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The mobility and adsorption of sewage sludge-derived copper and zinc in forest soil Evans, Rhian Emma 1994

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THE MOBILITY AND ADSORPTION OF SEWAGE SLUDGE-DERIVEDCOPPER AND ZINC IN A FOREST SOILbyRHIAN EMMA EVANSB.Sc.(Hons), University of London, 1990A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIES(Department of Soil Science)We accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAMarch 1994©Rhian Emma Evans, 1994In presenting this thesis u-i 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 I) cS i€,,i,c€The University of British ColumbiaVancouver, CanadaDate kc4tL9.DE-6 (2188)ABSTRACTForest land application of sewage sludge results in therecycling of nutrients essential for plant growth, and alsoprovides organic matter important for soil structure. However,land disposal of sewage sludge is not an ideal solution. Amajor environmental concern associated with sewage sludgeapplication to land, is the fate of sludge-derived heavy metalssuch as copper and zinc. Consequently, the mobility andretention mechanisms of sludge—derived metals in forest soilsare enormously important to questions of plant uptake andgroundwater contamination.The purpose of this study was to examine the mobility andadsorption of sludge-derived copper and zinc in a forest soil.To determine the mobility of the metals a column study wasconducted (Phase I) with one main objective:to determine the effect of time and rate of sludgeapplication on the movment and accumulation of copper andzinc in the underlying forest soil.The results of this study indicated significant differencesin metal accumulation between application rates in the upperorganic horizon (0—5 cm) of the soil. It also became evidentthat time had a significant effect in the redistribution ofmetals. Furthermore, and of considerable environmentalsignificance, was the fact that more than 75 % of both copper11and zinc remained in the sludge after 4 months.The main objective of Phase II of the research, theAdsorption Study, was to determine the adsorption behaviour ofcopper and zinc by FH and Bf horizons, and by woody material.The adsorption data was successfully fitted to the Langmuirisotherm. This model provided parameters which reflected boththe adsorption capacity and bonding energy of the metal.Results showed that the FH material had a greater bonding energyconstant and a higher adsorption capacity for both the metals,than did either of the other two materials. This is significantbecause the majority of the sludge, when applied to a forestsoil, is primarily in contact with the FH horizon. As expected,copper had both a greater bonding energy constant and adsorptionmaximum than zinc for all three materials.The mobility of sludge-derived copper and zinc in a forestsoil is highly dependent upon the adsorption characteristics ofthe metal with the adsorbate material. Therefore based on theresults of this research, it is possible to conclude that in aforest soil with a considerable FH horizon, copper, and to alesser extent zinc, will be readily adsorbed and retained bythis highly organic material.1)-iTable of ContentsAbstract iiTable of Contents ivList of Tables viiList of Figures ixAcknowledgements xChapter 1: Introduction 1Chapter 2: Literature Review 42.1. Basic Metal Chemistry and EnvironmentalSignificance 42.1.1. Copper 42.1.2. Zinc 42.1.3. Anthropogenic Sources of Copperand Zinc Atmospheric Deposition Agricultural Inputs 62.1.4. Input of Copper and Zinc fromSewage Sludge Sewage Disposal onForested Land 72.2. Adsorption of metals 122.2.1. Mechanisms of Metal Retention Adsorption Precipitation 142.2.2. Metal Adsorption by Organic Matter... Affinity Sequences Proposed Sites of Inner-sphere Complexation 222.2.3. Adsorption by Hydrous Oxides 232.2.4. Adsorption Isotherms Freundlich Isotherm... .302.2.4.2. Langmuir Isotherm 312.2.5. Effects of Ionic Strength on MetalAdsorpt ion 342.3. Fractionation of Copper and Zinc 352.3.1. Chemical Forms of Copper and Zincin Sludge and Sludge-amended Soils. . .372.3.2. Distribution of Copper and Zinc inSludge—amended Soil Fractions 412.3.3. Behaviour of Copper and Zinc inivSewageSludge.442.3.4.Sumxnary 47Chapter 3: Materials and Methods 493.1. Site Description 493.1.1. Geology 493.1.2. Soils 503.2.SampleCollection 503.3.ColumnStudy 513.3.1. Experimental Design 513.3.2. Column Structure 523.3.3. Column Materials 533.3.3.1. Soil Materials 533.3.3.2.SewageSludge 553.3.4. Experimental Procedure 553.3.4.1. Leachate Collection 563.3.5. Destructive Sampling of the Columns. .563.3.6. Laboratory Analysis 573.3.6.1. Leachate Analysis 573.3.6.2. Total Metal Analysis ofSoil and Sewage Sludge 573.4. Adsorption Studies 583.4.1. Materials 583.4.2. Method 583.5.NMRAnalysis 59Chapter 4: Results and Discussion: Column Study 614.1.1. Differences in Total Copper Conc. asa Function of Time and ApplicationRate 614.1.l.1.FHmaterial 614.1.l.2.Bflmaterial 744.1.l.3.Bf2material 744.1.2. Differences in Total Zinc Conc. as aFunction of Time and ApplicationRate 754.1.2.1. FH material 754.1.2.2. Bfl material 754.1.2.3. Bf2 material 764.1.3.MetalMovement 764.1.3.1. Leachate Analysis 764.1.3.2. Metal Accunmiulation in theSoil 814.1.4. Conclusion 87Chapter 5: Copper and Zinc Fractionation of the FH material.895.1. Method 895.2. Results and Discussion 90V5.3. Sources of Error .915.4. Suggestions to Improve the FractionationMethod 955.5.Conclusion 96Chapter 6: Results and Discussion: Adsorption Studies 976.1. Effect of CaC12 986.2. Adsorption of Copper and Zinc by FH,Bf and woody materials 986.3. Adsorption of Copper by FH material. .1016.4. Adsorption of Copper and Zinc by FHand woody material 1106.4.1. NMR Analysis 1106.4.2. Differences between theAdsorptive Behaviour ofthe FH and woody material 1156.5. Differences between Copper and ZincAdsorption by FH and woody material..1186.6. Adsorption of Copper and Zinc by Bfmaterial 1196.7. Effect of Adsorption on pH 1216.8. Summary of Observations 1276.9. Conclusion 1296.10. Recommendations 130Chapter 7: Summary and Conclusion 131Bibliography 134Appendix 1: Column Study- Copper 145Appendix 2: Column Study - Zinc 149Appendix 3: Leachate Analysis 152Appendix 4: Adsorption Study 161viList of Tables2.1: Guidelines of heavy metal concentrations in sewagesludge 82.2: Guidelines form USEPA 4OCFR for sludge disposal 92.3: Maximum recommended copper and zinc soilconcentrations by the GVRD 93 . 1: Experimental Design . 524.1: Analysis of variance— copper accumulation in FH 634.2: Mean values — copper accumulation in FH 634.3: Analysis of varinance — copper accumulation in Bfl...654.4: Mean values — copper accumulation in Bf 1 654.5: Analysis of variance — copper accumulation in Bf2....674.6: Mean values — copper accumulation in Bf 2 674.7: Analysis of variance— zinc accumulation in FH 694.8: Mean values — zinc accumulation in FH 694.9: Analysis of variance — zinc accumulation in Bf 1 714.10: Mean values— zinc accumulation in Bf 1 714.11: Analysis of variance— zinc accumulation in Bf2 734.12: Mean values— zinc accumulation in Bf2 734.13: Total metal accumulation in FH material 824.14: Total metal accumulation in Bf 1 material 824.15: Total metal accumulation in Bf2 material 834.16: Total amount of metal released from the sewagesludge 845.1: Fractions of copper: FH— 4 month material 945.2: Fractions of zinc: FH— 4 month material 946.1: Langniuir and Freundlich parameters for copper andzinc 99vii6.2: Relative percentage of C in chemical shift regionsof woody and FH material 1146.3: Copper adsorption by FH material in relation tofinalsolutionpH 1226.4: Copper adsorption by woody material in relation tofinal solution pH 1226.5: Copper adsorption by Bf material in relation tofinal solution pH 1236.6: Zinc adsorption by FH material in relation to finalsolution p11 1236.7: Zinc adsorption by woody material in relation tofinal solution pH 1246.8: Zinc adsorption by Bf material in relation to finalsolution p11 124viiiList of Fiqures2.1: Separation of metal ions into three categories:Class A, borderline and Class B 172.2: Phathalic Acid and Salicylic Acid 243.1: Column Design 544.1: FH — Changes in total copper 6242:Bfl—Changesintotalcopper 644.3:Bf2—Changesintotalcopper 664.4:FH—Changesintotalzinc 684.5:Bfl—Changesintotalzinc 704.6: Bf2 — Changes in total zinc 724.7: Leachate analysis 775.1: Copper fractionation: FH—4 month 925.2: Zinc fractionation: FH—4 month 936.1: Copper adsorption by FH: Langmuir isotherm 1026.2: Copper adsorption by Wood: Langmuir isotherm 1036.3: Copper adsorption by Bf: Langmuir isotherm 1046.4: Zinc adsorption by FH: Langmuir isotherm 1056.5: Zinc adsorption by Wood: Langmuir isotherm 1066.6: Zinc adsorption by Bf: Langmuir isotherm 1076.7: ‘3C (protonated) CPMAS NNR spectra of FH and woodymaterial 1126.8: 13C (unprotonated) CPMAS NMR spectra of FH and woodymaterial 1136.9: Guaiacyl lignin structural unit 114ixAcknowledgementsFirst, I would like to thank the GVRD for their financialassistance. Thanks also to Dr. Lawrence Lowe for his guidance, andto Drs. Lavkulich, Ballard and Kimmins, for their input and advice.Carol Dyck was of great help in the laboratory, and Bernie vonSpindler provided valuable technical assistance. I would like tothank Caroline Preston and J. Niu for providing NMR Analysis. I amgrateful also to Mike van Ham and especially Martin Hilmer for hismuch appreciated help and support. Thanks must also go to thegraduate students of the department, for their support and goodhumour.And finally, to my family and my friends all of whom remainedconstant in their belief and encouragement — Thankyou.xCHAPTER 1: INTRODUcTIONThe Greater Vancouver Regional District (GVRD) has apopulation of approximately 3 million and a population influxrate greater than that of any other Canadian city. As thepopulation of the region grows, so does waste production. Urbanwaste in the district undergoes primary sewage treatment, an endproduct of which is sludge. Therefore, as the populationincreases a suitable means of sludge disposal becomes a preeminent concern. Greater Vancouver, with its hinterland ofextensive and productive forests, may well look to theserelatively uninhabited areas as potential sites for sludgedisposal.Sewage sludge with a high nitrogen, phosphorus and potassiumcontent, is a viable and proven forest fertilizer (Henry andCole, 1983). Researchers have documented the favourable effectsof sewage sludge disposal on forested lands. They include:accelerated wood production cycles; increased forestproductivity; and the promotion of revegetation and stabilizationof sites after clearcutting and forest fires (Cole et al., 1983).Since 1973 Seattle Metro, with the University of WashingtonCollege of Forest Resources, has been involved in a researchprogramme to evaluate the feasiblity of applying sewage sludge toforested land. Based upon the findings of this research, SeattleMetro is currently disposing of approximately 65 % of theirmunicipal sludge onto forest lands (van Ham, personnel1communication).In a similar venture the GVRD, in conjunction with theForest Sciences Department of UBC, undertook a major researchproject to examine the feasibility and viability of sludgedisposal on forested land in B.C.. Beginning in 1989 sludge wasapplied to sites in the Malcolm Knapp UBC Research Forest, MapleRidge. The project took on many facets, including the effects ontree productivity, effects on small mammal populations,understory vegetation response and the fate of heavy metals.In ‘clean’, non—industrial sludges, such as that which wasapplied to sites at the UBC Research Forest, the predominantheavy metals which may present an environmental problem arecopper and zinc. The origin of copper in such sludges is thoughtto originate from the solubilization of copper piping which isplumbed into the majority of housing in North America and Europe.The most likely sources of zinc, in non—industrial sludge, arefrom galvanized products (Alloway, 1990) and the solubilizationof zinc oxide on roofs, used as a coating to combat moss andlichen growth.A major environmental concern associated with sewage sludgeapplication is the fate of these sludge-derived heavy metals. Agreat deal of the literature focuses on the behaviour of heavymetals from sludge applied to agricultural lands. Forest trees,on the other hand, are not significant human food chain crops andso heavy metal contamination of forest soils and vegetation isconsidered to be of less critical importance. Cole and Henry2(1988), for instance, stated that heavy metal loadings are not alimiting factor in sludge disposal on forested land. However,along with sludge application there is always the potential forsludge—derived metals, if present in high enough concentrations,to become an environmental hazard. Metals which are availablefor plant uptake and to percolating waters, are a potentialcontaminant to groundwater and the food chain. Throughadsorption onto insoluble clays, (hydrous) oxides and organics,both the availability and mobility of metals in the soil can bereduced. It is therefore important to assess the adsorptivebehaviour and mobility of the metal if one is to appreciate itspotential as a contaminant. Also, Wells et al. (1988) noted ascarcity of information concerning the movement of heavy metalsin the soil solution of forest systems.Therefore, in order to assess the mobility and retention ofsludge—derived copper and zinc in forest soils a research projectwas initiated with two major objectives:(1) to determine the effect of time and rate of sludgeapplication on the movement and accumulation of copper andzinc in the underlying forest soil;(2) to determine the adsorptive behaviour of the two metals byFH and Bf horizons, and by decomposed woody material.3Chapter 2: Literature Review2.1. Basic Metal Chemistry and Environmental Significance2.1.1. CopperCopper is the first element of subgroup lB of the PeriodicTable. Its electronic structure is is2, 2s, 2p6, 3s2, 3p6, 3d10,41, and like all elements of the first transition series itspreferred oxidation state is II. Although Cu is relativelystable in aqueous systems, the Cu(H2O)6 ion is considered thepredominant copper species in soil solutions (Parker, 1981).The average copper content in the lithosphere isapproximately 70 ppm (Lindsay, 1979). The concentration ofcopper in soils ranges from 2 to 100 ppm with an average of 30ppm (Lindsay, 1979). Krauskopf (1972) reported the relativeabundance of copper in basic igneous rocks as compared to acidigneous rocks, with the highest concentrations being found inbasalt and gabbro.2.1.2. ZincZinc, like copper, is a member of the first transitionseries. Its electronic configuration is similar to copper but hasa complete 4s2 orbital.In soils zinc is slightly more abundant than copper with anaverage content of 50 ppm (Lindsay, 1979). The total zinccontent in soils is largely dependent upon the composition of the4parent material. In igneous materials zinc is present at higherconcentrations in mafic rocks than in felsic rocks. Krauskopf(1972) cited two reasons for this: first Zn2, unlike Cu2, isable to substitute for Mg2 and Fe in ferro—magnesiam silicates,and second, zinc occurs as submicroscopic grains of sphaleriteassociated with basic igneous rocks. Like most other heavymetals, zinc is found in relatively high concentrations in shalesand other fine-grained sedimentary materials (Krauskopf, 1972).The average content of zinc in the lithosphere is 80 ppm(Lindsay, 1979).Zinc in soils occurs exclusively in its II oxidation state(Lindsay, 1979). It has the highest first ionization potentialof all metals in the first transition series, probably as adirect result of the complete 3d and 4s orbitals (Cotton andWilkinson, 1972). Its second ionization potential (i.e. to Zn2)is relatively low at 17.96 eV (Lindsay, 1979).2.1.3. Anthropoçrenic Sources of Copper and Zinc2.1.3.1. Atmospheric DepositionThe origins of air—borne copper include copper smelting,incineration of municipal waste, and general industrial and urbanactivities. Thornton (1979) reported copper concentrations insoils, within the vicinity of a copper smelting plant, to reachlevels of 2000 ppm. The total amount of copper deposited fromthe atmosphere in the United Kingdom ranged between 100 g/ha to5480 g/ha per annum (Shorrocks and Alloway, 1987).Sources of atmospheric zinc include the burning of fossilfuels and smelting of non—ferrous metals (Alloway, 1990).Deposition of the air—borne metal can lead to contamination ofsoils. Agricultural InputsIn past years, prior to the advent of sewage disposal onagricultural land, the most documented source of copper soilcontamination was through the application of the Bordeauxmixture. Used to control die-back, the use of this CuSO4.5H20mixled to an increase of copper in soils under crops treated withthe fungicide. Furthermore, Swaine (1969) reported theassociated increase of copper in soils treated with phosphaterock fertilizer products. Superphosphate and triplesuperphosphate were found to contain copper concentrations of upto 1000 ppm.Agrochemicals such as fertilizers and pesticides can alsoelevate zinc levels in soils (Alloway, 1990).2.1.4. Input of Copper and Zinc from Sewage SludgeSewage sludge disposal in Europe and North inerica has beenpredominantly on agricultural land. As a soil conditioner and acheap source of major nutrients, such as nitrogen and phosphorus,land application has become a greatly favoured means of disposal.In the UK nearly 50 % of all the sewage sludge produced is6applied to agricultural land, of which 20 % is disposed of on toarable land. Sewage sludge generally has a nitrogenconcentration ranging from 0.2 — 2.2 % and a P205 content of 0.2 —3.7 % (Alloway, 1990). Therefore in order to meet therequirements of crop production, a large quantity of sludge isrequired.One of the major drawbacks of sewage disposal on land is thepossibility of heavy metal contamination of the soil andsubsequent uptake by plants. Invariably the treatment ofagricultural soils with sewage sludge leads to an increase in theamount of metal in the food chain. With humans frequently theprimary consumers of the crop, elevated metal contents iscertainly a source for concern, Consequently many countrieshave drawn up guidelines to regulate sludge disposal onagricultural land (Table 2.1). sewage Disposal on Forested LandIn the past decade the disposal of sewage sludge on forestedland has increased. Interest has stemed mainly from the factthat sludge provides a cheap means of fertilization. Bastion(1986) identified the following two factors as beneficial effectsof sludge application on forest land. First, the use of sludgecan led to a reduction in wood production cycle times and anincrease in forest productivity; second, the application ofsludge onto clearcuts, encourages the stabilization andrevegetation of the land. Moreover, Cole et al. (1983)7Table2.1.GuidelinesofHeavyMetalConcentrationsinSewageSludgesABCDEFGHRangeCeilingAnnualUKECFranceGermanySludgeConc.LoadingRec.Man.Rate(mg/kg)(mg/kg)(kg/ha/yr)(mg/kg)(mg/kg)(mg/kg)(mg/kg)(mg/kg)Copper50—8000500—30001—9.3140/28050100100100Zinc91—49001000—100002.5—18.6280/560150300300300Notes:ColumnAshowstherangeofvaluesformaximumacceptableelementconcentrationsinsewagesludgeColumnBandCindicateceilingconcentrationsandannualloadingratesinuseindifferentcountriesColumnsD,E,F,GandHshowthemaximumallowablemetalconcentrationsinsludge—amendedsoilsinUK,EC,FranceandGermany.AlldataaretotalconcentrationsexceptintheUKcolumnswhichareEDTA—extractablevalues.Rec=Recommended,Man=MandatoryECvaluesSource:Alloway,BJ.(1990).HeavyMetalsinSoils.6ci) 0C, 0pCDG)CDar’) 3)CDC,033CDD.CD0.-ICD0.CDCD0-I’-‘03CCl)m.0C,0-‘ci,0.CD000C,N03’. r•CD-‘‘—.oC,‘).-CD%%Ø00S-CDC, N0CD-‘•%JC, 0 (JiC.’)CDo!.00-pCFP•%1’3—00CD33CD’-.I-CD0 -&4h.