002.5000552.00001.50001.00000.50000.000021 25 29Age (number of days after planting)21 25 29Age (number of days after planting)Experiment 2, Cherry Belle— 0—-TRT9— 0— TRT1O— &-TRT1I— *-TRT12—K---— TRT 13-0—— lET 14I TRT1517 29Figure 31. Total dry weight for radiah, cv. Cherry Belle experiment two, harveated between June 29 and July 7,l9g9. Each point repreaenta a treatment mean of 5-10 eubaanaplea (plants). Treatments 13, 14 and 15 areambient air controla.21 25Age (number of days after planting)122Experiment 3, Cherry BelleAge (number of days after planting)Figure 32. Total dry weight for radish, cv. Cherry Belle experiment three, harvested between June 29 and July7, 1989. Each point represents a treatment mean of 5-10 sub-samples (plants). Treatments 13, 14 and 15 areambient air controls.17 21 25 29Age (oomber of days after planting)Experiment 3, Cherry Belle3 .50003.00002.500034 2.0000- 1.50001.00000.50000.00003.50003.00002.500034 2.0000- 1.50001.00000.50000.0000173.5000Experiment 3, Cherry Belle3.00002.500034 2.00001.50001.00000.50000.000029—-TRT1— ci— TRT2— 1X-TRT3— X—-TRT4—)K——— TRT 13—0——— TRT 14I TRT15— 0—- TRT5— D— TRT6— a—-TRT7— x—-g—)—— TRT 13—0——— TRT 14I TRTI50— TRT9—s--- TRT1O— a-—-TRT11—X—-TRT12—)K--— TRT 13—0——— TIlT 14I TRT1S21 25 29Age (number of days after planting)17 21 25123between treatments over time (data not displayed). In Expts. 2 and 3, few differences wereapparent between the controls and03-enriched treatments for TW until 29 d.a.p. (Figures 31 and32). The response in HW was similar to TW for each experiment except that the differencesbetween the controls and03-treated plants were much greater 29 d.a.p. (data not presented). LARdeclined as the harvests progressed in all three experiments with few marked differences betweentreatments. Considering the final harvest data from each experiment, TW, HW and HFW werehighest in Expt. 2 and lowest in Expt. 3. The relative proportion of total biomass allocated tohypocotyl weight (HI) was highest in Expt 2 (69%) and lowest in Expt 3 (60%) (Appendix 5,Table 5).In Expt. 1, RGR, and RGRH\, decreased between the second and third harvestintervals with the exception of treatment 15 (one of the controls) (Appendix 5, Table 1). For mosttreatments, AGRs increased with time. On average the controls (treatments 13-15) had lowerRGRTWs, RGRj..p,s and AGRs than03-enriched treatments in the second harvest interval buthigher in the third (Table 22).In Expts. 2 and 3, RGR decreased throughout the experimental period (Appendix 5,Table 1). In contrast, AGRs increased over time (Appendix 5, Table 1). On average the controls(treatments 13-15) had lower RGRrj,s, RGRpis and AGRs than03-enriched treatments in thefirst two harvest intervals but these differences were reversed in the last harvest interval (Table22).4.5.3.2 French BreakfastFrench Breakfast exhibited similar trends to those observed with cv. Cherry Belle in TW,HW and LAR over each harvest, for each of the three experiments. Like cv. Cherry Belle, in Expt.1 few differences were noted between treatments 29 d.a.p. for TW of French Breakfast, and inExpts. 2 and 3, differences only became apparent between the controls and 03-enriched treatmentsfor TW 29 d.a.p. Again, greater differences occurred 29 d.a.p. in HW than TW. LAR declined asthe harvests progressed in all three experiments. In addition, LAR was lower for the124Table 22. Summary of relative growth rates for total and hypocotyl weight and absolute growth ratefor each experiment by harvest interval and treatment averaged separately for the controls and ozoneenriched treatments for cvs. Cherry Belle and French Breakfast. Data are based on expected valuesextrapolated from fitted curves using ln-transformed weight versus time or weight versus time whereappropriate.CULT EXPT TRT RGR (gig/day) RGRTW (gig/day) AGR (g/day)Harvest Intervals1 2 3 1 2 3 1 2 3Cherry Belle1 Oz - 0.339 0.177 - 0.215 0.117 - 0.155 0.146AA - 0.287 0.239 - 0.165 0.162- 0.123 0.1732 Oz 0.484 0.297 0.111 0.288 0.195 0.102 0.110 0.137 0.164AA 0.399 0.295 0.190 0.220 0.198 0.177 0.057 0.168 0.2793 Oz 0.506 0.344 0.182 0.316 0.212 0.109 0.073 0.094 0.116AA 0.515 0.355 0.195 0.288 0.214 0.139 0.069 0.132 0.194French Breakfast1 Oz - 0.417 0.250 - 0.260 0.189- 0.139 0.235AA- 0.280 0,286 - 0.175 0.213 - 0.097 0.2432 Oz 0.450 0.328 0.206 0.277 0.223 0.169 0.087 0.164 0.241AA 0.454 0.35 1 0.247 0.275 0.242 0.209 0.072 0.203 0.3333 Oz 0.492 0.316 0.139 0.296 0.200 0.104 0.072 0.101 0.129AA 0.493 0.339 0.186 0.282 0.219 0.157 0.071 0.155 0.240NB. Data used in the treatment means are n=3 for the ambient air controls (AA) and n=12 for the 03-enriched (Oz) treatments. CULT = cultivar; EXPT = experiment.controls than for the03-treated plants. The final harvest data from each experiment again showedthat TW, 11W and HFW were highest in Expt. 2 and lowest in Expt. 3. The relative proportion oftotal biomass allocated to hypocotyl weight (HI) was again highest in Expt. 2 (5 7%) but the valueswere comparable in Expts.. 1 and 3 (Appendix 5, Table 2).In Expt. 1, on average the controls had lower RGR-’is, RGRprs and AGRS in thesecond harvest interval and higher in the third harvest interval than the03-enriched plants (Table22).125Like cv. Cherry Belle, Expts. 2 and 3 RGRj and RGRHW decreased and AGRincreased for all treatments and harvest intervals (Appendix 5, Table 2). Similarly, the RGR-pijs,RGRp,rs and AGRs in the controls were clearly greater than in the03-enriched treatments by thetime of final harvest (Table 22).In summary, apart from the exceptions noted above, in general RGRTJ and RGRj,decreased and AGR increased for both cultivars, in all harvests and experiments. In addition,,RGR-1-’çi,r, RGRp, and AGR were higher for the average control plants versus the average 03-.enriched plants in the final harvests for all experiments and both cultivars.4.5.4 EXPOSURE-RESPONSE RELATIONSHIPS4.5.4.1 Simple Linear Regression AnalysesIn Expt. 1 there were no significant effects of 03 treatments on any variables or derivedratios for either cultivar (Tables 23 through 26). Although the regression of HW for cv. FrenchBreakfast was significant at harvest 3, the sign of the coefficient was positive and the trend was notmaintained in subsequent harvests. This was therefore regarded as a questionable result.In contrast, for Expts. 2 and 3, there were clear decreases in TW, 11W and HI in the laterstages of growth of both cultivars (Tables 23 and 25). The increasingly significant trends in theadverse effects of 03 on TW and 11W in both experiments with cv. Cherry Belle are shown in thescatterplots in Figures 33, 34, 35 and 36.In Expts. 2 and 3, significant decreases in LW and LA were less consistent in eithercultivar (Table 24). However, in Expt. 3 effects on leafiness resulted in significant increases inLAR (Table 25) and LWR (Table 26), although these effects were not reflected in significantchanges in SLA (Table 26).126Cherry BeUe Total WeightHypocotyl WeightFrench Breakfast Total WeightHypocotyl WeightExpt. r2Table 23. Results of the simple linear regressions on total and hypocotyl dry weight (g) for radish versusD5OC for each cultivar, experiment and harvest.Harvest a 1)1 p1 2 0.3838 +0.00690 0.216 0.08121 3 0.9439 +0.00830 0.107 0.23371 4 1.6546 -0.00042 0.000 0.94492 1 0.2045 -0.00240 0.153 0.14942 2 0.6168 -0.00300 0.017 0.64702 3 1.1059 +0.00310 0.010 0.72472 4 2.3100 -0.03110 0.452 0.00603 1 0.1251 -0.00280 0.451 0.00613 2 0.4591 -0.00860 0.277 0.04383 3 0.9044 -0.00680 0.188 0.10653 4 1.7737 -0.02730 0.672 0.00021 2 0.1219 +0.00410 0.279 0.04291 3 0.5111 +0.00490 0.067 0.34981 4 1.0760 +0.00034 0.000 0.93882 1 0.0495 -0.00090 0.140 0.16942 2 0.3126 -0.00190 0.022 0.60182 3 0.7459 -0.00170 0.005 0.79622 4 1.8112 -0.02530 0.424 0.00863 1 0.0160 -0.00030 0.162 0.13653 2 0.1584 -0.00470 0.288 0.03933 3 0.4726 -0.00670 0.292 0.03763 4 1.1656 -0.02260 0.729 0.00011 2 0.3241 +0.0022 0.018 0.63071 3 0.7669 +0.0082 0.182 0.11241 4 1.6891 +0.0084 0.103 0.24332 1 0.1701 -0.0008 0.014 0.67102 2 0.5384 -0.0067 0.112 0.22312 3 1.3 190 -0.0068 0.035 0.50562 4 2.9084 -0.0363 0.380 0.01443 1 0.1428 -0.0023 0.241 0.06293 2 0.4793 -0.0060 0.289 0.03873 3 1.0177 -0.0117 0.312 0.03043 4 2.0300 -0.0334 0.641 0.00031 2 0.0857 +0.00090 0.0 17 0.64251 3 0.3097 +0.00560 0.272 0.04621 4 0.8570 +0.00450 0.077 0.31602 1 0.0325 -0.00030 0.033 0.51822 2 0.1932 -0.00250 0.106 0.23602 3 0.6879 -0.00570 0.069 0.34442 4 1.6734 -0.02170 0.328 0.02563 1 0.0222 -0.00060 0.235 0.06683 2 0.1523 -0.00230 0.154 0.14733 3 0.5042 -0.00900 0.479 0.00433 4 1.0820 -0.01930 0.586 0.0009127Table 24. Results of the simple linear regressions on leaf dry weight (g) and leafarea (sq. cm) for radish versus D5OC for each cultivar, experiment and harvest.Expt. Harvest a b1 r2Leaf Weight 1 2 0.24561 3 0.42561 4 0.56672 1 0.15142 2 0.29892 3 0.35592 4 0.68693 1 0.10603 2 0.29483 3 0.42543 4 0.6008Leaf Area 1 2 0.00591 3 0.00801 4 0.01212 1 0.00242 2 0.00632 3 0.00712 4 0.01373 1 0.00303 2 0.00593 3 0.00963 4 0.01191 2 0.22871 3 0.44601 4 0.81652 1 0.13292 2 0.33532 3 0.61982 4 1.20063 1 0.11723 2 0.32013 3 0.50543 4 0.92771 2 0.00531 3 0.00811 4 0.01272 1 0.00292 2 0.00692 3 0.01322 4 0.01603 1 0.00323 2 0.00653 3 0.01063 4 0.0161+0.00270 0.128 0.1910+0.00320 0.171 0.1252-0.00062 0.088 0.7559-0.00150 0.126 0.1947-0.00110 0.009 0.7433+0.00480 0.191 0.1035-0.00570 0.343 0.02 18-0.00230 0.488 0.0038-0.00380 0.220 0.0775-0.00020 0.001 0.8913-0.00470 0.340 0.0226+0.00005 0.09 1 0.2749+0.00006 0.116 0.2148-0.00011 0.118 0.2107+0.00000 0.001 0.8914+0.00002 0.003 0.8362+0.00010 0.112 0.2228-0.00012 0.253 0.0562-0.00006 0.425 0.0084-0.00005 0.101 0.2474+0.00001 0.006 0.7899-0.00004 0.096 0.262 1+0.00070 0.006 0.7923+0.00270 0.079 0.309 1+0.00377 0.110 0.2268-0.00040 0.007 0.7737-0.00420 0.116 0.2134-0.00110 0.005 0.8114-0.01410 0.407 0.0105-0.00170 0.224 0.0748-0.00370 0.357 0.0187-0.00270 0.078 0.3137-0.01380 0.671 0.0002+0.00003 0.022 0.5967+0.00005 0.044 0.4509+0.00008 0.202 0.0925+0.00000 0.001 0.8949-0.00005 0.03 1 0.5283-0.00004 0.011 0.7041-0.00033 0.387 0.0132-0.00003 0.112 0.2228-0.00005 0.267 0.0486-0.00002 0.010 0.7275-0.00016 0.443 0.0068Cherry BellepFrench Breakfast Leaf WeightLeaf Area128Table 25. Results of the simple linear regressions on harvest index and leaf area ratio(sq. cmlg) for radish versus D5OC for each cultivar, experiment and harvest.+0.00340 0.243 0.0618+0.00010 0.000 0.9773+0.00040 0.031 0.5274-0.00180 0.053 0.4083-0.00060 0.003 0.8411-0.00360 0.281 0.0421-0.00230 0.155 0.1470+0.00040 0.009 0.7305-0.00600 0.274 0.0453-0.00420 0.324 0.0267-0.00450 0.587 0.0009-0.00013 0.160 0.1400-0.00001 0.002 0.8829-0.00008 0.131 0.1856+0.00012 0.095 0.2634+0.00011 0.089 0.2800+0.00008 0.199 0.0955+0.00004 0.227 0.0724+0.00006 0.099 0.2532+0.00020 0.456 0.0057+0.00011 0.282 0.0415+0.00015 0.461 0.0054+0.00100 0.011 0.7155+0.00250 0.255 0.055 1+0.00006 0.000 0.9396-0.00100 0.036 0.4967-0.00050 0.019 0.6208-0.00250 0.154 0.1485-0.00090 0.049 0.4289-0.00170 0.164 0.1348-0.00 120 0.026 0.5675-0.00380 0.425 0.0085-0.00140 0.130 0.1865-0.00010 0.087 0.2871-0.00006 0.159 0.1408+0.00001 0.018 0.6289+0.00003 0.0 10 0.7280+0.00014 0.161 0.1385+0.00003 0.109 0.2298-0.00000 0.004 0.8162+0.00014 0.104 0.2401+0.00008 0.110 0.2270+0.00013 0.345 0.0213+0.00010 0.325 0.0265Expt. Harvest a b1 r2 pCheny Belle Harvest Index 1 2 0.32 171 3 0.54281 4 0.64952 1 0.23992 2 0.50252 3 0.67682 4 0.73053 1 0.12503 2 0.34273 3 0.52133 4 0.6641Leaf area ratio 1 2 0.01591 3 0.00901 4 0.00762 1 0.01212 2 0.01022 3 0.00632 4 0.00533 1 0.02413 2 0.01303 3 0.01083 4 0.0065French Breakfast Harvest Index 1 2 0.25551 3 0.40421 4 0.50722 1 0.19122 2 0.36292 3 0.52732 4 0.58223 1 0.15463 2 0.31573 3 0.49233 4 0.5336Leafarea ratio 1 2 0.01801 3 0.01111 4 0.00772 1 0.01742 2 0.01272 3 0.01012 4 0.00883 1 0.02253 2 0.01413 3 0.01073 4 0.0080129Table 26. Results of the simple linear regressions on specific leaf area (sq. cmlg) andleaf weight ratio for (gig) radish versus D5OC for each cultivar, experiment and harvest.Expt. Harvest a b1 r2 pCherry Belle Specific leaf area 1 2 0.0244 -0.00008 0.050 0.42381 3 0.0 192 +0.00000 0.000 0.95761 4 0.0236 -0.000281 0.124 0.19842 1 0.0161 +0.00011 0.079 0.31122 2 0.0211 +0.00014 0.109 0.22922 3 0.0197 +0.00001 0.003 0.85032 4 0.0202 -0.00002 0.010 0.72383 1 0.0282 +0.00006 0.153 0.14913 2 0.0207 +0.00034 0.030 0.53703 3 0.0226 +0.00003 0.053 0.40743 4 0.0198 +0.00012 0.214 0.0824Leaf weight ratio 1 2 0.6526 -0.00340 0.228 0.07 181 3 0.4682 -0.00029 0.001 0.89471 4 0.3452 -0.00036 0.020 0.61152 1 0.7543 +0.00 160 0.059 0.38282 2 0.4986 +0.00075 0.004 0.81552 3 0.3202 +0.00384 0.294 0.03672 4 0.2622 +0.00250 0.162 0.13693 1 0.8531 +0.00015 0.002 0.88273 2 0.6554 +0.00055 0.274 0.04533 3 0.4767 +0.00410 0.346 0.02123 4 0.3333 +0.00490 0.546 0.0016French Breakfast Specific leaf area 1 2 0.0242 -0.000010 0.003 0.85891 3 0.0183 +0.00000 0.000 0.94851 4 0.0158 ÷0.00002 0.036 0.50092 1 0.0218 +0.00002 0.003 0.85852 2 0.0202 +0.000 19 0.265 0.04962 3 0.0216 -0.00004 0.061 0.37582 4 0.0217 -0.00006 0.122 0.20133 1 0.0272 +0.000 10 0.060 0.37823 2 0.0207 +0.00009 0.168 0.12883 3 0.0210 +0.00011 0.188 0.10633 4 0.0176 +0.00013 0.377 0.0149Leaf weight ratio 1 2 0.7384 -0.00350 0.119 0.20741 3 0.6085 -0.00320 0.250 0.05761 4 0.4853 -0.00010 0.001 0.90242 1 0.7985 +0.00066 0.016 0.65612 2 0.6347 +0.00027 0.005 0.80302 3 0.4669 +0.00251 0.163 0.13542 4 0.4055 +0.00110 0.063 0.36713 1 0.8272 +0.00220 0.220 0.07813 2 0.6776 +0.00120 0.028 0.54973 3 0.5060 +0.00360 0.388 0.01313 4 0.4562 +0.00195 0.180 0.1146130Harvest - 1 Cultivar -1 Harvest- 3 Cultivar -1Figure 33a-d. Scattergrams of total dry weight for radish, cv. Cheffy Belle versus D5OC, for harvests I through 4 in expernnent 2. Eachpoint represents a treatment mean of 5 - 10 sub-samples (plants)... .• ! :.. ...•• •....0.250.2as0.150.10.0500.80.70.6as0.50.40.10150 5 10D50CHarvest -2 Cultivar -101.81.61.4.9 1.2.15 10.80.60.40.2032.51.50.50255 10 15 20050CHarvest -4 Cultivar -1.• : •... ....... ..• •••.••:•0 5 10 15 20D5OC0 5 10 15 20 25 30DSOC131Harvest - I Cultivar -1 Harvest -3 Cultivar -1Figure 34a-d. Scattergrams of total dry weight for radish, cv. Cherry Belle versus D5OC, for harvests I through 4 in experiment 3. Eachpoint represents a treatment mean of 5 - 10 sub-samples (plants).•.. •..•.•. •.0.250.2us0.150.10.0500.60.50.40.30.20.10150 5 10D5OCHarvest -2 Cultivar -10D5OC10 15 20 251.20.80.60.40.20C21.81.61.4be.H 1.2us0.80.60.40.20—dHarvest -4 Cultivar -1L : •... ..••••... .:•••..0 5 10D5OC15 20 0 5 10 15 20 25 301350C132Harvest - 1 Cultivar -1 Harvest- 3 Cultivar -1Figure 35a-d. Scattergrams of hypocotyl dry weight for radish cv. Cherry Belle versus D5OC, for harvests 1 through 4 in experiment 2. Eachpoint represents a treatment mean of 5 - 10 sub-samples (plants)....•• I...•• •.0 5 10 15D5OCHarvest -2 Cultivar -10.070.063 0.050.040.030.020.0100.50.4so0.30.20.10....‘:::0 5 10 15 20 25D5OCHarvest -4 Cultivar -11.20.80.60.4H0.2021.5.I....•.....I..Csobe0a>00.>.Hd.0.5....0 5 10 15 20D5OC0 5 10 15 20 25 30D5OC133Harvest - 1 Cultivar -1 Harvest -3 Cultivar -IFigure 36a-d. Scattergrams of hypocotyl dry weight for radish, cv. Cherry Belle versus D5OC, for harvests 1 through 4 in experiment 3.Each point represents a treatment mean of 5 - 10 subsamples (plants).[, ...••.•...0.0250.02as0.0150.010.00500.25a0.2be0.150.10.050150 5 10D5OCHarvest -2 Cultivar -10O.6j..0.4 •beS ..0.205 10 15 20 25D50CHarvest -4 Cultivar -11.51.2.0.9S •• ••.0.60..0.30......•0 5 10 15 20D5OC0 5 10 15 20 25 30D5OC134There was no significant effect of 03 on HFW in the first experiment for either cultivar,but significant linear decreases in HFW were found with increasing 03 in Expts. 2 and 3 for bothcultivars (Table 27). This is consistent with the decline in HW found for these same harvests andexperiments (Table 23).Table 27. Results of the simple linear regressions on hypocotyl fresh weight (g) forradish versus D5OC for harvest 4, for two cultivars and three experiments.a b1 r2 p16.630 +0.080 0.063 0.368038. 167 -0.558 0.453 0.006021.605 -0.389 0.567 0.001014.468 +0. 107 0.099 0.253341.147 -0.531 0.321 0.028022.566 -0.393 0.526 0.002023French Breakfast 123No significant linear models were found for AGR and D5OC in Expt. 1, but by the laterharvest intervals in Expts. 2 and 3 significant negative relationships were found for both cultivars(Table 28).Although similar negative linear trends were also found for RGR’pij the only significantregression was for cv. Cherry Belle in Expt. 2. No significant relationships were found forRGRHW in any experiment with either cultivar (Table 28).4.5.4.2 Non-Linear Regression AnalysesNon-linear regression analysis using both gamma and Weibull models was attempted withthe TW, HW and HFW data. No meaningful convergence was found for TW or HW in any oftheexperiments nor for HFW in the first two experiments. However, in Expt. 3, modest improvementsin fit were found for HFW using the Weibull model. The models are as follows:Cherry Belle: HFW=2 1 .778*exp((D50C/40.656) .082);ro=0.572French Breakfast: HF\V23 .042*exp((D50C/46.428)’913);r20O.535 (cf. Table27).CultivarCherry BelleExpt.1135Table 28. Results of the simple linear regressions on growth rates between harvestsintervals for radish AGR (gig), RGRTW (gig/day) and RGRHW versus D5OC.The sign of the coefficient (b1) and p value are presented.Harvest Interval1 2 3Cherry BelleExperiment 1AGR- (+) 0.6733 (-) 0.1784RGRTW - (-) 0.8108 (-) 0.1189RGRHW - (-) 0.4762 (-) 0.3 157Experiment 2AGR (+) 0.1129 (-) 0.0160 (-) 0.0005RGRTW (+) 0.2893 (-) 0.448 1 (-) 0.0486RGRHW (+) 0.3843 (-) 0.5802 (-) 0.0939Experiment 3AGR (+) 0.5328 (-) 0.0003 (-) 0.0004RGRTW (+) 0.0588 (-) 0.5760 (-) 0.0916RGRpW (-) 0.2740 (-) 0.1919 (-) 0.8718French BreakfastExperiment 1AGR- (+) 0.1539 (+) 0.7833RGRTW - (+) 0.6592 (-) 0.4726RGRHW - (+) 0.6476 (-) 0.1912Experiment 2AGR (+) 0.5661 (-) 0.013 1 (-) 0.0082RGRTW (-) 0.6495 (-) 0.0895 (-) 0.2257RGRHW (-) 0.5293 (-) 0. 1060 (-) 0.2953Experiment 3AGR (+) 0.8425 (-) 0.0005 (-) 0.0012RGRTW (+) 0.5192 (-) 0.1318 (-) 0.0512RGRp (+) 0.7252 (-) 0.4112 (-) 0.2835NB. RGRTW = Relative growth rate for total weight (g.g4.day)ROR w = Relative growth rate forhypocotyl weight (g.g4.day)AGR = Absolute growth rate (g.g1.day14.5.5 WHOLE PLANT CONDUCTANCEAs previously discussed in Section 3.7.5.1, conductance measurements were undertakenonly in the last two experiments. A summary of the whole plant conductance data over all datesand hours measured for the two experiments is presented in Table 29. For each cultivar in Expt. 2,the conductance measurements are lowest for the control plants versus the four 03 treatmentplants. In Expt. 3, the control plants were second and third lowest out of a total of five treatments.136However, inspection of the standard errors shows that there is overlap between conductance valuesfor the control plants and some of the03-enriched plants (Table 29). The magnitude of theseconductance values is consistent with those obtained elsewhere for radish (see Pell et al., 1993).Table 29. Whole plant conductance (cm s) of radish plants for 2 cultivars and twoexperiments. Values are means ± standard errors (n=40).Treatment Expt. 2 (July 1 1-August 8) Expt. 3 (August 15-September 12)Cherry Belle French Breakfast Cherry Belle French Breakfast1 0.66 1 ±0.034 0.642 ±0.029 0.610 ±0.025 0.479 ±0.0272 0.825 ±0.050 0.637 ±0.028 0.596 ±0.021 0.447 ±0.0293 0.706 ±0.039 0.584 ±0.023 0.568 ±0.017 0.530 ±0.0174 0.662 ±0.033 0.571 ±0.016 0.555 ±0.023 0.506 ±0.025AA 0.624 ±0.036 0.496 ±0.024 0.557 ±0.025 0.498 ±0.028NB. Treatment means are computed by averaging the mean of 2 subsamples (plants) over each hour of the 10-hmeasurement period over the 4 sampling dates by cultivar and experiment. AA = ambient air control, 1-4 = 03.enriched treatments.With the exception of Cherry Belle in Expt. , in general conductance declined with leafmaturity for both cultivars (Figure 37). Mean conductances of both cultivars were generallyhigher in Expt. 2 than 3 and mean conductances of cv. Cherry Belle were consistently higher thancv. French Breakfast (Table 29). Mean conductance increased with increasing 03 concentration inExpt. 