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Diesel exhaust particles and related air pollution from traffic sources in the Lower Mainland Brauer, Michael 2008

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Diesel Exhaust Particles and Related Air Pollution from Traffic Sources in the Lower Mainland      Final Report Submitted to:  Health Canada, Environment and Sustainability Program      Western Region      Health Protection Branch 3155 Willingdon Green, Burnaby BC V5G 4P2      Attn: Jack Nickel, Manager  Reference:        HECSB-SEP-BC/YUK02  Financial Code:       837010-02406    Michael Brauer Sarah Henderson The University of British Columbia School of Occupational and Environmental Hygiene 2206 East Mall, Vancouver BC V6T1Z3 tel. 604 822 9585 fax. 604 822 9588 email. brauer@interchange.ubc.ca   February 5, 2003 Acknowledgements  This work was made possible with the help of several colleagues and contacts in the Vancouver area.  We would like to offer special thanks to the Air Quality division of the GVRD for lending its equipment and staff to the study.  The technical support of Michiyo McGaughey, Fred Prystarz, Tim Jensen and Al Percival was invaluable and much appreciated.  We would also like to recognize the cooperation and participation of the following community partners:  - King Lum at the King Edward & Kingsway Revy Home Centre - Joe Pach at Telus - Geoff Thompson at Chevron Canada Ltd. - Tom Carras at the Vancouver School Board i1 Summary  Due to increasing attention devoted to the direct health risks associated with air pollution from local traffic sources, a pilot study was conducted during summer 2002 to develop and test monitoring methods for evaluating ambient roadside levels of traffic-related air pollutants in the Lower Mainland.  These methods were used to examine the range of expected concentrations of particles and other traffic-related air pollutants at roadside and non-roadside locations, and to link measured concentrations to geographic variables (traffic intensity measures, population density) in order to evaluate the ability of geographic data to estimate measured ambient concentrations for future epidemiologic studies and risk assessment.  Potential traffic and background monitoring sites were identified using population density data from Statistics Canada and traffic data provided by Translink, based on the output of a traffic demand model. Initial evaluation of these data indicated that a large percentage of the Vancouver population resides in close proximity to roads carrying 15,000 or more vehicles per day.  Five traffic and three background sites within the City of Vancouver were selected for monitoring. At each location, 2-week measurements were made for NO/ NO2/NOx, PM10, PM2.5, PM1.0 and filter absorbance, which is a surrogate for particle elemental carbon.  As measurements were not made simultaneously at all monitoring sites, measured concentrations were adjusted for temporal variability between the different measurement periods based on the temporal pattern of measured concentrations at GVRD monitoring sites. Ratios of mean traffic to background concentrations were 1.26 for NO2 (measured with the continuous monitor; 1.43 for NO2 measured with passive samplers), 2.73 for NO, 1.11 for PM10, 0.83 for PM2.5 and 1.73 for estimated elemental carbon. While there are slightly higher measured concentrations of NO2 at the traffic locations, there were much greater differences for NO and NOx, which is expected given the primary emissions of NO from mobile sources. For particulate matter, the greatest difference between traffic and background locations was seen with (estimated from filter absorbance) elemental carbon; somewhat higher concentrations of PM10 are seen at the traffic locations while the concentrations of PM2.5 were slightly higher at background locations. These results indicate that, of the measured pollutants, NO and elemental carbon were the most sensitive indicators of traffic-related sources. The mean 2-week average elemental carbon concentration at the traffic sites was 1.21 µg/m3 (range: 0.7 – 2.1) and 0.7 (range 0.6 – 0.8) at the background sites. Mean NO concentrations were 38.0 (range: 19.2 – 72.8) and 13.9 ppb (range: 11.6 – 15.4) for traffic and background sites, respectively.  These results may be compared to estimates prepared by the Onroad Diesel Emissions Evaluation Task Force (Levelton, 2000). In this report, it was estimated that the regional average concentration of diesel particulate in the BC Lower Mainland was approximately 1 µg/m3, with maximum 24-hr diesel particulate concentrations of 2.4 and 0.7 µg/m3 at roadside and 20 m away from the road centreline, respectively. Based on the elemental carbon measurements reported here, it is apparent that the previous model results underestimate the measured concentrations, considering that these were 2-week averages ii collected at approximately 10-15m from the road centreline. The model estimates were maximum 24-hour concentrations during any 24-hr period within a single calendar year and were suggested to occur very infrequently. Comparisons with the measurements suggest that these, and higher, concentrations may be experienced more frequently and in greater proximity to residences.  The regional average diesel particulate concentration estimated previously in the Onroad Diesel Emissions Evaluation Task Force Report is in good agreement with our measurements, which suggest this concentration to be 0.7-0.9 µg/m3 based on measurements collected at locations not impacted by major roads. These measurements confirm the conclusion of the Onroad Diesel Emissions Evaluation Task Force Report that the average diesel particulate concentration in the Lower Mainland is similar in magnitude to that observed in large U.S. cities.  Although the small number of sampling locations limited the ability to construct regression models predicting air pollutant concentrations from geographic variables, this initial effort demonstrated the potential of the modelling approach. Regression models including buffer calculations of traffic and population density were able to predict a substantial fraction of the variability in measured concentrations of NO2, NO, PM2.5, PM10 and (estimated) elemental carbon, as indicated in the table below.  Table A – Summary of multivariate regression models predicting measured pollutant concentrations with traffic and population density data Pollutant Variables Model Adjusted R2 (Palmes Tube) NO2 traf.100 + traf.500-100 + pop.500 + pop.1000 0.85 (Palmes Tube) NO2 am.rush (based on measured City data) 0.19 NO traf.500 + traf.100 0.20 NO am.rush (based on measured City data) 0.43 PM2.5 traf.1000-500 + traf.100 + pop.3000 + pop.1000 + pop.500 0.97 PM2.5 pop.3000 + pop.500 (based on measured City data) 0.41 PM10 pop.500 + traf.500-100 + pop.1000 + traf.1000+pop 3000 0.73 PM10 pop.500 (based on measured City data) 0.49 Absorbance pop.500 + pop.1000 + traf.1000 0.76 Absorbance  am.rush (based on measured City data) 0.34 Traffic variables (traf) indicate the number of peak morning. rush hour vehicles in the buffer zone surrounding the measurement site. 100 refers to a radius of 100m, and 500-100 refers to a donut shaped buffer extending from 100m to 500m from the measurement site, as described in Table 5.  The variable am.rush refers to the measured vehicle counts during the peak morning period (see Appendix E).   For all pollutants except NO, the model-based traffic data resulted in improved correlations relative to the models that included measured traffic at the intersection of interest. This may reflect the fact that the regression models using the modelled traffic data allowed for buffer calculations of various distances around the measurement to be included whereas the models including only measured traffic data were restricted to the use of traffic data for the specific intersection only.  