‘,considered the following points as viable reasons for sludgedisposal on forested sites:1. many forests are lacking in major nutrients, especiallynitrogen and phosphorus,2. forests are often on well—drained sites which are notsubject to periodic flooding,3. forest trees are not food chain crops, therefore publichealth concerns and application regulations are not ascritical as those for agricultural land,4. forests commonly have a large amount of organic carbonwhich will immobilize nitrogen and prevent excessiveleaching of nitrates and,5. the perennial root system of forest trees allows foryear round uptake of available nutrients.These same authors went on to conclude that sludge disposal canbe used to increase forest productivity without causingsignificant environmental problems.In 1973 the University of Washington College of ForestResources began a major research programme to investigate thefeasibility of sludge disposal on forest lands. The researchconducted at the Pack Forest in Washington revealed an increasein growth response of Douglas fir from a single application of 20dry tonnes/acre of sewage sludge. This response continued formore than 5 years (Henry and Cole, 1983).Based on other findings from this research project, Henryand Cole (1986) stated that heavy metal loadings are typically10not limiting factors for most forest applications of sewage.Moreover, they did not report any phytotoxicities even atexcessive levels of metal loadings.Based in part on the research at Pack Forest the EPA haveproduced guidelines for sludge disposal. Tables 2.2 shows themandatory heavy metal loading rates and concentrations, and Table2.3 illustrates the guidelines produced by GVRD for the maximumrecommended metal concentrations in sludge.In both the US and Canada, forests occupy a significantproportion of the land (Bastion, 1988). Therefore, the disposalof sewage sludge on forested land may well be a viable option forcertain communities in North America.112.2. The Adsorption of Metals2.2.1. Mechanisms of Metal RetentionMetal retention refers to reactions between metals insolution and the surfaces of clays, (hydrous) oxides andorganics. These reactions change the distribution andconcentration of the metals in solution and hence theiravailability and mobility in the soil. AdsorptionAdsorption is a general term for the uptake of material(adsorptive) from a fluid by a condensed phase (adsorbent)(Harmsen, 1977). The nature of adsorption between a metallic ionand the charged surface of a soil particle may involve one of twoprocesses: either the formation of a relatively weak outer—spherecomplex through an ion exchange reaction, or alternatively theformation of a strongly bound inner—sphere complex by way ofligand exchange (Evans, 1989). An ion exchange reaction involvesthe simultaneous desorption of an equivalent amount of anotherionic species. The ions in solution are attracted byelectrostatic or coulombic forces to the negatively charged soilparticle. They form only a weak association with the surface ofthe soil particle. As the ions do not form covalent bonds withthe surface, they retain their inner hydration sphere to formonly outer-sphere complexes. Work done by Clementz et al. (1973)and McBride (1979) found evidence for the formation of outer12sphere complexes on clay minerals, as the adsorbed cations had ahigh degree of rotational mobility. This bonding is alsoreferred to as nonspecific adsorption.When a greater amount of desorbed ions are released uponadsorption of other ions, the process is termed ‘specificadsorption’ (Harmsen, 1977). The adsorbed cations are consideredto be non-exchangeable with reduced rotational mobility (McBride,1989). The specific adsorption of cations often alters theeffective surface charge of the particle surface (Harmsen, 1977;McBride, 1989). Metals which are retained this way areconsidered to be held by way of coordination or covalent bonds toform inner—sphere complexes. The term ‘specific adsorption’ isgenerally used to describe the non—exchangeable adsorption ofcations onto inorganic materials, namely clays and (hydrous)oxides.Terms such as ‘chemisorption’ and ‘physisorption’ have beenused in soil science to describe the adsorption of ions.Chemisorption is considered to involve the formation of covalentbonds and to result in the formation of a surface complex(Harmsen, 1977). Physisorption, on the other hand, considerssorption by way of weak intermolecular bonds, such as van derWaals forces. Although chemisorption is analogous to specificadsorption, physisorption is not comparable to nonspecificadsorption. This is because forces stronger than van der Waals,such as coulombic and electrostatic attraction, are involved inion exchange.13Finally the term ‘simple complexation’ is considered byMcBride (1989) as a monodentate bonding of a metal ion to aligand by predominantly ionic or covalent forces. PrecipitationAnother mechanism of metal retention in soils isprecipitation. A continuum is believed to exist betweenadsorption and precipitation, as most precipitates are initiatedby the surface adsorption of the ion (Leckie, 1988). As pHincreases, chemisorption inevitably merges into precipitation(McBride, 1989). Most adsorption experiments are unable tomake a distinction between adsorption and precipitation. In thesoil system, however, precipitation of copper and zinc is notconsidered to be an important mechanism of retention, as the soilsolutions are undersaturated with respect to known solubilityproducts of these metals (Harmsen, 1977). In contrast, Evans(1989) predicts the occurrence of Cu2 and Zn2 hydroxides undersome unstated soil conditions. Gilmour and Kittrick (1979) alsoindicate Zn2 solubility to be controlled by ZnS in anaerobicsoils. In agreement with Harmsen (1977), McBride and Blasiak(1979) found that in acidic mineral and organic soils, even withelevated metal contents, Cu2 and Zn2 are generally not presentas hydroxides or carbonates.2.2.2. Metal Adsorption by Organic MatterThe adsorption of metals by organic matter leads to the14formation of metal—organic complexes of varying stability andstructure.Such adsorption can occur through ion exchange; however thehigh degree of selectivity exhibited by organic matter forcertain metals suggests that they are also held as inner—spherecomplexes (McBride, 1989).Senesi et al. (1986) classified two metal binding sites forCu2 and Fe3, on soil humjc acid. The first class wascharacterized by structurally stable CU2 —organic complexes witha strong element of covalency. The second class was a weakercomplex, in which the hydrated copper ion was held byelectrostatic forces on surface functional groups.Work done by Lakatos et al. (1977) on the bonding of metalsto huinjc acids revealed how 3d—transition metals are bound tohumic acids as inner—sphere chelate complexes. According to theIrving—Williams series, those metals later in the 4th period ofthe Periodic Table form increasingly stable complexes withorganic matter. This is as a result of both the ionic radius andthe electron configuration of the metal.Stevenson (1976) noted that at low CU2 concentrations thestability constants (log K) of the Cu2—organic complexes werehigher than at high metal concentrations. Based on thisevidence, Stevenson stated that humic acids contain multiplesorption sites and that the first increment of metal added wouldbe bound to certain sites preferentially to form the strongestcomplex.15This phenomenon of preferential adsorption at low metalconcentrations can be explained in part by Pearson’sclassification of Hard and Soft Acids and Bases (1969). Pearsoncategorizes metals into three classes; Class A, Class B andborderline. Class A metal ions are considered ‘Hard Acids’ andconsist of the alkaline earths, alkali metals, lanthanides,actinides and aluminium. The ‘Soft Acids’ (Class B) include Ag,Hg2 and Pt2. The first row of transition metals, including Cu2and Zn2 is considered ‘borderline’. This separation of metalions into distinct groups is based upon empirical thermodynamicdata (i.e. the magnitude of stability constants of the metalligand complexes.) The greater the stability constant the morestable the metal-ligand complex. The theory states that hardacids prefer hard bases while soft acids prefer soft bases.Class A metals are those which, on the basis of the magnitude ofthe stability constants, have the following ligand affinitysequence:O>N> Swhereas that of Class B is:S >N>OThe borderline metals form an intermediate group, with each metalhaving its own individual affinity sequence.The basis on which metals are categorized isillustrated in Figure 2.1 (Niebor and Richardson, 1980) Thedelineating parameter is the Covalent Index i.e. the degree towhich the metal exhibits characteristics of Class B. The16Figure 2.1. Separation of Metal Ions into Three Categories:9°F4.5 -•AuClass A, Borderline and Class B.CLASS A OR IONIC INDEX, Z2/r* Where the Class B index,against the Class A index)ç2r is plotted for each ionz2/r.Source: Niebor and Richardson (1980)• Ag4.0Pd’.Pt1+Hg’ +•BP.TI,,zI—LU-J4>0C.)0U)U,C)•Pb(IICLASS B3. •Cu•Pb’ +•Sb{UBSn. •Cu+CdCo’ •As(m)•FeNj,+ Sn(IVCrl+W#Zn’Mn’’”BORDERLINE— Gd’ LU”•••Sc”Cs Ba’ •Mg’ yi+ At’• K ••C&% N&’ Sr’ •B&•L1CLASS AI I I I I ,II2 4 6 8 10 12 14 16 20 23017Covalent Index, )ç2r (where X1 is the electronegativity of themetal ion, and r is its ionic radius) is a measure of the energyof the empty valence orbital of a metal ion, which is often takenas a measure of the metal ion’s ability to accept electrons andthus form covalent bonds. The Covalent Index is calculated bydividing the valence orbital energy by the ionic energy, thelatter being a measure of the electrostatic energy between ametal ion and an anion. Therefore the magnitude of the CovalentIndex reflects the importance of covalent interactions relativeto ionic interactions. Along the X axis of Figure 2.1. is plottedthe Ionic Index, Z2r (where, Z is the charge of the metal ion andr its ionic radius). Those metals with a high Ionic Indexrelative to that of the Covalent Index are considered to belongto Class A. The Ionic Index is considered to reflect the energyof electrostatic interactions between a metal and a ligand.A similar concept is that of the Misono softness parameterY defined byY=iO___(5)z+1where R is the ionic radius, Z is valence and 12 is theionization potential of the metal ion. When Y < 0.25 nm themetal ion usually has a corresponding high electronegativity, lowpolarizability and forms predominantly ionic bonds as opposed tocovalent bonds. It is also considered a hard acid. Theborderline metals such as Cu2 and Zn2 have a Y value of between180.25 nm and 0.32 nm. The extent of covalency in these borderlinemetals depends upon the electronic configuration of the metalion, the stereochemistry and the specific solvent (Sposito,1989)Nieboer and Richardson (1980) stated that Zn2 thoughcategorized as a borderline metal, had many featurescharacteristic of Class A. On the other hand Cu2, again aborderline metal, had a Covalent Index closer to that found inClass B metals. Evidence such as this, though not conclusive,does provide an explanation to account for the formation ofstrong complexes between Cu2 and N—containing ligands.Electron Spin Resonance (ESR) evidence from Senesi et al(1986) indicated a higher involvement of N-containing functionalgroups — as compared to 0—containing functional groups — in thecomplexation of CU2. These authors also noted the preferentialcomplexation of Cu2 rather than Fe3 by nitrogen—enriched sites.Goodman and Cheshire (1973) noted that in peaty soils theporphyrin groups strongly chelated CU2. McBride (1989) usesevidence from ESR studies of Baes (1983) to state that at low Cu2levels the metal will complex to amine-type N groups inpreference to 0—containing ligands. Of considerable interest arethe findings of Stevenson and Chen (1991). They state that thereare differences in the ability of humic substances to bind Cu2.Their findings suggest that huxaic acids form stronger complexeswith Cu2 than do fulvic acids. An explanation of the differencestakes into account the amount of N—containing functional groups,19implying that the higher the N content the greater the stabilityconstant.The functional groups of soil organic matter arepredominantly oxygen—containing, with a lesser amount of nitrogenand sulphur containing groups. With this is mind, the use ofPearson’s classification scheme sufficiently explains copper’saffinity for nitrogen but does not account for the affinitysequences of organic matter for metals. This is because the highconcentrations of metals used to establish metal—affinitysequences, saturate those ligands which adsorb preferentially butare few in number. Therefore, it is left to the abundantcarboxylate groups to determine the apparent order of metalpreference (McBride, 1989). Affinity SequencesStevenson and Ardakani (1972) predicted the followingaffinity sequence of metals onto organic matter at pH 5:Cu2 > Ni2 > Pb2 > Co2 > Ca2 > Zn2 > Mn2 > Mg2and at pH 3.0:Fe3 > Al3 > Cu2 > Fe > Ni2 > Pb2 > Co2 > Ca2 > Zn2Schnitzer (1969) produced this affinity sequence of metals ontofulvic acid at a pH of 5.0:Fe3 > Al > Cu > Pb2 > Fe2 > Ni > Mn2 > Ca2 > Zn220Finally, Stevenson (1976) reported the following order ofaffinity onto huirtic acid:Cu2 > Pb2 >> Zn > Cd2Many researchers have sought an explanation to account forthe sequence of metal affinity. James and Healy (1972), Harmsen(1977), McBride (1989), Elliot et al. (1986), Abd—Elfattah andWada (1981) Kinniburgh et al. (1976) and Schnitzer and Kerndorff(1981) all observed a parallel between the order of preferenceand the hydrolysis properties of the metal ions.Schnitzer and Kerndorff (1981) correlated the affinitysequence for metals adsorbed by water—insoluble fulvic acid withthe first hydrolysis constants (pK1). The pK1 of CU2, Zn2 and Al3are 7.34, 9.14, and 4.50 respectively (Geological Survey, 1974).It is evident that Al3 with the lowest p1< is more stronglypreferred than is Cu2, which in turn has a higher affinity forfulvic acid than does Zn2 (Schnitzer, 1969).“It is not known with certainty why hydroxo complexes aremore strongly adsorbed than their unbound counterparts” (Elliotet al., 1986). James and Healy (1972) however, did present amathematical model describing the specific adsorption ofhydrolysed metals on an oxide surface. They suggested that theaddition of an -OH group would lead to a reduction in the freeenergy requirement of adsorption because the hydration sheath ofthe ion would be reduced. This in turn would lower the energybarrier of adsorption, thereby allowing the hydrolysed ions to21approach closer to the site of adsorption. Due to the increasedproximity of the metal ion to the adsorbate surface there wouldbe a concomitant increase in the coulombic interaction energies,which would thereby encourage the adsorption of the hydrolysedmetal ion. Proposed Sites of Inner-Sphere ComplexationIt is generally held that the 0-containing functional groups(predominant in humic and fulvic acids) are responsible for theretention of metal ions by soil organic matter (Stevenson, 1982;Bloom, 1981). Both the carboxylic acid groups (R-COOH) and thephenolic—OH groups are major contributors to the acidic nature ofhumic substances (Stevenson, 1982). The carboxylate group isconsidered to be the most important as it is the stronger acidand predominates in fulvic acid. N—containing groups do make acontribution to the bonding of metals such as CU2, but are notconsidered significant because they constitute only a smallpercentage of the total acidity.McBride (1978) provided evidence indicating the formation ofinner—sphere complexes between Cu2 and humic acid, but was unableto provide direct evidence of multidentate adsorption orchelation. Conversely, other researchers (Gamble et al., 1970;Schnitzer and Skinner, 1965 and Stevenson and Ardakani, 1972)implicated carboxylate and phenolic groups in the chelation ofmetal ions. Furthermore, Boyd et al. (1981) through ESR studiesprovided evidence of CU2 chelation. They proposed the22involvement of adjacent carboxylate groups each forming a singlebond with the metal ion. Gamble et al., (1970) considered bothsalicylic and phthalic acids important chelating agents of Cu2,as the stereochemistry of the phenolic and carboxylate functionalgroups encourage the formation of bidentate complexes. Figure2.2. illustrates the ortho position of the two functional groups.2.2.3. Adsorption by Hydrous OxidesIt is well established that metal oxides and hydroxides playan important role in the retention of metals in mineral soils.A great deal of evidence exists to suggest that metals areretained by way of specific adsorption. For example, Kinniburghet al. (1976), noted the high degree of selectivity exhibited byoxides for certain metals. More recently Stahl and James (1991)examined the exchangeability of zinc in a B horizon with an Feoxide content of 49 g/kg. At a pH of 5.7 only 4% of the totaladded zinc was in an exchangeable form, while the remaining 96%was in a non-exchangeable form. Also McBride and Blasiak (1979)reported evidence of cheiuisorption as zinc was preferentiallyadsorbed over Ca. They considered the species of adsorbed zincto be Zn(OH). McBride (1989) stated that the occurrence ofchemisorption depends upon the degree of crystallinity of theoxides and the surface morphology. Venkataramani (1978) providedevidence to support McBride’s claim. The saturation capacitiesof CU2 were much lower in those iron oxides with a higher degreeof crystallinity. He postulated that the more amorphous iron23:OOH000H‘OHPhthalicAcidSalicylicAcidFigure2.2.tsjoxides possessed a greater active surface area and therefore hada greater adsorption capacity for CU24. He also produced anaffinity sequence of metals by iron oxides:Cu24 > Zn24 > Ni24 >Gadde and Laitinen (1974) suggested the following affinitysequence of metal on hydrous iron and manganese oxides:Pb24 > Zn24 > Cd24 > Ti24.Kinniburgh et al (1976) found the following affinity sequence ofmetals onto Al hydroxides:Cu24 > Pb24 > Zn24 > Ni24 > Co > Cd24 > Mg24 > SrThis sequence was in accordance with that proposed by McKenzie(1980), who produced several affinity sequences. Firstly forhematite (pH 6):Pb2 > Cu > Zn24 > Co24 > Ni2 >and for goethite (pH 6):CU24 > Pb24 > Zn > Co24 > Ni > Mn24In agreement with other researchers, McKenzie (1980)concluded that the affinity sequences generally paralleled thefirst hydrolysis constants of the metal ions. This was supportedby James and Healy (1972) who stated that hydroxo complexes werefavoured as they had lower hydration energies than the free ionicspecies. Kinniburgh and Jackson (1981) noted that multivalentcations were often adsorbed at a pH below the Point of Zero NetCharge (PZNC), i.e. where the oxide surface had a net positivecharge. As a result such adsorptive behaviour was bettercorrelated to the hydrolysis character of the individual metal25ion than with the charge properties of the oxide surface. Kuoand Baker (1980) demonstrated how CU2 and Zn2 were adsorbed byoxides at a pH well below that of the PZNC. Adsorption of metalsproceeded even in the presence of electrostatic repulsion betweenthe positively charged surfaces and the metal cation. Inagreement, McBride (1989) stated that the overall specificadsorption of a metal will not show an obvious dependence on thePZNC of the oxide if adsorption is strongly favoured.Further evidence for the specific adsorption of metals by(hydrous) oxides comes from the release of more than one hydrogenion for each metal ion adsorbed (Benjamin and Leckie, 1981;McKenzie, 1980; Quirk and Posner, 1975; Kalbasi, 1978; Forbes etal, 1976, and Hohl and Stuxnm, 1976). Hohi and Stunuu (1976) notedthat as pH increased so did the number of moles of H+ desorbedfrom the surface of aluminium oxide. Kalbasi (1978) alsoobserved this phenomena and postulated two mechanisms of metalretention. The first mechanism involved the adsorption of Zn2and C1 or ZnC1 with the release of one H+ ion (C1 was thecommon anion used in Kalbasi’s experiments). The chloride ionwas attracted to the positively charged sites of the oxidesurface and to the zinc cation to form ZnCl. Adsorption ofZnCl+ was considered to be nonspecific for Zn2 as the ZnClcomplex could be replaced with either Ba2, Ca2 or Mg2. Thisadsorption was only significant at low pH values and it wasconsidered that only the aquo groups were involved.26_7 f-’I +OH 2 H +Fe/Al + or ==== Fe/Al —0 — ZnCI + HOH 2 Zn 2+ or Cl-OH 2The second mechanism of adsorption was considered specificinsofar as Zn2 could not be replaced by any of the aforementionedcations, and the number of H ions released rose to two. As Zn2was adsorbed at a pH below the PZNC of both A1203 and Fe203, theauthor postulated that Zn2 was held by strong chemical bonding.The proposed mechanism for this type of adsorption was based uponwork done by Quirk and Posner (1975) to describe the adsorptionof zinc onto goethite.H HFe_OH___Fe OH+ Zn2 < >OFe_O)Zfl + 2H/\i /\HHHThis involved the formation of a ring structure with the zinc ionacting as a bridging ligand between two hydroxo groups. Thesites of adsorption were without charge, although the ion couldhave been adsorbed at two positively charged sites or at 1postive and 1 neutral site. This mechanism of retention was notreversible and was considered to be a growth or extension of the27oxide surface (Kalbasi, 1978). Quirk and Posner (1975) statedthat the source of desorbed W accompanying this mechanism couldbe either hydroxo or aquo. They postulated that the aquo groupswere the more likely source as they would be more abundant at pHvalues lower than the PZNC. The lack of any obvious involvementof electrostatic forces is consistent with the concept of surfacecomplexat ion.Hohl and Stu!nm (1976) postulated that a release of more thanone H ion could be due to either bidentate bonding (aspostulated by Kalbasi, 1978) or to simultaneous adsorption andhydrolysis i.e.SOH + Me2 + H20 = SOMe-OH + 2H (6)McKenzie (1980) also invoked surface hydrolysation to explain H+ion release.SOH + Me2 = [SO—Me2j + H (low pH) (7)SOH ÷ Me2 + H20 = {S0-MeOHj + 2H (high pH) (8)McKenzie dismissed the idea of bidentate binding and insteadconsidered the reaction of Equations 7 and 8 the most logical inexplaining the increase H ion release with increasing pH.The increase in the number of moles of H released per moleof metal ion adsorbed parallels the adsorption selectivity ofmetal ions by oxides (Kinniburgh and Jackson, 1988). The sameauthors postulated that the affinity of a cation is related toits ability to displace H ions and reiterated that the more28strongly adsorbed cations are those which tend to hydrolyse morereadily. Forbes et al., (1976) provided further evidence tosupport this statement. These researchers studied H iondesorption upon adsorption of copper and zinc onto FeOOH. Theirresults showed that the number of wf ions released uponadsorption of copper was 2.4, whereas that for zinc was only Adsorption IsothermsAdsorption isotherms are a convenient and useful way todescribe the adsorption of solutes in quantitative ormathematical 4erms (Ellis and Knezek, 1972).The two most commonly used adsorption models are theLangmuir and the Freundlich. Their popularity has grown mainlyfrom their ability to fit a wide variety of adsorption data andfrom the ease with which their adjustable parameters can beestimated (Kinniburgh, 1986). Many authors have argued that theisotherms should be used only to summarize data and makequalitative interpretations (Mott, 1981 and Barrow, 1985).Travis and Etnier (1981), for example, stated that a good fit ofsorption data to a particular isotherm does not necessarily provethe existence of a specific sorptive mechanism. Veith andSposito (1977) also demonstrated the inability of the isothermsto distinguish adsorption from secondary precipitation.Despite these shortcomings, the capacity for qualitativeinterpretation of isotherm parameters, and the need to predictthe behaviour of metals in soils promotes the use of such models. Freundlich IsothermThe Freundlich adsorption equation is an empirical model,describing the adsorption of gases on solid—gas interfaces and ofdissolved components on solid—liquid interfaces (Harmsen, 1977).The Freundlich isotherm is defined by the following nonlinearrelationship:x/m = kC’ (1)where x/m is the amount of adsorbate adsorbed per unit weight ofadsorbent, C is the concentration of solute in the soil solutionat equilibrium, and k and n are empirical constants. For ease,the Freundlich model is often used in its linear form:log (x/m) = (1/n) log C + log k (2)A plot of log(x/m) against log C should yield a straight linewith a slope equal to 1/n and an intercept equal to log k.The Freuridlich equation is consistent with the principlethat the free enthalpy of adsorption decreases with increasingsurface coverage, due to surface heterogeneity or to interactionsamong the adsorbed mons (Harmsen, 1977). This implies that theadsorption of ions on the initial sites are energetically morefavourable and that wath increasing surface coverage thespecificity of the adsorption reaction decreases. The model alsodescribes conditions where the amount of solute adsorbedincreases indefinitely with increasing concentration.30Schulthess and Sparks (1991) believed that although theFreundlich model could predict adsorption behaviour at lowaqueous concentrations, it would overestimate the adsorptioncapacity of the particles at high adsorbate concentrations.This, they conjectured, is because of the finite number ofmonolayer sites on the surface of a particle. As a result, manyauthors have cautioned against extrapolation beyond theexperimental data (Kinniburgh, 1986). Possibly the greatestlimitation of the Freundlich equation is the lack of a predictedadsorption maximum (Schulthess and Sparks, 1991; Harmsen, 1977;Bohn, 1979; Travis and Etnier, 1981; Ellis and Knezek, 1972).Sidle and Kardos (1977) used the Freundlich adsorptionisotherm to describe copper, zinc and cadmium adsorption by aforest soil. Zabowski and Zasoski (1987) also used theFreundlich equation to describe the adsorption of the same threemetals from a sludge leachate by a forest soil. Lancimuir IsothermThe Langmuir adsorption isotherm was developed to describethe adsorption of gases on solids (Langinuir, 1918). It is basedupon kinetics and follows the laws of mass action (Schulthess andSparks, 1991). The Langmuir equation as used in soil science isas follows:kCbx/m =----- (3)l+kCwhere k is a constant relating to the binding strength and b is31the adsorption maximixm in mg/g (the other terms as defined onpage 30). Linearity in a plot of C/(x/m) vs C implies that theadsorbent will adsorb only a given amount of ion, and that thiswill be as a monolayer with a uniform bonding energy.Alternatively, a curved relationship suggests that the adsorbentwill adsorb a small amount with a constant and high energy andthat as the adsorptive concentration increases more will beadsorbed but at a lower energy.Langmuir (1918) assumed that the surface of a solidpossesses a finite number of adsorption sites and that if a gasmolecule struck an unoccupied site it would be adsorbed, whereasif it struck an occupied site it would be reflected back into thegas phase (Travis and Etnier, 1981). Once the surface is coveredwith a closely packed layer of molecules the adsorption maximum(b) has been reached. The Langmuir model also assumes that thebinding energy (k) is constant throughout the entire adsorptionprocess. As the binding energy constant is a function of theheat of adsorption and the effects of neighbouring adsorbedmolecules, it also follows that the heat of adsorption remainsconstant and that each adsorbed molecule is independent of theothers (Schuithess and Sparks, 1991). As one would expect, somesoil scientists consider these assumptions unreasonable in a soilsystem (Hsu and Rennie, 1962; Stumm and Morgan, 1970). Evidencefrom Brunauer et al. (1967), however, showed that the opposingenergies of lateral interactions and surface heterogeneity did infact maintain the heat of adsorption at a level constant enough32for the adsorption process to conform to this assumption. Otherassumptions of the Langmuir model are:(i) each space can hold only one adsorbed molecule,(ii) the adsorption reaction is reversible,(iii) the adsorbed molecules are not free to movelaterally on the surface.These too are not necessarily valid in a soil system. Therefore,the successful use of the Langmuir model is not a function of theassumptions being met, “but is [successful) because it happens tobe a good empirical equation!” (Harter and Smith, 1981).Harter (1979) used the single-site Langmuir equation todescribe the adsorption of copper and lead by Ap and B2 horizonsof several northeastern United States soils. Cavallaro andMcBride (1978) also successfully used the Langmuir model todescribe adsorption of copper and cadmium by selected acid andcalcareous soils, and correlated the adsorption maxima withvarious chemical and mineralogical properties.The problem of nonlinearity in plots of C/(x/m) vs C haveled to the development of multi-site Langmuir models. Thesemodels account for the presence of either several energeticallydistinct sites or two or more separate mechanisms of adsorptionon similar sites. Langmuir (1918) proposed the followingequation:k1bC k2bCx/m = + , (4)l+k1C l+k2C33where b1 and b2 are the adsorption maximums of the two componentsand k1 and k2 are bonding energy constants of the two components.Holford et al. (1974) used Langmuir’s multi-site equation todescribe phosphate adsorption. In doing so, they assumed thatthe intital sites (Phase I) were saturated before phosphate wasadsorbed by the second set of sites (Phase II). Shuman (1975)successfully described two adsorption sites for Zn and Mn, butused a multiple application of the monolayer Langmuir isotherm.In summary, the Freundlich model often provides a ‘good fit’as a direct result of the use of log - log plots, yet does notprovide parameters which reflect important properties of theadsorption mechanism. The Langmuir equation, despitelimitations, does provide the researcher with parameters whichreflect both the adsorption capacity and the affinity of theadsorbent for the adsorbate. Various authors have warnedagainst using such parameters in a quantitative manner, but havealso commented on their value as a qualitative means of assessingthe comparative differences between different adsorbates andadsorbents (Harter, 1979). For this reason the use of theLangmuir model, if applicable, would be a suitable choice topresent adsorption data.2.2.5. The Effects of Ionic Strength on Metal Adsorption.The primary effect of a significantly high ionic strength,is to reduce the activity of ions in solution. As theelectrolyte concentration increases, the system moves away from34an ‘ideal solution’ as the ions interact with one another.A second effect of ionic strength is to reduce metaladsorption due to the competion for adsorption sites by thecations of the electrolyte. Harter (1979) calculated a reductionof 50 to 75 percent in the adsorption capacities of copper byselected soils, when the experiment was conducted in O.O1M Cad2.Petruzzelli et al.(1979) also found a reduction in the Langmuirparameters, b and k, for copper with increasing Ca2concentration. Likewise, Shuman (1986) reported a decrease inzinc adsorption with increasing ionic strength. The same studyalso revealed a much greater effect on zinc adsorption whenelectrolyte concentrations were equal to, or greater than 0.05M.These results are in sharp contrast to those of Elrashidi andO’Conner (1982), who stated than Zn2 adsorption was not affectedby the presence of 0.O1M CaCl2. In agreement, Swallow et al.(1980) found that the adsorption of CU2, by hydrous ferricoxides, was not effected by ionic strengths less than O.5M.2.3. Fractionation of Copper and ZincFractionation techniques have been employed by manyresearchers to determine the chemical forms of metals in the soilenvironment. Such knowledge allows for the prediction of ametal’s potential as an environmental hazard, whilst alsoproviding detailed information as to its chemical behaviour.The most commonly employed technique of chemicalfractionation is sequential extraction. This is a method by35which the sample is subjected to a series of increasingly strongreagents under specified conditions. Problems have beenencountered, however, by researchers using this method. Theyare:(i) choosing a reagent which is both effective for thespecified form and sufficiently selective so as not tosolubilize other unintended fractions,(ii) readsorption of the solubilized fraction,(iii) validation of the soluble fractions which are, by thenature of the technique employed, only operationallydefined.Sequential extractions in general have met with a certainamount of criticism and distrust. An instance of this is thearticle by Nirel and Morel (1990) titled “Pitfalls of SequentialExtractions”. This paper highlights the lack of chemical meaningin the term “operationally—defined” and states that suchtechniques cannot confidently be extrapolated into the soilenvironment.Other researchers, however, (Carlson and Morrison, 1992;Liang et al., 1991) have argued that with adequate crossvalidation of the operationally-defined fractions to the soilenvironment, sequential extractions are a viable means ofassessing a metal’s environmental hazard potential.362.3.1. Chemical Forms of Copper and Zinc in Sludge and Sludge-Amended SoilsBeckett (1989) recognized six forms of metals which mayoccur in sludge or sludge—amended soils. The following providesa brief description of those forms.1. SolubleMetals in sludge/soil solution are present as either freeions or as soluble organic complexes (Beckett, 1989; Stevensonand Fitch, 1986). Through complexation with soluble organics,metals with low solubilities at common soil pH values, aremaintained in solution. Stevenson and Fitch (1986) identifiedtwo groups of organic compounds which form stable complexes withmetals in solution: (i) biochemicals from living organisms, suchas aliphatic acids, amino acids, sugar acids and polyphenols, and(ii) fulvic acids. The fulvic acids are considered to be moreeffective complexers of metals than huinic acids.Much of the literature considers these soluble metal—organiccomplexes to exist as chelates, based upon the stereochemistry ofthe complexing organic ligands. Schnitzer (1969) proposed thatone of the most important metal—fulvic acid interactions involvedboth phenolic OH and acidic COOH groups in an ortho position; ofless importance was the interaction with two acidic COOH groups.The extent to which metal ions are bound by fulvic acidsdepends upon the pH, ionic strength, molecular weight andfunctional group content (Stevenson and Fitch, 1986). Those37metal with a greater tendency to form strong coordinationcomplexes with soluble organics (e.g. Cu2j will be predominantlyin a complexed form in the soil/sludge solution.Metals may also exist in the soil solution as complexes withinorganic anions. Beckett (1989) noted that chloride andsulphate anions tend to complex metals more strongly thannitrate.2. ExchangeableExchangeable metals are those held on negatively chargedsurfaces of soil particles by predominantly electrostatic forces.This mechanism of retention is considered to be non—specificbecause the metal retains its inner hydration sphere andmaintains a high degree of rotational mobility (Harmsen, 1977).Implicit in the term ‘exchangeable’ is the relative ease by whichmetal cations can be displaced from the exchange sites by othercations present in the sludge/soil solution. As this exchange ofcations is usually rapid and complete, the activation energy ofthe reaction is relatively low (Beckett, 1989).A thermodynamic equilibrium exists between metal ions insolution and those on the exchange sites. This equilibrium isgoverned by ionic potential of the ions, their affinity for theexchange sites, the pH and composition of the solution (includingother electrolytes and complexing ligands) and the concentrationof the exchanging metal ion (Pickering, 1979).Metals can be held on the negatively charged sites of 2:138and 1:1 clays, (hydrous) oxides and soil organic matter.3. Specific AdsorptionSpecific adsorption involves the retention of metal ions bysurface ligands (usually 0 and OH groups) on inorganic materials.The ions are held relatively strongly by predominantly covalentor coordinate bonds (Miller et al, 1986).Specific adsorption is pH dependent and is closely relatedto the hydrolysis constant of the metal ions (Brununer, 1986).The latter point is reflected in the affinity sequence of metalions adsorbed onto (hydrous) oxides as it generally follows thefirst hydrolysis constant. A explanation as to the reasons whywas offered in Section 2.2.3.Often specific adsorption of metals results in ions beingadsorbed to a far greater extent than would be expected from theCEC of the soil (Alloway, 1990). Brununer (1986) found how theadsorptive capacities of Al and Fe oxides for zinc were 7 and 26times higher, respectively, than their CECs at pH 7.6.The term ‘specific adsorption’ is usually reserved forinner—sphere metal complexation onto inorganic surfaces where theexact nature of the adsorbent is known.4. Organically BoundIn addition to being an important medium for the retentionof exchangeable cations, organic matter can also adsorb metals by39way of chemisorption. Exchangeable metals are held predominantlyby electrostatic attractions, whereas chemisorption involves theformation of partly covalent bonds. As a result, the metals areheld much more tightly and are considered non—exchangeable. Thismode of adsorption is often associated with the formation ofinner—sphere complexes, which implies the lack of a watermolecule between the metal ion and the ligand. The adsorption ofmetal ions by organic matter was discussed at length in Section2.2.2.5. Oxide occludedOxide occluded is a term used to describe metals which arecoprecipitated during the formation of oxides. Many researchersconsider specific adsorption and coprecipitation to be part of acontinuum, the major difference being that adsorption is a two-dimensional process whereas coprecipitation is three dimensional(Corey, 1981; Brummer, 1986). Bruinmer (1986) identified threesteps of metal retention by goethite. The first step involvedsurface adsorption of the metal ion; the second, diffusion intothe mineral particle; the third, fixation at a certain positionwithin the crystal lattice.Copper, nickel and zinc have all been found to coprecipitatewith iron hydrous oxides (Sposito, 1983), with copper and zincalso showing a strong preference for coprecipitation withmanganese oxides (Drever, 1988).The occlusion of metals is generally regarded as40irreversible, as only a sharp drop in pH or a decrease inoxidation potential is capable of liberating the occluded metals.6. PrecipitatesBeckett (1989) considers this category to include newprecipitates of carbonates, suiphides, phosphates and hydroxides.The precipitates may be of the trace metal itself, orincorporated into the precipitate of a common cation such ascalcium, mangnesium, iron or aluminium.7. ResidualThis is the final and most inert form of metal identified byresearchers. Those workers utilizing a fractionation schemesubject the material to the harshest of reagents and name theliberated metals as those belonging to the ‘residual’ fraction.Silicate clays and unweathered minerals are considered thepredominant source of residual metals.2.3.2. Distribution of copper and zinc in sludge-amended soilfractions.Throughout the literature the general consensus is thatcopper is found predominantly in the organic fraction of theupper horizon of sludge-amended soils (Schalscha et al., 1982;Emmerich et al., 1982). Emmerich et al., 1982 reported thatbetween 50 % and 52 % of the total copper was associated with41organic matter. Sposito et al., (1982) found that more than 60% of the total copper was associated with the organic fraction ata sludge application rate of 90 tons/ha/year. This is hardlysurprising when one considers the stability constants of copperwith known fractions of organic matter.In many of the fractionation schemes designed for soilswithout sludge amendment, provision is made to extract that metalfraction which may be bound to Fe and Mn oxides (Hickey andKittrick, 1984; Shuman, 1985). In sludge amended soils, however,few schemes exist which include a reagent to extract for thatparticular fraction. One possible reason may be that sludgesgenerally do not contain