2 and declined with an increase in 03 exposure in Expt. 3 for both cultivars (Figure 38).The same trends were noted in the relationship of conductance and 03 concentrations in theprevious hour for both cultivars (Figure 39). There were no clear diurnal trends in the conductancemeasurements for either cultivar or experiment. The results for cv.137a bcv. Cherry Belle, Expt. 2 cv. French Breakfast, Expt. 2Figure 37a-d. Mean conductance versus the number of days after planting for radish, cvs. Cherry Belleand French Breakfast, for one ambient air control and four ozone-enriched treatments in Expts. 2 (a-b)and 3 (c-d).Saa1.4121.00.80.60.4020.0 -151,412E 1.00.8060.40.20.020 25 30Number of days after plantingAA0 0Z4DOZ34 0Z2— 0OZ130Ccv. Cherry Belle, Expt. 320 264imber of days after plantingcv. French Breakfast. Bxpt. 3° AA0 0Z4C 0Z3I 0Z2o OZ1d° AA0 0Z4O 0Z3I OZZo OZ1151.4 -121.00.80.60.4020.0 -151.41.28 1.00.8a0.600.40.20.0Eaa15 20 25Nkxnber of days after planting30° AA0 0Z4C 0Z3I 0Z20 0Z120 26ti&nnber of days after planting30138a bcv. Cherry Belle, Expt. 2 cv. French Breakfast, Expt. 21.0 1000.9 0— 002 00 08a o n 0 0 0u0 8 00.7 0 006 0cP000 :: cP003 030 50 100 150 0 50 100 150Mean hotrty ozone concentration (ppb) Mean hoi.rty ozone concentration (ppb)c dcv. Cherry Belle, Expt, 3 cv, French Breakfast, Expt. 31.0 1.00.9 0.90.8 ‘08 0a 00.7 00 0.7 0QJ2,oO06 c 0 06 00 005 0 000 0 0 os0 008 004 0.4 003 030 20 40 60 80 100 0 20 40 60 80 100Mean ho,rly ozone concentration (ppb) Mean houly ozone concentration (ppb>Figure 38a-d. Mean conductance versus mean hourly ozone concentration (ppb) for radish cvs. CherryBelle and French Breakfast for one ambient air control and four ozone enriched treatments in Expts 2 (a-b)and 3(c-d).139bFigure 39a-d. Mean conductance versus hourly ozone concentration (ppb) in the previous hour forradish cvs. Cherry Belle and French Breakfast for one ambient air control and four ozone-enrichedtreatments in Expts. 2 (a-b) and 3 (c-d).cv. Cherry Belle. Expt. 21.0 1000.9 0.90 00,8 o 080.7 0 0.706 0 °cP 0 06o0.5 cP0.4 0.403 030 60 100 150Mean ozone concentration previous 1 Ii (ppb>0cv. Cherry Belle, Expt. 3dcv. French Breakfast. Bxpt. 208o 050 100 150Mean ozone concentration prevIous 1 I’ (ppb>cv. French Breakfast, Expt. 30000 050 100 150Mean ozone concentration previous 1 l (ppb)1.00.90.80.70.60.50403aa100.90807060504030 50 100Mean ozone concentration previous 1 ti (pb)150 0140Cherry Belle, Expt. 2, shown in Figure 40 are typical for both cultivars.Prior to addressing Objective 3 (the development of surrogates for conductance based onmeteorological variables), the degree of association between 03 and a number of environmentalvariables measured concurrently were assessed using correlation analysis. The results arepresented in Table 30. The association between 03 and all environmental variables was poor inExpt. 2 (r < 0.06), although there were weak but significant positive associations (r = 0.237-0.281)between 03 in the previous hour, AT and SUN in the previous two hours (Table 30).In Expt. 3, with the exception of RH, WS, NO and NO2,a number of significant positiveassociations were found between the meteorological variables measured and both 03 and 03 in theprevious hour. The associations between 03, or 03 in the previous hour and ST, AT, SUN1B,STJN2B and WD ranged from r = 0.418 to r = 0.638 (p 0.01) (Table 30). The role of solarradiation in the production of 03 is well-known. However, air temperature is also a usefulindicator of environmental conditions conducive to 03 formation. High 03 concentrations tend tooccur during periods of high temperatures (Chock, 1989) as high temperatures are associated withanticyclonic conditions with clear skies and moderate winds, and photochemical reaction rates aretemperature dependent (Robeson and Steyn, 1990).The inconsistencies in associations with 03 between experiments are difficult to explain.The enriched concentrations were much higher in Expt. 2 than 3. Although windspeed, airtemperature and solar radiation were slightly higher in Expt. 2, little else differed between theseexperiments (Figure 30). Despite the poor correlations found in Expt. 2, 03 in the previous hourshowed the strongest associations with AT, ST, SUN lB and SUN2B over both experiments.The relationships between micro-meteorological measurements and conductance wereinitially investigated utilizing simple linear regression for each cultivar and experiment. In anattempt to deal with the scatter in the data, an average over all sampling dates by hour, for eachcultivar and experiment was used in the regression analyses. The results are presented in Tables31 and 32.With the exception of 03 (regardless of the hour), RH and WD, conductance decreased141EaaS° 0.5a10 15Time of day (hour)Date 4O 0Z4o 0Z30Z2o OZ1EaSa0 0Z40 0Z3d 0Z2OZI10 15Time of day (hour)O 0Z4o 0Z3< 0Z2o OZ1Figure 40a-d. Mean conductance versus time of day for radish, cv. Cherry Belle for one ambient aircontrol and four ozone-enriched treatments, and four measurement peroids (a-d), in Expt. 2.Date 1 Date 31.5 -1.00.50015ab1.0C0 0Z4O 0Z30Z2c OZ1d200.055 10 15Time of day (hour)Date 2201.51.00.50.01.51.00.50.0—5 10 15Time of day (hour)20 5 20142Table 30. Correlation analysis between ozone and a number of enviromnental variablesmeasured concurrently over 4 measurement days and 2 experiments. Values arethe Pearson product moment correlation coefficients and the sign indicates the typeof association between variables.Environmental 03 03 previous 03 03 previousVariable hour hourExperiment 2 Experiment 3Soil temperature (ST) +0.027 +0.178 +0.538** +O.638**Air temperature (AT) -0.041 +0.237* +0.468** +0.638**Absolute humidity (ABS) +0.058 +0.171 +0.258* +0.169Relative humidity (RH) +0.044 -0.227 0.260* 0.413**Solar radiation (SUN) -0.036 +0. 156 +0.044 +0.180SUN previous hour (SUN1B) 0.029* +0.281* +0.418** +0.542**SUNprevious2h(SUN2B) +0.001 +0.258* +0.486** +0.625**Wind speed (WS) +0.018 +0.199 -0.195 -0.018Wind direction (WD) -0.027 -0.107 +0.450** +0.540**Nitric Oxide (NO)-0.002 +0.093 -0.079 -0.216Nitrogen dioxide (NO2) -0.027 +0.206 -0.0 19 -0.195NB. Data used in the analyses are averages over the 4 measurement dates for each hour andexperiment; (n=50 observations, 5 treatments and 10 hours/treatment). *=p 0.05; **=p 0.01.with all environmental variables measured for cv. Cherry Belle in Expt. 2 (Table 33). In Expt. 3,the relationship with 03 reversed, resulting in several significant negative linear relationships beingfound though the fits never exceeded r2 = 0.366 (Table 31). The best relationships found forconductance in Expt. 2 were with RH and WS although neither of these models had anr2> 0.35.In Expt. 3, these same models were not significant, and the best relationships with conductancewere with SUN lB and SUN2B, accounting for 36% of the variation in conductance. In both casesan increase in the magnitude of the independent variable resulted in a significant decrease inconductance.143Table 31. Simple linear regressions of conductance on various environmentalvariables for radish cv. Cherry Belle in two experiments.Environmental a b1 r2 p a b1 r2 pvariableExperiment 2 Experiment 3Airtemperature(AT) 1.593 -0.04460 0.130 0.0100 1.453 -0.0396 0.266 0.0001Absolute humidity (ABS) 1.320 -0.04760 0.263 0.0001 0.541 +0.0038 0.002 0.7547Nitricoxide(N0) 0.697 -0.04370 0.000 0.9224 0.112 +0.0645 0.136 0.0084Nitrogen dioxide (NO2) 0.836 -0.48760 0.091 0.0338 0.414 +0.0827 0.109 0.0191Ozone (03) 0.552 +0.00250 0.249 0.0002 0.650 -0.0020 0.113 0.017003 previous hour (031B) 0.681 +0.00020 0.003 0.7297 0.667 -0.0028 0.187 0.0017Relative humidity (RH) -1.476 +0.02930 0.346 0.0000 0.469 +0.00 18 0.020 0.3236Soil temperature (ST) 1.345 -0.03160 0.290 0.0001 0.867 -0.0155 0.167 0.0033Solarradiation(SUN) 0.706 -0.00002 0.001 0.8299 0.710 -0.00030 0.123 0.0124SUNprevioushour(SUN1B) 0.821 -0.00023 0.138 0.0080 0.717 -0.00030 0.366 0.0000SUN previous 2 hour (SUN2B) 0.828 -0.00030 0.274 0.0001 0.681 -0.00030 0.362 0.0000Wind direction (WD) 0.437 +0.00210 0.259 0.0002 0.701 -0.00098 0.218 0.0006Wind speed (WS) 0.995 -0.23700 0.344 0.0000 0.522 +0.04350 0.037 0.1781NB. Conductance data were available for 5 treatments. Data used in the regressions are the 5 treatmentmeans (n=2 subsamples per treatment and cuhivar) computed by averaging over the 4 sampling dates fora ten hour measurement period for each experiment (N=50),Similar relationships were found between cv. French Breakfast conductance and theenvironmental variables for each experiment (Table 32). However, the fits for most models werepoorer in Exp. 2 than for cv. Cheny Belle but, with the exception of 03, improved in Expt. 3. Forexample, 55% and 50% of the variation in conductance was explained by the regressions withSUN1B and SUN2B for cv. French Breakfast (Table 32).4.5.6 FLUX-RESPONSE RELATIONSHIPSSimple linear regressions of total plant weight and hypocotyl weight for each cultivar andharvest date (based upon treatment means) versus FLUXSUMC and D5OC are compared in Tables33 and 34. The regressions using D5OC are different from144Table 32. Simple linear regressions of conductance on various environmental variables forradish cv. French Breakfast in two experiments.Environmental a b1 p a b1 pvariableExperiment 2 Experiment 3Airtemperature(AT) 1.257 -0.03330 0.133 0.0091 1.637 -0.05160 0.328 0.0000Absolute humidity (ABS) 0.887 -0.02290 0.112 0.0176 0.238 +0.02260 0.054 0.1055Nitrous oxide (NO) 0.597 -0.13890 0.004 0.6748 -0.353 +0.11730 0.328 0.0000Nitrogen dioxide (NO2) 0.682 -0.33020 0.076 0.0525 0.191 +0.15210 0.269 0.0001Ozone (03) 0.494 +0.00160 0.191 0.0015 0.557 -0.00150 0.049 0.121103 previous 1 hour (O31B) 0.579 +0.00011 0.001 0.8242 0.572 -0.00220 0.085 0.0402Relative humidity (RH) -0.547 +0.01530 0.173 0.0027 0.369 +0.00210 0.021 0.3155Soiltemperature(ST) 0.900 -0.01520 0.124 0.0123 0.844 -0.01860 0.173 0.0026Solar radiation (SUN) 0.636 -0.00009 0.030 0.2279 0.707 -0.00044 0.230 0.0004SUNprevioushour(SUNIB) 0.686 -0.00019 0.159 0.0041 0.696 -0.00050 0.549 0.0000SUN previous 2 hour (SUN2B) 0.669 -0.00020 0.193 0.0014 0.639 -0.00040 0.499 0.0000Winddirection(WD) 0.467 +0.00097 0.101 0.0248 0.691 -0.00160 0.401 0.0000Wind speed (WS) 0.729 -0.11320 0.144 0.0066 0.419 +0.06050 0.053 0.1081NB. Conductance data were available for 5 treatments. Data used in the regressions are treatmentmeans computed by averaging over the 4 sampling dates for a ten hour measurement period for eachtreatments and experiment (N—SO).those previously presented in Table 33, since they are based only on data from the plants on whichthe porometer measurements were made (an average of 2 subsamples for each of 5 treatments, 2cultivars and 4 harvests/experiment). Only simple linear regressions were computed due to thelimited number of data points available (n=5 treatments). Few models were significant, probablybecause of the limited number of degrees of freedom for error used to test the regression (n5).Although considerable improvement in fit was found using FLUXSUMC versus D5OC inharvest 1 for TW in Expt. 2 for Cherry Belle, this trend did not continue for the remainingharvests. In Expt. 3, the only significant regression for Cherry Belle occurred with D5OC inharvest 1 (Table 33). With the exception of an improvement in the relationship between flux andTable 33. Simple linear regressions of porometer radish total weight (g) versus flux indices145and D5OC for 2 cultivars and 2 experimentsExposure Indices FLUXSUMC D5OCHarvest a b1 r2 p a b1 r2 pExperiment 2Cherry Belle1 0.2232 -0.0006 0.763 0.0530 0.2067 -0.0022 0.365 0.28022 0.4691 +0.0007 0.034 0.7662 0.6541 -0.0085 0.133 0.54653 0.9985 +0.0013 0.132 0.5485 1.229 -0.0055 0.076 0.65344 1.6380 +0.0006 0.010 0.8753 2.0678 -0.0172 0.279 0.3604French Breakfast1 0.1617 -0.0017 0.406 0.2474 0.1389 -0.0036 0.749 0.05792 0.4972 -0.0033 0.096 0.6121 0.4324 -0.0054 0.134 0.54423 1.2450 -0.0087 0.502 0.1805 1.0257 -0.0106 0.481 0.19394 2.1304 -0.0154 0.496 0.1843 1.7836 -0.0226 0.802 0.0399Experiment 3Cherry Belle1 0.1880 -0.0005 0.319 0.3216 0.1928 -0.0027 0.810 0.03742 0.5718 -0.0016 0.198 0.4529 0.6225 -0.0108 0.517 0.17093 1.1814 -0.0002 0.001 0.9715 1.4665 -0.0169 0.413 0.24204 1.8551 1-0.0023 0.031 0.7758 2.5339 -0.0223 0.169 0.4911French Breakfast1 0.2067 -0.0022 0.774 0.0491 0. 1612 -0.0023 0.453 0.21322 0.6269 -0.0055 0.432 0.2279 0.5327 -0.0085 0.751 0.05733 1.4380 -0.0140 0.569 0.1409 1.1789 -0.0190 0.736 0.06284 2.4290 -0.0222 0.389 0.2613 2.0881 -0.0343 0.704 0.0755NB. The treatments used in the regression analyses were from 403-enriched plots and 1 ambient air control plot where conductancemeasurements were made. The data used in the analyses are treatment means computed by averaging the 2 sub-samples for whichporometer measurements were made, for each treatment, harvest and experiment; (n=5).146Table 34. Simple linear regressions of porometer radish hypocotyl weight (g) versus fluxindices and D5OC for 2 cultivars and 2 experiments.FLUXSUMC D5OCHarvest a b1 r2 p a b1 r2 pExperiment 2Cherry Belle1 0.0577 -0.0002 0.565 0.1429 0.0512 -0.0009 0.296 0.34302 0.2691 -0.0001 0.000 0.9738 0.3687 -0.0072 0.240 0.40213 0.6209 +0.0007 0.055 0.7035 0.8362 -0.0078 0.193 0.45964 1.1094 +0.0006 0.015 0.8470 1.4759 -0.0142 0.283 0.3564French Breakfast1 0.0341 -0.0009 0.069 0.6697 0.0370 -0.0007 0.342 0.30052 0.2146 -0.0007 0.295 0.3444 0.2246 -0.0040 0.512 0.17443 0.5679 -0.0005 0.023 0.8077 0.7239 -0.0095 0.481 0.19394 0.9669 +0.0021 0.070 0.6673 1.4073 -0.0116 0.116 0.5748Experiment 3Cherry Belle1 0.0297 -0.0005 0.739 0.0619 0.0204 -0.0006 0.545 0.15442 0.1956 -0.0022 0.140 0.5345 0.1557 -0.0036 0.215 0.43193 0.7226 -0.0067 0.535 0.1601 0.5460 -0.0079 0.464 0.20534 1.4611 -0.0131 0.541 0.1564 1.1351 -0.0175 0.729 0.0657French Breakfast1 0.0469 -0.0009 0.855 0.0244 0.0288 -0.0008 0444 0.21922 0.2199 -0.0024 0.315 0.3246 0.1765 -0.0036 0.505 0.17823 0.7640 -0.0088 0.559 0.1464 0.6196 -0.0130 0.869 0.02114 1.3632 -0.0137 0.358 0.2865 1.1438 -0.0207 0.620 0.1138NB. The treatments used in the regression analyses were from 403-enriched plots and 1 ambient air control plot where conductancemeasurements were made. The data used in the analyses are treatment means computed by averaging the 2 sub-samples for whichporometer measurements were made for each of 5 treatments, 4 harvests and 2 experiments.147Breakfast TW in harvest 3 and 1 over that of D5OC, in experiments 2 and 3 respectively, ingeneral more regressions with D5OC were significant at p = 0.10 (Table 33).The results for HW showed few significant relationships although the trends weregenerally consistent with those observed with TW (Table 34). No improvement was obtainedusing flux as the independent variable in the model for French Breakfast HW in Expt. 2, whereasthere was some improvement in harvest 1 for Expt. 3 (Table 34). In 24 of the 32 comparisonspresented in Tables 38 and 39, the use of the D5OC index was superior.To explore the possibility of extending the use of flux-based indices by means ofsurrogates for conductance, stepwise multiple linear regression was used to assess the relativeimportance of a number of meteorological variables in predicting conductance, leading to thefollowing models:Cherry Belle: Expt 2; Conductance 0.8550+ 0.00257(03)-0.2406(WS);r2=0.604, p0.000Expt 3; Conductance = 0.7167-0.00032(SUN1B);r2=0.366, p=O.000French Breakfast: Expt 2; Conductance 0.5772+0.00163(0)-0.000l6(SUN2B); r0.384, p0.000Expt 3; Conductance = 0.6958-0.00046(SUN1B); ?0.549, p0000It was hoped to use environmental variables as surrogates for flux in flux-response models. Inspite of the fact that the r2 values for two of the models fitted were an improvement over the simplelinear regressions (Table 31 and 32) no universal model was selected for both experiments andcultivars. The differences in response of conductance between experiments coupled with the weakrelationships found between conductance and most variables, and more importantly the absence ofan improvement of fit in models using flux indices based on actual conductance measurementsrather than on D5OC exposures indicated that it was unlikely that improved fits would be obtainedwith surrogates. As a result, this objective was not pursued further with radish.1484.6 Douglas-fir4.6.1 AIR QUALITYSummaries of the 1988 and 1989 air quality are presented in Tables 35 and 36. In 1988the D5OC ranged from 48 days where the 03 concentration exceeded 50 ppb to a low of 1 for theambient air treatment. The seasonal M12 03 concentrations in the experimental plots ranged from41 ppb in treatment 8 in contrast to the ambient concentration of approximately 18 ppb. Theaddition of 03 had a modest effect on the M24 03 concentration which ranged from 25 ppb intreatment 8 to 13 ppb for ambient air. In terms of TD, the high was 48 for treatment 8 and 25 forthe ambient air plots.Table 35. Seasonal mean pollutant concentrations (ppb) in 1988 for each treatment, for Douglasfir between 178-269 Julian daysTreatmentAAI AA2AA3 1 2 3 4 5 6 7 8 9 10 11 12D5OC (days)5 15 1 4 31 25 25 34 38 37 48 3 6 11 12M24 (ppb)13 15 13 15 20 17 18 18 18 21 25 14 15 20 15M12 (ppb)18 21 18 21 29 25 27 28 29 33 41 20 22 25 22TD (ppm-h)(25 28 25 28 39 32 34 34 36 41 48 27 30 32 29Table 36. Seasonal mean pollutant concentrations (ppb) in 1989 for each treatment, for Douglasfir between 159 - 257 Julian daysTreatmentAA1 AA2AA3 1 2 3 4 5 6 7 8 9 10 11 12D5OC (days)7 19 9 63 72 76 84 92 87 73 79 52 58 79 57M24 (ppb)22 23 22 29 30 34 34 45 38 31 32 28 29 42 28M12 (ppb)28 29 27 37 40 47 48 66 52 39 45 35 37 57 36TD (ppm-h)54 55 53 69 72 81 83 108 92 73 78 66 69 100 68149In 1989 the D5OC ranged from 92 in treatment 5 down to 7 in ambient air. The seasonalM12 03 concentrations in the experimental plots ranged from 66 ppb in treatment 5 to 27 ppb inambient air (Table 36). The result of addition of 03 on the M24 03 concentration was a factor oftwo increase, ranging from 22 in ambient air to 45 ppb in treatment 5 (Table 36).The range of M12, TD, D5OC and SUM5O indices over the experimental period over alltreatments achieved for Douglas-fir at each harvest in 1989 are summarized in Table 37. Thetables clearly illustrate that the magnitude of the indices is dependent on the method of expression.For the cumulative exposure index (SUM5 0) the largest increase in exposures took place betweenharvests 4 and 5 where the index showed an increase in excess of 13500 ppb-h (Table 37). Theincrease between harvest 3 and 4 occurred after the second flush of growth which started in lateJune. The D5OC does not reflect the change between these harvests as effectively and showed moreconsistent increases between harvests.Table 37. Summary of the range of ozone exposures, M12, TD, D5OC and SUM5O for Douglasfir across all treatments for each harvest in 1989.M12 (ppb) TD (ppm-h) D5OC (days) SUM5O (ppb-h)Harvest Julian Days (12-h period) (24-h period) (12-h period) (12-h period)1 (April, 1989) No enrichment2 157-172 32-57 10-15 2-14 464-65953 157-197 30-58 24-39 4-37 1382-192684 157-214 29-65 33-60 5-52 1433-331455 157-236 28-68 43-86 7-72 1485-468816 157-257 27-66 53-108 7-92 1721-553007 (July, 1990) No enrichment4.6.2 BASELINE DATA 1988The mean, standard deviation, minimum and maximum values for each of the treevariables measured prior to treatments commencing in 1988 are presented in Table 38.150Table 38. Baseline data for Douglas-fir seedlings, planted in 1988.Variable Mean Standard Mm MaxdeviationTree height (cm) 41.8 7.84 25.0 61.1Stem diameter (nun) 5.9 1.13 3.18 8.73Needle area (sq. cm) 148.2 57.9 47.2 296.1Stem weight (g) 4.1 1.97 1.23 11.53Needle weight (g) 4.0 1.53 2.07 8.08Totalweight(g) 8.1 3.32 3.32 18.98NB. Data represent the mean of 60 tree seedlingsAs can be seen from these data the seedling weight was fairly evenly distributed betweenthe needles and stem. However, there was greater variation in stem weight between seedlings. Thedisparity between the minimum and maximum values for each