While these results suggest that this type of modelling procedure may be useful to predict pollutant concentrations at locations without measurements, the results are somewhat confusing as the pollutants thought to be iii most sensitive to local traffic (NO and absorbance) are not as well explained by traffic and population variables as are pollutants thought to be more spatially homogeneous (NO2, PM2.5). Again, this may be a limitation of the very small number of sampling sites used for this preliminary modelling.  These results indicate the presence of spatial variability in traffic-related air pollutants within the Lower Mainland airshed. For example, elemental carbon measurements at traffic sites were 73% higher, on average, than measured concentrations at urban background locations. Preliminary modelling suggests that traffic and population density data may be useful in predicting this variability in localized air pollutant concentrations.                                                                                      Page 1 of 28 2 Introduction  2.1 Objectives  1. To develop and pilot test monitoring methods to be used to evaluate ambient roadside levels of traffic-related air pollutants in the Lower Mainland.  2. To apply these monitoring methods to examine the range of expected concentrations of particles and other traffic-related air pollutants. These monitoring locations would include roadside and non-roadside locations.  3. To link the measured concentrations to geographic variables (traffic intensity measures, population density) to evaluate the ability of geographic data to estimate measured ambient concentrations for future epidemiologic studies and risk assessment.  There has been increasing attention devoted to the direct health risks associated with air pollution from local traffic sources. This interest stems in part from the fact that mobile sources are the major contributor to emissions in many urban areas. Further, recent research has demonstrated associations between traffic-related air pollution and a range of health outcomes including increased mortality, cancer incidence, asthma and allergy prevalence, lung function, chronic symptoms, and adverse birth outcomes, as indicated in Appendix A.  As shown in Appendix A, many different approaches have been used to estimate exposures in the different studies. One of the major difficulties in studies of traffic-related air pollution is the specificity of the exposure assessment and specifically, the ability to apply exposure estimates to large study populations. Studies have used a variety of approaches including (subjective) self- reported measures of nearby traffic intensity, self-reported proximity to “major” roads, and a variety of surrogate variables such as distance to nearest road, traffic intensity on the nearest major road, etc.  In most cases surrogate variables for exposure to air pollution originating from traffic have not been directly validated for their use as exposure measures in epidemiological studies.  A further difficulty in the assessment of exposure to traffic-related air pollution is the inability of existing monitoring networks to assess the variability of air pollution concentrations within urban areas. Most ambient monitoring sites, particularly in North America, are situated to measure urban background concentrations, and are specifically located to avoid measuring the impact of individual road sources.  However, several studies have indicated that particle concentrations exhibit substantial spatial variability within urban areas, with higher concentrations found in city centers (Cyrys et al, 1998, Bauer et al, 2000; Fischer et al, 2000) or in proximity to local sources, such as neighbourhoods with residential wood burning.  A potentially useful approach to incorporate spatial variability in ambient pollution concentrations and to attribute this measured variability to specific sources is the use of Geographic Information Systems (GIS) which can allow exposure estimates to be applied to home addresses of large study populations Briggs et al, 2000; Briggs et al, 1997).  Geographic modeling approaches have been applied in several studies in Europe (Lebret et al, 2000). While limited information from North American studies suggests                                                                                      Page 2 of 28 that within-city spatial variability that is not reflected by ambient monitoring networks does exits, the geographic modeling approach has not yet been applied in the US or Canada.   2.2 Exposure Assessment  Existing epidemiological analyses of traffic-related air pollutants often use relatively simple estimates of exposure – for example simple measures of proximity to roads (Nitta et al, 1993; Oosterlee et al, 1996; van Vliet et al, 1997), traffic intensity on the nearest major road (Brunekreef et al, 1997; Wjst et al, 1993), or somewhat more complex measures such as distance weighted-traffic intensity (English et al, 1999).  Alternatively, exposure can be estimated with dispersion (Hruba et al, 2001), or air pollution and time-activity models (Korc et al, 1996). While such models may be useful, they are seldom validated with actual measurements and require input data, specifically for emissions, which may not be readily available.  In dispersion modelling, emissions parameters are input into dispersion or other types of atmospheric models to predict concentrations at individual “receptor” points. While this approach is common in the evaluation of air quality management programs and for risk assessment it has not been used with frequency in epidemiological studies. Unfortunately the usefulness of dispersion modelling in epidemiology is limited since input data, such as traffic intensity, street configurations and emissions inventory data, are usually not available or not specific to the location of interest. Dispersion models require large amounts of location-specific input data such as detailed information on the specific makeup of the motor vehicle fleet, the specific emissions of representative vehicle types, traffic volumes, detailed meteorological and topographical information (National Academy Press, 2000).  Examples of dispersion model applications in Epidemiology include the LUCAS study in Stockholm (Bellander et al, 2001) and a Danish study of childhood cancer (Raschou-Nielsen et al, 2000). As part of this study dispersion modelling estimates were compared with measured NO2 concentrations in Copenhagen and in several rural areas in Denmark. The analysis suggested that the estimated exposures correctly (based on measurements) classified exposures for approximately 80% of the study subjects. A third approach to assess exposure involved interpolating concentrations based on measurements conducted by monitoring networks (Liu et al, 1995; Brown et al, 1994).  These methods are useful to assess regional air pollution patterns but cannot identify small-scale variations in concentrations given the density of most typical monitoring networks and given the spatial distribution of traffic sources.  In addition to the approaches described above a recent methodology, which has been useful in European epidemiological analyses, is the geographic modelling approach.  Geographic modeling approaches make the application of models to large study populations feasible since the geographic information is typically readily available, in contrast to spatially- detailed air pollution concentration information. However, even exposure assessments based on geographic models may be inadequate unless these models have been validated as surrogates of exposure to air pollutants.  