Mobile Monitoring of Particle Light Absorption Coefficient in an Urban

Apr 6, 2009 - Phone: 604-822-9585 Fax: 604-822-9588. email: [email protected]. This article is part of the Particulate Matter and Human Health...
0 downloads 0 Views 4MB Size
Download PDF
Environ. Sci. Technol. 2009, 43, 4672–4678

Mobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use Regression TIMOTHY LARSON, SARAH B. HENDERSON, AND MICHAEL BRAUER* Department of Civil and Environmental Engineering, University of Washington, Seattle, WA; School of Environmental Health, The University of British Columbia, Vancouver, B.C.

Received October 30, 2008. Revised manuscript received February 19, 2009. Accepted March 10, 2009.

Land use regression (LUR) is used to map spatial variability in air pollutant concentrations for risk assessment, epidemiology, and air quality management. Conventional LUR requires longterm measurements at multiple locations, so application to particulate matter has been limited. Here we use mobile monitoring to characterize spatial variability in black carbon concentrations for LUR modeling. A particle soot absorption photometer in a moving vehicle was used to measure the absorption coefficient (σap) during summertime periods of peak afternoon traffic at 39 locations. LUR was used to model the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10 s measurements at each location. Model performance (measured by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, respectively) than for the mean, 75th and 90th percentiles (0.51, 0.55, and 0.54, respectively). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean σap from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate the development of LUR models for particulate matter.

Introduction A number of recent studies have measured and reported considerable spatial variability in the concentrations of particulate matter and its chemical constituents within urban areas (1-8), largely resulting from traffic sources (9-23). While air quality monitoring networks can provide detailed information on the temporal variablility of pollutant concentrations, the small-scale spatial variability tends not to be well-characterized. It follows that spatial variation in population exposure to particulate matter and its trafficrelated components is also not well-characterized by regulatory monitoring networks. Land use regression (LUR) was first developed to generate individual-scale estimates of longterm average air pollution exposure throughout populated * Address for correspondence: Michael Brauer. School of Environmental Health, The University of British Columbia, 3rd Floor, 2206 East Mall, Vancouver, BC V6T 1Z3, Canada. Phone: 604-8229585Fax: 604-822-9588. email: [email protected]. 4672

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 13, 2009

areas for use in epidemiological and risk assessment studies (24), and the method has recently gained attention in the air quality management and urban planning communities (20, 25, 26). There is no standard method for conducting LUR, and detailed descriptions of the approach can be found elsewhere (9-11, 17-20, 24-35). In brief, a pollutant is measured at multiple sites specifically selected to capture the complete intraurban range of its concentrations. Geographic attributes that might be associated with those concentrations are measured around each site in a geographic information system (GIS). Typical geographic predictor variables describe site location, surrounding land use, topography, population density, and traffic patterns (11, 20). Linear regression is used to correlate measured concentrations with the most predictive variables, and the resulting equation can be used to estimate pollutant concentrations anywhere that all of the predictors can be measured. Concentration maps with high spatial resolution can be generated by rendering the regression model in GIS. Early development of LUR favored traffic-related pollutants because (A) mobile sources obviously contribute to within-city variability in pollution concentrations and (B) pollution from traffic has important public health effects (36-40). The method is now being explored for other applications like mapping the spatial variability of residential wood smoke (7, 8) and other point source pollution (34, 41, 42). One major limitation of LUR is that it requires pollutant measurements at a large number of locations within the study area. The availability of inexpensive passive samplers for NO2 (43) has resulted in the vast majority of LUR models being developed on NO2 measurements as an indicator of trafficrelated air pollution (20, 24-32). Particulate pollution is likely to have spatial characteristics that are different from NO2 (10, 25) and that are of specific interest from regulatory and public health perspectives. However, only a very limited number of models have been developed for particle mass or particle characteristics, such as black carbon (9-11, 35, 44). In one such case, researchers combined LUR modeling with source apportionment to estimate the percent contribution of traffic to elemental carbon (EC) as a more specific indicator of diesel exhaust particles (11). In theory, similar approaches could be applied to determine spatial patterns of other sources (e.g., marine emissions) that can be resolved by source apportionment methods. To date, the main limitation in the development of LUR models for particulate matter is the lack of simple and inexpensive sampling devices that can be deployed in large quantities (20). Previous efforts have relied on rotating a small number of sampling devices between locations. While this approach is feasible it requires long study periods and poses logistical constraints. To overcome these limitations we developed a mobile monitoring approach to measure concentrations of particle light absorption coefficient using a particle soot absorption photometer (PSAP). The means and quantiles of the continuous measurements taken at each of 39 sites are used to develop multiple regression models for these parameters.