of these measures is not surprisinggiven the variable outbred nature of the tree seedling population.4.6.2.1 EXPOSURE-RESPONSE RELATIONSHIPS 1988The incremental changes in stem diameter and tree height for 1988 were examined bycomputing the difference between the two measurement dates (June 16, 1988 and August 17, 1988)for each tree seedling. These differences were then averaged by treatment (N=15). Each valuerepresented a mean of 104 tree seedlings. These measurements took place approximately 57 daysfollowing the commencement of 03 treatments in 1988.Using D5 OC as the independent variable no significant linear or non-linear trends werefound for either the change in height or stem diameter (data not presented). The coefficients werenegative and positive respectively, both with a coefficient of determination of less than one.1514.6.3 EXPOSURE-RESPONSE RELATIONSHIPS 19894.6.3.1 Simple Linear Regression AnalysesTo evaluate the effects of treatments applied pre- and post-bud break in early 1989, simplelinear regressions were computed by harvest for a number of growth variables versus D5OC. Theresults are presented in Table 39. No statistically significant regressions were obtained. Fromthese results, it was not possible to demonstrate any possible residual effects of 03 exposure in1988 on early growth in 1989.Although not significant, consistently negative linear trends were found between totalweight of the second flush (TW2f) in 1989 and 03 exposure, expressed as D5OC (Table 40). Onaverage the control plants were larger for this variable, particularly for harvests 3 through 5.Interval exposure indices for D50 provided no consistent improvements in fit.Figure 41 presents 3-dimensional surfaces of the second flush growth against D50expressed as the cumulative (1988 and 1989), the seasonal index (1989 only) and the incrementalindex (between harvests in 1989), over all five harvests in 1989. Although the negative impact of03 exposure shows most clearly in the case of the incremental indices (Figure 41 c), the overalltrends are not dramatic. Nevertheless, the multiple linear regression of second flush growth onJulian day and DSOinc was significant:y = -28.7- 0.182(D50)+ 0.168 (Julian day); r2 = 0.46; p <0.05 for all coefficients.However, examination of the residuals of the linear regressions showed that the harvest 3, 5 and 6data for treatment 15 (one of the controls) were outliers, based on the criterion of havingstudentized residuals> 3.0 (Systat Inc., 1987). These points are highlighted in Figure 41.Their removal led to the more steeply inclined response surfaces shown in Figure 42, and to theimproved multiple linear regression with DSOinc:y = 30.6 0.304 (DSOinc) + 0.185 (Julian day); r2 = 0.57; p < 0.001 for all coefficients..Removal of the outliers also led to improved linear fits for harvests 5 and 6. A significantregression was obtained for harvest 5:y = 19.447 - 0.199(D5OC); r2 = 0.534; p 0.003; n14.152Table 39. Compilation of simple linear regression results from the seven tree harvests inthe 1989 growing season of total weight and stem volume ratios versus D5OC.Harvest a b1 r2 pRatio of final tree volume-initial tree volume/final tree volume (arc-sin transformation)1 0.89531 +0.0011000 0.071 0.33742 1.06119 +0.0084500 0.231 0.06943 1.23350 -0.0018400 0.089 0.28104 1.22430 +0.0000924 0.001 0.89595 1.24960 +0.0003 150 0.013 0.68586 1.24270 +0.0006620 0.092 0.27227 1.39557 -0.0003470 0.115 0.2347Ratio of total new weight in 1989- total weight/total weight (arc-sin transformation)2 0.55660 -0.0034990 0.080 0.30643 0.56986 -0.0015100 0.112 0.22274 0.52921 +0.0005080 0.045 0.44725 0.59740 -0.0008220 0.203 0.09 166 0.56488 -0.0002532 0.024 0.5847Final stem volume (cm3)(FD2H)1 87.45720 +0.2173000 0.037 0.49492 167.43350 +3.4221000 0.125 0.19663 257.72870 +0.4758800 0.002 0.86074 331.91430 -0.4877600 0.006 0.78015 383.93980 +0.6396000 0.011 0.71506 407.91370 +1.2617000 0.038 0.48797 978.3893 -1.0002700 0.106 0.2556Total weight (g) (TW)1 33.28320 +0.1322600 0.133 0.18062 69.75009 +0. 1057690 0.001 0.90613 82.75330 +0.1920000 0.007 0.77224 101.53600 -0.0649900 0.001 0.90885 113.77370 +0.0806900 0.003 0.84416 109.93790 +0.3 153800 0.045 0.4485Total weight first flush (g) (TW1f)2 37.58182 -0.1779200 0.011 0.70693 41.79165 +0.0460400 0.002 0.88254 44.77320 +0.0284900 0.001 0.91225 49.81970 +0.0727590 0.012 0.69226 45.99440 +0. 1704900 0.074 0.3258Total new weight in 1989 growing season (g) (TWnew)2 37.65331 -0.1796970 0.011 0.70493 44.11630 +0.0145560 0.000 0.96234 51.96916 -0.0119200 0.000 0.96585 64.12671 -0.0372400 0.002 0.86646 60.77937 +0.1212400 0.022 0.5984NB. Data used in the regressions are treatment means(n=l5), which representthe average of 26 trees for harvests I and 7 and 4 trees for harvests 2 through 6.153434IFigure 4 la-c. Three-dimensional response surfaces across five harvests of total weight of thesecond flush of Douglas fir versus D50 expressed as a) cumulative (1988 and 1989exposures), b) seasonal and c) incremental indices. The response surfaces were generatedusing Sygraph (SYSTAT Inc., 1987), with distance weighted least squares smoothing.Outliers are highlighted - all related to Treatment 15.154IFigure 42a-c. Three-dimensional response surfaces across five harvests of total weight of thesecond flush of Douglas fir versus D50 expressed as a) cumulative (1988 and 1989exposures), b) seasonal and c) incremental indices, with the outliers noted in Figure 41removed from the data set.155Table 40. Results of the simple linear regressions between totalweight of the second flush and leader length versus D5OC.Harvest a r2 pTotal weight of the second flush in 1989 (g) (TW22 0.071485 -0.00178 0.001 0.92303 2.32466 -0.03149 0.129 0.18854 7.19595 -0.04041 0.049 0.42625 14.30701 -0.11000 0.206 0.08916 14.78496 -0.04924 0.040 0.4763Leader length (cm) (HTERM)2 33.9971 +0.19074 0.020 0.61753 46.8665 +0.05624 0.006 0.77854 55.0235 +0.02063 0.001 0.90105 67.4837 -0.08110 0.056 0.39556 66.0598 -0.03250 0.004 0.81747 69.9161 -0.05070 0.068 0.3664NB. Data used in the regressions are treatment means(n’l5), for harvests1 through 6; n=14 for harvest 7) which represent the average of 4-26 trees, dependingon the harvest.where y is the total dry weight of the second flush. For harvest 6, the regression failed to reachp = 0.05:y = 20.738 - 0.129(D5OC); r2 = 0.232; p = 0.08 1; n = 14;These analyses together with scatter plots of the data for the last three harvests based on the D5OCindex (Figure 43) show that the adverse effects of 03 exposure began to become clearly evident bythe August 25, 1989 harvest (harvest 5), and were maintained through harvest 6 on September 15.Only non-destructive measurements were made on July 9, 1990 (Harvest 7, 298 daysfollowing the last exposure to elevated 03 levels). Since the primary adverse effect of 03 on treegrowth appeared to be an impact on the second flush, it was anticipated that this effect might bereflected in reduced extension growth of the terminal bud in the following summer, if recovery hadnot been completely successful.15612I0.0U-00•000U.0U252GFigure 43a-e. Total dry weight of second flush of Douglas fir versus D5OC in harvests 4-6.a,b and c) complete data sets (n=15) showing outliers. d and e) data sets with outliers deleted.Linear regressions are described in the text. Non-linear regressions are described in Section4.6.3.210Harvest 4: a4..420250 10 20 30 40 50 60Harvest 5; n15 b20.0 10 20 30 40 50 60 70 8030Harvest 6: n=15 c0200 .00 20 40 60DSOClinear1510503020100Harvest 6: n=14 eI80 100gamma10020 40 60 80DSOCWeibull157Although no significant negative linear relationships were found between exposure andleader length (HTERM) the best fit was obtained for harvest 7 in 1990 (Table 40). Examination ofthe residuals again revealed the presence of outliers in each harvest (displayed in Figure 44 a-c).Although the removal of the outliers improved the linear fits, only in harvest 7 (in 1990) did thesereach significance, with the cumulative (1988 and 1989) indices:y = 70.832 - 0.048 (D508889);r2=0.361; p=O.030; n=13;y = 69.916 - 0.05 1 (D5OC); r2 = 0.198; p =0.127; n =13;where y is leader length (cm).The RGRs for stem volume (RGRSV) were examined by computing the difference betweenIn-transformed stem volume between the initial and final harvests, for each tree seedling (calculatedfrom August 22, 1988 up to July 9, 1990 for harvest 7). August 22, 1988 was the first date of thestem diameter and height measurements following the initiation of 03 -enrichment treatments.These data were used in the calculations of RGRSV for all 7 harvests in 1989. The finalmeasurements on July 9, 1990 took place 296 days following the completion of03-enrichmenttreatments. No significant regressions were found other than a positive linear relationship forharvest 2 (Table 41).Table 41. Results of the simple linear regressions between Douglas-firRGR versus D5OC, for 7 harvests.Harvest a b1 r2 p1 0.00494 +0.00001 0.076 0.32122 0.00549 +0.00011 0.265 0.04953 0.00726 -0.00002 0.082 0.30234 0.00688 +0.00000 0.001 0.93 145 0.00689 +0.00000 0.015 0.66266 0.00662 +0.00001 0.061 0.37537 0.00559 -0.00001 0.140 0.1867NB. n15 in harvests 1-6; n”14 in harvest 7. Each value represents a mean of 4tree seedlings in harvests 2 through 6 and a mean of 26 tree seedlings for each treatmentin harvests 1 and 7.158UUI-.UU8UUUUaUUIUUFigure 44a-f. Leader length of Douglas fir versus D508889 in harvests 5 and 6 (1989) and 7(1990). a, b and c) are complete data sets (n=15 in harvests 5 and 6; n=14 in harvest 7),showing outliers. d, e and t) are data sets with the outliers deleted. Linear regressions aredescribed in the text. Non-linear regressions are described in Section 4.6.3.2.Harvest 5: n=14 d.-‘-—-—..-----.-I.0 20 40 60 80 100 120b0 20 40 60 80 100 120‘Harvest 6: n=14 e -II100806040200-100806040200-100 -806040 F20100806040200100•Harvest 6: n=1580 r ‘60 ,-40200 *0 20 40 60 80 100 120 140lOOrHarvest 7: n=14 C80 F40-2000 20 40 60 80 100 120 140D50g,s,linear gamma0 20 40 60 80 100 120 140Harvest 7: n=13 f00 20 40 60 80 100 120 140D50m,Weibull1594.6.3.2 Non-Linear Regression AnalysesNo improvements in fit were found between final volume, total weight, total new weightand total weight of the first flush for 1989 versus 03 concentration using the Weibull or gammanon-linear regression models.Curvature was evident in the relationships of total weight of the second flush and exposure(Figure 41 and 42). However, no improvements in fit over the linear model were provided by nonlinear models for harvests 4 through 6, as shown in Figure 43 a-c. Corrected r2 values for thegamma and Weibull models were virtually unchanged from those for the linear models (Table 40).However, removal of the outliers led to improvements over the linear model, especially for harvest5 where both gamma and Weibull models gave r2 values of 0.5 96 versus 0.53 (the fitted non-linearmodels are virtually superimposed on each other in Figure 41). In both harvests 5 and 6 thegamma models reflected the shape of the response surface in Figure 42 c (outliers deleted).The plots of leader length versus D508889 also showed some suggestions of curvature(Figure 44). Although Weibull models for harvests 5 and 7 yielded corrected r2 values of 0.378and 0.099 (versus 0.019 and 0.022 for the linear models respectively), in the former case, themodel was essentially linear with zero slope until the highest exposures. No convergence wasobtained with harvest 6 data. Gamma models showed slight improvements in fit over the linearmodels using the complete data sets for each harvest. When the outliers were removed, the gammaand Weibull models resulted in improved fits which, in the case of harvest 7, increased fromr2=0.361 (linear) to 0.507 and 0.411 respectively.4.6.4 WHOLE-PLANT CONDUCTANCEConductance data were summarized over all dates and hours when measurements weretaken. The means and standard errors are presented in Table 42. Average conductances and plantgrowth variables by harvest date are presented in Appendix 6.As can be seen in Table 42 the overall seasonal average conductance was lowest for the160Table 42. Conductance (cms1) of Douglas-fir over seven measurement periods.Values are means and standard errors (N=67)Treatment Conductance (cm s)(7 measurement periods) (4 measurement periods)*1 O.178±+0.006 0.222±0.0102 0.149±+0.005 0.151±0.0083 0.194±1-0.003 0.198±0.0054 0.244±1-0.011 0.179±0.006AA 0.134±0.008 0.196±0.010NB. Data are treatment means computed by averaging over the 2 subsamples (trees) forthe 10-h measurement period for 7/4 sampling dates. AA = ambient air control,1-4 = Ozone enriched treatments. * subset, based on four measurement periods; see text.control plants and those in treatment 2. It was noted that average conductance was consistentlyhigher for some treatments than others throughout portions of the experiment (Figure 45). Duringsampling period two (consists of 3 days of conductance measurements on the same tree seedlingsmade prior to harvest 4, for each of 5 treatments) there was evidence that the control plants wereundergoing water stress. No rain had occurred during this time, the control plants were located ina drier area of the field, and they were larger than the other treatments (treatment 4 plants had thesmallest biomass). Because of these uncontrolled differences in environmental conditions, datacollected in this sampling period (the 3 days corresponded to Julian days 202, 206 and 214, andrelate to harvest 4) were excluded from subsequent analyses and calculations of flux. Means forthe remaining four measurement periods are also presented in Table 42. The magnitudes of thesevalues are consistent with those obtained elsewhere for Douglas-fir (Jarvis et al., 1976; Leverenz,1981; Livingston et al,, 1984).On average conductance showed a decline in all treatments between noon and 1700161a0.40.3a)C)CaC.)0.20.10.060.000.0 —180I I190 200 210 220 230 240 250Julian day0.30024-0)2C-)0.18CoC)-D0.12Caa)O AAo TRT4TRT3TRT2o TRT1bAA1- 00Z4o 0Z31 072I I oOZl5Figure 45a-b. a) Mean daily conductance measurements for Douglas fir using a modified whole-plant porometer over 7 sampling dates in 1989. b) Mean hourly diurnal conductance for Douglasfir, over 4 sampling dates in 1989, for 5 treatments.10 15 20Time of day (hour)162hours (Figure 45). For each of the 4 measurement periods the control conductance was secondhighest in the first and last period and lowest for the middle two (Appendix 6).As with radish, the associations between 03 and environmental variables measuredconcurrently, were assessed using correlation analysis. The results are presented in Table 43.Significant positive associations were found between the meteorological variables measured and 03concentration. There was a significant negative association between relative humidity and 03.Although conductance tended to decrease with increasing 03 concentration in the previous 2-h(Figure 46 a) it also tended to decrease with increasing solar radiation in the previous 2-h (Figure46 b). As would be expected 03 and solar radiation are positively associated; the degree ofassociation improving with SUN2B (Table 43). However, it is unclear at this point whether thestomata are responding to an increase in solar radiation, absolute humidity, or 03, or all of thesefactors. Figure 47 a-c illustrates the positive associations between 03 in the previous 2-h, solarradiation, absolute humidity and soil temperature.Simple linear regressions between conductance and various meteorological variables arepresented in Table 44 for four measurement periods. No improvements in fit were obtained withquadratic and other non-linear models (data not presented).Conductance significantly decreased with increases in all environmental variablesmeasured over the experimental period with the exception of 03 during the previous hour, currenthour 03, current solar radiation and wind direction (Table 46). Ozone in the previous 2-h wasborderline significant (p = 0.055). The inherent variability in the data is revealed by the fact thatalthough the best fit was obtained between absolute humidity and conductance (Table 44), theregression only accounted for about 27% of the variation in conductance.163aSolar radiation previous 2 hr (W/m2>Figure 46a-b. Mean conductance for Douglas fir measured over four sampling dates versus a)ozone concentration and b) solar radiation in the previous 2 hours.0.300.260.220.180.140.100 00o000000 0o0000010 20 4030 50Ozone concentration previous 2 hr (ppb)60bCo0CoC.)CCOC)0C00Ca0)030 -0.26 -0)SC.)022o 0.18 -o 0CCO0)0.140000 o 08 0 00 0 00O 00 0p0 00.100 200 400 600 800164a6050 00 000408305 0 0 o8085 20S10200 300 400 500 600 700 800Solar radiation (W/m2>b605O- 00 000 040-000 80C)000 00 0C,20 81011 12 13 14 15Absolute humidity (g/m3)C_—60.050-0I 0C) 00 040 -8°30- 8 0C 0 08 0 800 0 8 0C 20 -1016 17 16 19 20 21Soil temperature ( C)Figure 47a-c. Seasonal mean ozone concentation in the previous 2 hours versus mean a) solarradiation, b) absolute humidity and c) soil temperature.165Table 43. Correlation analysis between ozone and a number of environmental variablesmeasured concurrently on four days during which conductance was measured.Values are Pearson product moment correlation coefficients and the sign indicatesthe type of association between variables.Environmental 03 03 (previous 03 (previousvariables hour) 2 hours)Solar radiation (SUN) +O.482** +0.483** +0.416**SUNprevious hour (SUN1B) +0.707** +0.751** +0.753**SUN previous 2 h (SUN2B) +0.736** +0.815** +o.853**Absolute humidity (ABS) +0.694** +0.792** +0.854**Soil Temperature (ST) +0.709** +0.804** +0.868**Air Temperature (AT) +0.731** +0.784** +0.786**Relative humidity (RH) 0.728** 0.8Ol** 0.824**Wind speed (WS) +0.705** +0.732** +0.738**Wind direction (WD) +0.246* +0.305* +0.371*Nitric oxide (NO) +0.715** +0773** +0.803**Nitrogen dioxide (NO2) +0.731** +O.802** +0.846**NB. Data used in the analyses are averages over the 4 measurement periods (dates) for each hour; (n50observations). *=p 0.05; ** p 0.01.Table 44. Simple linear regressions of mean conductance for a number of on environmentalvariables, over four measurement periods during 1989.Environmental a b1 r2 pvariableAbsolute Humidity (ABS) 0.33654 -0.01246 0.271 0.0001Soil temp (ST) 0.36388 -0.00991 0.243 0.0003Sun previous 2 hours (SUN2B) 0.20248 -0.00005 0.227 0.0005Nitrogen dioxide (NO2) 0,21176 -0.04725 0.199 0.0012Nitrogen oxide (NO) 0.20580 -0.01217 0.164 0.0035Wind speed (WS) 0.22662 -0.03773 0.159 0.0042Relative humidity (RH) -0,00529 0.00241 0.156 0.0045Air temp (AT) 0.29656 -0.00623 0.150 0.0054Sun 1 h before (SUN1B) 0.20547 -0.00005 0.142 0.0070O3previous2hours(02B) 0.19540 -0.00062 0.074 0.055503 lhbefore(O1B) 0.19191 -0.00045 0.041 0.1592Ozone (03) 0.18800 -0.00030 0.015 0.3909Solar radiation (SUN) 0.18981 -0.00002 0.013 0.4245Wind direction (WD) 0.20930 -0.00021 0.010 0.4799NB. Conductance data were available for 5 treatments. Data used in the regressions are treatment means computedby averaging over the 4 measurement periods (dates) for the ten hour measurement period (n50).1664.6.5 FLUX-RESPONSE RELATIONSHIPS4.6.5.1 Models Using Actual Conductance DataTo address Objective 3, comparisons were made between exposure-response models basedon ambient exposure versus those based on uptake. In contrast to Tables 39 through 41 presentedin Section 4.6.3.1 the regressions using D5OC in Tables 45 through 47 are based only on trees onwhich the porometer measurements were made (n=2 subsamples/treatment vs. 4 for the former).Furthermore comparisons were only possible for treatments where the actual conductancemeasurements were made (n=5). Comparisons are made between harvests 2, 5 and 6 only, as thebiomass data collected in these harvests corresponds to data collected in the four conductancemeasurement periods. That is, flux estimates for harvest 2 are based on conductance data collectedin measurement period 1; for harvest 5 they are based on data collected in measurement periods 2and 3; for harvest 6 they are based on data collected in measurement period 