appreciable quantities of (hydrous)oxides, and secondly the soil horizons which would usuallyexhibit the effects of sludge treatment are the upper organichorizons. However, the results of fractionation schemes devisedfor soils without sludge additions, show zinc to be predominantlyin the oxide fraction, whereas in sludge—amended soils thecarbonate fraction (Chang et al., 1984) and the residual fractionseem to be the main sinks of zinc (Eimuerich et al., 1982).The smallest fractions of both copper and zinc were thesoluble and exchangeable fractions. Sposito et al. (1982) foundthat 3.6% of total copper and only 1.6% of total zinc were inthese two fractions. This is in agreement with the findings ofSilvera and Sommers (1977) who fractionated a sludge—amendedsilty loam and found less than 2% of total copper and zinc in thesoluble and exchangeable form. They also reported that the42amount of soluble zinc increased over a 28 day period. This theyattributed to the fall in pH, as Lindsay (1972) had reported thatfor every unit decrease in pH the amount of zinc in solutionincreased 100 fold. In contrast is the decrease in solublecopper over time. The authors proposed several reasons toaccount for this. They are: the formation of copper—organiccomplexes; sorption by hydrous oxides; and precipitation.Various authors have reported the change in metal forms overtime, (Silvera and Sommers, 1977; Emmerich et al, 1982) althoughthere is some debate as to whether the metals become more labileor more stable. Emmerich et al., (1982) found that afterleaching a sludge—amended soil for 25 months, the amount ofcopper and zinc increased in the residual fraction to more than65 % of the total. Conversely, Gaynor and Halstead (1976) afterincubating a sandy loam with sewage sludge for 8 weeks, foundthat the amount of DTPA—extractable copper and zinc increased by3 to 7 times and 7 to 21 times respectively. In accordance withthese results are those of Silvera and Sommers (1977), in whichboth DTPA-extractable copper and zinc increased with time. Theauthors attributed this to oxidation of suiphides, dissolution ofprecipitates and the release of organically bound metals.However, Emmerich et al., (1982) questioned whether theincubation period of 28 days, employed by Silvera and Soinmers,was sufficient time for the metals to stabilize into their finalchemical form. In a much longer study, Sposito et al., (1982)found that application of sewage sludge to an arid—zone field43soiloveraperiodofthreetofouryears,reducedthesizeoftheHNO 3-extractablefraction(suiphide)forbothcopperandzinc.Concomitantwiththiswasanincreaseinthepercentagesofcopperandzincintheorganicandcarbonatefractions.Thisledtheauthorstoconcludethatapplicationofsewagesludgeoverseveralyearsshiftsthemetalformsawayfromsuiphide/residualtothosewhicharemorelabileandpotentiallymorebioavailable.2.3.3.ThebehaviourofcopperandzincinsewagesludgeThroughouttheliterature,referencesaremadeastotheneedtoknowthephysiochemicalformsofthemetalsinsewagesludge,inordertoappreciatetheimpactthatsludge-derivedmetalshaveonthesoilenvironment.Certaintechniqueshavebeenemployedbyresearchers,includingfractionationofthemetalforms(Ruddetal.,1988;AngelidisandGibbs,1989)andthecharacterizationofmetalcomplexationwithsludgeorganicmatter(GouldandGenetelli,1978;FletcherandBeckett,1987).Asapreludetothefollowingdiscussion,oneshouldbearinmindtheconclusionsmadebyHoltzclawetal.,(1978)andLagerwerffetal.,(1976).Afterstatingtheirfindings,theyconcludedthatthereishighdegreeofvariabilitybetweensludgesfromonetreatmentplanttoanother,andwarnedagainstusingtheirresultstomakebroadgeneralizationsonthenatureofmetalsinsewagesludge.TheuseofasequentialextractiontechniquebyAngelidis44and Gibbs (1989) identified organic matter and suiphides as thetwo most important fractions of metals in anaerobically treatedsewage sludge. Their results revealed that greater than 85 % ofboth total copper and total zinc were in the oxidizable phase(30% H20 in HNO3). This phase corresponds to those metalsretained by organic matter and precipitated as or withinsuiphides. Rudd et al., (1988) also employed a sequentialextraction scheme to evaluate the forms of certain metals inseven different sewage sludges. In concordance with the findingsof Angelidis and Gibbs (1989), they discovered that the majorforms of copper were the organic and sulphide phases, whereaszinc was predominantly in the organically adsorbed fraction whichcomprised up to 52% of the total zinc content.Gould and Genetelli (1978) considered organo—metalliccomplexation to be the major mechanism of metal association withsludge solids. Based on stability constants of the metals withorganic solids the authors produced the following affinitysequence:Cu > Zn > Cd > NiThey also concluded that copper was able to form 2:1 complexeswhereas the other metals formed 1:1 complexes. pH was shown toinfluence the behaviour of metal complexation; as at a low pH theextent of metal complexation decreased. The authors postulatedthat this was due to competition for complexation sites byhydrogen ions. It was also noted that of all four metalscomplexation of zinc was the most pH-dependent. With a 1 unit45drop in pH, the solution concentration of zinc increased by anorder of magnitude. In a subsequent paper by the same authors(Gould and Genetelli, 1984) it was reported that the order ofbinding capacity of metals to sewage sludge was:Cu > Cd >> Zn > NiThey also examined the effect of competition by other metals forbinding sites and demonstrated that copper had the strongesteffect when present as the competing metal.Other researchers have examined complexation of metals withcertain sludge organic matter fractions. Holtclaw et al., (1978)stated that greater than 95% of the copper associated withorganic matter was in the humic acid fraction.Many other researchers, due to the environmentalsignificance of metal complexation with soluble organic matter,have focused their efforts accordingly (Fletcher and Beckett,1987a; Fletcher and Beckett, 1987b; Sposito et al., 1976 and Tanet al., 1971). Tan et al. (1977), based on complexation of zinc,considered the most active organic material in sludge solution tobe low molecular weight fulvic acid with strong polysaccharidefeatures. They stated, based on free energy values (Z2Gj, thatchelation of zinc was more favoured than was simple complexation(unidentate bonding).Fletcher and Beckett (1987) reported that soluble organicmatter from digested sewage sludge contained two distinct groupsof exchange sites. The first group was capable of binding Ca2,Mg2, Cu2, Zn2, Ni2, Co2, Mn2, Cd2, Pb2, and Fe3, whereas the46second class of sites was only able to bind Cu2, Pb2 andprotons. They considered both sites to bind by way of ionexchange but gave little indication as to the exact nature of thesites involved. Fletcher and Beckett (1989) in a second paperexamined the complexation of Cu by soluble organics from digestedsewage sludge. Again, they considered the complexation of copperthrough ion—exchange of protons. They found that the maximumuptake of CU2 occurred at a pH 6.5, beyond which copper hydroxidewas formed. At pH 6.5 the maximum amount of copper retained was4.43 x l0 mol/g, which is analogous to that of soil organics(Stevenson and Fitch, 1986). They predicted that at a pH of 5.0the amount of copper bound to the soluble organics would be only14% rising to 99% at a pH of 7.0.Lagerwerff et al. (1976) considered the complex formation ofcopper with soluble organic matter to be a continuous process.Analyses of sewage sludge after one year of storage showed anincrease in the complexation of copper by soluble organics overtime. In contrast, zinc remained in its (II) oxidation stateover the same time span.2.3.4. summaryThrough the application of sewage sludge to land,potentially harmful heavy metals are introduced to the soilenvironment. Whether the metals are, or become, detrimental tothe environment depends in part on their chemical forms. Despitethe shortcomings inherent in sequential extraction, such47techniques provide detailed information as to the metal’savailability and mobility, and thus to its potential as anenvironmental hazard.48CHAPTER 3: MATERIALS and METHODSIn 1989 the GVRD, together with the Department of ForestSciences (UBC), initiated a Sludge Recycling Project to study theenvironmental, ecological and silvicultural implications ofdigested municipal sewage sludge on forested land.3.1. Site DescriptionThe field research was conducted at sites within the UBCMalcolm Knapp Research Forest, north of Maple Ridge, BC. Theannual precipitation of the area is c. 2260 mm. The average meandaily temperature is 17°C during the summer months, and 0°C duringthe winter.The sampling sites were located on a gently undulating southfacing slope of approximately 20%. The tall forest of thesampling area was predominantly Douglas Fir interspersed withnatural Western Hemlock. The stand was approximately 17 yearsold.3.1.1. GeologyAs most of the area was covered by ice during thePleistocene, the superficial geology of the area consists mainlyof basal till overlain by ablation till (Armstrong, 1984) Thebasal till is highly compacted and impermeable to water. It alsoforms a barrier to root penetration. Overlying this is ablationtill, deposited later in the Pleistocene by the retreating49glacier. This forms the present soil mantle (Feller, 1975). Itis reworked in places by meltwater streams and is also mixed incertain areas with colluvium. The underlying bedrock ispredominantly acid igneous material, namely quartz diorite,diorite and granodiorite (Armstrong, 1984).3.1.2. SoilsThe soils of the sampling site are typically Orthic HumoFerric Podzols of the Cannell Soil Series (Luttmerding, 1981).They are typically well-drained and aerated, with a sandy loamtexture. They have a well-developed Bf horizon (> 10 cm) and alaterally discontinuous Ae horizon.The forest floor of these sites is a mor, and has a variableL horizon, ranging in depth from 0 cm to 2 cm. The F+H horizonstogether range in thickness from 5 cm to 8 cm.3.2. Sample CollectionMaterial for both the column experiment and the adsorptionstudy was collected from the buffer zone. The buffer zone is astrip of land 5 metres wide between the non—treated and sludge—treated plots.Due to variability in occurence of the L and the Ae horizon,it was decided to collect only the FH and Bf horizons. Thesematerials were used for both the column and the adsorption study.Decomposed woody material was also collected for use in theadsorption study, as previous logging practices had resulted in50a significant proportion of the forest floor being covered withdecaying logs.3.3. Column StudyColumn studies have been employed by various researchers,most noteably Emmerich et al. (1982), to examine the movement ofheavy metals from surface—applied sewage sludge into the soilbelow. Column studies are particularly useful when evaluating acomplete metal mass balance. There are, however, limitationsinherent in most column studies. Possibly the most documentedshortcoming is the problem of edge—flow effects. Cameron et al.(1990) states that “edge-flow of water and solutes between thesoil and the casing [of the lysimeter) can lead to large errorsin the measurement of solute leaching rates”. This problem of‘edge—flow effects’ is also relevant to those column studiesdesigned to examine the movement and redistribution of metals.3.3.1. Experimental DesignThe design of the experiment was completely randomizedwith a 4 x 3 x 3 factorial arrangement of treatments. In orderto maintain homogeneous conditions, the columns were placed in aplant growth chamber. The number of replications for eachtreatment combination was limited to 3 because of the size of thegrowth chamber.51Table 3.1: Experimental DesignSoil Type I: PHSoil Type II: Bf1(O—2.5cm)Soil Type III: f2(2.5—5.Ocm)Time IntervalsLoading Rate 4 weeks 8 weeks 12 weeks 16 weeks0 kgN/ha x3 x3 x3 x3500 kgN/ha x3 x3 x3 x32000 kgN/ha x3 x3 x3 x33.3.2. Column StructureThe column was designed to allow for the redistribution ofmetals by gravity from the sewage sludge into the soil below.The diameter of the column was 5.7 cm. Figure 3.1 illustratesthe structure of the column and the materials used.From the bottom of the unit to the top, each columnconsisted of the following:(i) 250 niL polyethylene bottle in which to catch theleachate.(ii) tygon tubing and parafilni to secure the drainage ofthe leachate into the bottle(iii) plexiglass plate which acted as the base of thecolumn(iv) very fine nylon mesh to filter out particulatematerial52(v) 5 cm of acid—washed glass beads (0.25 mm to 0.5 nundiameter) to promote the downward percolation offluid through the column and to retain coarseparticles carried down from the material above.(vi) 5cmof<41nmBf(vii) 5cmof<4mmFH(viii) aeration holes around the column to promote aerobicconditions within the PH and Bf horizons by allowingdiffusion of air into the soil.(ix) after the sewage sludge was applied, the distance fromthe surface of the sludge to the top of the column wasbetween 3 cm to 6 cm.3.3.3. Column Materials3.3.3.1. Soil MaterialsBoth the FH and Bf soil horizon types were sieved to a sizeof < 4 mm in order to maintain large pore spaces. Such largepore spaces promote drainage and aerobic conditions typical of aforest soil. 142.5 g of Bf and 45 g of PH (air dried weight)were packed into each column at a constant thickness of 5 cm.The moisture status of the soils was at approximately fieldconditions in order to minimize hydrophobicity, especially in theFH material.53AerationHolesNylonMeshPlexiglassPlateSewageSludgeFigure 3.1: Column DesignFH IBf5cm5 cm85cmTyg onTu bin g543.3.3.2. sewage SludgeThe sewage sludge used in the columns was from the samebatch which had been applied to the sites at the Malcolm KnappResearch Forest. After thoroughly hand mixing the sludge toensure homogeneity, Total Kjeldahl Nitrogen (TKN) was determinedusing the semi-micro method (Page, 1982). The sewage sludge wasthen applied to the assigned columns at a rate of 500 kg N/ha and2000 kg N/ha respectively. The moisture content of the sludge atthe time of application was the same as that applied to sludgetreated sites in the field, i.e. 90 %. The lower rate of 500 kgN/ha was the predominant level of application used in the SludgeRecycling Project, whereas the 2000 kg N/ha was the highest rateof application.3.3.4. Experimental ProcedureAfter sludge application the columns were left for 4 daysin order for the liquid from the sludge to drain through thecolumn. This liquid was then collected from the 500 kg N/hacolumns and termed ‘initial leachate’. However, due tosolidification of the sludge in the 2000 kg N/ha columns thesewage sludge was disturbed using a rubber policeman to promoteaerobic conditions. These columns were then left a further 4days before leachate collection.At the same time as sludge application, the control columns(0 kg N/ha) received 120 mL of deionized water poured through aWhatman No.2 filter paper in order to prevent surface disturbance55of the soil.120 mL of deionized water was applied to all the columnsevery 4 days. This quantity of water was not meant to simulateprecipitation levels, but was chosen to enhance the movement ofmetals from the sludge and through the column. Leachate CollectionFor the remainder of the experiment, the leachate was takenfrom all columns at the following time intervals:1st month, leachate collected every 8 days2nd month, leachate collected every 12 days3rd month, leachate collected every 12 days4th month, leachate collected every 12 daysThe collection of leachate every 12 days during the final 3months of the experiment was initiated when metal concentrationsapproached detection limits.3.3.5. Destructive Sampling of the ColumnsAt the end of each month the respective columns were cutopen and the soil and sewage sludge were sampled as follows:Sludge: as 1 sampleFH : as 1 sampleBf : 0 cm to 2.5 cm as 1 sample2.5 cm to 5.0 cm as 1 sampleThe samples were then air—dried and passed through a 2—mm sieve.563.3.6. Laboratory Analysis3.3.6.1. Leachate AnalysisThe leachate was analysed directly for soluble copper andzinc using a Perkin-Elmer 306 Atomic Absorption Spectrophotometer(AAS).It was observed that in the initial and 2nd leachatescollected from the 2000 kg N/ha columns, particulate matter waspresent. This non—colloidal material was separated from theleachate by centrifugation at c. 19 800 g for 15 minutes. Afterdecanting the leachate, the particulate matter was washed into anevaporating dish and air dried. It was then digested with 30 mLof 15M HNO3 at 80°C for 16 hours and then analyzed for copper andzinc using the AAS. The results were reported on an oven—drybasis. Total Metal Analysis of Soil and Sewage SludgeTotal copper and zinc were determined by acid digestion(Application Note GM-i, CEM Manual) using MDS-81D MicrowaveInstrument. The method of digestion involved adding 10 mL of 15MHNO3 to the sample which was then microwaved for 2 mins and 30 5at 100 % power, followed by 10 mins at 80 % power. Once cooled,5mL of 30 % H20 was added, and the sample left until theeffervescence subsided. The solution was then filtered andanalysed by AAS.573.4. Adsorption Studies: Batch ExperimentsBatch adsorption studies were conducted on FH and Bfmaterial and decomposed woody tissue. The objective was toquantitatively evaluate the adsorption of copper and zinc by eachof the three materials at a constant ionic strength.3.4.1. MaterialsWhen sludge is applied to forested land it is depositedmainly onto the forest floor. Consequently, the nature of therole played by the (L) FH horizon in metal retention is critical.As a result of previous logging practices on the sites ofsludge application at the UBC Reseach Forest, the forest floor islittered with decaying wood. It was therefore decided to examinethe adsorption behaviour of this material, to assess its likelycontribution to metal retention.The nature of metal adsorption by Bf material was alsostudied. This horizon is constant throughout the sites of sludgeapplication, and may, if metals migrate to such depths, beimportant in the retention of both copper and zinc.3.4.2. Method1 g subsamples was weighed in to 50 mL polycarbonatecentrifuge tubes to which 30 ml of O.5M Cad2 was added. Theywere allowed to sit overnight, then centrifuged and thesupernatant decanted. Following this, the samples were washedrepeatedly with 0.O1M CaC12 until the electrical conductivity of58the supernatant equaled that of 0.O1M CaC12. The metal was thenadded, in O.O1M Cad2, at concentrations ranging from 0 ppm to300 ppm. The samples were shaken at 25°C for 24 hours on areciprocating shaker, then centrifuged at 19,800 g for 15 minsand filtered. The reaction time of 24 hours was consideredoptimum based on the work of Harter (1973, 1992). Theconcentrations of Ca, Cu and Zn in the supernatant solution weredetermined using AAS. pH was measured using a Radiometer PHN 62Standard meter. The total amount of metal adsorbed was assumedto be the difference between that added and that measured in theequilibrium supernatant.The concentration of Ca2 in the system was designed tomaintain constant ionic strength at the various metal additions.The choice of 0.O1M Cad2 was based in part on the observationsmade by Petruzzelli (1972). Petruzzelli warns against using anionic strength exceeding 0.O1M, as a greater concentration of Ca2in the system severely represses metal adsorption.The range of metal concentrations used in the study werechosen to reflect the various levels found in soil/sewagesolutions. The upper limit was set at 300 mg/L because inprevious trial runs metal concentrations exceeding 300 ppmresulted in anomalous data.3.5. NMR AnalysisThe FH and woody materials were air—dried and passed through59a 130-.,Um sieve. Solid state 13C NMR with cross-polarization andmagic angle spinning (CPMAS) was then performed on the twomaterials by a Bruker CXP-100 spectrometer.60CHAPTER 4: RESULTS AND DISCUSSION4.1. COLUMN STUDYThe mobility of copper and zinc in soils is critical totheir potential as major groundwater contaminants. The objectiveof this study was to assess the mobility of the metals at twodifferent application rates over a 1,2,3 and 4 month time period.Data from the column study were statistically analysed inorder to identify differences in total copper and zincconcentration as a function of time and application rate for thethree different soil depths. As the data did not meet therequired assumptions for parametric analysis, the data waslog(x+1) transformed (Zar, 1984). Values of r2 are quoted inorder to evaluate what percentage of the variation in means isdue to treatment effects, and what percentage is due to error.Whenever there was a significant difference betweentreatments, the means were separated using Tukey’s (HSD) test.All parametric statistical analyses were performed by SAS.4.1.1. Differences in total copper concentration as a function oftime and application rate. Copper: FH Material (r2 = 0.88)Graph 4.1 reveals how copper concentrations of the FHmaterial were consistently greater for the higher applicationrates at all time intervals. This is reflected in the ANOVA61Fia4.1:FH-ChanaesinTotalCoooer70 60 50 40 30 20 10 0TotalCopper(mg/kg)Time(months)1month2month3month4month0IcgIl/Ia500kgN/ha2000kgN/haTable4.1:AnalysisofVarianceCopperAccumulationinFHSumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.0042820.0021380.260.7711n.s.Time0.0109330.0364354.480.0134*Rate1.1169620.55847968.680.0001*Time*Rate0.1878260.0313033.850.0089*n.s.