Materials and Methods Mobile Monitoring. A particle soot absorption photometer (PSAP) (Radiance Research, Seattle, WA) was used to measure the particle light absorption coefficient (σap) at locations throughout Vancouver, British Columbia. The PSAP with an accompanying pump were placed inside a conventional 10.1021/es803068e CCC: $40.75

 2009 American Chemical Society

Published on Web 04/06/2009

FIGURE 1. Location of the sampled intersections and the NO2/NOx passive sampler sites used by Henderson et al. (10). The largest red circle indicates the location of the reference intersection visited during each sample run. gasoline powered vehicle and powered with a marine battery connected through an inverter. The instrument sampled air through the otherwise-sealed driver side rear window and the readings were recorded each second. Instrument response time was between 5 and 10 seconds, due primarily to the sample residence time between the inlet and the PSAP filter. The vehicle was driven on eight different days between July 14th and August 16th, 2005 during periods of peak afternoon traffic (∼4 to 7 p.m.). Initial attempts at sampling during morning rush hour resulted in measurements that systematically decreased over time, presumably due to the rapidly increasing morning mixing height (45). Filter-based measurements of light absorbing carbon are highly correlated with thermal optical reflectance measurement of elemental carbon in this airshed (46) as well as in others (47, 48). Based on comparison between fixed-site filter σap and thermal optical reflectance measurements of elemental carbon in this study area, σap of 10 × 10-6 m-1 is equivalent to approximately 0.8 µg m-3 elemental carbon (46). PSAP precision as estimated by the standard deviation of 10 s averages measured when sampling filtered air for an hour is 0.74 × 10-6 m-1. Elemental carbon is often used as a surrogate for diesel exhaust particles. There are, however, additional sources of elemental carbon such as spark-ignition vehicles and wood combustion and therefore light absorbing carbon is not a unique tracer for diesel exhaust particles (49). We restricted our measurements to summer sampling to minimize the potential impact of residential wood combustion on our measurements (7). Sampling was conducted at 39 urban locations that included heavily-, moderately-, and lightly trafficked intersections and their immediate surroundings. All 39 were included in the set of 116 locations previously sampled by Henderson et al. (10) for NO2 and NO using a simultaneous, fixed-site sampler array (Figure 1). One central location (Figure 1) was visited during each afternoon of mobile sampling, either at the beginning or end of the sampling period (with the order chosen randomly). The number of additional locations sampled on any given day ranged from two to eight (Supporting Information Table 1). To capture the effect of local traffic as well as sources in the larger surrounding area, the vehicle traced a cloverleaf pattern through a central intersection, circling the four adjacent blocks surrounding the intersection and thus passing through the actual intersection four times (Figure 2). A clock in the vehicle was synchronized with the PSAP internal clock and the times entering (point A in Figure 2) and leaving

FIGURE 2. Cloverleaf pattern traversed by the vehicle at an intersection of interest: vehicle enters intersection at A and moves to locations in alphabetical order, returning to A. (returning to point A from point L) the cloverleaf were recorded manually. Cloverleaf traverse times ranged from 5 to 13 min (Supporting Information Table 1). Data Reduction. We calculated moving 10 s averages from the raw measurements (taken at 1 s intervals) recorded for each cloverleaf. All observations were included in the final analysis. To account for temporal variation between sampling days, all measurements were adjusted as follows. First, we divided the measurements at each location by the median at the reference location for the sampling day in question. Second, we multiplied these ratios by the average median σap at the reference intersection (8.55 × 10-6 m-1) across all sampling days (see Supporting Information Table 2). This final “adjusted” σap metric for each location was used as the dependent variable in the land use regression model. This approach is similar to that used in many LUR models, where the “adjusted” concentrations are computed by multiplying the measured values for a given sampling period by the ratio of the period average to the long-term average at a fixed, central location. In this case, we did not have long-term monitoring data from which to make such an adjustment. To characterize the influence of short-term peaks in the distribution of the measurements, we developed separate land use regression models for the 25th, 50th, 75th, and 90th percentiles of the distribution of adjusted measurements, as well as the mean. Land Use Regression Model. To develop a land-use regression model for σap, we examined spatial predictor variables that were previously extracted via GIS by Henderson et al. (10). These researchers generated 50 variables in 4 categories and 10 subcategories to characterize the spatial distribution of roadways, traffic intensity, land use, and VOL. 43, NO. 13, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4673

FIGURE 3. Selected percentiles in the observed distribution of adjusted 10 s average σap values for each site across all 39 locations. p-Values refer to the Kolmogorov-Smirnov test of normality. All distributions are consistent with either normality or log-normality as reported. population density. All variables in each category were derived from a single spatial data set in vector format. Information on road location, land use, and population density were taken from the 2001 census package prepared by DMTI Spatial (Markham, Ontario) and distributed by Statistics Canada. Information on traffic volumes were generated by the Metro Vancouver transit authority’s EMME/2 model (INRO Consultants, Montreal, Canada) of morning rush-hour traffic volume. Uniform circular buffers centered on the middle of each traversed intersection were used to extract the relevant spatial variables from these maps. Details on data processing within GIS are given in Henderson et al. (10). We also used the model-building algorithm described in Henderson et al. (10), specifically (A) Rank all variables by the absolute strength of their correlation with the measured pollutant. (B) Identify the highest-ranking variable in each subcategory. (C) Eliminate other variables in each subcategory that are correlated (Pearson’s r g 0.6) with the most highly ranked variable. (D) Enter all remaining variables into a stepwise linear regression. (E) Remove from the available pool any variables that have insignificant (p > 0.1) t-statistics. (F) Repeat steps D and E to convergence and remove any variable that contributes less than 1% to the R2 value for a parsimonious final model.