4.Although scatter plots of some of the data suggest non-linearity only simple linearregressions were computed due to the limited number of data points available (n5).Few models were significant for any of the three harvests. Nevertheless the models fittedusing flux as the independent variable resulted in consistent improvements over the linearregressions of TW, TW1f, T’Wnew, HTERM, FHT and FD2Hbased on D5OC (Tables 45 through47). Although most of the relationships were not significant even at p 0.10, significantrelationships were found using flux as the independent variable in harvest 6 for total weight, leafarea and leaf weight of the second flush (Table 46). However, at earlier harvests the D5 OC indexprovided better fits for second flush growth data, which reached significance for LA2fat harvest 2(Table 46). The relationships with ‘I’\V2f may be compared with similar trends observed in thelater harvests in 1989, based on larger sample sizes (Table 40).Although the relationship between 03 and conductance was negative, the degree of fit waspoor (Table 44). Even though the use of the FLUXSUMC index based on conductance aloneresulted in improved linear fits, other factors are undoubtedly also contributing to plant response.These may include residual resistance to 03 or the fact that exposure levels used in this experiment167were too low to consistently affect conductance.Table 45. Simple linear regressions of total weight, total weight of first flush andtotal new weight of porometer tree variables versus D5OC and FLUXSUMC.Harvest Index a b1 pTotal weight (g) (TW)2 FLUXSUMC 71.0160 -0.4576 0.414 0.24165 FLUXSUMC 146.8961 -0.7853 0.427 0.23 176 FLUXSUMC 171.6941 -0.8375 0.453 0.21322 D5OC 56.8360 -0.1920 0.011 0.86605 D5OC 118.242 -0.5430 0.130 0.55106 D5OC 110.341 -0.0350 0.001 0.9670Total weight 1st flush (g) (TW1f)2 FLUXSUMC 32.3997 -0.1745 0.338 0.30405 FLUXSUMC 62.5035 -0.3116 0.378 0.26956 FLUXSUMC 72.9091 -0.3324 0.405 0.24822 D5OC 26.1030 +0.0120 0.000 0.97905 D5OC 50.0060 -0.1860 0.086 0.63306 D5OC 46.8270 +0.0180 0.001 0.9590Total new weight (g) (TWnew)2 FLUXSUMC 33.4225 -0.1743 0.322 0.31875 FLUXSUMC 81.5339 -0.4663 0.369 0.27696 FLUXSUMC 92.1025 -0.4600 0.538 0.15822 D5OC 27.9160 -0.0630 0.006 0.89905 D5OC 69.6420 -0.4580 0.227 0.41706 D5OC 61.0610 -0.0690 0.010 0.8720NB. Data used in the regressions are treatment means of two porometer trees (subsamples) for each treatment andharvest where porometer data were available. n=5168Table 46. Simple linear regressions of total weight of the second flush,leaf area and leaf weight of the second flush and leader lengthversus D5OC and FLUXSUMC.Harvest Index a r2 pTotal weight of second flush (g) (TW2f)2 FLUXSUMC 1.02286 +0.00111 0.001 0.96465 FLUXSUMC 19.0304 -0.15469 0.201 0.44946 FLUXSUMC 19.1934 -0.12755 0.943 0.00582 D5OC 1.8140 -0.0750 0.539 0.15805 D5OC 19.635 -0.2720 0.397 0.25506 D5OC 14.233 -0.0870 0.372 0.2750Leaf area (m2) of the second flush (LA2f)2 FLUXSUMC 118.8729 -1.8594 0.322 0.31855 FLUXSUMC 660.8892 -5.6644 0.239 0.40316 FLUXSUMC 499.9681 -3.4065 0.944 0.00582 D5OC 135.566 -8.2120 0.958 0.00405 D5OC 669.940 -9.6230 0.441 0.22206 D5OC 367.272 -2.3 150 0.370 0.2760Leaf weight (g) of the second flush (LW2f)2 FLUXSUMC 0.96566 -0.00354 0.010 0.87045 FLUXSUMC 13.5455 -0.11390 0.218 0.42806 FLUXSUMC 10.9579 -0.07382 0.902 0.01342 D5OC 1.5850 -0.0740 0.699 0.07805 D5OC 13.841 -0.1970 0.414 0.24106 D5OC 7.9440 -0.0480 0.3 19 0.3220Leader length (cm) (HTERM)2 FLUXSUMC 82.730 -0.6133 0.020 0.82135 FLUXSUMC 65.865 -0.1711 0.109 0.56686 FLTJXSIJMC 84.448 -0.2132 0.618 0.11512 D5OC 56.3530 +0.4800 0.002 0.94505 D5OC 64.4590 -0.2450 0.145 0.52806 D5OC 74.2100 -0.1090 0.137 0.5400NB. Data used in the regressions are means of two porometer trees (subsample) for each treatmentand harvest where porometer data were available. n5169Table 47. Simple linear regressions of total weight, ratios, treeheight and stem volume versus D5OC and FLLJXSUMC.Harvest Index a r2 pRatio between final tree volume-initial tree volume (June 1988)/final tree volume (arc sin) (ASFJD2H)2 FLUXSUMC 1.1498 +000083 0.021 0.81485 FLUXSUMC 1.1257 +0.00193 0.825 0.03306 FLUXSUMC 1.3670 -0.00088 0.692 0.08072 D5OC 1.2370 -0.00600 0.163 0.50005 D5OC 1.1740 +0.00200 0.516 0.17206 D5OC 1.3560 -0.00100 0.799 0.0410Final tree height (cm) (FHT,2 FLUXSUMC 96.3509 -0.28139 0.242 0.39995 FLUXSUMC 126.829 -0.39250 0.436 0.22486 FLUXSUMC 143.471 -0.33330 0.618 0.11472 D5OC 83.0490 +0.3400 0.054 0.70705 D5OC 116.226 -0.3700 0.247 0.39406 D5OC 122.534 -0.0790 0.029 0.7830Final stem volume (cm3) (FD2R)2 FLUXSUMC 254.9391 -1.4028 0.216 0.43015 FLUXSUMC 447.2372 -2.0807 0.3 16 0.32426 FLUXSTJMC 749.2386 -3.8402 0.558 0.14702 D5OC 201.0780 +4.0370 0.067 0.67305 D5OC 452.3550 -2.3870 0.092 0.61906 D5OC 604.2180 -2.2960 0.535 0.1600NB. Data used in the regressions are means of two porometer trees (subsample) for each treatment whereporometer data were available. n54.6.5.2 Models Using Estimated Conductance ValuesIn order to explore the possibility of extending the use of flux-based indices by means ofsurrogates for conductance based on environmental variables, stepwise multiple linear regressionwas used to assess the relative importance of meteorological variables in predicting conductance.170This led to the model:Conductance 0.27092+0.0082(AT)+0.00088(032B)-0.01374(ST)-0.00003(SUN2B)+0.00028(WD)-0.03341(WS)(?= 0.477, p = 0.0000).It is important to note that in the model the relationship between 03 in the previous second hourwas positive not negative, as was found with simple linear regression (Table 44). Nevertheless, thepresence of 03 in the equation suggests that it affects conductance even though it also has apositive association with other meteorological variables. The use of conductance estimates basedon this model permitted the computation of estimates of flux (FLUXSUMM) for the periods up toeach of the harvests in 1989. The results of the use of this index in predicting seedling responseare presented in Table 48.Compared with the linear models using D5OC, modest improvements were found usingFLUXSUMM (Table 48), but in no case was the regression significant at p = 0.05. In the laterharvests in 1989 in particular, the relationships with FLUXSUMM improved, approachingsignificance in the case of TW in harvestS and reaching significance in the case of TW2f.However, a still better fit in the latter case was obtained using the D5OC index (Table 48). Giventhat the multiple linear regression model included a positive coefficient for 03 and that only 48%of the variation in conductance was explained by the regression, the reversal in the slope of anumber of models for the early harvests and the lack of improvement in many of the relationshipsdescribed is not surprising. Nevertheless, overall the use of the FLUXSUMM index outperformedthe use of the D5OC index in 75% of the cases examined.171Table 48. Results of the simple linear regressions between tree growthvariables versus FLUXSUMM and D5OC.FLUXSUMM D5OCHarvest a b1 r2 p r2 pTotal weight (g) (TW)2 66.0256 +0. 1209 0.008 0.7666 (+) 0.005 0.8 1583 101.8852 -0.2749 0.028 0.5698 (+) 0.000 0.98174 82.0846 +0.2423 0.036 0.5 145 (+) 0.000 0.97455 167.6034 -0.5798 0.275 0.0542 (-) 0.03 1 0.54776 147.3693 -0.1568 0.012 0.7046 (-) 0.003 0.8753Total weight second flush (g) (TW2f)2 0.1905 -0.0037 0.017 0.6616 (-) 0.002 0.86833 0.6585 +0.0161 0.134 0.1983 (-) 0.007 0.77434 7.2185 -0.0179 0.025 0.5928 (-) 0.126 0.21345 18.114 -0,1067 0.312 0.0377 (-) 0.534 0.00306 18.506 -0.0723 0.084 0.3152 (-) 0.232 0.0810Final stem volume (FD2B)2 143.3846 +1.5093 0.127 0.2110 (-) 0.115 0.23513 363.1937 -1.7860 0.071 0.3579 (-) 0.002 0.88284 307.1804 +0.0778 0.000 0.9460 (-) 0.000 0.97505 590,7393 -2.0556 0.177 0.1342 (-) 0.003 0.84976 546.7121 -0.4698 0.006 0.7939 (-) 0.007 0.7811Leader length (cm) (HTERM)2 29.8776 +0. 1647 0.077 0.3367 (+) 0.0 14 0.68963 53.6790 +0.0223 0.004 0.8355 (-) 0.001 0.93034 55.6773 -0.1418 0.083 0.3173 (-) 0.025 0.56685 70.4857 -0.0871 0.097 0.2781 (-) 0.044 0.47396 74.4838 -0.0508 0.014 0.6854 (-) 0.136 0. 1945NB. All data used in the regressions analyses are treatment means; nl4, as the outlier has been deleted.* For the D5OC index, the sign of the coefficient (b1), r2 and p values are presented. FLUXSUMM is calculatedfrom estimates of conductance using the multiple linear regression equation described above.1725. DISCUSSION5.1 Performance of the ZAPSThe mixing of the supplementary gas with the ambient air in open air types of systems hasbeen highlighted as a potential source of concern (McLeod and Baker, 1988). In the ZAPS used inthe present studies, the horizontal distribution was evaluated in 1986 at two locations in block 2, asshown in Figures 6a and b. The data from these locations suggest that the supplementary 03 wasreasonably uniformly distributed over the sample plots with a grand mean of 19.5 and coefficientof variation of 38.9 and 30.7 for locations A and B respectively over the time periods during whichthe measurements were made. The variability reported here compares well with that reported forother chamberless field systems (Runeckles et al., 1990). Variation above the harvested sub-plotsat each location was much less than that occurring towards the outer edge of the block.In any experimentation involving pollutant enrichment under field conditions it is importantthat the experimental exposures obtained reflect the characteristics of the pollutant in ambient air.In the present study frequency distributions using the 24-h data set of hourly average 03concentrations achieved in 1989 for all enriched treatments were plotted and compared with theambient air control plots (Figure 9). The observed cumulative frequencies for these data clearlyshowed they generally followed a Weibull distribution (Figure 9). This has also been reported forthe 1986 data set using a 24-h data set of 2-mm. averages (Wright, 1988). Nosal (1984) andTaylor et al. (1986) have also found that the Weibull distribution was appropriate for thedescription of various ambient 03 data sets.In the present study, the distributions observed were unimodal, and similar in form to theambient air plot, thus indicating that the ZAPS was providing realistic enrichment of the airsurrounding the plants, without the modifications in microclimate induced by chambers (Figure 9).In addition, due to carry-over from one plot to the next within a block caused by variations in windspeed and direction, the actual pattern of concentrations achieved in each treatment was unique. Incontrast, some studies using OTCs have produced bimodal distributions in a number of thetreatments with higher rates of enrichment (Heagle et al., 1987; Lefohn et al., 1988), thus biasing173the responses observed. Furthermore, the same relative treatments are applied to each chamber,which fails to simulate the stochastic nature ofnaturally occurring exposures (Runeckles andWright., 1988).In all three years of experiments covered in the present studies the season-long patterns ofmean 03 concentrations were unique for each treatment because of cany-over effects and the levelof enrichment being a function of the ambient concentration. Nevertheless, the seasonal diurnalpatterns of 03 concentrations achieved consistently resembled that of ambient air as shown inFigure 8.Seasonal exposures varied between the experiments and years. Over the three growingseasons (1986, 1988, 1989) and individual experiments none of the treatments received exposureswith seasonal mean concentrations (M12) exceeding 66 ppb, with the exception of the secondradish experiment in which M12 reached 84 ppb, due to the malfunctioning of the system in thelast couple of hours of the enrichment period on one day.5.2 Exposure Indices and Response ModelsThe merits of numerous exposure indices have been extensively reviewed (Krupa andKickert, 1987; Lefohn and Runeckles, 1987; Runeckles 1987; Hogsett et aL, 1988; Lee et a!.,1988; Musselman et a!., 1988 and Wright, 1988). Despite these efforts, there is no consensus onthe most appropriate summary for the development of exposure-response models for all species. Ithas been generally accepted that peak concentrations are important in determining plant response,which resulted in the development of exposure indices modified by various weighting functionssuch as SUMO6 (Lee et al., 1988 and reviewed in Hogsett et al., 1988). A previous evaluation ofthe suitability of more than 30 different exposure indices for developing exposure-responserelationships for several of the crop species grown in the Fraser Valley found that no indexperformed equally well for all crops (Wright, 1988). This was also the finding of Lee et al. (1988).For the present study the D5OC index was selected as the independent variable to investigate the174impact of low levels of03-exposure on plant growth. This index is simple to compute, accountsfor concentrations in excess of 50 ppb that lie within the range of levels suggested as beingpotentially injurious to plants (Grunhage and Jager, 1994; Krupa et al., 1994). It is a cumulativeindex and does not violate the statistical assumptions of normally distributed data in itscomputation.Low levels of 03 were used over the three years of experimentation to best relate to thelevels of 03 pollution experienced in the Fraser Valley, British Columbia. Consequently, thedramatic decreases in growth and yield reported by others for experiments involving repeatedexposures to hourly concentrations reaching or exceeding 200 ppb were not observed in the presentstudies. Although curvilinear trends were noted for some crop and tree seedling variables in thepresent study, thresholds in response were not evident (Figures 17, 18; 23, 29, 43 and 44).Although the Weibull function has been widely used as the basis of exposure-yieldresponses, its use is favoured by the inclusion of high level exposures in the experimental design(Heagle et al., 1988). The high levels of 03 typically used in NCLAN studies often resulted inreductions in yield in excess of 50% (Runeckles, 1990). Such concentrations are in excess of anyreleased over the long-term in the present study. Although some moderately high concentrationswere released periodically in all experiments, their occurrence in any one treatment wasunpredictable due to the use of the chamberless ZAPS and the moderating influence of theprevailing wind speed and direction. In the present studies where non-linear trends were observedin some response relationships, non-linear models including the Weibull and gamma were used,which in some instances resulted in improvements in fit (see Sections 5.3.1-5.3.5).5.3. Effects of Ozone on Plants5.3.1 PEAIn the present field experiments, all biomass variables were found to decrease significantly175with exposure to 03 in the later harvests (Tables 6 to 8). Although the level of enrichment used inthe present experiment was relatively low, it was delivered over 58 days of an 83-day growingseason.To the best of the author’s knowledge, the only field study concerning the effects of 03 onpeas is that of Skarby (1988) using OTCs, who found a significant decrease in the yield of freshand dry weight of peas when exposed to03-enriched unfiltered air as compared with the filteredair controls during the 1984 growing season. Ozone was added as a constant concentration 9h/day. However, comparable exposures during the 1983 growing season resulted in no significantdecrease in yield. The mean 03 concentrations in 1983 and 1984 were 57 ppb and 67 ppb(expressed as M7) respectively. In the present pea study, the highest M7 and Ml 2 obtained were37 and 42 ppb respectively. Other studies with pea have been conducted in growth chambers orgreenhouses, using prolonged exposures in excess of 100 ppb 03, and hence are of limitedrelevance to field conditions (Ormrod, 1976; Olszyk and Tibbitts, 1982; Kobriger and Tibbitts,1985)In the present experiment the greatest increase in 03 levels took place during floweringand pod set which resulted in significant linear reductions in pod number and pod weight (Table 7).This is consistent with the views of Cooley and Manning (1987) that exposure to low levels of 03during flowering or seed development may result in a reduction in the number of flowers, fruitsand/or seeds, but that those structures remaining are often able to attain normal or even larger size.Miller et a!. (1989), also found a decrease in seed yield, number of seeds, number of pods filled andweight of 100 seeds with soybean exposed in OTCs at twice the ambient concentration.There were no significant effects of 03 on harvest index, indicating that the distribution ofgrowth between reproductive and vegetative tissues was comparable for all treatments. Theobservation of Skarby (1988) that the starch content of the peas was found to increase withexposure is compatible with the findings in the present experiment, in which 03 treatments resultedin increased numbers of peas in size class 5, and increased average weight per seed (Table 8).The relationships between pea total weight and pod fresh and dry weight, pea fresh weight176and seed number showed modest improvements in fit using the Weibull model (Figures 16, 17 and18). Although the gamma models displayed in Figures 17 and 18 have the highest r2 values theydo not meet the criterion of becoming asymptotic to zero and consequently their use is not justified.Non linear relationships have not been previously reported for pea.With regards to growth rates, significant linear decreases in RGR were found in thisexperiment for pea in the first and second harvest intervals (Table 10). Velissariou and Davison(1993) exposed pea cv. Olympos to 03 for 6 h/d for 6 days to concentrations ranging from 50-150ppb in greenhouse chambers and also found that RGR decreased with increasing exposure to 03.The age of the plants used by Velissariou and Davison (1993) corresponds to the first harvestinterval in the present experiment. In the present experiment the early effect on RGR haddisappeared by the later harvests (Table 10)In the present experiment, although the03-stressed plants were smaller, they appear tohave adapted to the presence of 03 and were growing at the same rate as the control plants. Theaccommodative responses observed in RGR in the present study have been observed for otherspecies, as discussed in Section 5.3.6. In contrast to RGR’s, AGR’s decreased significantlythroughout the later harvest intervals (Table 10).5.3.2 POTATOThe response of the potato plants to the treatments applied in the present study was foundto be quite variable. The number of plants available for each harvest was limited, an infestation ofearly blight (Alternaria solani) and the foraging of rodents towards the end of the season resultedin an early termination of the experiment. The levels of 03 supplied in this experiment producedfew significant effects on the majority of the growth variables tested (Tables 12 and 13). RussetBurbank has been reported by Heggestad (1973) to be one of the more03-tolerant potatocultivars. Significant negative linear impacts of 03 on total plant weight (TW) in harvests 1 and 5(Table 12) were found in the present study. The effect on TW has also been reported by Foster etal. (1983) for cv. Centennial Russet and for cv. Norchip by Pell et al. (1988). Foster et al. (1983)177obtained a linear regression using TD (sum ofthe total exposures in 24 hours over the exposureperiod) as the independent variable:Total dry weight = 382 - 3.83 [I’D].In the present study, the use of TD for the final harvest of tuber dry weight gave the regression:Total dry weight = 391 - 4.82 [TD].Significant reductions in tuber dry and fresh weights and marketable weight/tuber werefound in the final harvest (Table 12 and 13). The linear reduction in weight per tuber wasborderline significant but there was no significant effect of 03 on tuber number at the final harvest(Table 13). Other studies with sensitive cultivars have described foliar injury and/or yield losses,characterized by both reductions