Geographic models have been compared to simpler exposure estimation approaches used in other studies, for example distance to nearest road (Nitta et al,                                                                                      Page 3 of 28 1993; Oosterlee et al, 1996; van Vliet et al, 1997; Wilkinson et al, 1999) and the intensity of traffic on the nearest road (Wjst et al, 1993; English et al, 1999; Brunekreef et al, 1997) or self- reported traffic intensity (Ciccone et al, 1998; Duhme et al, 1996; Weiland et al, 1994). These simpler models explained a much lower proportion of the variability in measured concentrations than did geographic models.  Accordingly, we have evaluated the feasibility of developing a geographic modelling approach to assess exposures to traffic related air pollution in Vancouver. This feasibility assessment includes the collection and assembly of relevant geographic data, preliminary analysis of these data, and development of a protocol to elated targeted air quality measurements to geographic variables.  While most previous studies of traffic-related air pollution have not been able to distinguish between gasoline and diesel-fuelled vehicles, there are some limited examples in which diesel exhaust has been specifically implicated (van Vliet etal, 1997; Brunekreef at al, 1997).  In 2000 in a risk assessment for diesel exhaust particles for the GVRD was conducted (Levelton, 2000). In a critique/review of this risk assessment it was suggested that local air monitoring for diesel exhaust particles be conducted, as no data are currently available (Brauer et al, 2000).  A complicating factor is the lack of a robust indicator for diesel exhaust particles. Currently, elemental carbon has been used as an indicator of diesel exhaust particles, however elemental carbon is also a minor component of particle emissions from gasoline-fuelled vehicles.  Despite these uncertainties, there has been relatively little local monitoring of elemental carbon with specific emphasis on understanding the spatial variability. Local monitoring would enhance current risk assessment capabilities as the aforementioned assessment was based on modeled concentrations that were suggested to underestimate real ambient levels.  In addition, local monitoring could be used in the future to link to existing or new epidemiological studies. In preparation for this work and to begin to establish a database of traffic-related air pollution measurements in the BC Lower Mainland we conducted a feasibility study to evaluate the range of potential exposures to traffic-related air pollutants in the Lower Mainland and to link these measurements to GIS-based data on traffic and population characteristics.                                                                                       Page 4 of 28 3 Methods  3.1 Site Identification and Selection  Potential high traffic sampling locations were identified in ArcView using output from a Vancouver-based transportation planning model (NET99, obtained from Translink) and data from the 1996 census. This model was built to reflect automobile and public transit volumes during morning rush hour.  All schools within Vancouver city limits were considered as potential background locations.  3.1.1 Meta-Data Output from the planning model was received as a shape file (projected to UTM27, zone 10) with attribute variables for automobile volume (all, single, double and triple occupant), transit volume, and number of lanes.  Four digital cartographic files from the 1996 census were obtained from Statistics Canada (via the UBC Data Library) and used in conjunction with the Translink traffic model to identify street names, shore lines and enumeration boundaries: i)the street network file (publicly available as gsnf933r.e00), ii)the skeletal street network file (gvanssnf.e00), iii) the water file (gsnf933s.e00) and iv) the enumeration area file (gea_933b.e00). All files were converted from Arc/Info export format (.e00) to Arc/View feature data themes using Arc/View’s Import71 utility.  The resulting data were converted from latitude/longitude (referenced to NAD27) to UTM27 using the Projector! extension.  An ASCII file containing block-face population data (in which data for each city block is assigned to a single lat/long coordinate) from the 1996 census was also used.  3.1.2 Identification of Potential Traffic Sites The EMME/2 planning model was jointly developed by the Greater Vancouver Regional District (GVRD), TransLink, and the BC Ministry of Transportation and Highways to improve the understanding to transportation-related issues in Vancouver.  Output estimating automobile volumes during morning rush hour was used for the purposes of this study.  The data were received in a shape file with individual polygons representing the traffic density each road section, as shown in Figure 1:                                                                                        Page 5 of 28  Figure 1 – Polygon model output for rush hour traffic density in Vancouver  The density of automobile traffic is indicated by the colour and width of the polygons seen above.  This display was used to visually pinpoint those intersections at which relatively heavy traffic could be expected throughout the day.  3.1.3 Investigation into relationships between modelled and actual traffic count values We conducted a limited evaluation of the EMME/2 model to assess the relationship between peak morning traffic counts (the model output) and 24-hour traffic levels using a selected number of urban traffic monitoring sites. As indicated above, the EMME/2 model only estimates traffic values during morning rush hour.  Since the geographic modeling methodology is focused on the relationship between long-term air quality levels and traffic patterns, we evaluated the relationship between 24-hour traffic density averages and the model predictions of peak morning traffic counts. In the model development procedure, Translink made 24 hourly measurements at a number of locations. Twenty representative points were chosen from across the city of Vancouver.  At these locations, we first regressed the model estimates of peak morning hourly traffic counts against the measured peak morning traffic counts (in the predominant direction between 07:30 and 08:30) as seen in Figure 2 on the following page.                                                                                       Page 6 of 28 Morning Rush Hour Traffic Volume from Translink Model vs. Actual Values from GVRD y = 0.929x R2 = 0.7206 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 GV RD  R ush Hour  T raf f ic V o lume  Figure 2 – Comparison between measured and estimated morning rush hour traffic counts   In  Figure 3 (below) we compared the actual peak morning traffic counts with 24-hour counts for these same locations, and in Figure 4 (following page) we compared the model peak morning hourly traffic values against the actual 24-hour measured averages.   Morning Rush Hour Traffic vs. Daily Traffic y = 11.181x R2 = 0.6923 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 0 500 1000 1500 2000 2500 3000 3500 4000 Rush Hour Traffic Volume (07:30 to 08:30) Da ily  T ra ffi c Vo lu m e (2 4 ho ur s)  Figure 3 – Comparison between measured morning and 24-hour traffic counts                                                                                       Page 7 of 28  y = 0.0554x + 519.26 R2 = 0.5531 0 500 1000 1500 2000 2500 3000 3500 4000 0 10000 20000 30000 40000 50000 60000 Daily traffic volume (24 hrs) Tr an sl in k m or ni ng  ru sh  h ou r t ra ffi c vo lu m e (m od el )  Figure 4 – Comparison between measured 24-hour and estimated morning rush-hour traffic counts   We found good agreement between the measured peak morning traffic counts and the measured 24-hour traffic counts and between the measured and modelled peak morning traffic counts (Figures 2 and 3). The agreement is not as good between the measured 24-hour traffic counts and the modelled peak morning counts (Figure 4), but still adequate for use in exposure assessment. While it is difficult to assess the validity of these modelled traffic data relative to the European data sources, it is likely that the Translink model is at a similar degree of accuracy relative to actual traffic counts.  