=notsignificantat the0.05levelof probability*=significantatthe0.05levelofprobabilityTable4.2:MeanValues(mg/kg)CopperAccumulationinFH1month2month3month4month0kgN/ha16.08(1.84)13.65(3.97)14.54(3.49)13.03(1.01)500kgNfha25.08(1.04)23.81(1.14)16.12(7.12)22.78(3.41)2000kgN/ha34.82(3.89)31.48(6.31)31.56(9.40)62.47(2.78)Standarddeviationinbrackets.0• ()Fia4.2:Bfl-ChanaesinTotalCooDer20 15 10 5 0TotalCopper(mg/kg)Time(months)1month2month3month4monthirrrrioIgIl/I—ia500kgN/ha2000kgN/han.s.=notsignificantat the0.05levelof probability*=significantat the0.05levelofprobabilityTable4.4.:MeanValues(mg/kg)CopperAccumulationinBfl1month2month3month4month0kgN/ha12.23(1.91)12.71(1.83)12.01(1.25)12.59(0.99)500kgN/ha14.15(1.80)17.02(1.34)10.59(0.88)12.03(0.72)2000kgN!ha15.02(2.03)14.33(1.07)13.69(1.44)15.19(1.29)StandardDeviationinbracketsTable4.3:AnalysisofVarianceCopperAccumulationinBf1SumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.0024494620.00122470.550.5859n.s.Time0.0312976530.01043254.670.0441*Rate0.031570220.01578517.060.0043*Time*Rate0.044215560.00736913.300.0181*0 U)-c.1-IC0ECC0C)I-czD)a00I-Cz0)-00I-Cz0)-cC0rø—C0ci.0Ec’J-cC0E0)0)a)0.00C)CD Csl 0 CO CJ 0‘— ‘- ¶_ ‘-66Table4.5:Analysisof VarianceCopper AccumulationinBf2SumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.0003821320.000191060.090.9111n.s.Time0.03671 8930.0123066.030.0037*Rate0.009442820.00472142.310.1227n.s.Time*Rate0.01314860.002191351.070.4083n.s.n.s.=notsignificantat the0.05level ofprobability*=significantatthe0.05Level ofprobabilityTable4.6:MeanValues(mg/kg)CopperAccumulationinBf21month2month3month4month0kgN/ha13.09(1.04) aI11.26(2.78) abI10.25(1.14)bI13.97(1.24) al500kgN/ha13.20(0.31) aI13.99(2.61) abI11.80(1.36) bI14.60(0.73) aI2000kqN/ha14.48(0.51)al12.11(1.69)abI12.79(0.27)bI13.59(1.42)alStandardDeviationinbrackets.+where, lowercaselettersrefer toTimeandRomannumeralsrefer toApplicationRate.*Thosefollowedbydifferentletters/RomannumeralsdiffersignificantlyusingTukey’s(HSD) Test(p<0.05).Fia.4.4:FH-ChanaesinTotalZinc100 80 60 40 200TotalZinc(mg/kg)Time(months)01g1\J/Iia5OIcg1’i/ha2000kgN/ha1month2month3month4monthcon.s.=notsignificantatthe0.05level ofprobability*=significantatthe0.05level ofprobabilityTable4.8:MeanValues(mg/kg)ZincAccumulationinFH1month2month3month4month0kgN/ha35.92(1.01) aI33.05(1.87) aI38.22(1.35) bI48.27(1.83) CI500kgN/ha42.83(2.67) all44.92(1.66) all52.88(2.47) bII61.15(3.56) cli2000kqN/ha58.56(5.31) aIII62.81(4.74) aIII70.53(5.51) bIll91.28(2.83) cIllStandardDeviationinbrackets.+where, lowercaselettersrefer toTimeandRomannumeralsrefertoApplicationRate.*Thosefollowedbydifferentletters/RomannumeralsdiffersignificantlyusingTukeyss(HSD)Test (p<0.05).Table4.7:AnalysisofVarianceZincAccumulationinFHSumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.0002081230.000104060.160.8563n.s.Time0.14783420.04927874.010.0001*Rate0.40116520.20058258301.240.0001*Time*Rate0.007067660.001177941.770.1522n.s.-c0EC)N(‘3-czc)000I(‘3-cz00I(‘3-Cz0)--c4-’C-on-Ic,)-C-I-’c0a)-oEC.J-c4-’C0E0)_0)C)CNaS0 LC) 0 tO 0C) CJ CJtO 070Table4.9:AnalysisofVarianceZincAccumulationinBflSumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.0015716220.00078581.510.2483n.s.Time0.0100403130.003346776.410.0027*Rate0.002750520.00137522.640.0941n.s.Time*Rate0.003773260.0006281.210.3405n.s.n.s.=notsignificantatthe0.05level ofprobability*=significantatthe0.05levelof probabilityTable4.10:MeanValues(mg/kg)ZincAccumulationinBfl1month2month3month4month0kgN/ha26.83(1.96) aI25.57(0.76) aI23.76(1.17) bI28.00(3.45)aI500kgN/ha26.05(1.01) aI26.67(1.11) aI23.64(1.48)bI25.49(0.48) aI2000kgN/ha27.42(0.85) aI26.81(1.14) aI25.79(0.74)bI26.84(0.44) aIStandardDeviationinbrackets+where,lowercaselettersrefertoTimeandRomannumeralsrefer toApplicationRate.*Thosefollowedbydifferentletters/RomannumeralsdiffersignificantlyusingTukey’s(HSD) Test (p<0.05).(;1NCLi--c4-.0E-C4-.C0E-c4-.C0Ec’JCl)-c4-.C0EI-(‘5z0)-‘000I(‘5-Cz0)00I(‘5-Cz0)0)0)C)CN(‘5-C0Eo to 0 ‘0 0C’) CSJ C’Jto o72Table4.11:AnalysisofVarianceZincAccumulationinBf2SumofMeanSourceSquaresD.F.SquaresF—RatioProbabilityRep0.00444720.00222353.530.0467*Time0.021048730.0070162411.150.0001*Rate0.0026036320.00130182.070.1503n.s.Time*Rate0.007325760.00122091.940.1189n.s.n.s.=notsignificantatthe0.05level ofprobability*=significantatthe0.05levelofprobabilityTable4.12:MeanValues(mg/kg)ZincAccumulationinBf21month2month3month4month0kgN/ha24.96(0.65)23.44(1.47)20.09(2.76)24.92(2.17)500kgN/ha25.39(1.17)23.70(0.23)22.49(1.41)24.63(1.78)2000kgN/ha26.40(0.68)23.72(0.50)23.55(0.75)23.70(1.43)StandardDeviationinbracketsanalysis (Table 4.1) which indicates a significant interactionbetween the two treatments at the 0.05 level of probability. Atthe 4 month time interval the copper concentration for the 2000kg N/ha application rate was nearly twice that of previousmonths for the same application rate. Copper: Bf, Material (r2 = 0.69)As Graph 4.2 illustrates, the total copper concentrations ofthe Bf1 material varied over time and between application rates.The ANOVA analysis found that the treatment interaction betweentime and application rate was statistically significant (p<0.05).It should be noted however, that the coefficient of determination(r2) is only 0.69 indicating that 31% of the variation is due notto treatment effects but to error. With this in mind one shouldquestion the viability of these results and use extreme cautionin interpretation. Copper: Bf2 Material (r2 = 0.57)Differences in the total copper concentration of the Bf2material due to application rate were not significant (p < 0.05).Treatment interaction between time and application rate was alsonot significant at the 0.05 level of probability. The ANOVAanalysis (Table 4.5) however, does indicate a significantdifference in copper concentration with time. Tukey’s (HSD) testreveals the 3 month time interval to be significantly different(p < 0.05) as the mean value is significantly lower than that of74other months.Again it is important to note the r2 value of the analysis.At only 0.57 the variation due to error is extremely large andtherefore the results of the analysis are not reliable.4.1.2. Differences in total zinc concentration as a function oftime and application rate. Zinc: FH material (r2 = 0.97)As illustrated in Graph 4.4 the overall trend of total zincconcentration seemed to be an increase with both time andapplication rate. By ANOVA the interaction between the twotreatments was found not to be significant, however both theindividual treatments were highly significant at p < 0.05.Tukey’s (HSD) test identified months 3 and 4 as beingsignificantly different from both one another and from months 1and 2. All three application rates were significantly differentfrom one another (Table 4.8). Zinc: Bf1 material (r2 = 0.61)The changes in total zinc concentration due to time werefound to be significant at the 0.05 level of probability.Differences due to application rate were not significant andthere was no significant interaction between the two treatments.Graph 4.5 shows the lack of any obvious trend, although Tukey’s(HSD) test did identify the 3 month time interval as being75significantly different. However, with a r2 value of only 0.61the effect of error becomes significant, accounting for 39 % ofthe variation. Therefore it would be unwise to make anyconclusive statements about treatment effects. Zinc: Bf2 material (r2 = 0.72)The ANOVA conducted on this set of data revealed asignificant difference between the replications at p < 0.05.Therefore any variation seen in the data cannot be attributed totreatment effects and renders all furthur statistical analysisvoid.4.1.3. Metal MovementIn order to compare differences between copper and zinc,values are expressed as a percentage of the total metal contentof the initial sludge. This way values are normalized anddifferences in the initial content are accounted for. Leachate AnalysisGraph 4.7 illustrates how the amount of metal leachedthrough the column had decreased significantly by the 12th day ofthe experiment. Also of interest are the significantly higherconcentrations, in the first 12 days, of both copper and zinc inthe leachate of the 2000 kg N/ha columns as compared to the 500kg N/ha columns. The initial leachate (Day 4) of the 2000 kgN/ha columns had a copper concentration nearly an order of76Ficiure4.7:LeachateAnalysis2000kaN/ha25 20 15 10 5 0MetalConc.(mg/L)412202840525664768496108112Time(Days)Copper““Zincmagnitude greater than of the 500 kg N/ha columns. Furthermore,approximately 75% of total leachate copper came through in thefirst 12 days of the experiment.The disparity in metal leachate concentrations betweenapplication rates can be explained, in part, by the higher liquidcontent of the 2000 kg N/ha sludge application. The voluiue ofliquid of this, the highest sludge application, far exceeded thepore volume of the column material, Consequently uponapplication of the sludge (at 90 % H20) saturated flow occurredthroughout the column. The effect of this was two-fold: firstsoluble copper and zinc flowed relatively unhindered through theFH and Bf material, and second and possibly of greatersignificance was the movement of fine particulate material. Thismaterial was found to contain approximately 78 % of the totalamount of copper, and 64 % of the total amount of zinc leachedover the first 12 days. The exact nature of the particulatematerial was not determined, although Harmsen (1977) reported themigration of sludge—derived metals in association with particlesof clay, oxides and organic matter. The amount of copperleached as particulate matter, as a percentage of the totalcopper content of the initial sludge, was 0.97%, slightly higherthan that of zinc at 0.91%. This difference, however slight, maybe a reflection of copper’s greater affinity for hydrous oxidesand organic matter.It was noted that the 2000 kg N/ha leachate contained agreater amount of copper (0.74%), as a percentage of the initial78sludge, than zinc (0.63%). This slight difference could beaccounted for by the nature of copper in the sludge/soilsolution. Lagerwerff et al. (1976) found that more than 60% ofsoluble copper in an initial sewage sludge aqueous solution wasin noncationic forms, compared to only 12 % of soluble zinc.Emmerich et al., (1982) discovered that only 2 % of copper, insoil solutions amended with sewage sludge, was in the free ionicform compared to 70 % of zinc. Likewise, Behel et al. (1983)found that 87 to 97 percent of soluble zinc in an acid soilamended with sewage sludge was as Zn2 and less than 2 % ofsoluble zinc was associated with soluble organic carbon. Thisall provides strong evidence for the existence of copper inchelated or organically complexed forms in the leachate of sewagesludge. Copper, as an uncharged or negatively charged solubleorgano—metallic complex, will have a greater chance of passingthrough the column without being retained on adsorption sites ofthe column material. Zinc, on the other hand, being in itspredominant Zn2 state, has a greater likelihood of being adsorbedon the negatively charged exchange sites of the FH material. Inagreement with this hypothesis are Hodgson et al. (1965) whostated that chelated forms of metals are much more soluble insoil systems, and therefore would move further through the soilthan their cationic counterparts. Also, Lund (1976) consideredthat metal enhancement beneath sewage ponds is due to themovement of soluble metal—organic complexes.Sposito et al. (1981) produced conditional stability79constants (logcK1, where i denotes the different classes ofcomplexes) for various bivalent metals with fulvic acid extractedfrom sewage sludge. The logcK1 for copper was 3.84, whereas thatfor zinc was 3.54. This supports Lagerweff’s statement that alarger percentage of soluble copper in sludge leachates exists inthe chelated form relative to zinc. Dudley et al. (1986) foundthrough incubation studies of sewage sludge, that the amount ofsoluble copper in the first two weeks reflected soluble carbonlevels. Again this supports the hypothesis that a highpercentage of copper in the leachate existed as soluble organo—metallic complexes.Many researchers however, provide evidence to suggest thatzinc, rather than copper, has the greater mobility in a soilsystem (Tyler and McBride, 1982; Ritter and Eastburn, 1978;Williams et al., 1984; Lagerwerff et al., 1976 and Dowdy et al.,1991). The offered explanation is that the high relativeaffinity of the copper ion for the functional groups of insolubleorganic matter and hydrous oxides encourages retention of themetal and thereby inhibits its movement through the soil.One can therefore conclude that there are in fact twofactors controlling the movement and retention of copper: first,copper, when complexed to soluble organic matter to formuncharged complexes, is able to move through the soil relativelyunhindered; conversely when organic matter is in its insolubleform it will again complex with the metal ion, but will insteadinhibit rather than enhance movement of the metal. Williams et80al., (1984) in agreement, stated that organic matter in itssoluble form may enhance the movement of copper, but as aninsoluble material will react with copper and effectivelyimmobilize the metal.The amount of copper and zinc leached from the controlcolumns was below detection limits. Metal Accumulation in the SoilAs is evident from Graphs 4.1 to 4.6, most metal accumulatedin the top 7.5 cm of the column, whereas relatively littleaccumulated at greater depths. Other researchers found similarresults: Williams et al., 1984; Fiskell et al., 1984; Sidle, 1976and Hinelsey et al., 1972. Williams et al., (1984) examined thedepth of metal movement in soils treated with sewage sludge overa period of six years. They found that copper was limited to adepth of 5 cm below the zone of sludge incorporation, whereaszinc had moved to a depth of 10 cm.Tables 4.13 to 4.16 show the net accumulation of copper andzinc in the different soil materials. The net accumulation inthe FH material is greater for zinc than for copper at bothapplication rates and for all 4 months (except 1 month at the 500kg N/ha where copper accumulation was slightly greater). Thisresult is rather surprising as the well-documented affinity ofcopper for organic matter would lead one to speculate that copperaccumulation in the FH material would far exceed that of zinc.There is, however, a body of evidence to suggest that copper81Table 4.13Total Metal Accumulation in FH material(expressed in mg and as a % of total initial metal in sludge)FHCopper ZinckgNlha mg mg500 0.20 4.56 0.16 4.501 month2000 0.41 2.34 0.50 3.51500 0.22 5.02 0.24 6.752 month2000 0.39 2.22 0.57 4.00500 0.04 0.91 0.32 9.003 month2000 0.37 2.10 0.77 5.41500 0.21 4.79 0.30 8.434 month2000 1.09 6.21 0.94 6.60Table 4.14.Total Metal Accummulation in Bf I material(expressed in mg and as % of total initial metal in sludge)Bf 1Copper ZinckgNlha mg mg %500 0.23 5.25 0.00 0.001 month2000 0.33 1.88 0.02 0.14500 0.51 11.63 0.06 1.692 month2000 0.19 1.08 0.06 0.42500 0.00 0.00 0.00 0.003 month2000 0.20 1.14 0.10 0.70500 0.00 0.00 0.00 0.004 month2000 0.16 0.91 0.09 0.6382Table 4.15Total Metal Accummulation in Bf2 material(expressed in mg and as % of total initial metal in sludge)Bf2Copper ZinckgN/ha mg % mg %500 0.00 0.00 0.06 1.691 month2000 0.07 0.40 0.10 0.70500 0.09 2.05 0.09 2.532 month2000 0.02 0.11 0.10 0.70500 0.10 2.28 0.14 3.933 month2000 0.15 0.86 0.21 1.47500 0.04 0.91 0.15 4.204 month2000 0.00 0.00 0.05 0.3583Table 4.16Total Amount of Metal released from the Sewaae Sludge(expressed in mg and as a % of total initial metal.)Copper Zinckg N/ha mg % mg %500 0.45 10.26 % 0.23 6.47 %1 month2000 1.29 7.35 % 0.99 6.95 %500 0.89 20.30 % 0.41 11.53 %2 month2000 1.37 7.81 % 1.06 7.45%500 0.20 4.56 % 0.48 13.50 %3 month2000 1.12 6.38% 1.39 9.76%500 0.33 7.53 % 0.47 13.21 %4 month2000 1.63 9.29% 1.42 9.98%84exists as an uncharged or negatively charged chelate in thesolutions of sewage sludge (Lagerwerff et al., 1976). Therefore,as a neutral or negatively charged organic complex, copper wouldhave been able to move through the soil without attraction oradsorbtion by the negatively charged functional groups of theorganic material.The level of accumulation of copper in the Bf1 horizon (5.0—7.5 cm) was greater than that of zinc. Explanations to accountfor this, relate to the presence of negatively charged copper—organic complexes. With a pH in the Bf likely below that of thePZNC, the exchange sites of the (hydrous) iron and aluminiumoxides would be positively charged. Consequently the negativelycharged metal complexes would be electrostatically attracted tothe exchange sites of the Bf material. Zinc, on the other hand,which is present in sludge solution predominantly as Zn2, wouldnot be attracted to the protonated functional groups of theoxides.Consistent with observations made by other researchers(Andersson and Nilsen, 1972), there was a greater accumulation ofzinc in the Bf2 (7.5-10 cm) horizon at both rates of application.Yet when one considers the small r2 value of the ANOVA for copperin the Bf2 horizon and the significant difference in replicationsfor zinc in the same material, the reliability of these resultsis certainly questionable.Of great interest and significance is the amount of metalreleased from the sewage sludge. In terms of absolute values the85amount of copper and zinc released from the sludge was greaterfor the 2000 kg N/ha application rate. However, when expressedas a percentage of the initial total metal content, it becameclear that the 500 kg N/ha sludge released more metal relative tothe initial amount (Table 4.16). The most likely explanationrelates to the mass of the 500 kg N/ha sludge which wasappreciably less than that of the 2000 kg N/ha. As a result, agreater percentage of the total mass of the 500 kg N/ha sludgewas decomposed (oxidized and mineralized) thereby releasing agreater percentage of the total metal present in the sludge. Theheight of the column above the surface of the 2000 kg N/ha sludgemay also have played a major role in determining the extent ofsludge decomposition. In order to accommodate the greater volumeof sludge of the 2000 kg N/ha sludge application rate, thecolumns were approximately 8 cm higher than those of the lowerapplication rate. This may have led to a reduction in the amountof light, and associated heat, reaching the surface of thesludge; which in turn would have reduced the rate ofdecomposition relative to that of the 500 kg N/ha sludge.864.1.4. ConclusionThe low r2 values for various ANOVA analyses, and theunaccountable anomalous values, both imply a large component oferror in the study. The conspicuously high accumulation ofcopper in the Bf1 material of the 2 month-500 kg N/ha rate,relates directly to the