in the number of tubers and smaller tubers depending on thecultivar (Hooker et al., 1973; Mosley et al. 1978; Clarke et al., 1990).Skarby (1982) found a decrease in the fresh and dry weight of potato tubers during twogrowing seasons when exposed to non-filtered air enriched with 03 in OTCs as compared withcontrols in charcoal-filtered air. In Skarbys experiments, 03 was added as a constantconcentration 9 h/day in OTCs, during the growing season. The M7 indices were 43 ppb and 51ppb for 1983 and 1984 respectively with the greater yield loss occurring in 1983. In the presentstudy for the final harvest the M7 indices ranged from 11 to 37 ppb (Appendix 4, Table 2).Foster et al. (1988) also observed reductions in number and weight of tubers for cv.Centennial Russet. Reductions in tuber weight and number have also been found for cv. Norland,Kennebec and Cherokee (Pell et al., 1980; Pell and Pearson, 1984). Pell et al. (1988) havesuggested that reduction in tuber number found in their previous experiments and those of Foster etal. (1983) may be an artifact of the growing conditions employed which used containers that mayhave restricted tuber initiation. Plants in the present experiment were grown under true fieldconditions and space was not a limiting factor.Accommodative responses in partitioning of ass iniilate have been previously reported byPelt et al. (1988), but in the present experiment no significant effects on HI were found.To the best of the author’s knowledge, non linear relationships have not been previously178reported between various potato variables and 03 concentration. In the present study, therelationships between total weight and tuber dry and fresh weight in the final harvest obtainedusing the Weibull model showed some improvement relative to the linear models (Figures 23 and24). However, as with the pea data discussed above, the simple linear regressions seemed adequatefor most of the variables measured in the present data set as little evidence of a threshold in plantresponse was apparent (Figures 23 and 24).No other researchers appear to have examined the effect of 03 on RGR in potatoes.Incremental RGRs may be indicators of03-induced plant stress in potato, based on the change insign of the non-significant trends in Table 14 from positive to negative, characterizing theinterruptive nature of 03 stress over the growing season. In contrast to pea, potato RGR tended toincrease with 03 exposure for the first two intervals and to decrease for the last two (Table 14).The response of AGR (Table 14) was similar to that noted for pea (Table 10), with significantdecreases with exposure found in the last two intervals. As noted in Section 5.3.1. for pea, thepresent study on potato indicated that although the03-stressed plants were smaller, they weregrowing at similar rates to the control plants.5.3.3 BEANBecause of the nutrient deficiency suspected in the early stages of growth in the presentexperiment, effects of 03 exposure on growth were examined over the last three harvests only. Nosignificant linear effects of 03 on TW, SW, LW, LA, LN, SLA, LWR, BW, FW, ROR and AGRwere found (Tables 16, 17 and 18), and no marked improvements resulted from attempts at fittingnon-linear models. However, significant adverse effects were noted for PW, HI and weight per podin the final harvest (Tables 18 and 19).The only reports in the literature of comparable findings in which adverse effects of 03only occurred on reproductive growth appears to be that of Sanders et al. (1 992b). Their studyinvolved experiments at two locations; at one of these, effects were detected only on bean seedyields, with no effects on overall growth, while at the other negative effects were observed on both179vegetative and reproductive growth. They suggested that the differences, in response at the twolocations was due to different dynamics of exposure. Where effects were observed only onreproductive growth, the 03 exposures were episodic, i.e. 3 days per week, rather than continuous.This therefore resembles the situation in the present experiment where the overall concentrationswere comparable, and the declines in HI, PW and weight per pod (Tables 18 and 19) coincidedwith the largest increment in the HRS5O and DAY5O indices (Table 15). Blum and Heck (1980)found that the magnitude of the effect of 03 on snap bean was dependent on the total exposure anddevelopmental stage of the plant. They reported that episodic events occurring at pod set anddevelopment resulted in a reduction in pod weight and final yield of bean. Work by Kohut andLaurence (1983) on kidney bean found that exposure of bean to elevated 03 concentrations duringpod filling resulted in decreases in yield. This observation is also supported by the work ofSanders et al. (1992a) and Younglove et al. (1994) who reported that higher exposures coincidentwith pod filling resulted in a decrease in yield.In contrast to the findings of the present investigation and those of Sanders et al. (1992b),almost all of the other reports of effects of 03 on bean growth indicate deleterious effects (Hans,1970; Blum and Heck, 1980; Kohut and Laurence, 1983; Amthor, 1988; Sanders et al., 1992c).However, the earlier studies involved relatively high 03 exposures.Notable exceptions are Kohut et al. (1988) who observed no effects on vegetative orreproductive growth by the final harvest, and Sanders et al. (1992c), who observed both vegetativeand reproductive growth stimulations at low levels of enrichment in OTCs.The interpretation by Kohut et al. (1988) of their results may be germane to the presentstudy. They suggested that the occasional significant effects observed at interim harvests in theirstudy disappeared subsequently because of repair which occurred during respite periods whenexposure levels were low. Integration of effects during stress and stress-free periods may result inno cumulative adverse effect on growth (Kohut et al., 1988).1805.3.4 RADISHBoth the growth of radish and the magnitude of response to 03 varied with the season ofexposure and with the genotype studied. In the first experiment no significant effects due to 03exposure were noted, whereas, in the remaining experiments negative effects on growth wereobserved for both genotypes.The significant negative effect of 03 on TW and HW of Cherry Belle and FrenchBreakfast in Expts. 2 and 3 are consistent with the findings of other researchers (Tingey et al.,1971; Walmsley et al., 1980; Atkinson et al., 1988; Pell et al., 1990; Pell et al., 1993).Overall, Cherry Belle appeared more sensitive to 03 exposure than French Breakfast.Reinert et al. (1972) examined the effect of 03 on the foliage of a number of radish cultivars.They found that Cherry Belle was the mostO3-sensitive and French Breakfast one of the leastsensitive in terms of percent foliar injury. In their experiments a single exposure at a concentrationof 350 ppb over 1 1/2 h was used. The present study shows that the cultivars also differ in growthform; French Breakfast invested more photosynthate in the production of leaves than Cherry Belle.It had a smaller 11W but higher RGR than Cherry Belle in all three experiments (Appendix 5,Table 5).The greater 03 effects detected in plants from Expt. 3 were not explainable by thedifferences in exposures between experiments. The average 03 concentrations were higher in thesecond experiment where the plants did not respond to 03 until the last harvest (Table 20).However, there was a more consistent negative response to 03 stress observed in the thirdexperiment, in spite of the gas exchange rates as inferred from increased conductances (Table 29)being slightly higher in Expt. 2 than 3, where the plants were less responsive to 03.Pell et al. (1992) have previously shown that when plants were responsive to 03, leafconductance declined. In the present study, conductance showed an increasing trend with 03 inExpt. 2 as compared with the decline noted in Expt. 3 (Figure 38). However, the increase anddecline may be concentration-related. Inspection of Figure 38 suggests that conductance tends to181decrease at concentrations up to about 60 ppb, and increase at higher levels. Hassan et al. (1994)have reported that stomatal conductance increased with 50 to 100 ppb 03, and that the increasewas the result of the collapse of epidennal cells.In the third radish experiment, LAR was higher in all harvests than in Expt 2, significantlyincreasing with 03 stress in 3 of the 4 harvests (Table 25). These increases in LAR were largelythe result of significant increases in LWR rather than SLA, although, SLA showed an increasewith increasing 03 and was larger in Expt. 3 than in Expt. 2. Atkinson et al. (1988) foundsignificant increases in LAR and SLA of radish exposed to 200 ppb 03, whereas Barnes andPfirrmann (1992) found a significant decrease in SLA but no effect on LAR. Aben et al. (1990)attributed the greater negative impact of 03 exposure between experiments with the sameconcentrations, to enhanced uptake rates as a result of differences in LAR of faba bean.Significant linear decreases in the root:shoot ratio (equivalent to HI) were also found for anumber of the harvests in Expts. 2 and 3 for Cheny Belle, but only one harvest in Expt. 2 forFrench Breakfast (Table 25). Tingey et al. (1971) have suggested that 03 stress initially results ina decline in photosynthesis and that this causes an alteration in assimilate partitioning. They alsofound a decrease in root:shoot ratio for cv. Cherry Belle following exposure to 03 at 50 ppb for afive-week period, and such decreases have been reported for radish by a number of otherresearchers (Tingey et al., 1973; Adedipe and Ormrod, 1974; Walmsley et al., 1980; Greitner andWinner, 1988; Pell et al., 1993).Like potato and pea, radish AGR initially showed an increasing trend with 03 andsubsequently significantly decreased in harvest intervals 2 and 3 for both cultivars in the presentstudy (Table 28). The response in RGR was similar to potato with an increasing trend in the firstharvest interval followed by a decrease in the remaining 2 intervals. Previous work by Pell et al(1990) found no effect of RGR on radish despite an decrease in hypocotyl weight, suggesting anaccommodative response to 03. Examination of the various growth functions by Walmsley et a!.(1980) also distinguished an accommodative response to long-term, low level 03 exposures forCherry Belle where NAR and RGR of the control and 03 treatments (170 ppb for 36 days) showed182no significant difference by the end of the experiment. RGRs were similar in magnitude to thosereported by Pell et al. (1993). In the present study, RGRs were lowest in the last experiment andhighest in Expt. 1, for both cultivars (Appendix 5, Table 5). This observation is supported by Pellet al. (1993) who found RGR to be higher in spring than in autumn experiments for Cherry Belleand Wild Type radish. In the present study, the lack of an impact of 03 growth in Expt. 1, and theconsistent negative impact noted in harvest intervals 2 and 3 for Expts.. 2 and 3 (Table 28) is alsosupported by Pell (1993) who found no significant effects of 03 on radish plants exposed in thespring, whereas there were a significant effects in the autumn experiment.Since negative effects of 03 on net photosynthesis are well documented in the literature(Atkinson et al., 1988; Pell et al., 1992), Pellet al. (1993) have suggested that an impact of 03 onphotosynthesis in slower growing plants (reduced RGR) may result in a greater reduction of thoseplants’ ability to compensate for the stress successfully. The higher RGR in French Breakfast mayin part account for the differences between cultivars in response to 03 stress (Appendix 5, Table5).In the present study, average wind speed was lowest during the day for Expt. 1 and highestfor Expt. 2 (Figure 30). Wind speed has been reported to modify the effects of SO2 on growth ofryegrass by Ashenden and Mansfield (1977), who noted significant reductions in growth at highwind speed and no effect at low wind speed. They discussed these results with respect to theanticipated differences in boundary layer resistance modifying uptake in these plants. Thevariation in wind speed observed in the present study may therefore have affected uptake by theseplants despite the similarities in 03 concentration. Higher flux densities in conjunction with lowerconcentrations between the 2 sampling years above a grassland ecosystem have also been reportedby Grunhage et al. (1994). They suggested that similar air temperatures but higher wind speedswould lead to enhanced turbulent vertical exchange, resulting in differences in flux densitiesbetween years. In their experiments the turbulent exchange coefficients were found to differrelative to the differences in wind velocity. In the present study although the mean air temperaturewas similar between Expts. 1 and 3, the mean wind speed was higher in experiment 3 perhaps183resulting in greater flux densities for these plants; contributing to the greater impact of 03exposure on growth despite the lower overall concentrations in Expt. 3 (Table 28).5.3.5 DOUGLAS-FIRNegative effects of 03 stress on plant biomass have been reported for a number of treespecies (Pye, 1988). However, the responses have been variable, and inconsistent between speciesand for different experiments using the same species (Table 3). In the case of Douglas-fir, itsmultiple flushing growth characteristic undoubtedly contributed to the variability observed in thetree responses in the present study (Table 39). Schaap and Wang (1989) commented that theinherent variability of Douglas-fir added to the likelihood of type II errors occurring in the analysisof 03 exposure experiments involving practical numbers of trees. The same is true of otherspecies. For example Adams et a!. (1988) observed decreases in stem volume expressed as(D2H), and secondary needle weight of Pinus taeda seedlings exposed to ambient air plus 60 ppb03 in OTCs for one growing season. Subsequent study of the same trees after two further seasonsrevealed no significant effects on height, stem diameter or total weight (Adams et al. 1990). Shaferet al. (1987) found linear decreases in stem diameter, height, and weight of component parts ofF.taeda seedlings after a single 5 month exposure to 03. More recently, Shafer and Heagle (1989)reported that after 3 years exposure to 03 stem height was variable and no longer significantlyrelated to 03. Shoot, branch and root weight and stem diameter were suppressed by 03 in 3 of the4 ecotypes. Non-significant decreases in stem, needle and root weights, following exposure tohigher levels of 03 than those used in the present study have been reported by Wilhour and Neely(1977) for Douglas-fir.The lack of any significant linear trend between incremental changes in stem diameter orheight and exposure observed in 1988 was not surprising considering that the exposure period waslmiited at the time of measurements to 57 days with total average exposures due to enrichment notexceeding an M 12 index of 41 ppb (see Section 4.6.2.). Furthermore, P. menziesii is typicallyslow growing in comparison to most crop plants. No significant effects of 03 exposure were seen184prior to bud break, and immediately following the first flush in 1989 (Tables 39 to 41).The decreases in second flush growth that reached significance towards the end of thesecond exposure period in the present study led to decreased leader growth in the following year(Table 40 and relevant text), 10 months following the completion of the enrichments. Thedependence of current growth on physiological activity during the preceding year has been reportedfor other conifer species. Tseng et al. (1988) found that the decreased photosynthetic rates ofAbiesfraseri caused by 03 did not result in concurrent growth reductions. Fuhrer et al. (1990)reported that photosynthetic rates ofPicea abies were unaffected by 03 until bud break afterwhich the photosynthetic capacity of the previous year’s needles was decreased by treatment,whereas the current year’s needles were unaffected. They concluded that budbreak was a criticalphase of tree phenology, with the previous year’s needles supplying the current flush of growth.However, approximately 70% is supplied from storage in the stems not leaves, as these authorshave suggested.With Douglas-fir, Hogsett et al. (1989) also reported that adverse effects on biomasstended to occur at the end of the period of bud elongation in the spring and summer following theperiod of exposure to above-ambient levels of 03. They suggested that bud elongation wasdependent on the effects of previous 03 exposure on the carbohydrate reserves accumulated in thebud during the exposure period.The results of the present study suggest both delayed and concurrent growth effects.Although the response surfaces produced with the cumulative exposure indices, DAY5 08889 andDAY5OC, have the greatest negative slopes (Figure 42), the surface produced with the incrementalindex, DAY50mc also shows a negative slope, suggesting that, at least towards the end of thegrowing season, second flush growth was also responding to some extent to the immediatelypreceding exposure history.With both second flush and leader length the improved fits obtained with the gamma andWeibull models are evidence of non-linearity of the response. However, although the gammamodels for second flush resulted in best fits, unlike the Weibull regressions, they are not185asymptotic to zero growth at high values of the exposure index much beyond the range of those inthe data set. In contrast, the better fits obtained with the gamma model for leader length do nothave this caveat, and suggest that at low exposures (DAYS 08889 <40) there may be as slightstimulatoiy effect of 03. Such effects have been reported for other species (Sanders et al., 1992b).The results clearly indicate that experimentation of this type needs to be conducted overmore than two years. The present experiment had to be terminated in the third year, precluding thepossibility of determining whether or not the delayed effects observed in the second season wouldhave been compounded in subsequent seasons. The fact that the regression of leader length in 1990with the cumulative index over both 1988 and 1989 seasons, DAY508889 was more significantthan the regression with the index for 1989 alone, DAY5OC, suggests that such compounding mayoccur.5.3.6 COMPARISONS BETWEEN THE CROPS AND TREES SUBJECT TO OZONESTRESSEvidence in the literature has suggested that an 03 induced reduction in photosynthesis(leaf photosynthetic efficiency) may result in a decrease in the amount of assimilate available fortranslocation (Walmsley et a!., 1980). This typically impacts sinks located furthermost from thesource, resulting in a decrease in the root:shoot ratio for a number of plant species. In the presentstudy although the roots were not fully recovered at each harvest for bean, pea and Douglas-firsignificant decreases in the root:shoot ratio were noted for potato and radish. Decreases inroot:shoot ratio have been observed for bean (Engle and Gabelman, 1967), soybean (Tingey et al,1973; Reich and Amundson, 1985; Greitner and Winner, 1988), Trfolium incarnatum and Loliummultifiorum (Bennett and Runeckles, 1977), parsley (Oshima et al., 1978), potato (Pell et a!.,1988), carrot (Bennett and Oshima, 1976), radish (Tingey et a!., 1971; Greitner and Winner, 1988;Pell et al., 1993) and Pinus taeda (Spence et al., 1990). Nevertheless, since the levels of 03-enrichment used in Spence et al’s. (1990) experiment with Pinus taeda exceeded those of thepresent study it is unlikely that Douglas-fir root biomass was impacted.186The response of plants to03-enrichment during flowering, seed or fruit development maylead to a diversion of photosynthate to these tissues. This is consistent with the present studywhere yield reductions in bean were associated with a reduction in pod weight but not pod number.Yield reductions in potato were also associated with a reduction in tuber weight rather thannumber. In contrast to bean and potato, yield reductions in pea were associated with both reducedpod number and weight, but seed weight/seed number increased, and partitioning betweenreproductive and vegetative tissue was unaffected. Ozone has also been shown to decrease theability of soybean to set seed, but once this has occurred, the seeds are the preferred sink (Cooleyand Manning, 1987). The differences in response of the two legume species in the present studymay be due to the occurrence of the largest increase in 03 concentrations which took placebetween harvests 3 and 4 for pea and between harvests 5 and 6 for bean. These coincided withflowering and pod set for pea and pod filling for bean. The differences in response between thepresent study on bean and radish and the work of Sanders et al. (1992b, 1992c) and Pell et al.