3.1.4 Estimates of population and traffic density We also conducted a limited assessment for Vancouver (City of Vancouver only) to identify the proportion of the population that lives within 100m of a street included in the Skeletal Streets Network File. Based on visual comparisons between the Skeletal Streets Network File and the EMME/2 traffic model, the Skeletal Streets Network corresponds to those streets with greater than 15,000 vehicles per day. Using this 100m buffer we selected all of the Census block face points that fell within the buffer and then summed the total populations (see Figure 5):  Total population of the City of Vancouver = 530,000 Total population within 100m of skeleton streets = 291,000  Based on these estimates, within the City of Vancouver 55% of the population lives within 100m of a road carrying more than 15,000 vehicles per day. Based on these initial estimates, we concluded that the available geographic data in the Lower Mainland are suitable for further assessment of the geographic modeling approach. Additionally, within the City of Vancouver,                                                                                      Page 8 of 28 there are apparently large numbers of people residing in close proximity (within 100m) to medium (<15,000 vehicles per day) or high traffic roads.   Figure 5 – Population of the City of Vancouver residing within 100m of a major road (approximately 15,000 or more vehicles per day)   Traffic density at the identified intersections was estimated using circular buffers with radii of 100, 500 and 1000 metres to select all polygons representing the roads within them, as shown in Figures 6 and 7 below.  The attribute variable for traffic volume was summed for the selected polygons, and the desirability of each location as was assessed using these values.    Figure 6 & Figure 7 – 100 and 500 metre buffers used to estimate traffic density                                                                                      Page 9 of 28  The overall objective of this study was to assess the feasibility of using pre-existing geographic data to estimate human exposure to traffic pollutants in Vancouver.  While traffic density is one obvious predictor for pollution exposure, population density has also been shown to be significantly associated with traffic pollution (2003, Brauer et al.).  The population density around intersections of interest was estimated using 500, 1000, and 3000 metre buffers to selected features of a population layer created with the block-face data, as shown in Figure 8.    Figure 8 – 500 metre buffer for estimating population density from block-face data  3.1.5 Traffic Site Selection After using ArcView to identify ten potential traffic sites, UBC and GVRD technicians visited each location.  Businesses nearby to those deemed feasible were approached with information about the study, and for permissions to use their facilities for a two-week monitoring period. Subsequent negotiations led to working agreements for five of the intersections (shown in Figure 9), the details of which are summarized in Table 1.   Table 1 – Selected traffic sites Intersection Cooperating Facility Kingsway & 25th Revy Home Centre Boundary & Kingsway Telus Head Office Knight & 57th Chevron Public Station Rupert & 1st Chevron Public Station Clark & 1st Chevron Card-Lock Station                                                                                       Page 10 of 28   Figure 9 – Selected traffic sites  3.1.6 Background Sites Potential background sites were located at least 100 metres from any road servicing more than 15,000 vehicles per day.  Given the summer sampling schedule it was decided that schools, which are generally located in low-traffic areas, would make ideal background sites.  The street network file was used to locate the addresses of all Vancouver public schools, and a 100 metre buffer zone was created around the busy streets so that suitable ones could be easily identified. After successful negotiations with the Vancouver School Board, three facilities (see Table 2) were selected according to their proximity to the traffic sites, as shown in Figure 10.  While site pairing was not mandated by the study objectives, it was thought that this approach would most realistically describe the differences in traffic-related pollution between roadside and background locations in Vancouver.  Because the three background locations were situated in areas not covered by the transportation model, traffic in the 100 metre buffer was estimated using the value for the nearest section of an identifiably minor road.   Table 2 – Selected school sites School Name Address Sir Matthew Begbie 1430 Lillooet Street Lord Selkirk 1750 East 22nd Avenue Sir Douglas Annex 7668 Borden Street                                                                                        Page 11 of 28  Figure 10 – Identification of three schools as background monitoring sites   3.2 Air monitoring  Prior to the initiation of a monitoring program, we evaluated several measurement techniques that would a) be feasible for the assessment of long-term concentrations of traffic-related air pollutants at roadside locations; b) provide specific information on air pollutants originating from traffic sources; and c) provide a means to distinguish traffic sources from regional background air pollution. Several continuous monitoring approaches were identified.  Continuous monitors would allow an internal comparison and validations of long-term integrated sampling approaches, including the intermittent sampling schedules that have been used in the European studies. Additionally, continuous monitors allow for the identification of more flexible exposure indicators (for example, comparison of concentrations during peak traffic periods only) that may enhance the variability between measurement locations.  We evaluated continuous monitoring devices for ultrafine particles, fine particles, nitrogen oxides and carbon monoxide as all these pollutants are associated with direct vehicle emissions.  With the cooperation of the Greater Vancouver Regional District (GVRD), the mini Mobile Air Monitoring Unit (mini-MAMU) was used as the main monitoring platform. Incorporated as part of mini-MAMU was a continuous NO/NO2 monitor (chemiluminesence). In addition, we deployed a photometer (TSI DustTrak) for continuous monitoring of fine particles (PM1.0). With regard to ultrafine particles, the expense (approximately $30,000) of purchasing suitable devices (Condensation particle counter) was beyond the scope of this pilot project.  Integrated monitors have been used in the European geographic modeling approaches and it was therefore desirable to replicate those approaches in this effort. Accordingly, we operated Harvard                                                                                      Page 12 of 28 Impactors for PM10 and PM2.5.  In addition, we deployed a PM1.0 sampler (BGI Triplex cyclones) to collect samples of particles that are more directly related to combustions sources than are PM2.5 (or PM10 samples). The addition of PM1.0 was included to potentially enhance the ability to measure variability in air pollution contributions that are specific to traffic sources. All of these integrated particle samples were analyzed for mass concentration as well as filter reflectance – a simple measurement that has been used as a surrogate for elemental carbon, a potential marker for diesel exhaust.  Previously, we have demonstrated a high correlation between co-located filter reflectance and elemental carbon measurements in the Lower Mainland (Figure 11).   