large amount of copper released from thesludge. For a variety of reasons, 20% of the total coppercontent was released. The exact cause is uncertain, but couldrelate to the lighting position of the columns in the growthchamber. Other flaws in the design of the experiment include thedifference in height between columns of different applicationrates. As previously stated, this would in effect reduce theamount of light reaching the surface of the 2000 kg N/ha sludge,compared to that of the 500 kg N/ha. Consequently there wouldhave been a differential between the rates of sludgedecomposition. Other sources of error include ‘edge effects’(Cameron et al., 1990), inherent in many column studies, andinconsistencies in the packing of column materials.Therefore, acknowledging the amount of error involved in thestudy, one cannot make conclusive statements based on theseresults. It would be more appropriate to consider theseobservations as a basis upon which to generate hypotheses, suchas:1. copper and zinc accumulate in the upper organic horizon(0-5 cm) with relatively little accumulating at greaterdepths;872. metals may initially move in association with fineparticles of sludge—derived oxides, clays and organicmatter;3. over a time period of 4 months, more than 75% of thetotal amount of copper and zinc remain in the sludge.88Chapter 5Copper and Zinc Fractionation of FH materialAfter total metal concentrations were determined on thematerial from the column study, it became apparent that there wasa significant increase in the copper and zinc content of the FHmaterial at the 2000 kg N/ha application rate of the 4 month timeinterval, as compared to that of subsequent months. Based onthis observation, an attempt was made to fractionate the metalsof this material for the three different application rates of the4 month time period. The following describes the chosenfractionation scheme.5.1. MethodThe FH material was air dried and lightly ground to passthrough a 2mm-sieve. A 2 g sample was then placed into a 100 mLpolyethylene centrifuge tube and extracted sequentially accordingto the sequence outlined below.Step 1: Soluble and Exchangeable30 niL of 1M KNO3 was added to the sample and placed on areciprocating shaker for 4 hours. It was then centrifuged for 15minutes at 14,500 g. The supernatant solution was decantedand analysed for copper and zinc by AAS.89Step 2: organically BoundThe next step involved adding 70 niL of O.5M NaOH to thesample which was then shaken for 16 hours, after which thesupernatant solution was decanted. The soil residue was thenwashed twice with 70 niL of deionized water. After each wash thesupernatant was kept and added to the NaOH extract. These threesupernatant solutions were then mixed and a 10 niL aliquot takenand placed in an evaporating dish. To the aliquot, 30 niL of 15MHNO3 was added and put on steam bath at a constant temperature of80°C. This mix was left to digest for 8 to 16 hours, or untilthe solution cleared. The solution was then made up to 50 mL withdeionized H20 and analysed by AAS.Step 3: ResidualThis fraction was determined by the same method used fortotal metal digestion (pg 55); i.e. 10 niL of 15M HNO3 added tothe soil residue and microwaved for 2 mins and 30 s in a MDS-81DMicrowave at 100 % power, followed by 10 nuns at 80 * power.Once cooled, 5mL of 30 % H20 were added, and left until theeffervescence subsided. The solution was then filtered andanalysed by the AAS.5.2. Results and DiscussionIn order to evaluate the variability of the data, standarddeviations were calculated. These results and mean values areshown in Tables 5.1 and 5.2. A second measure, standard error of90the mean, was also calculated, the results of which areillustrated on Figures 5.1. and 5.2.The most striking feature of these values is the magnitude.For instance, the standard deviation of the copper concentrationfrom the residual fraction of the 2000 kg N/ha rate is 69.91indicating an extremely large variation around the mean.Likewise, the standard deviation value of soluble copper from thecontrol FH (0.42) exceeds that of the mean (0.37 mg/kg).An example of such high variability in a sample set is seenin the data for residual fraction, copper concentration at the2000 kg N/ha level of application. Replications #1, #2 and #3are 34.72 mg/kg, 156.58 mg/kg and 36.28 mg/kg respectively. Thevery high value of 156.58 mg/kg for replication #2 appears to beanomalous and contributes to the high standard error. However,with a sample size of only three, the value was not discarded asit was not in fact an outlier.With such high variability the ‘usefulness’ of the data iscertainly questionable. Any differences between fractions orbetween copper and zinc concentrations may not be due totreatment effects, but merely an artifact of the highvariability. As a result it was decided not to perform anyfurther statistical analysis.5.3. Sources of ErrorPossibly the greatest source of error comes from theanalysis of metal concentrations at levels approaching the91Ficiure6..1;CorerFractionation80 60-Cuconcentration(mg/kg)FH:4Month40-2O-0kgN/haSoluble&Exch’able[1OrganicallyBoundResidual—F0-500kgN/ha2000kgN/haApplicationRate‘.060 50 40 30 20 10 0Figure5.2:ZincFractionationZnconcentration(mg/kg)FH:4MonthApplicationRateSoIiible&Exchan’bleOrganicallyBoundResidual0kgN/ha500kgN/ha2000kgN/haC.)Table 5.1: Fractions of Copper (mg/kg)COPPERSoluble & Organically Residual Sum of ActualExchangeable Bound Fractions Total *(mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg)Control 0.37 (0.42) 16.02 (7.93) 22.09 (23.36) 38.48 (15.39) 13.03 (1.01)500 kg NIha 0.00 (0.00) 18.31 (3.96) 17.89 (6.00) 36.20 (2.11) 22.78 (3.41)2000 kg NIha 0.09 (0.16) 18.32 (7.93) 75.86 (69.91) 94.27 (66.54) 62.47 (2.78)Standard Deviation in brackets*Actual Total Metal Concentrations determined by the Total Digestion Method (pg 57) on an independent sample.Table 5.2: Fractions of Zinc (mgjkg)ZINCSoluble & Organically Residual Sum of ActualExchangeable Bound Fractions Total *(mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg)Control 18.32 (4.13) 13.74 (6.87) 24.38 (0.14) 56.43 (10.45) 48.27 (1.83)500 kg N/ha 21.42 (0.49) 45.79 (14.30) 33.25 (2.38) 100.45 (13.07) 61.15 (3.56)2000 kg N/ha 23.77 (1.36) 35.59 (9.74) 55.72 (3.60) 115.07 (8.67) 91.28 (2.83)Standard Deviation in bracketsActual Total Metal Concentrations determined by the Total Digestion Method (pg 57) on an independent sample.94detection limits of the AAS. A good example of this is theanalysis of the organically bound fraction, where a difference of0.01 by the AAS, resulted in a difference of 6.3 mg/kg once thedilution factor was taken into account. The same is true forcopper analysis of the soluble fraction.A second and highly likely source of variation is from thenatural metal variability of the material. With a sample size ofonly three the inherent variability is difficult to assess, butfor the same reason, these highly variable sample sets should notbe discarded. Therefore, a possible means to reduce the errorwould be to increase the number of samples. This would in effectreduce the standard error and allow for the evaluation of‘actual’ differences.5.4. Suggestions to Improve the Fractionation MethodThe most obvious and simple way in which the method could beimproved would be to reduce the level of dilution, especially forthe soluble and organic fractions. For instance, the residueafter NaOH extraction (organic fraction) could be subjected toeither fewer washes, or alternatively the volume of water used ineach wash step could be reduced. Futhermore, the aliquot used todetermine the metal concentration could be larger, or the amountof 15M HNO3 used to digest the aliquot could be reduced.Likewise, the volume of reagent (KNO3) used to extract thesoluble fraction could be decreased, particularly whendetermining copper concentrations.955.5. ConclusionAlthough it was deemed inappropriate to make conclusivestatements about differences between fractions and between copperand zinc concentrations, this exercise was of some value. Itproduced, at best, a method (though not refined) by whichfractions of copper and zinc could be extracted from a highlyorganic material such as the FH. At worst, it gave an insight toerrors which may be avoided or at least reduced. Therefore,although this method indicates an approach to copper and zincfractionation, a great deal more ‘trial and error’ work is neededto achieve a method which is both effective and reproducible.96Chapter 6: Results and DiscussionAdsorption StudiesAn appreciation of the adsorptive behaviour of copper andzinc in forest soils is vital in the evaluation and prediciton ofmetals as potential contaminants. The two main objectives ofthis study were to examine differences in adsorption betweencopper and zinc and between the three different materials.In an attempt to describe the adsorption of copper and zincby the three different materials (FH, Bf and decomposed wood) thedata were fitted to the linear forms of the Langmuir andFreundlich equations. Correlation coefficients (r) were used toevaluate the fit of the data to the two models. The high valuesof r suggest conformance of the data to both models, althoughbased upon correlation coefficient values alone, the Freundlichequation is seemingly superior in describing the data. Thishowever is likely to be in part a result of the insensitivity ofthe Freundlich model caused by the use of log-log plots and thehigh degree of flexibility afforded by the two constants k and n(Bohn, 1979). One of the greatest limitations of the Freundlichmodel is its inability to predict an adsorption maximum. Forthis reason, coupled with the high correlation coefficient valuesfor the Langmuir model it was decided to use the Langmuirparameters, b and Ic, to describe the adsorption of copper andzinc by the three different materials.976.1. Effect of Cad2Based upon the evidence of other researchers, it isgenerally held that the adsorption of both copper and zinc issuppressed, to a small extent, in the presence of O.O1M Cad2(Harter, 1979; Petruzzelli et al., 1979). This experiment wasconducted in 0.O1M CaC12 only, therefore the effect of calcium,in this instance, is speculative.Evidence also exists which suggests that the formation ofmetal—chloride complexes enhances adsorption of metals such aszinc (Padmanabham, 1983). However, in this present study, theformation of metal—chloride complexes was deemed to beinsignificant. This is because the amount of chloride insolution was only 0.3 mmoles.6.2. Adsorption of Copper and Zinc by Fl!. Ef and woody materialAs anticipated the adsorption maxima (b) for the threedifferent materials reflected both the organic matter content andthe CEC. The Bf material had the lowest copper adsorptionmaximum value at 3.00 mg/g, whereas the woody material and the FHmaterial (Part II) had values of 7.05 mg/g and 8.93 mg/grespectively (Table 6.1).The zinc adsorption maximum (b) for FH, woody material andBf were 5.65 mg/g, 4.88 mg/g and 1.68 mg/g respectively. Againthese values reflect organic matter content and CEC. However themagnitude of the values are greater for copper suggesting ahigher affinity of copper for the adsorption sites. The zinc98Table6.1:Langmuir andFreundlichparametersforCuandZnwhereb,adsorptionmaximum, is inmg/g.**signfficant atthe99%confidencelevel.COPPERLangmuirFreundlichKbr2rnKr2rFH2.208.580.990.99**PartI61.493.720.990.99**————PartII8.918.930.990.99**————Bf1.323.000.870.93**1.891.450.990.99**Woody5.567.050.980.99**1.558.460.960.98**ZINCLangmuirFreundlichKbr2rnKr2rFH0.835.650.990.99**1.272.380.990.99Bf0.401.680.940.97**1.290.410.990.99**Woody0.674.880.970.98**1.251.810.990.99**bonding energy constant (k) for all three materials issignificantly lower, (Table 6.1) reflecting the suspected higherstability constants of copper with the functional groups oforganic matter and (hydrous) oxides. This difference betweencopper and zinc will be discussed at length in Section 6.3.7.The importance of soil organic matter for the adsorption ofmetals is well documented in the literature (Kuo and Baker, 1980;Shuman, 1975; Petruzzelli, 1978 and McBride, 1989). Harter(1979) stated that organic matter can be used as a major factorin explaining metal retention differences. Shuman (1975) relatedan increase in the Langmuir bonding energy parameter (k) of Zn2to an increase in organic matter. Furthermore, a study conductedby Sidle and Kardos (1977) on the adsorption of copper and zincby forest soils revealed a positive correlation of the Freundlichadsorption constant (k) with organic matter content and CEC.The Langmuir bonding energy constant (k) relates to thestrength of bond formed between the metal ion and the adsorbent.It also has been defined as the ratio of forward to reverse ratecoefficients, and so in actuality K can be considered as theequilibrium constant of the reaction (Harter & Smith, 1981).There is a highly significant difference between the copperbonding energy constant of the Bf material at 1.32 and that ofthe FH material (Part I, 61.49; Part II, 8.91). One couldspeculate that since the FH material is high in organic matter(70.52%) relative to that of the Bf (5.37%), the organic matterplays an important role in determining the bonding energy of100copper. However, when one considers the woody material with anorganic matter content greater than that of the FH, but with abonding energy constant significantly lower, it becomes clearthat it is the nature of the organic matter which is the crucialfactor.In an attempt to understand the nature of metal adsorptionit is important to ascertain the species of metal which isadsorbed. Harter (1983) stated that at a pH 4 - 5 the only metalspecies in solution would be Cu2 and Zn2. From this one canconclude that at the pH at which the adsorption experiments wereconducted, the predominant metal species would be the divalentcation.6.3. Adsorption of Copper by FH material.On examination of the data points of Graph 6.1. - CopperAdsorption by FH, one is able to recognize two distinct trends.The data points at low equilibrium concentrations (C) follow amuch steeper gradient (Part I) than those at higher C valueswhere the slope of the line is much less (Part II). In order torecognize the significance of these two seemingly separatetrends, the data were resolved into two parts using Langmuir’smulti-site equation:k1bC k2bCx/m = —- ÷1+kC 1+k2C,In applying the assumption made by Holford et al. (1970), that101Ficiure6.1:ConnerAdsorotionbvFHCI(x/m)LangmuirIsotherm25 20 15 10 5 0000. 040 30 20 10 0CI(x/m)010LangmuirIsotherm1-. 0CI(x/m)0LangmuirIsotherm3.50.511.522.53C(mmoles/L)Fiaure6.4:ZincAdsorDtionbvFH0 010.018Cf(x/m)LancimuirIsotherm10.0160.014-0.012-0.010.008-0.0060.0040.002 0r=0.9900.51.522.5C(mmoles/L)Fiaure6.5:ZincAdsorDtionbyWood0.020.0150.01C/(x/m)LangmuirIsothermr=O.980.005-0-0 0IIII00.511.522.5C(mmoles/L)Fiaure6.6:ZincAdsorDtionbyBtC/(x/m)LancimuirIsotherm0120. —101234C(mmoles/L)metal adsorption begins on the second group of sites once theinitial set of adsorption sites are saturated; this equation ineffect allows for the evaluation of the bonding energy constant(k2) and the adsorptive capacity (b2) of Part II withoutinterference from Part I. As a result of this partitioning, thecorrelation coefficient values increased from 0.98 to 0.99 forPart I and II.The bonding energy constant (k) was significantly higher forPart I, (61.49) than for Part II (8.91), suggesting that thestrength of binding at low Cu2 concentrations was much greater.This phenomenon is not uncommon. Davies et al. (1969) found thatthe strength of binding of Zn2 and Cu2 by soil humic acids wasmuch greater at low metal concentrations. Shuman (1975), alsousing a multi-site equation, described the adsorption of zinc byresolving the data into two parts. The bonding energy constantat low Zn2 concentrations was again much greater than at highZn2 concentrations.This change in the slope of the isotherm indicates eithertwo separate mechanisms of retention or two different adsorptionsites. One possible explanation is the formation of multi-layers, as a second monolayer of Cu2 may be held through metallicbonds to the Cu2t ions of the first monolayer. The initialmonolayer would be adsorbed to the organic matter by way of ionicor covalent bonds, whereas the second monolayer would be held bythe much weaker metallic bonds. This mechanism would accountfor the disparity in bonding energies of Part I and II of the108adsorption isotherm.A second and more plausible explanation would be the initialbonding of copper to highly specific sites of the organic matter,and, as copper concentration increases, further bonding to lessspecific sites. This second explanation is more acceptablebecause even the less specific sites of organic matter would havea greater affinity for Cu2 than would a copper ion.The exact nature of the sites represented by Part I of theadsorption isotherm is unclear. What is evident however, basedon the findings of Stevenson (1976) and Senesi et al. (1986), isthat these sites preferentially adsorb Cu2 to form stablecomplexes with a strong element of covalency. Of considerablesignificance in determining the nature of the sites, is evidencefrom Baes (1993). He stated that at low Cu2 concentrations themetal cation was preferentially adsorbed by amine—type N groupsrather than 0—containing groups. Stevenson and Chen (1993) alsoexplained the higher stability constants of CU2 to humic acidsby the higher nitrogen content of humic acids.Once these N—containing ligands become saturated with Cu2the more abundant 0—containing groups become the sites of Cu2adsorption, as reflected in Part II of the isotherm. Thesecarboxylate and phenolic—OH groups still form complexes with Cu2,but the stability constants and bonding energies (k2) are muchlower. The N-containing functional groups, which despite the factthat they adsorb Cu2 preferentially, are relatively few innumber. As a result, Part I of the isotherm has a much lower109adsorption maximum (b1=3 . 72). The 0-containing functionalgroups, which are major contributors to the acidic nature ofhumic substances (Stevenson, 1982), provide a greater number ofsites for copper adsorption, hence the higher adsorption maximum,b2=8. 