(1993) among others on these same species, are likely a reflection of the very low levels of 03-enrichment used in the present experiments and the differences in the type of exposure facility andmethodology (ZAPS versus OTCs).Reductions in ROR following fumigation with 03 suggest that the dry weight needed toproduce an additional unit of dry weight is increased by treatment. This may be due to a decreasein the photosynthetic rate, an increase in the respiration rate or a change in the partitioning ofphotosynthate between the stem and leaves (Barnes, 1972; Tingey et al. 1976). Essentially itsuggests a decline in the overall efficiency of growth processes with fumigation. These responseshave been shown by some researchers to be temporary in nature (Oshima et al., 1978,1979;Walmsley et al., 1980). This is compatible with the response of pea RGR in the present study.Significant reductions in ROR total weight were observed for radish cv. Cherry Belle in Expt. 2.In woody seedlings RGR declines because of the increase in woody stem tissue which adds dryweight to the seedling but adds nothing to the photosynthetic production of seedlings, except assupport for additional leaves. A trend for a decrease in RGR ofF. menziesii was noted in the187final harvest in July 1990 (Table 46).Leaf area ratio is an index of leafiness and expresses the relationship between assimilatorysurface to respiratory mass (Ledig, 1974; Hunt, 1978). The decline in LAR may be due to leafabscission or the termination of leafgrowth. Decreasing trends in LAR and LWR (statisticallysignificant in harvest 6) were noted for pea in the last two harvests of the growing season, which isconsistent with the significant decline in leaf number for these same harvests. All other species inthe present study showed increasing trends in LAR and LWR for most of the harvests, includingthe last one for the growing period, some of which were significant in the case of radish. Miller etal. (1988) has suggested that decreases in LWR and LAR in later harvests for cotton wereindicative of leaf abscission due to 03 stress.5.4 Effect of ozone on conductanceA decline in radish conductance with plant maturity was noted in Expts. 2 and 3 of thepresent study (Figure 37). Pell et al. (1993) observed that while stomatal conductance declinedwith leaf maturity, 03 effects were insignificant in the spring (May 22-June 22), but significantdecreases were obtained in the fall (August 31-October 6). Greitner and Winner (1988) alsoobserved a decrease in conductance with leaf age, ranging from approximately 1.1-1.9 cm/s for thecontrols and 0.66-1.2 cm/s for the 03 treated plants. These values are within the range of thosemeasured during the present experiments. The magnitude of the conductance values for Douglas-fir were also consistent with those obtained by Lassoie (1982) and Livingston et al. (1984), whichwere found to vary between 0-0.4 cmIn the present study, there was a lack of any distinct daily pattern in the conductance ofeither radish or Douglas-fir (Figures 40 and 45). Inconsistencies in diurnal trends wereparticularly obvious for radish. Schenone et a!. (1994) also found no distinct diurnal trend in theobserved physiological (net photosynthesis, stomata! conductance and transpiration) responses ofbean plants to ambient air pollution, suggesting that the physiological effects recorded in theexperiment were the result of long-term modifications. Water-stress is known to affect stomatal188response in plants (Tingey and Hogsett, 1985; Dobson et al. 1990). However, the radish plantswere watered to field-capacity the evening prior to conductance measurements being made whichsuggests that the inconsistencies among radish experiments must be attributed to other factors.Atkinson et al. (1988) reported a variable response in stomatal conductance for radish betweentreatments over the course of their experiment, but the concentrations used were much higher thanthose in the present study. Nevertheless, results of the present study and those of Atkinson et al.(1988) and Schenone et al. (1994) indicate the difficulties inherent in making comparisons betweentreatments on the basis of short-term measurements and extrapolating to the effects over the entireexposure period of a plant.In contrast to radish, the tree seedlings received no supplementary water during theexperimental period. As previously indicated, water stress is known to affect stomatal response.The lack of rainfall during the early part of the growing season and the variations in moisturestatus in the field combined with the disparity in size of the tree seedlings measured betweentreatments, may be key factors which led to the variable responses between treatments seen in 3 ofthe 7 measurement periods during the growing season (Figure 45).Both radish and Douglas-fir, decreases in conductance were noted at low concentrations of03 (Figures 38 and 46). However, in the case of radish, at higher concentrations the trend wasreversed (Figure 38). Eamus et al (1990) in a study on four-year old Picea abies exposed to 50ppb 03 in OTCs found no significant difference in mean stomatal conductance over the day forcurrent year needles, but with previous year’s needles there was a significant increase in meanstomatal conductance in the03-stressed plants. Increases in stomatal conductance of radish atconcentrations in excess of 50 ppb have also been reported (Hassan et al., 1994), and wereattributed to collapse of the epidermal cells at these higher concentrations.Because of the lack of a distinct diurnal trend in conductance for either species in thepresent studies, it is not surprising that poor relationships were noted for many of theenvironmental variables assessed. For radish, the r2 values were found to be consistently less than0.55. Douglas-fir showed even greater variability with r2 values never exceeding 0.27. Solar189radiation in the previous hour(s) was found to account for most consistent trend in conductancebetween radish cultivars and experiments (Tables 31 and 32). This was also found for Douglasfir, in addition to absolute humidity and soil temperature (Table 44). For both species, 03concentration regardless of the hour accounted for less than 25% of the variation in conductance.McCaughey and lacobelli (1994) used measurements of solar radiation, vapour pressure deficitand air temperature to model stomatal conductance in trembling aspen (Populus tremuloidesMichx.) and paper birch (Betulapapyrifera Marsh.). They also found poor fits between stomatalconductance and each of these environmental variables separately (with r2 values ranging from0.004 to 0.24). However, a multiple linear regression model incorporating all three variablesexplained 62% of the variation in stomatal conductance. In the present experiment with Douglas-fir, a multiple linear regression model incorporating air and soil temperature, solar radiation, 03,wind speed and direction only accounted for 48% of the variation in conductance (Section 4.6.5.2).With radish, the multiple linear regression models were fitted separately for each cultivar andexperiment due to the differences in response of conductance by experiment and between species.For Expt. 2 the models found for both cultivars resulted in some improvement in fit over thoseobserved in the simple linear regressions (Section 4.5.6), but, only one variable was selected inExpt. 3 for both species (Section 4.5.6).Overall, French Breakfast appeared to be less sensitive to 03 exposure than Cherry Belle.While this may be a reflection of the lower conductance values recorded for this cultivar (Table 31)which may have reduced uptake, it might also be accounted for by cultivar differences in liquid-phase or perturbation processes, not tested in the present study. In general the plants in those 03treatments that led to the higher conductance did not necessarily grow as well, suggesting that theeffect of 03 was on the photosynthetic process rather than gas exchange, as has been suggested byothers (Tingey et al. 1973; Taylor et al., 1982; Aben et al., 1990; Pell et al., 1992; Schenone et al.,1994).1905.5 Flux-response relationshipsThe porometer measurements were obtained under conditions where the boundary layerresistance was minimal, therefore the estimates obtained with the whole plant porometer reflectdiffusive resistance to water vapour only. The inclusion of boundary layer resistance in thedevelopment of a flux term is in keeping with the approach by Grunhage et al. (1994). Sinceinformation on boundary layer and residual resistances for these species were not avaliable fluxwas computed using estimated values for conductance only. This is compatible with the approachused by Reich (1987).Comparisons of the linear regressions of radish growth variables with the FLUXSUMCand D5OC indices, showed that the use of the latter index provided better fits in most cases,although only 8 of the 24 regressions with D5OC reached significance, even at p = 0.10 (Table 34).Although Grunhage et al. (1994) have reported that accumulated flux is the preferred index for usein injury-response models in tobacco, the findings with radish in the present study fail to confirmthis. However, the limited scope of the radish data and their variability may well have led to thefailure to demonstrate significant regressions based on flux estimates.In contrast, with Douglas-fir the linear relationships found using FLUXSUMC (based onfluxes computed from actual conductance data) as opposed to the D5OC resulted in a consistentimprovement in fit for most of the tree biomass variables, although few regressions weresignificant. The variables included TW, TW1f, TWnew FF11’, HTERM, and FD2H(Tables 45through 47). However, at harvest 6, the regressions of FLUXSUMC and second flush totalweight, leaf area and leaf weight were significant (Table 46). In contrast, the limited data set forthe trees used for porometry measurements failed to show any significant regressions for secondflush variables with the D5OC index, except for Leaf area at the first harvest (Table 46). Thiscontrasts sharply with the results obtained using larger harvests, presented in Section 4.6.3.1.Because of the poor performance of the FLUXSUMC index with radish, the extension ofthe work on flux to estimates based on the use of the multiple linear regression model described inSection 4.6.5.2, was limited to computing FLUXSUMM for all 15 treatments of Douglas-fir.191Although few regressions based on either FLUXSUMM or D5OC were significant when TW,‘‘2f’ FD2Hor HTERM were used as dependent variables (Table 48), in 15 of the 20comparisons of these variables for data from harvests 2 to 6, FLUXSUMM showed better fits.With the data set limited to the trees used for porometer measurements, the clear relationshipsestablished between D5OC and second flush growth using the larger sample sizes (Section 4.6.3.1)were less apparent. Nevertheless, the regressions ofTW2and D5OC for the last two harvestswere significant at p = 0.10, while the regression using FLUXSUMM was significant at p = 0.05for harvest 5.Conceptually the FLUXSUMM index is a more appropriate choice for the independentvariable in these relationships as it addresses the role of environmental factors in influencingpollutant uptake which is a not true for the D5OC index. Factors such as the limited number ofconductance measurements made, and the use of hourly averages (for meteorological variables) torelate to conductance may in part account for the weak relationships established betweenconductance and the meteorological variables. Refinement of these measurements may lead to animproved multiple linear relationship between these factors and consequently a more consistentimprovement in fit for models based on estimated flux versus exposure.One of the main disadvantages of using a porometer is that continuous measurements arenot possible, reducing the accuracy of the estimates obtained for flux density. This has beendemonstrated by Leuning et al. (1979) who found that the use of a single curve for canopyresistance over the whole season failed to reveal situations where reduced 03 flux occurred as aconsequence of variable weather conditions resulting in stomatal closure. This criticism can bemade of the present studies of both radish and Douglas-fir.From these experiments it is clear that the development of a flux term using conductancealone tended to improve the relationships with some growth measures for Douglas-fir, but, nocomparable improvements could be demonstrated for radish. Hence, these studies provide limitedsupport for the concepts originally proposed by Mukammal (1965) and more recently developed byGrunhage et al. (1994).1926. SUMMARY1) The ZAPS approach, a chamberless open-air system, was used in the present study toexamine the effects of 03 exposure on growth of pea, potato, bean, 2 cultivars of radish, andyoung Douglas-fir. The configuration of the ZAPS resulted in 12 unique exposure treatments inwhich the plants were exposed to different regimes of03-enriched air, in addition to control plotsexposed to ambient air.2) Exposure of pea cv. Puget, to low levels of03-enrichment resulted in advanced senescencein the03-treated plants and reductions in all biomass variables. These responses occurred early onin the experiment and were maintained until the final harvest. Alterations in LAR were coincidentwith declines in leaf number for the later harvests. Significant negative impacts of exposure onAGR and ROR in the early stages of growth were not detected in the later harvests for RGRsuggesting that the plants had accommodated to the stress. While exposure resulted in significantdeclines in pod fresh and dry weight and pod number, harvest index was unaffected suggesting thatthere was no alteration in assimilate partitioning between reproductive and vegetative weight withstress. Similarly, although the total number of seeds was significantly reduced with exposure to03, average seed weight increased significantly. Due to the low concentrations used in thisexperiment the response of all pea variables was well described using simple linear regressionmodels.3) Few significant effects of03-enricbment on potato cv. Russet Burbank were noted in thisstudy although significant reductions in total plant dry weight, total tuber dry weight and totaltuber fresh weight (i.e. yield) were found at the final harvest. Ozone exposure also had asignificant negative impact on AGR for the later harvests, but like pea these effects were not foundin the later harvests. The lack of significant trends in RGR may indicate that, like pea, the plantshad accommodated to the stress and although they were smaller they were growing at the same rateas the controls.4) In contrast to pea cv. Puget few significant effects were noted for the biomass variables ofbean cv. Galamore. However, this may be partly the result of the relatively low levels of 03193dispensed during the growing season in 1988. Although no significant effects of 03 on growthrates were observed during early growth, in the later stages significant decreases in harvest indexand pod weights were found as a result of increased levels of exposure during the pod fillingstages.5) Marked differences were found in the three experiments with radish cvs. Cherry Belle andFrench Breakfast. In both cultivars AGR was highest in Expt. 2 and lowest in Expt. 3, whereasRGRTW was highest in Expt. 1. AGRs were significantly reduced in the later harvest intervals (p<0.05), and this was reflected in reduced RGRs (p <0.10). The seasonal variation in response ofthe two radish cultivars was reflected in the relationships found between a number of plantvariables and D5OC. For the most part, no significant effects of 03 were found in Expt. 1 (latespring) for either cultivar, whereas significant linear reductions in total dry weight and hypocotyldry and fresh weight were found for both cultivars in the final harvest in Expts. 2 (summer) and 3(fall) versus D5OC. The responses in the intermediate harvests varied suggesting that somecompensation in growth may have occurred. Variable responses in biomass partitioning werenoted between the cultivars and experiments. In general, the hypocotyl was more affected than theleaves in these experiments and Cherry Belle was more impacted by the 03 concentrations appliedthan French Breakfast. The differences in response between cultivars may be due to the highergrowth rates and lower overall conductances for the more tolerant French Breakfast.6) The results with crops lend some support for the conceptual model of Krupa and Teng(1982) which indicated that the pattern and timing of03-exposure with regards to a crop’s stage ofdevelopment will determine the effects on growth and development. Furthermore, in each crop,there was a clear indication of the negative impact of 03 on final yield, whether of pods (as in thecase of pea and bean), tubers (potato) or hypocotyls (radish), in spite of the relatively low levels ofexposure obtained in the ZAPS.7) While03-enrichment in the present study on Douglas-fir had no effect on most biomassvariables, significant linear decreases in total weight of the second flush and leader length werenoted for the later harvests in 1989, and in 1990 respectively, suggesting that an impact on needles194supporting the current yea?s growth will have a long-term impact extending into the next growingseason, even in the absence of further03-stress.8) Although in some instances, Weibull and/or gamma non-linear models increased goodnessof fit in comparison with linear regressions, the low range of exposure levels obtained resulted inno consistent patterns of improved fits.9) The relationships between 03 and conductance of radish and Douglas-fir conductancewere weak. Mean conductance was found to be inversely related to solar radiation in the previoushour(s) for radish and to both solar radiation in the previous hour(s) and soil temperature forDouglas-fir.10) Flux of 03 based on conductance measurements (FLUXSUMC) were tested asindependent variables in linear regressions against various growth measures in comparison with theD5OC exposure index. With the radish cultivars, the D5OC index provided better fits thanFLUXSUMC. However, overall, there was an improvement in the relationships found with treebiomass using FLUXSUMC, versus D5OC as the independent variable.11) Estimates of cumulative 03 flux to Douglas-fir were calculated based upon conductanceestimates obtained by using a multiple linear regression between observed conductance and severalmeteorological variables (FLUXSUMM). Few significant effects of 03 on tree growth wereobserved. 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Growth response of Loblolly Pine(Pinus taeda L.) seedlings to ozone fumigation. Environ. Pollut. 71: 43-5 6.Wolff, G.T., P.J. Lioy and R. S. Taylor. 1987. The diurnal variations of ozone at differentaltitudes on a rural mountain in the eastern United States. J. Air Pollut. Control Assoc.37: 45-48.Wright, E. 1988. The effect of ozone on horticultural crops important in the Fraser Valley ofBritish Columbia. M.Sc. Thesis. University of British Columbia. 218 pp.Yang, Y.S., J.M. Skelly, B.I. Chevone and J.B. Birch. 