SOUTH BURNABY: Absorbance v Thermal Method EC y = 1.196x + 0.09145 R2 = 0.7692 0 2 4 6 8 10 12 14 16 18 20 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Elemental Carbon (ugm-3) Ab so rb an ce  (1 0- 6 m -1 )  Figure 11 – Comparison between elemental carbon and PM2.5 filter absorbance measurements at the South Burnaby GVRD monitoring network site.  Elemental carbon has been used previously as an indicator of diesel exhaust particles, although its utility as a diesel exhaust marker has been questioned. As in the European studies, we deployed passive NO2 samplers (Palmes Tubes) as these can be inexpensively deployed at many locations. As part of this pilot effort we compared Palmes Tube measurements with the NO2 measurements from the reference chemiluminescence monitor. Previous measurements have suggested that NO2 concentrations, as measured by Palmes tubes at sites with varying levels of impact from traffic sources, are highly correlated with other traffic-related pollutants.  Therefore, one objective of these pilot measurements was to assess the feasibility of using Palmes Tube measurements alone for a larger monitoring effort; an extremely efficient and much less expensive approach.  3.2.1 The Mobile Air Monitoring Unit (MAMU-2) All sampling was done in partnership with the GVRD air quality department, which dedicated its mobile air monitoring trailer (MAMU-2, pictured in Figure 12) to the study.  This light trailer measures approximately 3m long by 2m high by 1.5m wide, and was moved between locations with the aid of GVRD staff.  Monitoring equipment on loan from the GVRD was housed inside the trailer, drawing air from a rooftop manifold.  Hourly averages from continuous NOX and CO                                                                                      Page 13 of 28 analyzers were sent to an onboard data logger, which connected to the GVRD’s central computer twice daily via cellular uplink.  Figure 12 – The GVRD’s mobile air monitoring trailer  3.2.2 Rooftop Samplers Equipment for measuring particulate matter was housed in a unit designed to be mounted on the roof of MAMU-2.  PM10 and PM2.5 were sampled with Harvard impactors, while PM1.0 was sampled with a Cyclone and a DustTrak continuous analyzer.  Palmes tubes for the passive measurement on NO2 were also included.  The rooftop configuration of these instruments is shown in the figures below.  Five feet of surgical latex tubing attached he impactors and the cyclone to battery operated SKC programmable pumps.  Figure 13 – Bird’s eye view of rooftop sampling unit Pump Case Dusttrak Case PM1.0 Cyclone Palmes Tube (NO2) Dusttrak Intake PM2.5 Harvard Impactor PM10 Harvard Impactor                                                                                      Page 14 of 28    Figure 14 – Rooftop sampling unit as seen from the front   Monitoring lasted for approximately two weeks at each of the eight locations.  So as not to overload samplers, the sampling pumps were programmed to sample for two of every seven minutes, yielding the equivalent of a 48-hour sample, collected over a 1-week period.  All other samplers ran continuously for the entire 2-week period.  A list of the data expected from each monitoring site is given in Table 3.   Table 3 – Data expected from each monitoring site Sampler Data Expected Continuous NOX Analyzer Two weeks of hourly averages for NO, NO2 and NOX Continuous CO Analyzer Two weeks of hourly average for CO PM10 Harvard Impactor Two 48-hour samples collected on 41mm Teflon filters PM2.5 Harvard Impactor Two 48-hour samples collected on 41mm Teflon filters PM1.0 Cyclone Two 48-hour samples collected on 37mm Teflon filters DustTrak Two weeks of minutely averages for PM1.0 Palmes Tube One two-week average for NO2 Field Blanks One 41mm filter, one 37mm filter, and one Palmes tube   3.2.3 Sampling Schedule Sampling began in mid-May and ended in early September.  While operations were mainly incident-free, some important human and mechanical errors are noted in the sampling schedule, which is summarized in Table 5.    PM10 Impactor PM1.0 Cyclone Palmes Tube PM2.5 Impactor Sample Pumps                                                                                      Page 15 of 28 Table 4 – Sampling schedule Location Sampling Dates Notes Kingsway & 25th May 16 – May 30 Between May 16th and 24th the pump for the PM1.0 cyclone completed 1033 of 2400 sampling minutes due to flow faults. Boundary & Kingsway June 3 – June 19 The two PM1.0 samples completed 1392 and 1994 or 2400 sampling minutes due to flow faults. Knight & 57th June 20 – July 4 All samples complete. Rupert & 1st July 8 – July 22 All samples complete. Background #1 (Begbie) July 24 – Aug. 6 Between July 22nd and 29th the PM1.0 sample completed 946 of 2400 sampling minutes due to flow faults. Background #2 (Selkirk) Aug. 6 – Aug. 19 Between August 12th and 19th the PM1.0 sample completed 1034 of 2400 sampling minutes due to a cracked Cyclone casing.  Improper programming of the DustTrak resulted in only two days of data. Background #3 (Douglas) Aug. 20 – Sept. 3 All samples complete. Clark & 1st Sept. 04 – Sept. 18 Between Sept 4th and 11th the PM1.0 sample completed 1434 of 2400 sampling minutes due to flow faults. The PM2.5 sample completed 490 of 2400 sampling minutes due to a faulty battery.    3.3 Data Analysis 3.3.1 Sample Analysis Samples were analysed according to standard methods.  All particle filters were stored and weighed in a temperature- and humidity-controlled room, and were weighed in triplicate before and after sampling.  Filter reflectance was measured with an M43D Smokestain Reflectometer and filter absorbance was calculated with the following equation:  Absorbance = 100000 ereflectancfilter ereflectancblank  field average ln filter through passedair  of volume 2 area surfacefilter ××     Filter absorbance is strongly correlated with the concentration of elemental carbon (EC) in filtered air, which was estimated from the regression relationship measured at the GVRD’s South Burnaby station in 2001 (Figure 12):  EC = 1.196 0.09145 - Absorbance  Palmes tubes were stored in air-tight bags before and after sampling, and were extracted in a single batch.  Extracted samples were analysed using an ion chromatograph (Dionex DX-300) and concentrations determined by comparison with a standard curve of 0.16, 0.32, 0.64, 1.6 and 3.2 µg/ml NaNO2.  The ambient concentration of NO2 was determined by the following equation:                                                                                       Page 16 of 28 Palmes NO2 = (hours)duration  sampling 401.6(nmol)ion  nitrate extracted ×  Where the coefficient 401.6 accounts the tube dimensions and the diffusion coefficient for NO2.  3.3.2 Temporal Adjustments Sampling took place in two-week blocks between May 16th and September 3rd.  To account for wide-scale temporal variations in pollutant concentrations during this period all results were adjusted to data from two fixed monitoring stations in the GVRD.  Data from the station in South Burnaby (T18) were used to adjust all NO, NO2, NOX and PM10 values, and data from the station in Langley (T27) were used to adjust PM2.5, PM1.0, absorbance and EC.  Adjustment ratios were calculated for each pollutant for each week of sampling using the following formula:  Adjustment Ratio = period sampling entirefor station  fixedat ion concentrat average  weeksampling onefor station  fixedat ion concentrat average  Measured values were then divided by the adjustment ratio, resulting in an increase if the one- week average underrepresented the overall average, or a decrease if the opposite was true. This same procedure has been used previously for evaluating spatial variability with discontinuous sampling programs (Hoek at el, 2002; Brauer et al, 2003).  Station T18 was chosen for comparison because of its proximity to the sampling locations (see Figure 15) and the relatively complete array of compounds monitored.  