93.Part I may also represent sites of multi—dentatecomplexation i.e. chelation, with Part II reflecting sites ofmono—dentate or simple complexation. The involvement of two ormore sites in metal adsorption would account for the higherbonding energy values of Part I (Stevenson and Ardakani, 1972).6.4. Adsorption of Copper and Zinc by FH and woody materialAlthough organic matter does certainly influence the extentand nature of metal adsorption, it becomes clear from theLangmuir parameters b and k for FH and woody material, that it isnot the amount but the nature of the organic compounds which isthe critical factor. In an attempt to assess the basicdifferences between the organic nature of the two materials, ‘SC—NNR was conducted on both materials.6.4.1. NMR AnalysisNMR analysis was conducted on the FH and woody materials toprovide evidence which may account for differences in theiradsorptive behaviour.The spectra of the woody material are dominated by signalstypical of guaiacyl lignin (Figure 6.7 and 6.8.). This is not110surprising as the lignin of gymnosperms consists exclusively ofguaiacyl units (Preston, 1993). The reddish brown appearance ofthe wood indicates degradation by way of brown-rot fungi. Theseorganisms degrade polysaccharides and other carbohydrates toleave a dark reddish brown crumbly structure, the majority ofwhich is lignin (Preston et al., 1990).The signals of the guaiacyl units include methoxyl C at 56ppm and aromatic and phenolic C at 110-160 ppm (de Montigny etal., 1993). The phenolic region of 141—159 ppm on the woodyspectra is comprised of signals from a number of sources;guaiacyl C3 at 148 ppm, free C4—OH at 146 ppm and C4 in C—O—C4ether linkages at 153 ppm. The individual components are notresolved on the spectra, but together produce a single peak at148 ppm (de Montigny et al., 1993). The aromatic region (96—141ppm) encompasses guaiacyl C1, C2, C5 and C6.The broad shoulder at 0-50 ppm is the aliphatic region andmost likely reflects the selective preservation of resins andwaxes of the original wood (deMontigny et al., 1993). The smallpeak at 30 ppm on the protonated spectra of the woody material ischaracteristic of aliphatic- CH2 — units in long chains, such asfatty acids. However, the same peak on the FH spectra is muchlarger, reflecting the greater degree of decomposition (Prestonet al., 1989).Concurrent with lignin decomposition there is a decrease inthe methoxyl and phenolic C content. On the assumption thathumic substances are in part derived from decomposed woody1]1FH220 200 110 150 140 120 100 10 60 40 20 0 —20 —40ppNWoodyi ‘ r•220 200 160 160 140 i20 100 60 60 40 20 0 —20 -40Figure 6.7. ‘3C (protonated) CPMS NNR Spectraof FH and Woody Material.112FHWoodyI’I’I’Il’IjJ’J1’1lT220 200 80 60 40 120 100 eo 60 40 20 0 —20 —40PPMFiqure 6.8. ‘3C (non—protonated) CPNAS NNR Spectraof FH and Woody Material.113Table 6.2: Relative Percentages of C in Chemical ShiftRegions of Woody and FH materialChemical Shift Region I PPM RangeA B C D E F G0—50 50—60 60—96 96—141 141—159 159—185 185—210Wood 17 19 18 27 10 3 6FH 24 12 17 29 6 6 7HH— Ce—OHH—H— CaOH96Q2CN4OCH3{OH0—Figure 6.9: Gualacyl Lignin Structural Unit114tissue, the demethylation associated with decomposition isreflected in the relatively low percentage of methoxyl in the FHmaterial (12%) compared to that of the woody material (19%).Along with deinethylation there is also a change in the aromaticregion as the intensity moves from 115—125 ppm to 130 ppm. Thisshift in intensity becomes evident upon comparison between thetwo spectra (woody and FH), and reflects the higher degree ofdecomposition associated with the FH material (deMontigny et al.,1993)The peak at 175 ppm is representative of the carboxylcarbon. The presence of this functional group suggests oxidationand decomposition of the material. As expected, the carboxylcomponent is greater in the more humified FH material (6%) thanin the woody material (3%).In general, the differences between the two materialsrepresent the extent of decompostion and humification. Thegreater carboxyl content of the FH is concomitant with the lossof OCH3 and phenolic C. This is reflected in the functionalgroup content of the two materials and consequently in theirrespective behaviour of metal adsorption.6.4.2. Differences between the Adsorptive Behaviour of the FHand woody material.It is evident from the magnitude of the Langmuir parameters,b and k, that both CU2 and Zn2 had a higher bonding energy andadsorption maximum for the FH material. This suggests a higher115affinity for these sites and the formation of stronger complexesthan those of the woody material. The ‘3C—NNR spectra revealedbasic differences between the two materials which may account forthe differences seen in metal adsorption.As expected the woody material has a high proportion oflignin relative to the FH. Cameron and Sohn (1992) conductedwork on the functional group content of humic acids and producedconditional formation constants for Zn2. They categorized thehumic acids based upon lignin content and found larger formationconstants (K) for the lignin-poor humic acids. This differencewas explained in part by the high degree of aromaticity,characteristic of lignin. Although the carboxyl contents of thelignin-rich and lignin-poor humic acids did not differsignificantly, the authors considered the steric flexibilityafforded by carboxylic groups situated at the end of “rather longaliphatic chains” of the lignin-poor humic acids, made chelationof zinc easier to achieve. This occurrence of chelation oversimple complexation explained the higher formation constantsfound in the lignin-poor humic acids.Sohn (1985) explained low humic acid—metal formationconstants of some sedimentary humic acids by a high methoxyl-low phenolic content. The author stated that a high methoxylcontent resulted in fewer chelation sites and as a result metalswere retained by way of simple complexation. Sohn also suggestedthat the electron-donating methoxyl groups may destabilize thephenolate and carboxylate ions, thereby making them weaker116ligands. Furthermore, Davis et al. (1979) showed how methylationof humic acids reduced the adsorption of Cu2. With this in mind,the demethylation associated with the decomposition andhumification of organic material may explain in part the higherbonding energy constant and adsorption maximum of the FHmaterial.With humification of organic material there is a concomitantincrease in the oxygen content. Therefore as the materialbecomes more humified the number of carboxyl groups increasesrelative to the amount of phenolic groups. Work done by Sohn andWeese (1986) revealed the high degree of selectivity of copperfor carboxylic groups in humic acids, thereby providing furtherconfirmation for the higher adsorption maximum and bonding energyconstant associated with the FH material. Along with anincrease in oxygen, with increased humification, there is arelative increase in the nitrogen content. As nitrogen is an‘intermediate’ ligand (McBride, 1989), both copper and zinc havea high affinity for nitrogen-containing functional groups such asamine (-NH2). Cameron and Sohn (1992) explained in part, higherzinc formation constants with humic acids, by their high aminoacid content.Stereochemistry may also provide further evidence to accountfor the lower K and b parameters of the woody material. Variousauthors (Schnitzer and Skinner, 1965, Wood, 1961, Bloom, 1981 andSenesi, 1992) suggest the presence of salicylic acid and phthalicacid type groups in humified soil materials. Both acids have117functional groups in the ortho position, thereby encouragingchelation of metal cations.6.5. Differences between Copper Adsorption and Zinc Adsorptionby FH and woody material.The higher adsorption maximum and bonding energy for Cu2,as compared to Zn2, on the sorption sites of FH and woodymaterial reflect copper’s affinity for these sites. Althoughthere is no evidence from this study that CU2 forms multi—dentatecomplexes, what is clear from the higher bonding energyconstants, is that CU2 does form stronger complexes with organiccompounds than does Zn2. This observation is concordant with theformation constants (log K) reported by Schnitzer and Skinner(1967):pH 3.5 pH 5.0Cu2 5.78 8.69zn2 1.73 2.34This preference for Cu2 rather than Zn2 by adsorption sites oforganic matter is well documented in the literature. Work doneby Kuo and Baker (1980) on soils varying in organic mattercontent and CEC revealed how copper was preferentially adsorbedover zinc. Kurdi and Doner (1983) also found a greater amount ofcopper adsorbed relative to zinc. They also noted how copperadsorption was not significantly affected by the presence ofzinc, though copper severely suppressed zinc adsorption. Work118done by Zabowski and Zasoski (1987) on forest soils, againrevealed how copper was adsorbed in preference to zinc.6.6. Adsorption of Copper and Zinc by Bf materialThe differences in the adsorption parameters, b and k,between the highly organic materials (FH and wood) and themineral material (Bf) can be explained in part by the nature ofthe major constituents. The organic matter content of the Bfmaterial is more than an order of magnitude smaller than that ofthe FH. Moreover, the organic matter which is present is likelyto be associated with the iron and aluminium oxides. Conseqentlythe CEC, bonding energy constant and adsorption capacity are muchlower. Even so, copper and zinc may be retained throughadsorption by Fe and Al (hydrous) oxides which are the majorconstituents and sites of metal adsorption in Bf material.As with the FH and woody materials, adsorption of copper andzinc by the Bf material lead to an increase in H ion activity,indicative of specific adsorption (Kinniburgh and Jackson, 1981).McLaren and Crawford (1972) noted how the Langmuir constants band k for the specific adsorption of Cu2 decreased in the order:Mn oxides > organic matter > Fe oxides > clay mineralsTheir observations are concordant with those presented in Table6.1.The nature and number of adsorbing sites, though significantin determining the adsorption capacity and bonding energyconstant of metals, may not be the only controlling factor.119Various authors (Kuo and Baker, 1980; McBride and Blasiak, 1979;Duquette and Hendershot, 1990) have postulated that at a low pH,metal adsorption by soils with significant amounts of aluminium(hydrous) oxides may be inhibited by the Al3 ion. Kuo and Baker(1980) noted how at a pH < 5 the adsorption of copper by acidicsoils was suppressed due to the increase of soluble A13. Theyattributed this increase of Al3 to the dissolution of aluminium(hydrous) oxides, which was then able to compete with CU2 foradsorption sites. Duguette and Hendershot (1990) studied copperand zinc adsorption by the Bf horizon of an Orthic Ferro-HumicPodzol. They reported a decrease in metal adsorption withincreasing Al3 activity and considered the concentration of Al3in solution to be an important controlling factor in theadsorption of metals. This is consistent with the work ofMcBride and Blasiak (1980) who suggested that at a low pH (4.0 to5.4), Al3 can be dissolved from the solid phase and successfullycompete with Zn2 for adsorption sites.As Al3 seems capable of competing for sorption sites withboth Cu2 and Zn2 it seems highly likely that it would also beable to compete with Ca2. Therefore at the pH at which theseexperiments were conducted it would be unreasonable to assumethat all the exchange sites were saturated with calcium.The effect of Al3 on the adsorption of copper and zinc byboth the FH and woody materials is considered to be negligibledue to their highly organic nature and low mineral content.120As with the other two materials, the bonding energy constant(k) of the Bf material was greater for Cu2 than for Zn2. Thisreflects the higher affinity of CU2 for the adsorption sites ofthe Fe and Al (hydrous) oxides. Venkataramani’s affinitysequence (1978) of metals for Fe oxides supports this finding:Cu2 > zn2 > Ni2 > Mg2as does the following affinity sequence of metals onto Alhyroxides by Kinniburgh et al. (1976):Cu2 > Pb2 > Zn2 > Ni2 > Co2 > Cd2 > Mg2This order of sequence is thought to parallel the firsthydrolysis constant of the metal ions (McKenzie, 1980; James andHealy, 1972) as metal hydroxo complexes have a higher affinityfor adsorption sites than do their unbound counterparts (Elliotet al., 1986). However, as the pK1 of CU2 is 7.34 and Zn2 is9.14 (Geological survey, 1974) it would seem unlikely that suchhydroxo complexes existed at the pH at which the experiment wasconducted (pH 3.7 - 4.5). In fact at such a pH the probablespecies of copper and zinc in solution would be 100 % Cu2 andZn2 (Harter, 1983).6.7. Effect of Adsorption on pHThe adsorption of both CU2 and Zn2 by the three differentmaterials resulted in a decrease in pH (Tables 6.3 to 6.8). Thisindicates the displacement of H ions by the metal cation. TheH/Metal stoichiometry was not determined as back titrations ofthe final solutions were not performed. There is still some121* D -I.-D*-I. £1)-D—“SD.1—c)3 0 CD (I) B I--I CD 0 D 0 -h C’, 0 C — 0 D)LOOàOG)OJOCDo:o)cJ1C,)—’4P91:CYICD-’o)icDr)bocoO)Ci)LO.JC.OC))C,)OO)C)43 0 CD C,, I oO =-l CD 0) C, 0 U U CD-‘ CD 0 D — 0 -IlD 0 0 C — 0 D UOC)(DO(OO1*O)-I CD 0) Ci) C, 0 U U CD I* Ci)()1CDCCl)‘-CDD•=1) t%3—TO)j’OO)O-r-.-c)o)coLoCU=rcrI0.C’)C)CO—0.C)ICCUIwC)EI.01±Oo.Dc..C)—a)—CC(1)ctCV)C’JO)(DO)COCC.0o‘-DF-OC’Jc)CO.2o‘-ccjoa)c’JcIOOQOC’iCóC’i‘ONO-co.EEJc’)ci-LOLC)<<VI0_CbI_EE.0—.0U)—U)0..CU..1cocor-coLncoc’J—CUCI)F.CO’—C’J’-O)CD—C).OF’.CDC)i-COF’-.C).OF’.LOC)OI”-LD.S.ai..00Cw)Cw)E.DiC,,C4‘-4*-5I(LOO3‘-I.0oizc,iL.ib2.—.J-(OC)O%JCD-oCl)O)Qz101°13 I-I-500030__L.-LO.O°N2-N-‘.à-’-bioCDZ05Or)CDCJ1C1’CDQQ3CD.aD•1<.030CD3**C)CA)CC)C)C)oc3s4C)iO)CJiUi(jiC)iOvalue, however, in comparing pH values of the final Cu2+ and Zn2and between the three different materials. Caution, however,should be taken in comparisons between materials as theirbuffering capacities differ.The adsorption of Zn2 by FH material led to a drop in pH of0.17 units, whereas the corresponding amount of Cu2 adsorbed(mmoles/g) resulted in a decrease in pH of approx. 0.35 units.This contrast in values supports the theory of multi-dentatecomplexation of Cu2 (as opposed to simple monodentatecomplexation of Zn2j because a greater number of H ions arereleased upon adsorption of Cu2. Kurdi and Doner (1983) alsofound a greater decrease in pH upon adsorption of copper thanthey did for zinc. They attributed this to the higher affinityof copper for the adsorption sites. The same trend was alsofound in woody material, where the drop in pH was only slightlygreater for CU2 (0.19) than for Zn2 (0.16). This smalldifference in pH values between Cu2 and Zn2 for woody materialsuggests that there are fewer sites for multi—dentatecomplexation of CU2 and that both metals were retained by similarmechanisms. This is in agreement with Sohn (1985) who statedthat humic acids with a high methoxyl content have fewerchelation sites.One further point of note is the magnitude of the drop in pHby FH material upon adsorption of equal amounts of Zn2 (0.13),as compared to that of woody material (0.16). From this, one caninfer that the mechanism of retention of Zn2 is similar in both125materials. Based on the magnitude of the drop in pH the proposedmechanism of adsorption would be simple/monodentate coiuplexation.The largest drop in pH, for both Cu2 (0.74) and Zn2 (0.76),was found upon adsorption by the Bf material; yet the actualamount of metal adsorbed was far less than that adsorbed by theFH and woody materials. One explanation to account for thislarge drop in pH would be the chelation of both metals to theadsorption sites of Fe and Al (hydrous) oxides. Such anexplanation however, is in complete contrast to evidence cited inthe literature, which is constant in its opinion that organicmatter has a greater affinity for Zn2, and especially Cu2, thando Fe and Al (hydrous) oxides. Another more plausibleinterpretation of these results, which is in keeping with thefindings of Duquette and Hendershot (1990), would be, at the pHvalues of the equilibriuju solution, the dissolution of oxides andamorphous inorganic and organic forms of aluminium. Thesubsequent effect of this would be an increase in theconcentration of Al3 in solution. As the equilibrium pH of thesolutions were between 3.7 and 4.6, the Al3 would readilyhydrolyse and thereby contribute to the acidity of the solution.The similar magnitude in the drop in pH after equivalentamounts of Me2 were adsorbed, suggest a similar mechanism ofretention of Cu2 and Zn2 by the Bf material. Although more CU2than Zn2 was adsorbed at equivalent metal—ion additions thepresence of Al3 may have played an important role in determiningthe prevalent adsorption mechanism. In fact, Duquette and126Hendershot (1990) warn against making conclusive statementsregarding the nature of metal adsorption when sorptionmeasurements are made at a pH of less than summary of Observations1. The adsorption of copper and zinc by FH, Bf and woodymaterials can be described by both the Freundlich and theLangmuir adsorption models.2. The adsorption of both metals by the highly organic FH andwoody materials produces higher adsorption capacities (k) andbonding energy constants (b) than does adsorption by the Bf.3. By using the Langmuir model to describe the adsorption ofcopper on to FH material, it appears that the adsorption involvestwo separate phases. The initial phase, at low copperconcentrations, represents sites with a high affinity for copper,reflected in the greater bonding energy constant. Conversely, athigher equilibrium concentrations, copper is bonded by siteswhich have a lower affinity for the metal (lower bonding energyconstant) but have a higher adsorption capacity. Based onevidence in the literature, these highly specific sites —represented by Part I of the isotherm- are likely N-containingfunctional groups, whereas the second phase of adsorption isdominated by the carboxylic and phenolic groups.1274. All three materials adsorb copper at a higher bonding energyconstant and to a greater extent (higher adsorption capacity)than zinc. This indicates a greater affinity for copper by theadsorption sites of all three materials.5. The release of H ions upon metal adsorption by the FHmaterial is greater for copper than for zinc. This observation,in conjuction with the relatively high bonding energy constant ofcopper, supports the hypothesis that this metal is adsorbed bythe PH material through multi-dentate complexation. Zinc, on theother hand, is likely to be adsorbed by way of simple ormonodentate complexation.Based on the relative drop in pH upon adsorption of bothcopper and zinc by the woody material, it appears that bothmetals are retained by a similar mechanism.The mechanism of retention of metals by Bf material isobscured by the relatively high buffering capacity of thematerial. Problems stem from the solubilization of Al3 and thesubsequent hydrolysis of the cation.6. The results indicate a greater affinity for both copper andzinc by the FH material than by the woody material. Thisdifference can be explained simply in terms of the degree ofdecomposition evident in the results of the NNR spectra. Themore huiuified PH material has a lower lignin and methoxylcontent, and a higher proportion of carboxyl and phenolic groups.128Consequently, the bonding energy constants and adsorptioncapacities are much higher.6.9. ConclusionThe disposal of sewage sludge on forested land introducesthe potential for contamination of soil, groundwater and surfacewater by the sludge—borne heavy metals. The extent of thepotential contamination is in part dependent upon the adsorptionbehaviour of the metals by the forest soil.Based on the adsorption capacities and bonding energyconstants generated by the Langmuir model, the role played by theFH material in metal retention is paramount. Its highly organicnature makes it an ideal sink for metals such as copper and zinc.The same is also true for woody material, though to a lesserextent. It appears that the more decomposed the organic matterthe greater its contribution to metal retention.In accordance with observations reported by other researchers,copper was consistently adsorbed at a higher bonding energy andto a greater extent (higher adsorption capacity) than was zinc.Based on the relative magnitude of these two parameters, copperis less likely to move through the soil as a free ion.In conclusion, any evaluation of the pollution potential ofsludge—derived metals in a forest soil should incorporate anassessment of the depth and extent of the (L)FH horizon.1296.10. RecommendationsThough all the objectives of the study were realized, manyother questions arose from the results: the actual sites ofadsorption were unclear, as were the mechanisms of retention.The Langmuir model is useful insofar as it provides qualitativedata, but in order to gain a better understanding of the actualmechanisms and sites of adsorption, Electron Spin Resonancestudies are needed. Also of use would be kinetic experiments.These would provide quantitative data from which thermodynamicfunctions and adsorption energies can be calculated.However, if similar batch studies were to be performed, thenit may be more constructive to use a background electrolyte whichwould simulate that of sludge solution, such as the use of anitrate salt.130CHAPTER 7: SUMMARY AND CONCLUSIONDue to problems of variability and low metal concentrations,the results of the Column Study do not provide a basis upon whichdefinitive quantitative conclusions can be drawn. It wouldtherefore be more appropriate to view these results asqualitative observations, such as:(i) at the highest sewage sludge application rate, a smallproportion of the metals initially moved right throughthe column, of which a significant percentage wasassociated with fine particulates of sludge-derivedoxides, clays and organic matter.(ii) the majority (i.e. 75 %) of both copper and zinc,rather than being redistributed through the column,remained in the sewage sludge after 4 months.