1983a. Effects of short-term ozoneexposure on net photosynthesis, dark respiration and transpiration ofthree eastern white pine clones. Environ. Internat. 9: 265-269.Yang, Y.S., J.M. Skelly and B.I. Chevone. 1982. Clonal response of eastern white pine to lowdoses of ozone, sulphur dioxide and nitrogen dioxide, singly and in combination. Can. J.For. Res. 12: 803-806.Younglove, T., P. M. McCool, R. Musselman and M. E. KahI. 1994. Growth-stage dependentcrop yield response to ozone exposure. Environ. Pollut. 86: 287-295.2158. APPENDICES216Appendix 1.Summary of soil analyses completed prior to the initiation of the 1988 field seasonBlock pH Available P Total N K Ca Mgppm ppm ppm ppm ppm1 5.60 66 0.725 152.5 602.5 127.52 5.75 86 0.608 225.0 695.0 125.03 5.65 36 0.520 180.0 500.0 97.54 6.10 71 0.552 227.5 915.0 180.05 5.75 150 0.520 95.0 875.0 57.56 5.75 94 0.432 105.0 885.0 65.0217Appendix 2.Exposure indices commonly used as independent variables in linear and non-linear exposure-responsemodels. Except where noted, all statistics refer to values obtained during the daily 12-hour period(0900-205 9 h) in which ozone was released.Heck et al. (1984)Heagle et al. (1987)Hecketal. (1984)Heck et al. (1984)Lefohn andBenedict (1982)Oshima et at, (1976)Lee et al (1987)ExposureindexDescription ReferenceM7M12Ml [7]P1 [7]SUMxxAOTxxHRSxxTDD25, D50xCxxSeason-long mean of daily 7-h means (0900-1559 h)Season-long mean of daily 12-h meansSeason-long mean of daily 1-h maximum (0900-1559 h)Single-peak 1-h mean (0900-1559 h) for the seasonSum of all ozone concentrations when a threshold of50, 80 or 100 ppb is exceededSum of the ozone concentrations above thresholdsof 50, 80 or 100 ppb.Sum of the number of hourly ozone concentrationsabove a threshold of 80 or 100 ppbSum of all hourly means, over 24-h, for the seasonSum of the number of days where an hourlyozone concentration exceeded 25 or 50 ppbSum of the number of days with 2, 3 or 4 consecutivehours greater than 25 or 50 ppb ozoneFoster et ai. (1983a)Wright (1988)Wright (1988)218Appendix3.TableI.Summaxyofradishmeansovercultivarsandcontrolsvesussupplementaryozonetreatments, forcontrastsusedinANOVA.TRTCULTHARLNLASQMLARSLALWRTWRWLNRWHWLWHIFHW114.480.00244214.060.00290Con14.290.00286Oz14.260.00262125.890.00620225.080.00589Con25.350.00599Oz25.520.00606136.580.00901235.790.01005Con35.870.00866Oz36.270.009750.018850.022920.020620.024960.018770.023120.019980.024140.014410.023630.015870.023140.013880.021730.015450.023800.009440.020340.010970.020010.009450.019500.010400.020340.17610.16180.18280.16550.37610.30800.35380.33910.54180.45380.52760.4904147.190.01095246.680.01864Con46.740.01448Oz46.980.014870.007110.020300.010010.021860.007480.020160.008830.021310.353131.624160.0090-4.87771.06270.55250.468921.823330.0185-4.14770.96880.83610.386141.954960.0150-4.41631.18500.75500.417251.665940.0134-4.53680.97350.67910.643020.01810.528020.89960.609724.87830.579419.35400.811620.143390.0028-6.07480.02750.11310.824140.144030.0034-5.91750.02390.11670.803540.162210.0037-5.79200.03110.12750.821470.139090.0030-6.04720.02440.11180.610730.466130.0112-4.75920.18450.27040.682810.391530.0118-4.63190.12120.25860.638920.449070.0102-4.81880.16050.27830.648730.423760.0118-4.66470.15090.26110.460941.013020.0078-5.04980.55990.44540.550560.949940.0101-4.79780.43320.50670.485970.955550.0076-5.03020.50150.44640.510700.987970.0092-4.89720.49520.4835NB.TRT=Treatment(concontrol;0zsupp1ementaiy03treatments);CULT=Cultivar(1=ChenyBelle;2=FrenchBreakfast);HAR=Harvest;LN=Leafnumber;LASQM=Leafarea(sq.m);LAR=Leafarearatio(sq.m/g);SLA=Specificleafarea(sq.m/g);LWR=Leafweightratio(g/g);TW=Totalweight (g);RW=Rootweight(g);LNRW=inrootweight (g);HW=Hypocotylweight(g);LW=Leafweight (g);HI=Harvestindex;FHW=Freshhypocotylweight (g).219Table 2. Expected mean squares used in radish ANOVA on a per harvest basis assuming that treatmentsand cultivar are fixed effects.LinenumberdenominatorSource of df EMS F-testvariation1 Replicate r-1 X R 72 Ozone t-1 XTR T 33 Ozone * Rep (t-1)(r-1) X TR 74 Cultivar (c-i) X CR C 65 Cult * Ozone (c-l)(t-1) X CT 76 Cult * Rep (c-l)(r-1) X CR 77 Cult * Ozone * Rep (c-l)(t-1)(r-1) XTable 3. Summary of coefficients used in partitioning the treatment sums of squares into supplementary03 linear contrast in ANOVA run seperately for each harvest.Total dose (ppm) coefficientsHarvestTreatment1 2 3 41 -0.534660 -0.441520 -0.541500 -1.0128602 -0.757160 -0.932860 -1.335160 -1.4491903 +0.173333 -0.413190 -0.556830 +0.1501384 -1.592660 -0.988190 -1.370830 -1.4821905 +2.145833 +2.803472 +3.553833 +3.9138056 +1.888333 +2.845805 +3.372500 +4.0038057 +1.231833 +0.720472 +1.086833 +1.6114728- 0.253 160 - 0.208520 - 0.204830- 1.98 15209 +2.333333 +2.080138 +2.113833 +3.75247210- 1.600 160 - 2.007860 - 2.766 160- 3.87486011 -1.477660 -1.659520 -2.119500 -0.73552012 -1.557160 -1.798190 .1.232160 -2.895520NB. These were calculated using the grand mean for each harvest and subtracting the mean for eachtreatment averaged over all replicates and cultivars.220Table 4. Summary of radish means by harvest, over cultivars for treatment contrasts.Harvest 1 Harvest 2TRT LN LA LAR SLA LWR TW LN LA LAR SLA LWR TW1 4.40 0.0028 0.0205 0.0250 0.8120 0.1488 5.74 0.0067 0.0148 0.0234 0.6376 0.47482 4.20 0.0022 0.0202 0.0242 0.8269 0.1149 5.52 0.0062 0.0142 0.0227 0.6296 0.45333 4.07 0.0029 0.0218 0.0256 0.8439 0.1422 5.61 0.0061 0.0145 0.0221 0.6575 0.43144 4.23 0.0026 0.0209 0.0251 0.8271 0.1320 5.44 0.0063 0.0195 0.0311 0.6268 0.43875 4.05 0.0022 0.0200 0.0237 0.8359 0.1180 5.41 0.0049 0.0145 0.0227 0.6394 0.35676 4.23 0.0024 0.0183 0.0224 0.8055 0.1399 5.11 0.0053 0.0148 0.0225 0.6456 0.37197 4.38 0.0026 0.0195 0.0238 0.8126 0.1424 5.58 0.0059 0.0151 0.0226 0.6694 0.41208 4.30 0.0023 0.0192 0.0233 0.8144 0.1304 5.03 0.0049 0.0161 0.0238 0.6661 0.34309 4.53 0.0029 0.0195 0.0239 0.81 17 0.1585 5.68 0.0066 0.0168 0.0248 0.6779 0.428810 4.30 0.0029 0.0198 0.0243 0.8135 0.1486 5.64 0.0069 0.0144 0.0230 0.6257 0.498011 4.18 0.0025 0.0201 0.0241 0.8299 0.1323 5.60 0.0061 0.0154 0.0230 0.6721 0.421612 4.33 0.0032 0.0201 0.0243 0.8242 0.1611 5.60 0.0069 0.0146 0.0230 0.6405 0.499313 4.32 0.0030 0.0181 0.0227 0.7977 0.1712 5.53 0.0061 0.0132 0.0212 0.6238 0.486014 4.30 0.0028 0.0198 0.0242 0.8044 0.1491 5.21 0.0054 0.0133 0.0208 0.6382 0.406815 4.25 0.0028 0.0184 0.0225 0.8085 0.1664 5.45 0.0065 0.0139 0.0218 0.6445 0.4876Harvest 3 Harvest 4TRT LN LA LAR SLA LWR TW LN LA LAR SLA LWR TW1 6.50 0.0117 0.0119 0.0221 0.5281 1.0881 7.07 0.0152 0.0086 0.0203 0.4201 1.86762 6.27 0.0096 0.0111 0.0215 0.5051 0.9543 7.05 0.0434 0.0200 0.0494 0.4045 1.78393 6.10 0.0096 0.0108 0.0214 0.5090 0.9357 6.85 0.0130 0.0085 0.0204 0.4177 1.57924 6.37 0.0108 0.0104 0.0213 0.4915 1.0844 6.83 0.0123 0.0090 0.0202 0.4384 1.56545 6.13 0.0091 0.0105 0.0198 0.5231 0.9049 7.10 0.0119 0.0086 0.0198 0.4303 1.49006 6.18 0.0092 0.0104 0.0205 0.5039 0.9262 6.74 0.0107 0.0077 0.0183 0.4199 1.45717 6.42 0.0090 0.0103 0.0202 0.5065 0.9123 6.94 0.0120 0.0082 0.0189 0.4340 1.49518 6.10 0.0081 0.0106 0.0192 0.5488 0.7894 6.99 0.0111 0.0086 0.0185 0.4668 1.33909 6.39 0.0109 0.0107 0.0209 0.5122 1.0943 7.04 0.0137 0.0079 0.0201 0.3994 1.763910 6.30 0.0111 0.0101 0.0212 0.4801 1.1212 7.21 0.0136 0.0071 0.0186 0.3879 1.923211 6.17 0.0101 0.0102 0.0206 0.4999 1.0303 6.89 0.0131 0.0079 0.0199 0.4035 1.706212 6.53 0.0106 0.0106 0.0211 0.5032 1.0464 7.13 0.0141 0.0076 0.0198 0.3908 1.867813 6.04 0.0096 0.0091 0.0199 0.4616 1.0793 6.85 0.0143 0.0072 0.0188 0.3890 1.982114 5.88 0.0089 0.0092 0.0198 0.4628 0.9830 6.60 0.0124 0.0068 0.0189 0.3665 1.822415 6.09 0.0086 0.0100 0.0200 0.4983 0.9108 6.96 0.0176 0.0082 0.0224 0.3942 2.2003NB. TRT = Treatment; HAR = Harvest; LN = Leafnumber, LASQM = Leaf area (sq. m); LAR Leafarea ratio (sq. mlg); SLA = Specific leafarea (sq. m/g); LWR = Leaf weight ratio (gig); LNTW = In total weight (g); TW = Total weight (g).221Table 5. Sununaiy of radish means over cultivars for treatment contrasts in ANOVA.Harvest 1 Harvest 2TRT RW LNRW HW LW HI RW LNRW HW LW HI1 0.0037 -5.8461 0.0282 0.1169 0.1719 0.0123 -4.6704 0.1762 0.2863 0.35152 0.0024 -6.1541 0.0186 0.0939 0.1521 0.0140 4.5711 0.1659 0.2734 0.35713 0.0033 -5.9488 0.0212 0.1178 0.1425 0.0123 4.7130 0.1443 0.2748 0.33334 0.0026 -6.1632 0.0231 0.1063 0.1635 0.0123 4.6328 0.1602 0.2662 0.35285 0.0019 -6.4255 0.0197 0.0964 0.1586 0.0088 4.9053 0.1329 0.2151 0.33956 0.0030 -6.0186 0.0269 0.1101 0.1765 0.0094 4.8032 0.1310 0.2315 0.33877 0.0029 -6.0913 0.0266 0.1128 0.1777 0.0105 4.7686 0.1389 0.2626 0.31108 0.0028 -6.1225 0.0249 0.1027 0.1748 0.0085 -5.1323 0.1217 0.2128 0.32849 0.0034 -5.8555 0.0286 0.1265 0.1729 0.0090 4.9027 0.1503 0.2694 0.319910 0.0031 -6.0329 0.0266 0.1190 0.1783 0.0099 4.7884 0.1884 0.2996 0.369511 0.0034 -5.9640 0.0211 0.1078 0.1550 0.0090 4.8648 0.1464 0.2662 0.327812 0.0033 -5.9438 0.0267 0.1311 0.1618 0.0104 4.7095 0.1878 0.3011 0.339913 0.0037 -5.7865 0.0332 0.1344 0.1875 0.0089 4.9499 0.1853 0.2917 0.363614 0.0032 -5.9067 0.0294 0.1165 0.1891 0.0089 4.9549 0.1405 0.2574 0.348915 0.0042 -5.6828 0.0307 0.1315 0.1718 0.0097 4.8753 0.1754 0.3026 0.3491Harvest 3 Harvest 4TRT RW LNRW HW LW HI RW LNRW HW LW HI1 0.0087 -4.8895 0.5473 0.5320 0.4772 0.0135 4.6367 1.0893 0.7649 0.57602 0.0079 -5.0116 0.4929 0.4536 0.4995 0.0143 -4.4744 1.0530 0.7166 0.59093 0.0071 -5.1108 0.4736 0.4550 0.4997 0.0126 -4.5426 0.9209 0.6458 0.57834 0.0086 -4.9174 0.5538 0.5221 0.5003 0.0135 4.5621 0.9184 0.6336 0.56975 0.0094 -4.7989 0.4345 0.4610 0.4690 0.0107 4.6849 0.8531 0.6262 0.56796 0.0075 -5.0038 0.4601 0.4586 0.4909 0.0111 4.6952 0.8499 0.5961 0.57837 0.0086 -4.9440 0.4563 0.4474 0.4939 0.0121 4.6487 0.8367 0.6463 0.55968 0.0090 4.9584 0.3588 0.4217 0.4516 0.0111 4.6963 0.7160 0.6118 0.53419 0.0093 -4.8404 0.5596 0.5254 0.4839 0.0145 -4.4369 1.0674 0.6820 0.595410 0.0098 -4.8038 0.5859 0.5255 0.5173 0.0168 4.3381 1.1597 0.7467 0.605611 0.0141 4.8341 0.5196 0.4966 0.4984 0.0139 4.4800 1.0193 0.6730 0.592612 0.0090 -4.9084 0.5311 0.5063 0.5025 0.0152 4.3847 1.1350 0.7176 0.604413 0.0074 -5.0041 0.5874 0.4845 0.5381 0.0166 -4.3396 1.2005 0.7650 0.605414 0.0075 -5.0449 0.5252 0.4503 0.5345 0.0130 4.5190 1.1448 0.6645 0.626015 0.0078 -5.0902 0.4712 0.4318 0.5103 0.0166 4.3188 1.3105 0.8732 0.5978NB. TRT = Treatment; HAR Harvest; RW = Root weight (g);LNRW = in root weight (g); HW = Hypocotyl weight (g); LW = Leaf weight (g);HI = Harvest index.222Appendix 4.Summary of exposure indices for pea, potato bean and radish computed for each harvest.223Table 1. Summary of exposure indices for pea, computed for each harvest.Harvest 1TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 30.6 29.7 2.67 52 39.9 38.6 3.09 53 31.9 28.6 2.63 44 41.4 37.7 3.04 55 47.4 39.7 3.63 46 32.8 26.3 3.72 47 43.2 38.4 3.65 58 48.6 44.8 3.73 49 31.7 29.9 2.71 510 44.9 41.3 3.51 511 59.3 57.7 4.12 512 47.3 44.1 4.36 513 16.1 12.7 1.28 314 27.8 25.0 1.99 5Harvest 23 2 2 2 5 4 3 2 0 03 2 2 2 5 5 5 3 2 13 2 1 0 4 4 3 1 0 04 2 2 1 5 5 3 4 2 14 3 3 2 4 4 4 3 2 23 2 2 2 4 4 3 3 3 24 3 2 2 4 4 4 3 3 14 3 3 3 4 4 4 3 3 24 3 3 2 4 4 2 3 2 04 3 1 1 5 4 4 3 2 24 3 3 2 5 5 5 4 4 44 4 2 2 5 5 5 4 4 20 0 0 0 1 1 1 0 0 01 0 0 0 5 3 2 1 0 0TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 31.3 28.4 8.88 15 6 22 37.3 34.3 9.97 15 10 43 31.5 28.1 8.83 14 6 24 37.7 33.8 9.38 15 9 35 36.6 30.4 8.55 12 8 36 33.9 28.6 10.14 14 8 57 41.6 37.2 10.85 15 11 68 50.1 43.9 13.20 14 12 69 30.0 26.4 8.04 15 7 310 44.1 38.6 11.04 15 12 511 51.0 46.8 11.83 15 11 712 45.4 41.4 12.77 15 12 713 15.5 13.4 4.26 7 0 014 24.1 21.8 5.98 13 1 02 2 15 14 14 4 14 2 14 14 12 6 51 0 13 13 11 2 02 1 15 15 14 7 53 2 11 11 9 5 43 2 12 12 12 4 43 2 13 13 13 9 65 3 14 14 14 10 83 2 14 13 10 3 23 2 15 14 13 8 66 3 15 15 15 10 93 2 15 15 15 11 110 0 3 1 1 0 00 0 12 9 7 1 002023256068600224Table 1 cont. Suimnary of exposure indices for pea, computed for each harvest.Harvest 3TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 27.9 24.5 13.31 24 7 2 2 22 32.1 28.7 14.59 23 13 4 4 23 26.9 23.4 12.77 23 8 2 1 04 35.0 30.3 14.90 25 16 5 2 15 27.3 22.2 11.72 18 10 3 3 26 31.5 24.9 15.24 24 14 9 6 37 39.4 33.6 16.98 25 19 10 5 28 42.9 36.1 19.13 24 20 9 6 39 29.3 26.0 13.38 25 9 4 4 210 42.3 36.0 17.81 25 20 9 5 311 44.3 39.5 17.97 25 18 10 7 412 39.6 35.9 18.62 26 20 8 3 213 15.5 13.5 7.33 10 0 0 0 014 21.7 19.7 9.69 18 2 0 0 023 21 18 5 221 21 17 7 618 16 13 3 025 23 22 10 817 15 12 5 422 22 19 6 621 21 19 14 1123 23 21 17 1124 21 17 4 325 22 21 14 1124 23 22 17 1425 22 21 13 125 3 2 0 016 11 9 1 01 26.1 22.9 17.59 35 10 2 22 30.3 26.9 19.05 32 18 4 43 25.6 22.5 16.67 32 10 3 24 34.1 29.5 20.64 34 24 6 25 25.5 20.7 15.47 25 12 4 46 29.7 24.4 19.77 33 20 11 77 37.6 32.0 22.38 35 24 13 68 44.4 37.7 26.98 35 29 15 119 29.0 25.9 18.63 36 13 6 410 40.7 34.5 24.21 34 26 12 711 47.0 40.6 26.45 35 27 16 1312 42.0 37.6 26.78 37 30 15 813 14.9 13.2 10.15 12 0 0 014 19.9 18.0 12.78 21 2 0 02 32 28 232 29 29 240 24 20 161 34 31 303 24 21 164 30 30 252 29 29 267 34 34 312 34 30 254 34 30 298 34 33 316 36 33 310 6 4 20 18 13 106 2 19 7 33 0 013 11 67 5 39 8 318 14 1025 17 135 4 119 14 1225 22 1722 18 100 0 01 0 0120533881910700Harvest 4TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C450225Table 1 cont. Summaiy of exposure indices for pea, computed for each harvest.Harvest 5TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 23.9 20.9 19.76 39 10 2 2 22 27.6 24.2 21.04 37 18 4 4 23 23.2 20.3 18.33 35 10 3 2 04 32.0 27.6 23.74 40 27 6 2 15 23.8 19.5 17.62 30 15 4 4 36 27.2 22.5 21.98 38 22 11 7 47 34.9 30.0 25.21 40 28 14 7 28 41.9 35.8 30.77 43 34 18 12 79 27.2 24.2 21.37 41 14 6 4 210 37.5 31.9 27.38 42 31 13 8 411 44.8 38.6 30.75 43 32 18 14 812 40.9 36.4 31.38 46 37 16 9 613 13.8 12.2 11.61 12 0 0 0 014 18.1 16.3 14.31 22 2 0 0 035 30 24 6 234 32 27 9 725 20 16 3 040 37 36 14 1129 26 18 7 534 34 26 9 833 33 30 21 1742 40 37 27 1839 34 28 5 439 34 33 22 1542 41 38 30 2644 41 39 26 196 4 2 0. 018 13 10 1 0TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 22.0 19.2 22.51 44 11 2 22 25.2 22.3 23.77 42 19 4 43 21.3 18.9 20.74 39 11 3 24 29.9 26.1 27.57 47 31 7 25 21.6 18.1 19.92 35 16 4 46 25.0 21.0 24.90 45 24 12 77 33.4 29.3 29.60 47 33 17 88 38.6 33.3 35.06 50 41 21 139 25.3 22.7 24.61 48 16 6 410 34.8 29.9 31.41 50 36 15 911 41.7 36.5 35.52 50 38 21 1512 38.3 34.3 36.04 53 41 19 1013 12.9 11.4 13.52 14 0 0 014 16.5 15.0 16.35 24 2 0 02 39 33 27 7 2 12 38 36 31 10 7 30 29 22 18 3 0 01 46 43 42 17 12 73 32 29 20 8 6 34 39 39 30 10 8 33 40 39 36 26 19 137 49 45 42 32 20 142 46 40 33 6 4 15 46 40 39 24 17 148 49 47 44 34 30 256 51 47 45 30 22 140 7 5 3 0 0 00 19 14 11 1 0 0130633H13112211100Harvest 6226Table 2. Summaiy of exposure statistics for potato, computed for each harvest.Harvest 1TRT M12 Mi TD D25 050 080 0100 0120 C225 C325 C425 C250 C350 C4501 29.3 30.9 4.89 82 37.7 40.9 5.50 83 29.0 32.6 4.87 74 36.5 39.3 5.13 85 35.9 42.2 5.18 76 30.9 34.3 5.98 77 40.1 43.0 6.01 88 46.6 50.7 7.10 79 27.4 30.1 4.50 810 40.3 44.4 6.07 811 54.8 58.6 6.97 812 43.2 48.2 7.74 813 12.5 15.8 2.17 414 24.3 27.0 3.29 8TRT M12 Mi TD D25 050 0808 7 6 3 1 08 8 8 4 3 27 7 6 1 0 08 8 6 5 2 17 7 6 4 2 27 7 6 3 3 27 7 7 6 4 27 7 7 6 5 47 7 4 3 2 08 7 7 5 3 38 8 8 7 7 68 8 8 7 7 41 1 1 0 0 08 6 4 1 0 00100 0120 C225 C325 C425 C250 C350 C4501 24.2 27.3 12.67 23 6 22 28.4 31.6 13.96 22 12 43 23.1 26.4 12.19 22 7 24 29.5 34.1 14.09 24 15 45 22.2 27.3 11.29 17 10 36 24.3 30.6 14.49 23 13 87 32.1 37.9 15.97 24 18 98 35.6 42.4 18.37 23 19 99 25.5 28.4 12.58 24 8 310 34.5 40.8 16.71 24 19 811 39.0 44.0 17.21 24 17 912 35.6 39.3 17.92 25 19 713 13.0 14.9 6.89 9 0 014 19.4 21.3 9.20 17 1 02 2 22 20 17 4 1 04 2 20 20 16 6 5 21 0 17 15 12 2 0 02 1 24 22 21 9 7 43 2 16 14 11 5 4 36 3 21 21 18 5 5 24 2 20 20 18 13 10 76 3 22 22 20 16 10 73 2 23 20 16 3 2 04 2 24 21 20 13 10 87 4 23 22 21 16 13 93 2 24 21 20 12 11 60 0 4 2 1 0 0 0o o 15 10 8 1 0 04 2 26 3 33 2 16 2 25 3 35 4 37 4 37 4 45 3 37 4 17 5 47 6 30 0 01 0 02202223212.200Harvest 2227Table 2 cont. Summary of exposure statistics for potato, computed for each harvest.Harvest 3TRT M12 M7 TD D25 050 080 DI00 0120 C225 C325 C425 C250 C350 C4501 22.2 25.0 18.25 36 10 2 22 25.9 29.3 19.64 33 18 4 43 21.8 24.8 17.19 33 10 3 24 28.7 33.3 21.55 36 25 6 25 20.1 24.7 16.02 26 13 4 46 23.6 28.7 20.36 34 21 11 77 30.8 36.2 22.99 36 25 13 68 37.3 43.8 28.29 38 30 16 119 25.2 28.2 19.42 37 13 6 410 33.4 39.6 25.26 37 28 13 811 39.8 46.3 27.80 38 28 17 1412 37.5 42.1 28.39 40 33 16 913 12.8 14.5 10.59 12 0 0 014 17.5 19.3 13.26 21 2 0 02 32 28 23 6 22 30 30 25 9 70 24 20 16 3 01 36 33 32 13 113 25 22 16 7 54 31 31 25 9 82 30 30 27 19 157 37 36 33 26 182 35 31 26 5 44 35 31 30 20 148 37 36 33 26 236 39 36 34 24 180 6 4 2 0 00 18 13 10 1 01 19.1 21.8 21.98 43 10 2 22 22.1 24.9 23.19 41 18 4 43 18.7 21.0 20.22 38 10 3 24 25.7 29.4 26.73 46 30 6 25 17.7 21.3 19.39 34 15 4 46 20.9 24.7 24.32 44 23 11 77 28.6 32.6 28.54 46 32 16 78 33.3 38.3 34.31 49 40 20 129 22.5 25.0 24.00 47 15 6 410 29.3 34.2 30.48 49 35 14 911 35.8 41.1 34.52 49 37 20 1412 33.8 37.7 35.08 52 40 18 913 11.2 12.7 13.13 13 0 0 014 14.9 16.4 15.96 23 2 0 02 38 32 26 6 22 37 35 30 9 7O 28 21 17 3 01 45 42 41 16 113 31 28 19 7 54 38 38 29 9 82 39 38 35 25 187 48 44 41 31 192 45 39 32 5 45 45 39 38 23 168 48 46 43 33 296 50 46 44 29 21o 6 4 2 0 00 18 13 10 1 0306331013112181000Harvest 4TRT M12 Mi TI) 025 050 D80 0100 D120 C225 C325 C425 C250 C350 C4501306331214113241300228Table 2 cont. Summary of exposure statistics for potato, computed for each harvest.Harvest 5TRT M12 M7 TO 025 050 080 0100 0120 C225 C325 C425 C250 C350 C4501 19.0 21.9 28.60 562 21.8 24.6 29.78 563 19.4 21.7 26.75 544 27.4 31.1 36.72 645 16.7 19.9 23.96 406 21.2 24.8 31.31 597 30.1 33.8 38.24 648 31.9 36.8 42.96 669 23.4 25.9 32.59 6510 31.0 35.0 40.93 6811 36.8 41.0 45.87 6812 34.5 38.3 46.56 7113 11.4 13.1 17.90 1814 14.4 16.1 21.06 2912 2 2 220 4 4 214 3 2 038 9 3 118 4 4 328 13 8 441 20 9 450 23 13 720 6 4 243 16 10 547 24 17 952 23 12 60 0 0 02 0 0 048 40 32 7 248 43 35 11 837 28 21 4 062 59 57 22 1335 32 21 9 749 47 34 12 855 52 48 32 2264 56 52 35 2161 53 45 7 563 55 52 29 2166 63 59 41 3669 63 61 34 259 7 5 0 021 16 13 1 01408431515217291600229Table 3. Summary of exposure statistics for bean, computed for each harvest.Harvest 1TRT M12 M7 TD D25 D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 21.1 22.2 1.07 32 32.6 41.3 146 43 24.5 25.3 1.23 34 24.1 26.9 1.18 35 20.9 21.6 1.06 46 21.1 21.6 1.09 37 19.8 19.7 1.00 38 19.6 19.7 0.98 39 20.7 21.8 1.04 310 22.7 24.4 1.13 311 24.0 26.6 1.17 312 21.1 20.7 1.04 313 19.7 19.8 1.02 314 20.6 21.5 1.00 315 20.3 20.7 1.03 3Harvest 2ThT M12 M7 TD D250 0 0 0 3 1 1 0 0 02 2 2 2 2 2 1 1 0 00 0 0 0 3 3 3 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 1 0 0 0 00 0 0 0 3 3 0 0 0 00 0 0 0 2 1 0 0 0 00 0 0 0 2 2 0 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 2 2 1 0 0 00 0 0 0 2 0 0 0 0 01 0 0 0 2 1 1 0 0 00 0 0 0 3 2 1 0 0 0D50 D80 D100 D120 C225 C325 C425 C250 C350 C4501 21.1 22.2 1.07 32 32.6 41.3 1.46 43 24.5 25.3 1.23 34 24.1 26.9 1.18 35 20.9 21.6 1.06 46 21.1 21.6 1.09 37 19.8 19.7 1.00 38 19.6 19.7 0.98 39 20.7 21.8 1.04 310 22.7 24.4 1.13 311 24.0 26.6 1.17 312 21.1 20.7 1.04 313 19.7 19.8 1.02 314 20.6 21.5 1.00 315 20.3 20.7 1.03 30 0 0 0 3 1 1 0 0 02 2 2 2 2 2 1 1 0 00 0 0 0 3 3 3 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 1 0 0 0 00 0 0 0 3 3 0 0 0 00 0 0 0 2 1 0 0 0 00 0 0 0 2 2 0 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 3 3 2 0 0 00 0 0 0 2 2 1 0 0 00 0 0 0 2 0 0 0 0 01 0 0 0 2 1 1 0 0 00 0 0 0 3 2 1 0 0 0230Table 3 cont. Summary of exposure statistics for bean, computed for each harvest.Harvest 3TRT MI2 M7 TD 1)25 1)50 1)80 D100 D120 C225 C325 C425 C250 C350 C4501 18.9 17.8 4.16 82 27.0 25.0 5.58 103 20.4 19.2 4.39 94 22.2 20.9 4.68 95 27.0 25.0 5.58 106 20.8 15.9 4.37 77 19.4 16.2 4.02 98 20.0 16.2 4.13 99 20.4 19.2 4.39 910 19.9 18.8 4.37 811 21.5 20.0 4.55 812 18.3 16.1 3.95 713 18.4 16.9 3.97 714 18.0 15.7 3.74 815 16.8 15.5 3.79 7Harvest 4TRT M12 M7 TD D25 1)501 0 0 0 7 4 3 