Unfortunately PM2.5 is not measured at station T18 and, of the four available stations, T27 was chosen for related adjustments because its PM10 ratios were most highly correlated with those from T18 (Figure 16). We have previously documented the very low spatial variability in PM2.5 concentrations measured at GVRD monitoring sites (Ebelt et al, 2000).               Figure 15 – Location of fixed station T18 compared to sampling sites                                                                                      Page 17 of 28   Figure 16 – Comparison of PM10 ratios to determine best station for PM2.5 adjustments  3.3.3 Model Construction Multivariate regression models were constructed for Palmes NO2, continuous NO, PM10, PM2.5, and absorbance (from the PM2.5 filter) in S-plus using the most predictive of the variables described in Table 5.  Table 5 – Predictor variables used in model construction Variable Name Description traf.100 Traffic within a 100m radius of the site traf.500 Traffic within a 500m radius of the site traf.1000 Traffic within a 1km radius of the site traf.500.100 Traffic within a 500m – 100m donut around the site traf.1000.500 Traffic within a 1000m – 500m donut around the site pop.500 Population within a 500m radius pop.1000 Population within a 1km radius pop.3000 Population within a 3km radius   For each pollutant the predictor having the strongest univariate relationship with the response was used as the base model, to which subsequent predictors were added according to their resulting adjusted R2 value.  First the base variable was combined with each remaining predictor, and the bivariate model resulting in the greatest increase to the adjusted R2 was chosen.  This iterative process continued until the maximum R2 was observed.                                                                                          Page 18 of 28 4 Results  4.1 Traffic and population estimates  Table 6 presents summary statistics of traffic estimates and population density measures for the eight sampling sites. Estimated traffic counts from the Translink model as well as measured traffic counts obtained from the City of Vancouver (Appendix E) are presented. As can be seen in this table and in Figure 17, traffic measurements and estimates differ greatly, especially for the high traffic locations, with the Translink model overestimating traffic counts for high traffic locations. We have no readily apparent explanation for this discrepancy.   Table 6 – Summary statistics of traffic and population estimates for the sampling sites Location Traffic in a 100m Radius (from model) Traffic During Morning Rush Hour (from City measured data) Population in a 500m Radius (from Census data) Kingsway & 25th 11856 3876 4871 Boundary & Kingsway 13181 4965 3245 Knight & 57th 10701 4576 3866 Rupert & 1st 16935 4364 3079 Clark & 1st 7282 5660 3033 Background #1 (Begbie) 555 120 3936 Background #2 (Selkirk) 482 806 4569 Background #3 (Douglas) 591 732 3963    Figure 17 – Comparison of measured and estimated traffic counts for the eight sampling sites                                                                                      Page 19 of 28 Summary statistics of the particle and gaseous pollutant measurements are presented in Tables 7 and 8.   Table 7 – Mean weekly particle concentrations all locations Location PM10 PM2.5 PM1.0 (Cyclone) PM1.0 (DustTrak) 10.70 6.2 10.7 19.06 Kingsway & 25th 10.95 4.9 5.0 15.87 8.24 5.2 8.1 16.02 Boundary & Kingsway 19.09 5.8 6.5 15.29 16.55 17.1 7.9 14.65 Knight & 57th 13.18 7.5 7.2 10.30 13.94 9.1 7.8 12.39 Rupert & 1st 15.85 8.6 6.6 13.28 23.12 7.2 8.1 19.13 Clark & 1st 19.60 7.3 8.6 19.06 14.33 9.4 11.8 10.61 Background #1 (Begbie) 15.85 8.2 10.2 15.27 10.61 6.7 6.1  Background #2 (Selkirk) 11.36 10.3 14.8 15.23 15.1 5.5 16.33 Background #3 (Douglas) 14.11 7.3 5.9 15.73   As is evident, the filter-based PM1.0 concentrations (measured with the Triplex Cyclone) were consistently higher than the measured PM2.5 concentrations.  Given this erroneous result and the high number of sampling problems with the Triplex Cyclone, the unreliability of this data made it impossible to calibrate the DustTrak PM1.0 measurements. We have therefore excluded the PM1.0 measurements from all further analysis.  Table 8 and Figures 19-20 display mean measurement results, stratified by traffic and background locations for all measured parameters. The figures show measurements that are adjusted for temporal variability between the different measurement periods, as described in the Methods section.  Ratios of mean traffic to background concentrations were 1.26 for NO2 (measured with the continuous monitor; 1.43 for NO2 measured with passive samplers), 2.73 for NO, 1.11 for PM10, 0.83 for PM2.5 and 1.73 for estimated elemental carbon. While there are slightly higher measured concentrations of NO2 at the traffic locations, there are much greater differences for NO (and NOx). This is expected given the primary emissions of NO from mobile sources. For particulate matter, the greatest difference between traffic and background locations was seen with (estimated) elemental carbon; somewhat higher concentrations of PM10 are seen at the traffic locations while the concentrations of PM2.5 were slightly higher at background locations. These results indicate that, of the measured pollutants, NO and elemental carbon are the most sensitive indicators of traffic-related sources.                                                                                       Page 20 of 28 These results may be compared to estimates prepared by the Onroad Diesel Emissions Evaluation Task Force (Levelton, 2000). In this report, it was estimated that the regional average concentration of diesel particulate in the BC Lower Mainland was approximately 1 µg/m3, with maximum 24-hr diesel particulate concentrations of 2.4 and 0.7 µg/m3 at roadside and 20 m away from the road centreline, respectively. The estimated 2-weak average elemental carbon concentrations determined in this study can be used as an estimate of diesel particulate concentrations (Brauer et al, 2000). Based on this comparison, it is apparent that the previous model results underestimate the measured concentrations (2-week average concentration measurements of 2.1 µg/m3), considering that these were 2-week averages collected at approximately 10-15m from the road centreline.  It should be noted that the model estimates (Levelton, 2000) do exclude the contribution of diesel particulate emissions from nearby roads although this cannot explain the differences.  The model estimates also were based upon traffic counts for the Knight Street Bridge, a very high traffic corridor, and were maximum 24-hour concentrations during any 24-hr period within a single calendar year and were suggested to occur very infrequently. Comparisons with the measurements suggest that these, and higher, concentrations may be experienced more frequently and in greater proximity to residences.  The regional average diesel particulate concentration estimated previously is in good agreement with our measurements, which suggest this concentration to be 0.7-0.9 µg/m3 based on measurements collected at locations not impacted by major roads. These measurements confirm the conclusion of the Onroad Diesel Emissions Evaluation Task Force Report (Levelton, 2000) that the average diesel particulate concentrations in the Lower Mainland is similar in magnitude to that observed in large U.S. cities.  