(iii) of the 25 % of copper and zinc which moved out of thesewage sludge, the bulk accumulated in the upperorganic FH horizon.The accumulation of copper and zinc in the FH horizon can beexplained in part by examining the more definitive results of theAdsorption Study. It became evident that this highly organicmaterial was able to adsorb copper, and to a lesser extent zinc,at a higher bonding energy constant and at a greater capacitythan either the Bf or woody material. This higher affinity ofboth metals for the FH material, relative to that of the Bf131material, can be explained in part by the nature of the majorconstituents. The organic matter of the Bf material is more thanan order of magnitude smaller than that of the FM. Moreover, theorganic matter which is present is likely to be associated withthe iron and aluminium oxides. Consequently, the CEC, bondingenergy constant and adsorption capacity are much lower.Although organic matter does certainly influence the extentand nature of metal adsorption, it becomes clear from the higherbonding energy constant and adsorption maximum for the FHmaterial, compared to that of the woody material, that it is thenature of the organic matter which is the critical factor. ‘3Csolid—state NMR analysis demonstrated basic differences in theorganic nature of these two materials.The results showed that the FM material had a lower lignin andmethoxyl content, and a higher proportion of carboxyl andphenolic groups than the woody material. Consequently, thebonding energy constants and adsorption capacities of the FHmaterial are much higher.Using the Langmuir adsorption isotherm, the adsorption ofcopper by FM material displayed two distinct phases. The initialphase, at low copper concentrations, represents sites with a highaffinity for copper, reflected in the greater bonding energyconstant. Conversely, at higher equilibrium concentrations,copper is bonded by sites which have a lower affinity for themetal (lower bonding energy constant) but have a higheradsorption capacity. Based on evidence in the literature, these132highly specific sites- represented by Part I of the isotherm -are likely N—containing functional groups, whereas the secondphase of adsorption is dominated by carboxylic and phenolicgroups.The critical role played by the FH material in the retentionof heavy metals, has significant implications for the selectionof sites for sludge disposal on forested land. Sites in whichthe (L)FH horizon has been depleted, either through loggingactivities, site preparation or forest fires, should not beconsidered for sludge appplication, unless a horizon with similaradsorptive properties is present. 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Vol.128 No. 5: 257—266.145APPENDIX 1A: COLUMN STUDYCOPPER ACCUMULATION IN THE SOIL MATERIALSTotal Copper Concentrations(mg/kg)FH1 MONTHCONTROL#1 #2 #316.75 17.50 14.00500 kg N/ha#1 #2 #324.25 26.25 24.752000 kg N/ha#1 #2 #333.25 39.25 31.962 MONTHCONTROL#1 #2 #311.40 18.23 11.31500 kg N/ha#1 #2 #324.63 22.51 24.282000 kg N/ha#1 #2 #334.8 35.44 24.203 MONTHCONTROL#1 #2 #313.80 11.48 18.34500 kg N/ha#1 #2 #324.26 13.09 11.022000 kg N/ha#1 #2 #325.62 26.67 42.44 MONTHCONTROL#1 #2 #312.23 12.69 14.17500 kg N/ha#1 #2 #320.75 26.72 20.882000 kg NJha#1 #2 #364.73 63.32 59.36Total Copper Concentrations(mg/kg)Bfl (0 — 2.5 cm)1 MONTHCONTROL#1 #2 #314.36 11.64 10.68500 kg N/ha#1 #2 #312.62 16.13 13.692000 kg N/ha#1 #2 #314.34 17.30 13.412 MONTHCONTROL#1 #2 #314.82 11.54 11.78500 kg N/ha#1 #2 #317.01 18.37 15.692000 kg N/ha#1 #2 #313.88 13.56 15.553 MONTHCONTROL#1 #2 #310.58 12.55 12.89500 kg N/ha#1 #2 #310.77 11.37 9.632000 kg N/ha#1 #2 #315.81 12.61 13.284 MONTHCONTROL#1 #2 #313.47 11.51 12.78500 kg N/ha#1 #2 #311.98 11.33 12.772000 kg N/ha#1 #2 #314.04 16.58 14.94 146APPENDIX 2A: COLUMN STUDYZINC ACCUMULATION IN THE SOIL MATERIALSTotal Zinc Concentrations Total Zinc Concentrations(mg/kg) (mg/kg)FH Bfl (0 — 2.5 cm)1 MONTH 1 MONTHCONTROL CONTROL#1 #2 #3 #1 #2 #335.00 37.00 35.75 28.50 27.33 24.87500 kg N/ha 500 kg N/ha#1 #2 #3 #1 #2 #345.75 40.50 42.25 24.89 26.73 26.532000 kg N/ha 2000 kg N/ha#1 #2 #3 #1 #2 #352.50 80.75 62.43 27.40 28.27 26.582 MONTH 2 MONTHCONTROL CONTROL#1 #2 #3 #1 #2 #334.71 31.03 33.41 26.32 24.81 25.57500 kg N/ha 500 kg N/ha#1 #2 #3 #1 #2 #346.74 44.51 43.50 27.95 26.06 25.992000 kg N/ha 2000 kg N/ha#1 #2 #3 #1 #2 #365.84 65.24 57.34 27.76 27.13 25.553 MONTH 3 MONTHCONTROL CONTROL#1 #2 #3 #1 #2 #336.69 38.73 39.24 25.04 22.73 23.52500 kg N/ha 500 kg N/ha#1 #2 #3 #1 #2 #350.63 55.52 52.49 23.99 24.92 22.022000 kg N/ha 2000 kg N/ha#1 #2 #3 #1 #2 #372.91 84.23 74.46 26.36 24.96 26.054 MONTH 4 MONTHCONTROL CONTROL#1 #2 #3 #1 #2 #349.46 49.19 46.17 25.75 31.98 26.28500 kg N/ha 500 kg N/ha#1 #2 #3 #1 #2 #359.20 65.26 58.98 25.49 25.01 25.972000 kg N/ha 2000 kg N/ha#1 #2 #3 #1 #2 #390.33 94.46 89.04 26.42 26.79 27.30 149APPENDIX 2A: COLUMN STUDY (Cont.)ZINC ACCUMULATION IN THE SOIL MATERIALTotal Zinc Concentrations(mg/kg)Bf2 (2.5 — 5.0 cm)1 MONTHCONTROL#1 #2 #325.53 25.10 24.26500 kg N/ha#1 #2 #324.33 26.64 25.192000 kg N/ha#1 #2 #326.96 26.61 25.642 MONTHCONTROL#1 #2 #323.69 21.86 24.77500 kg N/ha#1 #2 #323.43 23.87 23.792000 kg N/ha#1 #2 #324.01 23.15 24.013 MONTHCONTROL#1 #2 #322.69 17.19 20.39500 kg N/ha#1 #2 #322.21 21.24 24.022000 kg N/ha#1 #2 #323.60 22.78 24.274 MONTHCONTROL#1 #2 #323.94 23.41 27.41500 kg N/ha#1 #2 #326.68 23.42 23.792000 kg N/ha#1 #2 #323.79 22.23 25.08 150APPENDIX 2B: COLUMN STUDYZinc Concentration in the Sewage Sludge at each Time Interval(mg/kg)SEWAGE SEWAGESLUDGE SLUDGE500 kg N/ha 2000 kg N/ha1 MONTH 1 MONTH#1 552.91 #1 508.64#2 567.62 #2 523.60#3 540.17 #3 503.832 MONTH 2 MONTH#1 575.18 #1 473.98#2 525.33 #2 496.00#3 473.48 #3 549.453 MONTH 3 MONTH#1 459.43 #1 482.47#2 507.77 #2 493.19#3 443.93 #3 486.274 MONTH 4 MONTH#1 422.21 #1 549.01#2 454.64 #2 494.46#3 501.30 #3 505.61APPENDIX 2C: COLUMN STUDY — ZINCZinc Concentration of Initial Sewage Sludge#1 #2 #3 Mean479.00 506.93 496.43 494.12Zinc Concentration of Original FH material#1 #2 #3 Mean11.22 13.17 10.48 11.62Zinc Concentration of Original Bf material#1 #2 #3 Mean12.48 13.69 14.58 13.58 151APPENDIX 3A: LEACHATE ANALYSISCOPPER2000 kg N/ha ColumnsVol. Vol. leachate Copper COPPER COPPERDays Leached Leached Made to Soluble Soluble Part.(mL) (L) (mL) (mg/kg) (mg) (mg)1 mth,#1 4 141.00 0.14 250.00 0.14 0.04 0.1712 132.00 0.13 250.00 0.08 0.02 0.0420 159.00 0.16 200.00 0.01 0.0028 164.00 0.16 200.00 0.17 0.031 mth,#2 4 145.00 0.15 250.00 0.16 0.04 0.1512 95.00 0.10 250.00 0.18 0.05 0.0420 177.00 0.18 200.00 0.02 0.0028 161.00 0.16 200.00 0.19 0.041 mth #3 4 138.00 0.14 250.00 0.13 0.03 0.1612 110.00 0.11 250.00 0.05 0.01 0.0320 161.00 0.16 200.00 0.03 0.0128 166.00 0.17 200.00 0.11 0.022 mth #1 4 140.00 0.14 250.00 0.14 0.04 0.1512 112.00 0.11 250.00 0.07 0.02 0.0320 139.00 0.14 200.00 0.03 0.0128 153.00 0.15 200.00 0.16 0.0340 248.00 0.25 250.00 0.04 0.0152 273.00 0.27 300.00 0.02 0.0156 99.00 0.10 100.00 0.05 0.012 mth #2 4 139.00 0.14 250.00 0.13 0.03 0.1412 90.00 0.09 250.00 0.03 0.01 0.0420 153.00 0.15 200.00 0.03 0.0128 164.00 0.16 200.00 0.13 0.0340 247.00 0.25 250.00 0.03 0.0152 240.00 0.24 250.00 0.01 0.0056 84.00 0.08 100.00 0.05 0.012 mth #3 4 140.00 0.14 250.00 0.16 0.04 0.1212 117.00 0.12 250.00 0.06 0.02 0.0320 137.00 0.14 200.00 0.04 0.0128 153.00 0.15 200.00 0.04 0.0140 232.00 0.23 250.00 0.04 0.0152 247.00 0.25 250.00 0.01 0.0056 80.00 0.08 100.00 0.05 0.01152APPENDLX 3A: LEACHATE ANALYSIS (Cont.)2000 kg N/haVol. Vol. leachate Copper COPPER COPPERDays Leached Leached Made to Soluble Soluble Part.(mL) (L) (mL) (mg/kg) (mg) (mg)3 mth #1 4 140.00 0.14 250.00 0.09 0.02 0.1512 121.00 0.12 250.00 0.12 0.03 0.0320 135.00 0.14 200.00 0.03 0.0128 157.00 0.16 200.00 0.03 0.0140 221.00 0.22 250.00 0.06 0.0252 231.00 0.23 250.00 0.01 0.0064 284.00 0.28 300.00 0.05 0.0276 207.00 0.21 250.00 0.01 0.0084 175.00 0.18 200.00 0.01 0.003 mth #2 4 147.00 0.15 250.00 0.12 0.03 0.1412 112.00 0.11 250.00 0.09 0.02 0.0320 137.00 0.14 200.00 0.04 0.0128 148.00 0.15 200.00 0.05 0.0140 230.00 0.23 250.00 0.02 0.0152 246.00 0.25 250.00 0.01 0.0064 261.00 0.26 300.00 0.05 0.0276 200.00 0.20 250.00 0.01 0.0084 170.00 0.17 200.00 0.01 0.003 mtn #3 4 145.00 0.15 250.00 0.14 0.04 0.1212 112.00 0.11 250.00 0.07 0.02 0.0320 156.00 0.16 200.00 0.01 0.0028 146.00 0.15 200.00 0.03 0.0140 230.00 0.23 250.00 0.04 0.0152 237.00 0.24 250.00 0.01 0.0064 257.00 0.26 300.00 0.02 0.0176 248.00 0.25 250.00 0.01 0.0084 168.00 0.17 200.00 0.01 0.004 mth #1 4 136.00 0.14 250.00 0.11 0.03 0.1612 104.00 0.10 250.00 0.07 0.02 0.0420 157.00 0.16 200.00 0.04 0.0128 148.00 0.15 200.00 0.03 0.0140 232.00 0.23 250.00 0.04 0.0152 232.00 0.23 250.00 0.02 0.0164 276.00 0.28 300.00 0.03 0.0176 217.00 0.22 250.00 0.01 0.0084 165.00 0.17 200.00 0.01 0.0096 262.00 0.26 250.00 0.01 0.00108 239.00 0.24 250.00 0.01 0.00112 83.00 0.08 100.00 0.04 0.00153APPENDIX 3A: LEACHATE ANALYSIS (Cont.)2000 kg N/haVol. Vol. leachate Copper COPPER COPPERDays Leached Leached Made to Soluble Soluble Part.(mL) (L) (mL) (mg/kg) (mg) (mg)4 mth #2 4 138.00 0.14 250.00 0.07 0.02 0.1512 122.00 0.12 250.00 0.06 0.02 0.0320 143.00 0.14 200.00 0.06 0.0128 147.00 0.15 200.00 0.05 0.0140 225.00 0.23 250.00 0.09 0.0252 236.00 0.24 250.00 0.02 0.0164 251.00 0.25 300.00 0.03 0.0176 233.00 0.23 250.00 0.02 0.0184 148.00 0.15 200.00 0.01 0.0096 227.00 0.23 200.00 0.01 0.00108 227.00 0.23 250.00 0.01 0.00112 70.00 0.07 100.00 0.04 0.004 mth #3 4 138.00 0.14 250.00 0.15 0.04 0.1112 143.00 0.14 250.00 0.03 0.01 0.0420 145.00 0.15 200.00 0.03 0.0128 136.00 0.14 200.00 0.03 0.0140 212.00 0.21 250.00 0.02 0.0152 211.00 0.21 250.00 0.02 0.0164 269.00 0.27 300.00 0.03 0.0176 219.00 0.22 250.00 0.01 0.0084 132.00 0.13 200.00 0.01 0.0096 237.00 0.24 250.00 0.01 0.00108 230.00 0.23 250.00 0.01 0.00112 80.00 0.08 100.00 0.05 0.01154APPENDIX 3B: LEACHATE ANALYSISZINC2000 kg N/ha ColumnsVol. Vol. Leachate Zinc Zinc ZincDays Leached Leached Made to Soluble Soluble Particulate(mL) (L) (mL) (mg/L) (mg) (mg)1 mth,#1 4 141.00 0.14 250.00 0.27 0.07 0.1312 132.00 0.13 250.00 0.06 0.02 0.0320 159.00 0.16 200.00 0.03 0.0128 164.00 0.16 200.00 0.01 0.001 mth,#2 4 145.00 0.15 250.00 0.26 0.07 0.1212 95.00 0.10 250.00 0.11 0.03 0.0220 177.00 0.18 200.00 0.01 0.0028 161.00 0.16 200.00 0.01 0.001 mth #3 4 138.00 0.14 250.00 0.24 0.06 0.1312 110.00 0.11 250.00 0.05 0.01 0.0220 161.00 0.16 200.00 0.03 0.0128 166.00 0.17 200.00 0.01 0.002 mth #1 4 140.00 0.14 250.00 0.29 0.07 0.1112 112.00 0.11 250.00 0.04 0.01 0.0220 139.00 0.14 200.00 0.03 0.0128 153.00 0.15 200.00 0.01 0.0040 248.00 0.25 250.00 0.01 0.0052 273.00 0.27 300.00 0.01 0.0056 99.00 0.10 100.00 0.01 0.002 mth #2 4 139.00 0.14 250.00 0.29 0.07 0.1012 90.00 0.09 250.00 0.06 0.02 0.0220 153.00 0.15 200.00 0.01 0.0028 164.00 0.16 200.00 0.01 0.0040 247.00 0.25 250.00 0.01 0.0052 240.00 0.24 250.00 0.01 0.0056 84.00 0.08 100.00 0.01 0.002 mth #3 4 140.00 0.14 250.00 0.21 0.05 0.1012 117.00 0.12 250.00 0.02 0.01 0.0220 137.00 0.14 200.00 0.04 0.0128 153.00 0.15 200.00 0.01 0.0040 232.00 0.23 250.00 0.01 0.0052 247.00 0.25 250.00 0.01 0.0056 80.00 0.08 100.00 0.01 0.00155APPENDIX 38: LEACHATE ANALYSIS (Cont.)2000 kg N/ha ColumnsVol. Vol. Leachate Zinc Zinc ZincDays Leached Leached Made to Soluble Soluble Particulate(mL) (L) (mL) (mg/L) (mg) (mg)3 mth #1 4 140.00 0.14 250.00 0.16 0.04 0.1212 121.00 0.12 250.00 0.06 0.02 0.0220 135.00 0.14 200.00 0.03 0.0128 157.00 0.16 200.00 0.01 0.0040 221.00 0.22 250.00 0.01 0.0052 231.00 0.23 250.00 0.01 0.0064 284.00 0.28 300.00 0.01 0.0076 207.00 0.21 250.00 0.01 0.0084 175.00 0.18 200.00 0.01 0.003 mth #2 4 147.00 0.15 250.00 0.17 0.04 0.1212 112.00 0.11 250.00 0.05 0.01 0.0220 137.00 0.14 200.00 0.04 0.0128 148.00 0.15 200.00 0.01 0.0040 230.00 0.23 250.00 0.01 0.0052 246.00 0.25 250.00 0.01 0.0064 261.00 0.26 300.00 0.01 0.0076 200.00 0.20 250.00 0.01 0.0084 170.00 0.17 200.00 0.01 0.003 mtn #3 4 145.00 0.15 250.00 0.26 0.07 0.0912 112.00 0.11 250.00 0.02 0.01 0.0220 156.00 0.16 200.00 0.02 0.0028 146.00 0.15 200.00 0.01 0.0040 230.00 0.23 250.00 0.01 0.0052 237.00 0.24 250.00 0.01 0.0064 257.00 0.26 300.00 0.01 0.0076 248.00 0.25 250.00 0.01 0.0084 168.00 0.17 200.00 0.01 0.004 mth #1 4 136.00 0.14 250.00 0.22 0.06 0.1212 104.00 0.10 250.00 0.01 0.00 0.0320 157.00 0.16 200.00 0.01 0.0028 148.00 0.15 200.00 0.01 0.0040 232.00 0.23 250.00 0.01 0.0052 232.00 0.23 250.00 0.01 0.0064 276.00 0.28 300.00 0.01 0.0076 217.00 0.22 250.00 0.01 0.0084 165.00 0.17 200.00 0.01 0.0096 262.00 0.26 250.00 0.01 0.00108 239.00 0.24 250.00 0.01 0.00112 83.00 0.08 100.00 0.01 0.00156APPENDIX 3B: LEACHATE ANALYSIS (Cont.)2000 kg N/haVol. Vol. Leachate Zinc Zinc ZincDays Leached Leached Made to Soluble Soluble Particulate(mL) (L) (mL) (mg/L) (mg) (mg)4 mth #2 4 138.00 0.14 250.00 0.15 0.04 0.1312 122.00 0.12 250.00 0.06 0.02 0.0220 143.00 0.14 200.00 0.01 0.0028 147.00 0.15 200.00 0.01 0.0040 225.00 0.23 250.00 0.01 0.0052 236.00 0.24 250.00 0.01 0.0064 251.00 0.25 300.00 0.01 0.0076 233.00 0.23 250.00 0.01 0.0084 148.00 0.15 200.00 0.01 0.0096 227.00 0.23 200.00 0.01 0.00108 227.00 0.23 250.00 0.01 0.00112 70.00 0.07 100.00 0.01 0.004 mth #3 4 138.00 0.14 250.00 0.16 0.04 0.0912 143.00 0.14 250.00 0.03 0.01 0.0320 145.00 0.15 200.00 0.01 0.0028 136.00 0.14 200.00 0.01 0.0040 212.00 0.21 250.00 0.01 0.0052 211.00 0.21 250.00 0.01 0.0064 269.00 0.27 300.00 0.01 0.0076 219.00 0.22 250.00 0.01 0.0084 132.00 0.13 200.00 0.01 0.0096 237.00 0.24 250.00 0.01 0.00108 230.00 0.23 250.00 0.01 0.00112 80.00 0.08 100.00 0.01 0.00157APPENDIX 3C: LEACHATE ANALYSISCOPPER AND ZINC500 kg N/ha columnsVol. Vol. Leachate Copper Copper Zinc ZincDays Leached Leached Made to Soluble Soluble Soluble Soluble(mL) (L) (mL) (mg/L) (mg) (mg/L) (mg)1 mth,#1 4 10.60 0.01 25.00 0.00 0.00 0.00 0.0012 119.00 0.12 200.00 0.03 0.01 0.00 0.0020 83.00 0.08 200.00 0.00 0.00 0.00 0.0028 79.00 0.08 200.00 0.00 0.00 0.00 0.001 mth,#2 4 13.00 0.01 25.00 0.02 0.00 0.00 0.0012 119.00 0.12 200.00 0.03 0.01 0.00 0.0020 80.00 0.08 200.00 0.00 0.00 0.00 0.0028 80.00 0.08 200.00 0.00 0.00 0.00 0.001 mth #3 4 5.60 0.01 25.00 0.09 0.00 0.00 0.0012 92.00 0.09 200.00 0.04 0.01 0.00 0.0020 91.00 0.09 200.00 0.00 0.00 0.00 0.0028 109.00 0.11 200.00 0.00 0.00 0.00 0.002 mth #1 4 4.80 0.00 25.00 0.10 0.00 0.00 0.0012 128.00 0.13 200.00 0.05 0.01 0.00 0.0020 86.00 0.09 200.00 0.00 0.00 0.00 0.0028 72.00 0.07 200.00 0.00 0.00 0.00 0.0040 152.00 0.15 200.00 0.00 0.00 0.00 0.0052 160.00 0.16 200.00 0.00 0.00 0.00 0.0056 71.00 0.07 100.00 0.00 0.00 0.00 0.002 mth #2 4 9.80 0.01 25.00 0.20 0.01 0.00 0.0012 120.00 0.12 200.00 0.10 0.02 0.00 0.0020 91.00 0.09 200.00 0.00 0.00 0.00 0.0028 74.00 0.07 200.00 0.00 0.00 0.00 0.0040 154.00 0.15 200.00 0.00 0.00 0.00 0.0052 160.00 0.16 200.00 0.00 0.00 0.00 0.0056 64.00 0.06 100.00 0.00 0.00 0.00 0.002mth#3 4 9.00 0.01 25.00 0.14 0.00 0.00 0.0012 111.00 0.11 200.00 0.07 0.01 0.00 0.0020 77.00 0.08 200.00 0.00 0.00 0.00 0.0028 83.00 0.08 200.00 0.00 0.00 0.00 0.0040 143.00 0.14 200.00 0.00 0.00 0.00 0.0052 170.00 0.17 200.00 0.00 0.00 0.00 0.0056 67.00 0.07 100.00 0.00 0.00 0.00 0.00158APPENDD( 3C: LEACHATE ANALYSIS (Cont.)500 kg N/ha columnsVol. Vol. Leachate Copper Copper Zinc ZincDays Leached Leached Made to Soluble Soluble Soluble Soluble(mL) (L) (mL) (mg/L) (mg) (mgIL) (mg)3 mth #1 4 11.80 0.01 25.00 0.08 0.00 0.00 0.0012 138.00 0.14 200.00 0.06 0.01 0.00 0.0020 95.00 0.10 200.00 0.00 0.00 0.00 0.0028 96.00 0.10 200.00 0.00 0.00 0.00 0.0040 139.00 0.14 200.00 0.00 0.00 0.00 0.0052 194.00 0.19 250.00 0.00 0.00 0.00 0.0064 201.00 0.20 250.00 0.00 0.00 0.00 0.0076 214.00 0.21 250.00 0.03 0.01 0.00 0.0084 129.00 0.13 200.00 0.00 0.00 0.00 0.003 mth #2 4 12.80 0.01 25.00 0.12 0.00 0.00 0.0012 135.00 0.14 200.00 0.06 0.01 0.00 0.0020 82.00 0.08 200.00 0.00 0.00 0.00 0.0028 64.00 0.06 200.00 0.00 0.00 0.00 0.0040 121.00 0.12 200.00 0.00 0.00 0.00 0.0052 160.00 0.16 200.00 0.00 0.00 0.00 0.0064 200.00 0.20 250.00 0.00 0.00 0.00 0.0076 205.00 0.21 250.00 0.02 0.01 0.00 0.0084 121.00 0.12 200.00 0.00 0.00 0.00 0.003mtn#3 4 12.20 0.01 25.00 0.13 0.00 0.00 0.0012 119.00 0.12 200.00 0.05 0.01 0.00 0.0020 67.00 0.07 200.00 0.00 0.00 0.00 0.0028 72.00 0.07 200.00 0.00 0.00 0.00 0.0040 148.00 0.15 200.00 0.00 0.00 0.00 0.0052 160.00 0.16 200.00 0.03 0.01 0.00 0.0064 186.00 0.19 250.00 0.00 0.00 0.00 0.0076 161.00 0.16 200.00 0.00 0.00 0.00 0.0084 114.00 0.11 200.00 0.00 0.00 0.00 0.004 mth #1 4 7.60 0.01 25.00 0.29 0.01 0.00 0.0012 128.00 0.13 200.00 0.06 0.01 0.00 0.0020 132.00 0.13 200.00 0.00 0.00 0.00 0.0028 114.00 0.11 200.00 0.00 0.00 0.00 0.0040 203.00 0.20 250.00 0.00 0.00 0.00 0.0052 203.00 0.20 250.00 0.00 0.00 0.00 0.0064 189.00 0.19 250.00 0.00 0.00 0.00 0.0076 219.00 0.22 250.00 0.01 0.00 0.00 0.0084 139.00 0.14 200.00 0.00 0.00 0.00 0.0096 231.00 0.23 250.00 0.00 0.00 0.00 0.00108 229.00 0.23 250.00 0.00 0.00 0.00 0.00112 73.00 0.07 100.00 0.02 0.00 0.00 0.00159APPENDD( 3C: LEACHATE ANALYSIS (Cont.)500 kg N/ha columnsVol. Vol. Leachate Copper Copper Zinc ZincDays Leached Leached Made to Soluble Soluble Soluble Soluble(mL) (L) (mL) (mg/L) (mg) (mg/L) (mg)4 mth #2 4 7.00 0.01 25.00 0.18 0.00 0.00 0.0012 124.00 0.12 200.00 0.13 0.03 0.00 0.0020 76.00 0.08 200.00 0.00 0.00 0.00 0.0028 66.00 0.07 200.00 0.00 0.00 0.00 0.0040 133.00 0.13 200.00 0.00 0.00 0.00 0.0052 157.00 0.16 200.00 0.00 0.00 0.00 0.0064 208.00 0.21 250.00 0.00 0.00 0.00 0.0076 204.00 0.20 250.00 0.00 0.00 0.00 0.0084 146.00 0.15 200.00 0.00 0.00 0.00 0.0096 177.00 0.18 200.00 0.00 0.00 0.00 0.00106 208.00 0.21 250.00 0.00 0.00 0.00 0.00112 71.00 0.07 100.00 0.02 0.00 0.00 0.004 mth #3 4 14.40 0.01 25.00 0.10 0.00 0.00 0.0012 124.00 0.12 200.00 0.06 0.01 0.00 0.0020 86.00 0.09 200.00 0.00 0.00 0.00 0.0028 58.00 0.06 200.00 0.00 0.00 0.00 0.0040 123.00 0.12 200.00 0.00 0.00 0.00 0.0052 160.00 0.16 200.00 0.00 0.00 0.00 0.0064 180.00 0.18 250.00 0.00 0.00 0.00 0.0076 115.00 0.12 200.00 0.00 0.00 0.00 0.0084 142.00 0.14 200.00 0.00 0.00 0.00 0.0096 199.00 0.20 250.00 0.00 0.00 0.00 0.00108 213.00 0.21 250.00 0.00 0.00 0.00 0.00112 78.00 0.08 100.00 0.02 0.00 0.00 0.00160APPENDIX 4A: ADSORPTION STUDYCopper Adsorption by FH materialInitial Equil. ArnountofCopper Copper CopperConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*3.644.40 0.01 4.40 3.5747.90 0.71 47.19 3.4299.50 3.00 96.51 3.38147.00 9.68 137.33 3.29198.00 15.10 182.90 3.24245.00 25.60 219.40 3.17299.00 39.55 259.45 3.14* Initial pHAPPENDIX 4B: ADSORPTION STUDYCopper Adsorption by Woody materialInitial Equil. Amount ofCopper Copper CopperConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*3455.15 0.16 4.99 3.4250.00 1.71 48.29 3.3495.00 6.00 89.00 3.31150.00 12.15 137.85 3.21191.00 23.60 167.40 3.14242.00 36.75 205.25 3.08303.00 54.90 248.10 3.02* Initial pH161APPENDIX 4C: ADSORPTION STUDYCopper Adsorption by Bf materialInitial Equil. Amount ofCopper Copper CopperConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*4514.94 0.52 4.42 3.8449.60 17.00 32.60 3.79106.00 51.55 54.45 3.74151.00 92.30 58.70 3.71200.00 118.00 82.00 3.70245.00 152.50 92.50 3.69300.00 191.50 108.50 3.70* Initial pHAPPENDIX 4D: ADSORPTION STUDYZinc Adsorption by FH materialInitial Equil. Amount ofZinc Zinc ZincConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*3.624.84 1.22 3.62 3.6149.88 15.14 34.74 3.5798.88 33.69 65.19 3.54151.59 56.18 95.41 3.53197.00 79.59 117.41 3.49248.05 114.08 133.97 3.48297.82 150.83 146.99 3.45* Initial pH162APPENDIX 4E: ADSORPTION STUDYZinc Adsorption by Woody materialInitial Equil. Amount ofZinc Zinc ZincConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*3534.85 1.57 3.28 3.5148.80 19.20 29.60 3.4595.00 39.40 55.60 3.44142.00 60.10 81.90 3.43180.00 91.50 88.50 3.39226.00 119.50 106.50 3.37265.00 145.50 119.50 3.37* Initial pHAPPENDIX 4F: ADSORPTION STUDYZinc Adsorption by Bf materialInitial Equil Amount ofZinc Zinc ZincConc. Conc. Adsorbed pH(mg/L) (mg/L) (mg/kg)*4.604.8 3.26 1.54 3.9549.50 38.75 10.75 3.85100.00 78.75 21.25 3.85150.00 119.00 31.00 3.85197.00 162.50 34.50 3.86247.00 208.50 38.50 3.85292.00 252.50 39.50 3.84* Initial pH163


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