0 0 05 4 3 3 7 7 5 3 2 22 0 0 0 8 7 7 1 0 02 0 0 0 7 7 6 1 0 05 4 3 3 7 7 5 3 2 22 2 1 1 7 7 4 2 2 11 1 0 0 7 4 3 1 1 11 1 0 0 7 7 3 1 1 12 0 0 0 8 7 7 1 0 00 0 0 0 7 7 5 0 0 01 0 0 0 7 7 6 1 1 00 0 0 0 6 5 4 0 0 01 0 0 0 6 3 2 1 0 02 0 0 0 6 5 4 0 0 00 0 0 0 6 5 3 0 0 01380 1)100 13120 C225 C325 C425 C250 C350 C4501 19.3 18.4 7.33 15 22 23.7 22.5 8.78 16 73 21.6 20.7 7.91 18 24 24.3 23.4 8.73 17 55 20.1 17.9 7.55 17 26 20.0 16.6 7.37 14 47 19.9 17.5 7.13 16 18 20.7 18.1 7.41 16 39 18.1 17.6 6.97 12 110 19.3 18.7 7.44 14 111 21.9 20.9 8.01 16 112 18.0 16.1 6.83 12 013 17.5 16.1 6.63 11 114 16.5 14.7 6.06 12 215 16.5 15.5 6.48 11 00 0 0 13 9 7 05 3 3 12 9 7 30 0 0 16 15 12 11 0 0 15 13 12 30 0 0 13 10 7 22 1 1 13 12 8 21 0 0 13 9 7 12 0 0 13 12 8 30 0 0 0 10 8 60 0 0 11 10 7 00 0 0 15 14 12 10 0 0 11 8 6 00 0 0 10 7 4 10 0 0 7 5 4 00 0 0 8 7 5 00 02 20 01 11 12 11 12 20 00 01 00 00 00 00 0231Table 3 cont. Summary of exposure statistics for bean, computed for each harvest.Harvest 5TRT M12 Mi TO 025 050 080 0100 0120 C225 C325 C425 C250 C350 C4501 20.9 20.0 10.04 22 32 24.0 22.8 11.50 22 83 23.8 22.6 10.94 26 44 28.4 27.4 12.75 25 105 22.0 20.2 10.38 24 46 23.1 19.9 10.53 21 77 19.1 17.2 9.20 18 28 20.2 18.3 9.69 18 59 19.6 19.2 9.57 19 210 20.7 20.3 10.13 20 211 24.7 23.1 11.33 23 412 19.3 17.4 9.28 18 013 18.5 17.2 8.87 16 114 16.3 15.0 8.09 14 315 17.8 17.0 8.89 17 10 0 0 19 13 11 15 3 3 17 12 10 40 0 0 22 21 16 32 0 0 22 20 18 80 0 0 20 15 12 23 2 1 19 18 14 52 0 0 14 10 8 13 0 0 15 13 9 40 0 0 15 12 10 10 0 0 16 15 11 10 0 0 21 20 18 30 0 0 17 11 9 00 0 0 14 11 8 11 0 0 8 5 4 10 0 0 12 11 9 11 02 22 25 41 14 31 12 20 00 03 20 00 00 00 01 21.4 20.6 13.19 30 3 02 25.4 24.1 16.83 30 13 73 26.3 24.2 15.28 34 11 14 29.8 28.7 17.12 33 14 35 24.1 21.8 14.38 32 9 16 29.1 25.4 16.41 29 15 77 23.2 20.7 13.79 25 9 58 26.4 23.8 15.35 25 12 79 20.2 19.9 12.62 25 2 010 21.7 21.3 13.53 28 2 011 25.7 24.1 15.03 31 6 012 20.8 18.7 12.65 26 2 013 18.6 17.3 11.51 21 1 014 16.2 14.8 10.26 19 3 115 17.9 17.2 11.48 23 1 00 0 26 17 14 1 15 5 25 20 18 6 30 0 30 29 23 5 30 0 30 28 26 10 71 0 28 23 20 4 15 1 27 26 22 13 112 1 21 17 15 5 33 1 22 20 16 11 90 0 19 16 13 1 00 0 24 20 16 1 00 0 29 28 25 3 30 0 25 19 14 1 10 0 17 13 10 1 00 0 11 8 5 1 00 0 15 13 10 1 0Harvest 6TRT M12 Mi TO 025 050 080 D100 D120 C225 C325 C425 C250 C350 C450032619280020000232Table 4. Sununary of exposure statistics for radish, computed for each harvest.Harvest 2TRT Expt. D50 C250 C350 C450 H801 1 13 6 6 6 72 1 11 7 6 6 103 1 11 10 9 4 104 1 15 7 5 4 05 1 17 11 8 6 16 1 14 12 9 7 27 1 10 16 13 9 88 1 14 9 6 6 49 1 4 12 9 7 410 1 5 16 13 11 711 1 8 20 17 13 1012 1 9 6 6 6 713 1 1 7 6 6 1014 1 4 10 9 4 1015 1 4 7 5 4 0Harvest 31 1 15 11 8 6 12 1 13 12 9 7 23 1 15 16 13 9 84 1 19 9 6 6 45 1 21 12 9 7 46 1 18 16 13 11 77 1 14 20 17 13 108 1 18 7 6 3 19 1 6 8 7 4 410 1 7 8 7 7 411 1 10 10 7 6 1312 1 13 13 10 9 1813 1 2 15 12 10 2514 1 5 19 15 13 3715 1 5 4 0 0 0Harvest 41 1 17 5 0 0 02 1 16 9 4 1 13 1 19 13 7 3 34 1 23 7 6 3 15 1 25 8 7 4 46 1 22 8 7 7 47 1 18 10 7 6 138 1 21 13 10 9 189 1 9 15 12 10 2510 1 8 19 15 13 3711 1 10 4 0 0 012 1 17 5 0 0 013 1 2 9 4 1 114 1 5 13 7 3 315 1 5 6 5 4 11233Table 4 cont. Summary of exposure statistics for radish, computed for each harvest.Experiment 2TRT Expt. D50 C250 C350 C450 H80Harvest 1D50 C250 C350 C450 H80Harvest 21 2 10 23 20 17 642 2 12 13 11 9 253 2 11 18 16 14 434 2 8 21 19 17 605 2 14 25 23 19 616 2 14 10 4 3 47 2 11 13 6 4 58 2 11 16 9 4 59 2 14 18 10 4 710 2 7 15 12 11 3711 2 10 19 16 13 4512 2 8 23 20 17 6413 2 1 13 11 9 2514 2 4 18 16 14 4315 2 0 21 19 17 60Harvest 31 2 18 9 4 2 02 2 21 12 9 8 203 2 18 16 13 11 314 2 16 20 16 14 325 2 23 14 14 13 566 2 23 19 19 18 967 2 20 23 23 22 1138 2 20 27 27 26 1489 2 22 3 0 0 010 2 14 4 0 0 011 2 18 7 2 1 012 2 17 9 4 2 013 2 2 9 8 7 1214 2 10 11 9 7 1415 2 1 15 12 9 1515 25 23 19 6117 10 4 3 416 13 6 4 513 16 9 4 519 18 10 4 719 12 9 8 2016 16 13 11 3116 20 16 14 3219 14 14 13 5611 19 19 18 9615 23 23 22 11313 27 27 26 1482 3 0 0 08 4 0 0 01 7 2 1 0Harvest 422 8 8 8 1825 13 12 11 2922 15 13 12 3420 19 17 16 5127 12 10 7 1127 16 14 10 1424 20 18 14 1724 24 21 17 2426 9 8 7 1218 11 9 7 1422 15 12 9 1521 8 8 8 184 13 12 11 2910 15 13 12 342 19 17 16 51234Table 4 cont. Sununaiy of exposure statistics for radish, computed for each harvest.Experiment 3TRT Expt. D50 C250 C350 C450 H80Harvest 1D50 C250 C350 C450 H80Harvest 21 3 10 5 4 2 4 14 9 7 4 02 3 6 5 3 2 4 9 13 8 4 03 3 13 5 2 2 0 17 3 1 0 04 3 9 9 3 3 0 13 3 1 0 05 3 13 9 3 3 0 17 6 2 0 16 3 8 13 4 3 0 10 8 4 0 17 3 14 3 1 0 0 18 4 2 2 18 3 12 3 1 0 0 16 5 2 2 39 3 11 6 2 1 0 15 16 12 11 310 3 7 7 3 1 0 9 3 3 3 011 3 7 4 2 2 1 9 7 6 4 012 3 6 5 2 2 3 9 9 7 4 013 3 0 16 12 11 3 0 13 8 4 014 3 0 3 3 3 0 1 3 1 0 015 3 0 7 6 4 0 0 3 1 0 0Harvest 3 Harvest 41 3 18 6 2 0 1 22 5 4 2 372 3 13 8 4 0 1 17 8 7 5 13 3 21 8 5 4 3 25 11 9 7 34 3 17 12 9 8 23 21 12 10 8 45 3 21 5 4 2 37 25 15 13 11 76 3 14 8 7 5 1 18 1 0 0 07 3 22 11 9 7 3 26 1 0 0 08 3 20 12 10 8 4 24 3 1 0 09 3 19 15 13 11 7 23 5 3 0 010 3 12 1 0 0 0 16 1 1 1 011 3 13 1 0 0 0 17 2 2 1 312 3 13 3 1 0 0 17 2 2 1 313 3 0 5 3 0 0 0 1 1 0 014 3 1 8 5 4 3 1 2 2 0 015 3 1 12 9 8 23 1 2 2 0 0235Appendix 5.Summaiy of radish means used in the regression analyses and radish porometer data.236Experiment 2 RGR TW (g/g/day)Hat- mt Hat- mt Hat- hit1 2 31 0.260 0.188 0.1162 0.355 0.208 0.0623 0.180 0.210 0.2394 0.273 0.208 0.1435 0.288 0.197 0.1056 0.221 0.164 0.1077 0.271 0.163 0.0558 0.247 0.161 0.0759 0.322 0.199 0.07710 0.366 0.215 0.06511 0.359 0.219 0.07912 0.308 0.206 0.10413 0.270 0.197 0.12314 0.202 0.207 0.21215 0.188 0.191 0.195ROR HW (gig/day) AGR (g/day)Hat- hit Hat- hit Hat- hit Hat- hit Hat-mt Hat- t1 2 3 1 2 30.412 0.277 0.142 0.124 0.153 0.1810.562 0.315 0.068 0.120 0.129 0.1390.345 0.335 0.325 0.049 0.139 0.2290.506 0.327 0.147 0.103 0.157 0.2110.524 0.291 0.058 0.102 0.129 0.1560.403 0.248 0.093 0.082 0.102 0.1220.449 0.246 0.044 0.117 0.101 0.0860.400 0.226 0.051 0.078 0.089 0.1010.503 0.308 0.113 0.177 0.171 0.1650.583 0.337 0.091 0.146 0.152 0.1580.584 0.339 0.094 0.119 0.157 0.1950.532 0.318 0.103 0.103 0.166 0.2280.487 0.294 0.102 0.100 0.167 0.2340.371 0.300 0.228 0.030 0.151 0.2730.339 0.290 0.240 0.041 0.186 0.330Experiment 3TRTRGR TW (g/g/day)Harmt Hat-t Hat-hit1 2 31 0.308 0.205 0.1012 0.325 0.224 0.1243 0.348 0.196 0.0454 0.318 0.203 0.0875 0.319 0.204 0.0906 0.295 0.240 0.1847 0.322 0.224 0.1268 0.295 0.206 0.1189 0.334 0.221 0.10910 0.352 0.214 0.07711 0.287 0.206 0.12412 0.288 0.205 0.12113 0.277 0.214 0.15214 0.269 0.214 0.15915 0.319 0.213 0.107ROR HW (g/g/day)Hat-hit Hat-hit1 20.502 0.3270.592 0.3820.555 0.3140.550 0.3230.479 0.3400.488 0.3890.425 0.3530.468 0.3410.471 0.3390.554 0.3290.506 0.3540.487 0.3420.511 0.3470.460 0.3660.575 0.352AGR (g/day)Hat-hit Hat-hit Hat-hit Harint3 1 2 30.152 0.064 0.106 0.1480.172 0.048 0.095 0.1410.074 0.095 0.086 0.0780.095 0.083 0.062 0.04 10.202 0.065 0.071 0.0770.290 0.050 0.108 0.1660.281 0.064 0.087 0.1100.215 0.079 0.085 0.0910.207 0.085 0.108 0.1320.104 0.103 0.117 0.1310.203 0.069 0.094 0.1180.196 0.069 0.112 0.1550.183 0.073 0.136 0.1980.273 0.064 0.133 0.2020.129 0.071 0.126 0.181NB. RGR TW Relative growth rate for total weight (g.g.day1)RGR HW = Relative growth rate for hypocotylExperiment 1Table 1. Summary of relative growth rates for total and hypocotyl weight and absolute growth rate foreach experiment by harvest interval and treatment, for radish cv. Cherry Belie. Data are based onexpected values extrapolated from fitted curves using quadratic regressions of weight or in-transformedweight versus time for each treatment.RGR TW (g/g/day) ROR HW (gig/day) AGR (g/day)Hat- hit Hat- hit Hat- hit Hat- hit Hat- hit Hat- hit Hat- hit Hat- hit Hat- hit1 2 3 1 2 3 1 2 31 - 0.277 0.128 - 0.426 0.203 - 0.245 0.1682 - 0.199 0.149 0.285 0.238 - 0.155 0.2043 - 0.215 0.079 - 0.389 0.118 - 0.159 0.1104 - 0.193 0.047 - 0.299 0.098 - 0.168 0.0685 - 0.149 0.126 - 0.198 0.192 - 0.116 0.1506 - 0.191 0.087 - 0.304 0.120 - 0.150 0.1187 - 0.220 0.128 - 0.351 0.171 - 0.145 0.1628 - 0.185 0.176 - 0.309 0.278 - 0.106 0.1869 - 0.298 0.105 - 0.503 0.138 - 0.180 0.13010- 0.188 0.112 - 0.275 0.158 - 0.138 0.14311 - 0.263 0.141 - 0.391 0.209 - 0.156 0.16012 - 0.208 0.128 - 0.338 0.201 - 0.145 0.15113 - 0.197 0.154 - 0.401 0.197 - 0.135 0.18614 - 0.167 0.114 - 0.293 0.152 - 0.116 0.13415 - 0.131 0.217 - 0.169 0.366 - 0.120 0.199weight (g.g1.day)AGR = Absolute growth rate (g.g4.day); Hat- hit = harvest terval.237Table 2. Summary of relative growth rates for total and hypocotyl weight and absolute growth rate each experimentby harvest interval and treatment, for radish cv. French Breakfast. Data are based on expected values extrapolatedfrom fitted curves using quadratic regressions of weight or In-transfonned weight versus time for each treatment.Experiment 1 RGR TW (g/g/day)TRT Har mt Har hit1 21 0.2102- 0.2253- 0.1704- 0.2495- 0.2496- 0.1867- 0.2488- 0.3329- 0.36610- 0.25611- 0.30212- 0.32213- 0.20514- 0.15615- 0.163ROR HW (gig/day)Harmt Harint Harint3 1 20.247 - 0.3380.190 - 0.3640.218- 0.1900.121 - 0.3340.216- 0.3910.214- 0.3670.183 - 0.4580.141- 0.5630.181 - 0.5700.165 - 0.3940.179- 0.5030.209- 0.5320.230- 0.3 130.199- 0.2590.212- 0.268AGR(g/day)-Har hit Har hit Har mt Har hit3 1 2 30.276- 0.105 0.3120.213- 0.129 0.2680.363- 0.107 0.2370.160- 0.178 0.1910.261 - 0.123 0.2650.245- 0.117 0.2870.222- 0.146 0.2420.173- 0.154 0.1560.310- 0.138 0.1620.217 0.155 0.2380.259- 0.152 0.2070.303- 0.159 0.2540.318 0.094 0.2530.260 0.104 0.2130.279- 0.092 0.262Experiment 2TRT23456789101112131415Experiment 3TRT23456789101112131415RGR TW (gig/day) ROR HW (gig/thy) AGR (g/day)Har 1 Har hit Har hit Har hit Har hit Har hit Har hit Har hit Har hithit 2 3 1 2 3 1 2 30.257 0.226 0.196 0.404 0.322 0.239 0.109 0.183 0.2580.320 0.240 0.160 0.494 0.351 0.208 0.106 0.193 0.2810.281 0.212 0.143 0.500 0.334 0.168 0.082 0.139 0.1960.300 0.228 0.157 0.460 0.321 0.183 0.113 0.176 0.2400.282 0.241 0.199 0.478 0.353 0.227 0.062 0.131 0.1990.261 0.210 0.159 0.376 0.292 0.207 0.106 0.121 0.1370.292 0.209 0.127 0.519 0.323 0.127 0.058 0.132 0.2060.219 0.215 0.211 0.371 0.317 0.262 0.028 0.112 0.1970.240 0.208 0.177 0.414 0.315 0.217 0.083 0.198 0.3130.291 0.246 0.201 0.466 0.360 0.254 0.067 0.212 0.3570.283 0.228 0.172 0.449 0.340 0.232 0.116 0.180 0.2440.293 0.213 0.132 0.470 0.310 0.151 0.113 0.187 0.2610.318 0.223 0.127 0.467 0.325 0.183 0.118 0.195 0.2720.258 0.255 0.251 0.429 0.360 0.291 0.080 0.180 0.2800.247 0.248 0.248 0.467 0.366 0.266 0.018 0.233 0.447RGR TW (g/g/day) RGR HW (g/g/day) AGR (g/day)Har lot Har hit Har hit Har hit Har mt Har hit Har mt Har mt Har hit1 2 3 1 2 3 1 2 30.345 0.216 0.087 0.525 0.339 0.152 0.080 0.101 0.1210.277 0.213 0.149 0.433 0.318 0.203 0.038 0.106 0.1740.300 0.199 0.097 0.604 0.351 0.098 0.068 0.100 0.1320.289 0.175 0.061 0.480 0.277 0.074 0.073 0.073 0.0740.310 0.219 0.128 0.486 0.339 0.192 0.080 0.104 0.1280.258 0.169 0.080 0.421 0.270 0.118 0.058 0.068 0.0780.253 0.184 0.115 0.402 0.296 0.189 0.078 0.089 0.1000.305 0.198 0.090 0.542 0.307 0.072 0.066 0.091 0.1150.305 0.215 0.125 0.504 0.340 0.176 0.067 0.114 0.1620.264 0.200 0.135 0.441 0.303 0.165 0.080 0.137 0.1930.340 0.218 0.097 0.554 0.340 0.126 0.086 0.110 0.1330.308 0.198 0.088 0.514 0.310 0.107 0.095 0.117 0.1390.301 0.216 0.130 0.519 0.331 0.144 0.092 0.154 0.2160.324 0.217 0.109 0.589 0.352 0.115 0.094 0.139 0.1840.220 0.226 0.232 0.370 0.335 0.299 0.026 0.172 0.3 19NB. RGR TW= Relative growth rate for total weight (g.g4.day1RGR HW Relative growth rate for hypocotylweight (g.g1.day)AGR = Absolute growth rate (g.g.day);Har mt = harvest hiterval.238Table 3. Sununary of treatment means used in the porometer regressions for cv. Cheny Belle.TRT EXPT HAR JD AGE LA HW TW COND OZONE1 1 1 2071 1 2 2121 1 3 2161 1 4 2202 1 1 2072 1 2 2122 1 3 2162 1 4 2203 1 1 2073 1 2 2123 1 3 2163 1 4 2204 1 1 2074 1 2 2124 1 3 2164 1 4 2205 1 1 2075 1 2 2125 1 3 2165 1 4 2201 2 1 2431 2 2 2471 2 3 2511 2 4 2552 2 1 2432 2 2 2472 2 3 2512 2 4 2553 2 1 2433 2 2 2473 2 3 2513 2 4 2554 2 1 2434 2 2 2474 2 3 2514 2 4 2555 2 1 2435 2 2 2475 2 3 2515 2 4 25517 21.320 0.047 0.194 0.89821 34.350 0.232 0.419 0.71825 73.140 0.592 0.977 0.50329 79.320 0.931 1.364 0.52317 23.230 0.030 0.155 1.32621 88.650 0.322 0.695 0.79025 110.640 0.877 1.371 0.61529 108.600 1.386 1.957 0.62017 24.660 0.034 0.177 1.05421 43.983 0.102 0.309 0.69325 93.300 0.517 0.958 0.54329 114.450 1.246 1.812 0.57017 22.840 0.048 0.195 0.93821 62.950 0.295 0.574 0.49525 88.120 0.660 1.115 0.65629 91.350 0.873 1.368 0.56117 24. 170 0.050 0.203 0.89721 62.100 0.374 0.664 0.62425 75.780 0.847 1.242 0.49529 125.680 1.439 2.016 0.47817 21.250 0.009 0.084 0.73821 40.200 0.054 0.239 0.64625 96.180 0.321 0.737 0.47429 93.790 0.825 1.357 0.52817 27.360 0.020 0.120 0.53121 59.400 0.167 0.458 0.60425 106.200 0.557 1.033 0.52629 109.770 0.936 1.485 0.74917 26.960 0.013 0.103 0.63321 66.120 0.151 0.466 0.55225 102.200 0.405 0.837 0.47829 121.140 0.591 1.132 0.58217 19.520 0.012 0.082 0.64021 46.560 0.042 0.259 0.61725 83.680 0.333 0.723 0.53129 101.270 0.592 1.088 0.39717 32.240 0.019 0.138 0.69521 51.980 0.143 0.412 0.52025 100.880 0.510 0.980 0.48329 122.080 1.104 1.757 0.471737738431369583699350574054483131342328264935242628272823534261542529384420242825NB. TRT”treatment; REP=experiment; HAR = harvest; JD = julian day, Age number of days after planting; LA=leaf area; HW hypocotyl weight (g); TW’ total weight (g); Cond conductance (cms1);Ozone mean 1 h 03concentration239Table 4. Sununaiy of treatment means used in the porometer regressions for cv. French Breakfast.TRT EXPT HAR JD AGE LA HW TW COND OZONE1 1 1 207 171 1 2 212 211 I 3 216 251 1 4 220 292 1 1 207 172 1 2 212 212 1 3 216 252 1 4 220 293 1 1 207 173 1 2 212 213 1 3 216 253 1 4 220 294 1 1 207 174 1 2 212 214 1 3 216 254 1 4 220 295 1 1 207 175 1 2 212 215 1 3 216 255 1 4 220 291 2 1 243 171 2 2 247 211 2 3 251 251 2 4 255 292 2 1 243 172 2 2 247 212 2 3 251 252 2 4 255 293 2 1 243 173 2 2 247 213 2 3 251 253 2 4 255 294 2 1 243 174 2 2 247 214 2 3 251 254 2 4 255 295 2 1 243 175 2 2 247 215 2 3 251 255 2 4 255 2924.840 0.035 0.162 0.57642.383 0.102 0.295 0.708131.500 0.519 1.214 0.621118.250 0.815 1.473 0.66528.500 0.027 0.155 0.84272.550 0.165 0.484 .577133.960 0.595 1.237 .480213.710 1.610 2.727 .66930.780 0.026 0.166 .64860.983 0.181 0.471 .464112.120 0.561 1.063 .540172.080 1.046 1.824 .68926.040 0.024 0.152 .58769.050 0.176 0.494 .64094.480 0.389 0.854 .480164.260 0.940 1.791 .57628.540 0.038 0.192 .67775.040 0.210 0.589 .377155.025 0.722 1.482 .433220.330 1.396 2.495 .49626.860 0.017 0.120 .60157.760 0.091 0.344 .53075.900 0.274 0.628 .304104.630 0.409 0.930 .42735.990 0.029 0.165 .56163.520 0.171 0.474 .492114.800 0.483 1.038 .186140.950 1.016 1.799 .49933.920 0.012 0.122 .60358.360 0.138 0.427 .51094.480 0.35 1 0.777 .448127.520 0.701 1.337 .52935.250 0.022 0.142 .62558.260 0.096 0.378 .565101.180 0.385 0.850 .348109.550 0.584 1.175 .43633.390 0.025 0.152 .57665.420 0.167 0.526 .656101.080 0.604 1.135 .345140.810 1.081 2.005 .382737738431369583699350574054483131342328264935242628272823534261542529384420242825NB. TRTtreatment; EXPTexperiment; HAR = harvest; 3D = julian day; Age = number of days after planting; LAleaf area; HW= hypocotyl weight (g); TW= total weight (g); Cond = conductance (cms1);Ozone= mean 1 h 03concentration.240Table 5. Summary of radish means over all treatments for harvest 4 data for HI, TW and HW, AGR,RGR, RGR.CULT EXPT III W/ HW AGR RGR-HW RGR-TW(g) (g) (g/day) (g/g/day) (g/g/day)CB 1 0.655 1.649 1.081 0.151 0.189 0.1262 0.686 1.901 1.316 0.187 0.127 0.1173 0.588 1.313 0.785 0.131 0.185 0.115FB 1 0.508 1.812 0.923 0.237 0.257 0.1942 0.565 2.196 1.248 0.259 0.214 0.1773 0.511 1.467 0.757 0.151 0.149 0.115NB. Data used in the calculations of these means were all treatments (n15) by experiment (EXPT) and cultivar(CULT).241242Appendix 6.Summary of treatment means for tree porometer data used in the regression analyses.TRT JD FLUXSUMC 03 ASRTW Cond ASFJD2H TW NW ID2H FD2H HTERM FHTTRT JD LAp LAIf LA2f SWp SWIf SW2f LWp LW1f LW2f TW2f TWnew LAnewNB. TRT—treatment; JD=julian day; FLUXSUMC=cumulative flux index;03=hourly mean 03 concentration (ppb); ASRTWarc sinratio of total new growth/total weight; Condconductance (c1ns1);ASFJD2H=arc sin ratio of final-intital stem volume/final stem volume;TW=total weight (g); NW—needle weight (g); ID2H=initial stem diameter; FD2H=fmal stem volume (cni3); HTERM=leader length(cm); FHT=seedlingheight(cm); LAp= leaf area ofprimary growth (sq. cm); LAlfleafarea ofthe first flush (sq. cm); LA2f’leaf area ofthe second flush (sq. cm); SWp=stem weight of primary growth (g);SWlf=stem weight ofthe first flush (g); SW2f=stem weight of thesecond flush (g); LWp=leafweight of primary growth (g); LW1f= leafweight of the first flush (g); LW2f=leafweight of the secondflush; TW2f=total weight ofthe second flush; TWnew=total new weight in 1989; LAnew=leaf area of new needles in 1989.1 196 48.675 74.4 0.554 0.2459 1.214 45.13 24.00 10.65 168.75 37.25 78.7512 196 29.574 48.9 0.530 0.1412 1.166 57.21 29.43 16.70 206.15 39.50 87.503 196 31.210 44.4 0.500 0.1910 1.189 66.66 29.84 18.90 263.15 41.50 89.254 196 38.315 28.2 0.504 0.1893 1.073 48.48 21.80 24.95 205.70 153.50 95.755 196 23.014 32.0 0.521 0.2119 1.252 57.11 27.11 9.25 184.15 34.00 81.001 214 71.246 76.5 0.490 0.1494 1.246 88.55 40.81 16.80 321.30 52.50 100.252 214 46.505 78.1 0.644 0.1516 1.251 83.57 37.66 13.73 271.25 61.25 112.753 214 49.111 48.9 0.448 0.1995 1.196 55.70 24.11 15.70 226.40 31.75 77.504 214 65.803 42.0 0.538 0.3355 1.166 63.45 27.13 15.25 188.70 56.50 87.505 214 26.771 23.4 0.455 0.0791 1.183 96.19 45.07 16.70 225.44 48.50 96.001 237 96.683 23.5 0.581 0.1588 1.321 66.38 27.11 7.15 229.90 51.25 88.252 237 62.671 20.2 0.591 0.1436 1.255 132.39 58.15 21.70 438.75 68.00 117.503 237 63.464 27.6 0.542 0.1745 1.259 71.72 31.09 11.40 236.30 51.50 100.254 237 80.496 26.9 0.454 0.1505 1.204 101.44 41.84 21.35 320.70 38.50 89.255 237 33.485 18.9 0.643 0.1427 1.195 116.70 47.25 25.00 359.10 66.50 116.001 258 118.731 39.6 0.550 0.2606 1.266 59.50 20.20 11.00 238.95 57.00 99.502 258 71.698 36.2 0.530 0.1686 1.304 159.20 49.35 23.20 657.35 69.50 130.503 258 74.268 64.7 0.580 0.2298 1.300 118.00 43.05 17.55 480.60 78.00 127.004 258 92.044 37.9 0.555 0.1970 1.285 97.50 37.60 18.20 448.35 65.25 114.005 258 43.480 31.2 0.579 0.2320 1.348 108.05 36.10 11.60 470.95 72.00 120.501 196 257.2 1103.0 22.3 16.35 4.46 0.32 5.05 18.10 0.86 22.56 23.73 1125.32 196 332.7 1728.0 35.0 22.06 5.64 0.09 6.22 22.77 0.44 28.41 28.94 1763.03 196 410.0 1888.5 45.4 28.32 8.19 0.31 6.41 22.59 0.86 30.78 31.95 1933.94 196 320.1 1234.2 43.5 20.04 6.50 0.15 5.02 16.24 0.55 22.73 23.43 1277.75 196 246.9 1252.0 121.1 23.80 5.97 0.24 4.90 20.71 1.50 26.67 28.41 1373.11 214 370.8 1846.8 72.1 29.41 11.09 0.44 7.49 28.89 1.28 39.98 41.69 1918.92 214 398.1 1884.8 162.5 34.96 16.95 0.82 8.42 29.77 2.62 46.72 50.15 2047.33 214 317.6 1057.3 74.8 25.12 6.23 0.27 6.45 16.07 1.57 22.30 24.13 1132.24 214 225.9 887.6 250.7 26.63 8.39 1.30 4.29 18.13 4.72 26.52 32.53 1138.35 214 638.4 2313.6 104.6 36.32 13.92 0.89 17.60 25.49 1.98 39.41 42.27 2418.11 237 330.1 1359.6 186.0 24.48 12.57 2.23 5.46 17.57 4.09 30.13 36.45 1545.62 237 745.5 2917.5 484.1 47.50 22.11 4.63 11.16 36.60 10.39 58.71 73.72 3401.63 237 328.3 1504.3 165.8 28.46 10.48 1.70 6.27 21.71 3.12 32.19 37.00 1670.14 237 547.2 2468.5 7.6 46.57 13.00 0.04 10.37 31.30 0.17 44.30 44.50 2476.15 237 164.9 1740.7 687.6 41.52 21.88 6.07 5.16 27.76 14.31 49.63 70.01 2428.21 258 201.8 982.5 77.0 24.62 13.06 1.64 3.77 14.70 1.72 27.76 31.12 1059.52 258 431.7 2194.4 303.0 70.86 33.63 5.35 7.88 35.06 6.42 68.69 80.45 2497.43 258 373.1 1665.0 263.4 46.02 25.14 3.77 7.28 29.13 6.68 54.27 64.72 1928.44 258 405.0 1840.3 230.3 38.96 16.71 4.23 7.19 25.64 4.81 42.35 51.38 2070.55 258 187.7 1582.2 340.0 45.34 20.70 5.90 3.53 25.26 7.31 45.96 59.17 1922.2242

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