Table 8 – Summary statistics for the all pollutants by traffic and background sites, including measurements adjusted for temporal patterns  Traffic Background   Mean Range Mean Range Unadjusted 24.96 20.67 - 32.68 18.90 18.35 - 19.66 Palmes NO2 (ppb) Adjusted 25.78 20.83 - 32.22 18.06 15.15 - 22.65 Unadjusted 22.98 17.43 - 27.80 19.78 18.29 - 21.84 Continuous NO2 (ppb) Adjusted 23.60 19.78 - 26.82 18.65 16.22 - 21.65 Unadjusted 37.73 13.48 - 91.23 13.47 12.08 - 15.46 Continuous NO (ppb) Adjusted 37.98 19.20 - 72.85 13.89 11.59 - 15.45 Unadjusted 60.71 30.19 - 119.04 33.33 31.30 - 34.71 Continuous NOX (ppb) Adjusted 59.77 37.02 - 97.57 31.65 28.88 - 36.01 Unadjusted 14.23 8.16 - 20.51 15.70 13.06 - 18.62 PM10 (µg/m3) Adjusted 15.12 10.82 - 21.36 13.58 10.98 - 15.09 Unadjusted 7.66 4.71 - 11.44 10.57 9.18 - 13.20 PM2.5 (µg/m3) Adjusted 7.90 5.52 - 12.31 9.51 8.50 - 11.24 Unadjusted 1.51 0.81 - 2.66 1.03 0.92 - 1.12 Absorbance (10-6/m) Adjusted 1.58 0.80 - 2.75 0.94 0.84 - 1.00 Unadjusted 1.18 0.60 - 2.14 0.78 0.69 - 0.86 Estimated Elemental Carbon (µg/m3) Adjusted 1.21 0.67 - 2.10 0.70 0.64 - 0.78                                                                                       Page 21 of 28   Figure 18 – Mean (adjusted for temporal variation) concentration of nitrogen oxides at traffic and background locations    Figure 19 – Mean (adjusted for temporal variation) concentration of PM10 and PM2.5 at traffic and background locations                                                                                      Page 22 of 28  Figure 20 – Mean (adjusted for temporal variation) concentration of elemental carbon at traffic and background locations   There was generally good agreement between NO2 measured by the passive Palmes Tubes and by the continuous monitors, supporting the use of passive samplers in spatial survey (Figure 22). The mean difference between continuous and passive samplers was 2.6 ppb (range: 0.2 – 7.3).    Figure 21 – Adjusted NO2 measurements from continuous and passive samplers                                                                                      Page 23 of 28 4.2 Modeling air pollutant concentrations from traffic data  As an initial step, we evaluated the relationship between filter absorbance (an elemental carbon surrogate) and localized traffic measurements in order to assess the usefulness of the measured and modeled (from EMME2) traffic data for further modeling (Figures 23-24). As can be seen, the measured traffic shows a much higher correlation, suggesting the inaccuracy of the modelled traffic counts at very localized scales.  These plots also indicate the relative inability of measured traffic at the nearest road to adequately explain the measured air pollutant concentrations. Similar results have been reported elsewhere in which it has been shown that more detailed multivariate regression models incorporating different buffer zone traffic counts substantially improve the ability to explain the variability in measured concentrations (Brauer et al, 2003).  It is for this reason that the further modelling was conducted.    Figure 22 – Absorbance and measured traffic counts at sampling location intersections                                                                                      Page 24 of 28  Figure 23 – Absorbance and modeled traffic counts at sampling location intersections   One objective of this pilot study was to evaluate specific pollutants as indicators of traffic-related air pollution. As can be seen from Table 9, many of the measured pollutants were highly correlated with each other. Most importantly, (estimated) elemental carbon was strongly correlated with NO (and NOX), which can be easily measured with passive samplers. The relatively high correlation between (estimated) elemental carbon and PM10 probably is indicative a common source – vehicle traffic, although PM10 likely arises from re-suspended road dust rather than elemental carbon which arises from emissions.  This is supported by the very low correlation between elemental carbon and PM2.5, which is more focused on combustion source particles, but is spatially homogeneous in the airshed and is not a good traffic indicator.   Table 9 – Correlation table for the adjusted 2-week averages of measured pollutants at all sites  Palmes NO2 NO2 NO NOX PM10 PM2.5 Absorbance Elemental Carbon Palmes NO2 1.000 Continuous NO2 0.772 1.000 NO 0.251 0.632 1.000 NOX 0.308 0.691 0.997 1.000 PM10 -0.079 0.256 0.748 0.737 1.000 PM2.5 -0.381 -0.315 -0.231 -0.234 0.135 1.000 Absorbance 0.154 0.520 0.938 0.931 0.792 0.100 1.000 Elemental Carbon 0.144 0.510 0.934 0.926 0.797 0.113 1.000 1.000                                                                                       Page 25 of 28 The final regression models are summarized in Table 10 and presented in Appendix D. The interpretation of these models should be made cautiously given the small number of measurement sites. For all cases except NO, the EMME2 model-based traffic data resulted in improved correlations relative to the models that included measured traffic at the intersection of interest. This may reflect the fact that the models using the EMME2 data allowed for buffer calculations of various distances around the measurement to be included whereas the models including only measured traffic data were restricted to the use of traffic data for the specific intersection only. In models using the EMME2 data it is clear that buffer zones extending beyond the specifi intersection are important to the predictions. In the case of the NO models, it is possible that the use of measured data improved the predictive power of the model as the concentrations of No are highly localized, and therefore not well captured by the EMME2 model. While these results suggest that this type of modelling procedure may be useful to predict pollutant concentrations at locations without measurements, the results are somewhat confusing as the pollutants thought to be most sensitive to local traffic (NO and absorbance) are not as well explained by traffic and population variables as pollutants thought to be more spatially homogeneous (NO2, PM2.5). Again, this may be a limitation of the very small number of sampling sites used for this preliminary modelling.  Table 10 – Summary of multivariate regression models predicting measured pollutant concentrations with traffic and population density data. Traffic variables (traf) indicate the number of peak morning rush hour vehicles in the buffer zone surrounding the measurement site. 100 refers to a radius of 100m, and 500-100 refers to a donut shaped buffer extending from 100m to 500m from the measurement site, as described in Table 5.  The variable am.rush refers to the measured vehicle counts during the peak morning period (Appendix E). Pollutant Variables Model Adjusted R2 (Palmes Tube) NO2 traf.100 + traf.500-100 + pop.500 + pop.1000 0.85 (Palmes Tube) NO2 am.rush (based on measured City data) 0.19 NO traf.500 + traf.100 0.20 NO am.rush (based on measured City data) 0.43 PM2.5 traf.1000-500 + traf.100 + pop.3000 + pop.1000 + pop.500 0.97 PM2.5 pop.3000 + pop.500 (based on measured City data) 0.41 PM10 pop.500 + traf.500-100 + pop.1000 + traf.1000+pop 3000 0.73 PM10 pop.500 (based on measured City data) 0.49 Absorbance pop.500 + pop.1000 + traf.1000 0.76 Absorbance  am.rush (based on measured City data) 0.34                                                                                        Page 26 of 28 References  Bellander T, Berglind N, Gustavsson P, Jonson T, Nyberg F, Pershagen G, Jarup L. 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Page A1  Appendix A: Summary of studies literature regarding health effects of traffic-related air pollution  Asthma Location Exposure Outcome Finding Reference Birmingham, UK Living near major road, traffic density Asthma hospital admissions, children >5 yrs + Edwards 1994 Sweden Estimated NO2 exposure (dispersion model) Wheezy bronchitis hospital admissions, children 4-48 months + for girls only Pershagen, 1995 UK Presence of motorway in electoral ward MD diagnosed asthma, 13-14 yrs - Waldron, 1995 London Distance to nearest road/major road, traffic volume within 150 metres Asthma hospital admissions, 5-14 yrs - Wilkinson, 1999 Dresden Measured NO2, SO2, CO, O3, benzene at 200 grid points Asthmatic symptoms, 5-7, 9- 11 yrs (ISAAC) + cough, bronchitis - atopy, bronchial hyperresponsiveness Hirsch, 1999 California Traffic flow in 550 ft buffer Asthma diagnosis, asthma medical visits - asthma diagnosis + medical visits for asthmatics English Nottingham Traffic flow in 1 km -- 2 grids Wheeze prevalence in children 4-16 yrs - Venn, 2000 The Netherlands For schools within 300 metres of major roads Respiratory symptoms +  Oosterlee, 1996 The Netherlands  Respiratory symptoms  Van Vliet, 1997 The Netherlands  Lung Function  Brunekreef, 1997 The Netherlands GIS-based individual estimates of NO2, PM2.5, “soot” Asthma, Respiratory and allergic symptoms, Respiratory infections + asthma (2 years) + respiratory infections Brauer, 2002 Munich GIS-based individual estimates of NO2, PM2.5, “soot”   Gehring, 2002   Page A2    Mortality Location Exposure Outcome Finding Reference Amsterdam Time series stratified by proximity to major road Mortality  Roemer, 6 US Cities Prospective Cohort – PM stratified by source factors Mortality 10 µg/m3 increase in PM2.5: + Mobile sources: 3.4% increase in daily mortality [95% confidence interval (CI), 1.7-5.2%], + Coal combustion: 1.1% increase [CI, 0.3-2.0%).  - Crustal particles Laden, 2000 Switzerland, Austria, France, ? Time series/Impact Assessment Estimated contribution of motor vehicles to ambient PM concentrations Mortality  Kunzli, 2000 The Netherlands Regional, Urban and Traffic (within 50m of major road, 100m of freeway) components of exposure Prospective cohort - mortality + traffic component had higher OR than urban air or regional background Heok et al, 2001 16 U.S cities (NMMAPS) Percentage of PM emissions from highway diesel vehicles Regression of PM:hospitalization coefficients on external data (traffic, etc.)  Jannsen, 2001   Cancer Location Exposure Outcome Finding Reference Stockholm Dispersion models estimates of NO2 (traffic) and SO2 (residential heating) Lung Cancer + for NO2 Bellander, 2000 Denmark Dispersion model estimates of NO2 and benzene Leukemia, CNS tumors, lymphomas,  all cancers + for lymphoma - for all other outcomes Raaschou-Nielsen, 2001 Page A3    Birth outcomes Location Exposure Outcome Finding Reference Los Angeles CO, PM10, NO2, O3 concentrations during pregnancy (restricted to population within 2-mile radius of monitoring site) Preterm birth + PM10 + CO Ritz, 2000 Los Angeles CO concentrations during pregnancy (restricted to population within 2-mile radius of monitoring site) Low birthweight + Ritz, 1999   Page E1  Appendix B: Adjustments for temporal variation  The white lines indicate continuous measurements used to adjust discontinuous (2 week) measurements. NO, NO2, NOX and PM10 are adjusted to T18 in South Burnaby.  PM2.5, PM1.0 and absorbance are adjusted to T27 in Langley.        Page E2     Page E1  Appendix C: Comparison between adjusted and unadjusted values       Page E2        Page E3       Page E1  Appendix D: Regression model development  Best model development for Palmes NO2 (Translink data) Model Adjusted R2 traf.100 0.7103 traf.500 -0.1091 traf.1000 -0.1303 traf.500.100 0.2326 traf.1000.500 -0.1644 pop.500 -0.08832 pop.1000 -0.1446 pop.3000 -0.1604 traf.100 0.7103 + traf.500 0.7742 + traf.1000 0.7123 + traf.500.100 0.7742 + traf.1000.500 0.6539 + pop.500 0.6661 + pop.1000 0.6697 + pop.3000 0.6559 traf.100 + traf.500.100 0.7742 + traf.500 0.7742 + traf.1000 0.772 + traf.1000.500 0.772 + pop.500 0.7779 + pop.1000 0.7387 + pop.3000 0.7181 traf.100 + traf.500.100 + pop.500 0.7779 + traf.500 0.7779 + traf.1000 0.8296 + traf.1000.500 0.8296 + pop.1000 0.8473 + pop.3000 0.7653 traf.100 + traf.500.100 + pop.500 + pop.1000 0.8473 + traf.500 0.8473 + traf.1000 0.7811 + traf.1000.500 0.7811 + pop.3000 0.7724   Best model development for Palmes NO2 (City data) Model Adjusted R2 am.rush 0.186 pop.500 -0.08832 pop.1000 -0.1446 pop.3000 -0.1604 am.rush 0.186 + pop.500 0.02527 + pop.1000 0.1375 + pop.3000 0.02354 Page E2   Best model development for continuous NO (Translink data) Model Adjusted R2 traf.100 -0.01237 traf.500 0.139 traf.1000 -0.1664 traf.500.100 -0.1659 traf.1000.500 -0.09066 pop.500 0.0985 pop.1000 -0.1102 pop.3000 -0.1103 traf.500 0.139 + traf.100 0.1992 + traf.1000 -0.03086 + traf.500.100 0.1992 + traf.1000.500 -0.03086 + pop.500 0.1657 + pop.1000 0.036 + pop.3000 -0.01497 traf.500 + traf.100 0.1992 + traf.1000 0.0006755 + traf.500.100 0.1992 + traf.1000.500 0.0006755 + pop.500 0.08583 + pop.1000 0.09572 + pop.3000 0.01514    Best model development for continuous NO (City data) Model Adjusted R2 am.rush 0.4355 pop.500 0.0985 pop.1000 -0.1102 pop.3000 -0.1103 am.rush 0.4355 + pop.500 0.3405 + pop.1000 0.3248 + pop.3000 0.3518           Page E3   Best model development for PM2.5 (Translink data) Model Adjusted R2 traf.100 -0.05222 traf.500 0.1119 traf.1000 0.2474 traf.500.100 -0.1667 traf.1000.500 0.5796 pop.500 -0.1664 pop.1000 0.0599 pop.3000 0.3704 traf.1000.500 0.5796 + traf.100 0.6414 + traf.500 0.533 + traf.1000 0.533 + traf.500.100 0.5454 + pop.500 0.5024 + pop.1000 0.5474 + pop.3000 0.6173 traf.1000.500 + traf.100 0.6414 + traf.500 0.6163 + traf.1000 0.6163 + traf.500.100 0.6163 + pop.500 0.5595 + pop.1000 0.6101 + pop.3000 0.6915 traf.1000.500 + traf.100 + pop.3000 0.6915 + traf.500/1000/500.100 0.6798 + pop.500 0.6158 + pop.1000 0.9591 traf.1000.500 + traf.100 + pop.3000 + pop.1000 0.9591 + traf.500/1000/500.100 0.9444 + pop.500 0.9707 traf.1000.500 + traf.100 + pop.3000 + pop.1000 + pop.500 0.9707 + traf.100/500/1000 0.9453   Best model development for PM2.5 (City data) Model Adjusted R2 am.rush -0.04454 pop.500 -0.1664 pop.1000 0.0599 pop.3000 0.3704 pop.3000 0.3704 + am.rush 0.3341 + pop.500 0.4075 + pop.1000 0.248 pop.3000 + pop.500 0.4075 + am.rush 0.2617 + pop.1000 0.2618 Page E4   Best model development for PM10 (Translink data) Model Adjusted R2 traf.100 -0.1665 traf.500 0.113 traf.1000 -0.1097 traf.500.100 -0.09584 traf.1000.500 -0.1629 pop.500 0.4931 pop.1000 -0.1222 pop.3000 -0.02671 pop.500 0.4931 + traf.100 0.533 + traf.500 0.5518 + traf.1000 0.3941 + traf.500.100 0.6367 + traf.1000.500 0.4184 + pop.1000 0.4459 + pop.3000 0.3917 pop.500 + traf.500.100 0.6367 + traf.100 0.5568 + traf.500 0.5568 + traf.1000 0.5465 + traf.1000.500 0.55 + pop.1000 0.6456 + pop.3000 0.5461 pop.500 + traf.500.100 + pop.1000 0.6456 + traf.100 0.5285 + traf.500 0.5285 + traf.1000 0.7103 + traf.1000.500 0.6711 + pop.3000 0.5961 pop.500 + traf.500.100 + pop.1000 + traf.1000 0.7103 + traf.100/500/1000.500 0.5743 + pop.3000 0.7289 pop.500 + traf.500.100 + pop.1000 + traf.1000 + pop.3000 0.7289 + traf.100/500/1000.500 0.4737    Best model development for PM10 (City data) Model Adjusted R2 am.rush 0.01835 pop.500 0.4931 pop.1000 -0.1222 pop.3000 -0.02671 pop.500 0.4931 + am.rush 0.3917 + pop.1000 0.4459 + pop.3000 0.3917  Page E5   Best model development for Absorbance from PM2.5 filters (Translink data) Model Adjusted R2 traf.100 -0.05901 traf.500 -0.05382 traf.1000 -0.1207 traf.500.100 -0.1587 traf.1000.500 -0.1663 pop.500 0.1415 pop.1000 -0.1544 pop.3000 -0.1664 pop.500 0.1415 + traf.100 -0.01465 + traf.500 0.0298 + traf.1000 -0.02179 + traf.500.100 -0.02901 + traf.1000.500 -0.0281 + pop.1000 0.1992 + pop.3000 0.08686 pop.500 + pop.1000 0.1992 + traf.100 -0.0002248 + traf.500 0.04447 + traf.1000 0.7625 + traf.500.100 0.007206 + traf.1000.500 0.3148 + pop.3000 0.003597 pop.500 + pop.1000 + traf.1000 0.7625 + traf.100 0.6834 + traf.500 0.6836 + traf.500.100 0.6835 + traf.1000.500 0.6836 + pop.3000 0.6897    Best model development for Absorbance from PM2.5 filters (City data) Model Adjusted R2 am.rush 0.3405 pop.500 0.1415 pop.1000 -0.1544 pop.3000 -0.1664 am.rush 0.3405 + pop.500 0.2612 + pop.1000 0.2147 + pop.3000 0.2128 Page E1  Appendix E: Measured traffic counts at sampling locations Page E2  Page E3   Page E4  Page E5  Page E6  

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