Results Figure 3 summarizes selected percentiles in the observed distribution of adjusted 10-s average σap values for each site 4674

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 13, 2009

across all 39 locations. The 25th, 50th, and 75th percentiles are normally distributed across locations, whereas the mean and 90th percentile values follow a log-normal distribution. Table 1 summarizes the final LUR models. The stepwise regression models of the adjusted 25th and 50th percentile values selected the predictor variables with the largest spatial scales, including a term for latitude and elevation as well as total vehicle traffic. Models of the adjusted mean, 75th and 90th percentile values also include measures of truck traffic, consistent with the observation of the presence of small scale truck exhaust plumes at locations with higher truck traffic. At one location (site E2 in Supporting Information Table 2), we sampled behind a very smoky tour bus that produced the highest observed peak reading (σap∼10-3m-1; peak-to-mean ratio >10) that lasted for about 40 s. This particular location had an adjusted mean value larger than the 90th percentile. If we remove this one location from the analysis, the model performance improves for the prediction of the adjusted log(mean), but remains essentially the same for all other metrics. The alternate models of adjusted log(mean) and adjusted log(90th percentile) for the remaining 38 sites are shown in Table 1. An estimate of model robustness based on an R2 estimated by a leave-one-out approach (10, 20) is also shown in Table 1. We also tested LUR models with the unadjusted data. LUR predictions of the 25th and 50th percentiles were not much different than for the adjusted data, but there was significant loss of prediction power for the upper end of the unadjusted distribution, including the

TABLE 1. Land-Use Regression Model for σap (10-6 m-1) dependent variable 25th percentile median 75th percentile log(mean) log(90th percentile) log(mean)c log(90th percentile)c

LUR equationa σap ) -164.0 + 1.801(RD1_500) + 1.130(RD2_750) -0.025 (ELEV) + 3.399(X) σap ) -179.1 + 2.876(RD1_500) + 4.650(RD2_300) -0.031 (ELEV) + 3.744 (X) σap ) -210.6 + 1.978(TD_300) + 0.075(DENS_RD123) -0.054 (ELEV) + 4.28 (X) log(σap) ) -19.1 + 0.195(TD_300) +0.585(RD2_300) + 0.418 (X) log(σap) ) -20.21 + 0.196(TD_300) +0.856(RD2_300) + 0.446 (X) log(σap) ) -22.2+ 0.153(TD_300) +0.699(RD2_300) + 0.480 (X) log(σap) ) -21.8 + 0.188(TD_300) +0.890(RD2_300) + 0.477 (X)

R2

RMSEd

robustb R 2

0.72

2.22

0.63

0.68

2.89

0.56

0.55

5.08

0.44

0.51 0.54 0.66 0.54

0.53 0.58 0.40 0.58

0.40 0.42 0.57 0.42

a Where RD1_XXX is the total length (km) of highways and freeways within a XXX meter buffer around the intersection; RD2_XXX is the total length (km) of major roads within an XXX buffer; TD_XXX is the density of truck traffic (vehicles/ hectare) within an XXX buffer; DENS_ RD123 is the density of all roads around the intersection (km/hectare); ELEV is the elevation above sea level (m); X is the UTM Zone 10 latitude divided by 10000; p < 0.05 for all terms. b Leave one out method. c Excluding one location (see text for details). d RMSE is the root mean standard.

FIGURE 4. Map depicting land use regression model for median σap, as reported in Table 1. mean (LUR model R2 < 0.25 in these cases). This emphasizes the need for some temporal adjustments of the measurements. The model performance for the adjusted 75th percentile, log(mean) and log(90th percentile) values as measured by R2 (0.51-0.55) is similar to that reported by Henderson et al. (10) for NO2 and NO in this region (R2 ranged from 0.56 to 0.62). In addition, our model performed better than that previously reported by the same investigators for σap (R2 ranged from 0.39 to 0.41) for 25 sites with fixed-location samplers. A map of the median predictions is presented in Figure 4.

Discussion We have described a simple mobile sampling strategy for light absorbing carbon, results of which can be successfully incorporated into a land use regression model to characterize spatial variability in ambient concentrations. In a recent review, Ryan et al. (26) show that the number of sampling locations across published LUR models is uncorrelated with the model R2, suggesting that the specific location of sampling sites, rather than the actual number may be more important for model performance. Still, existing LUR models for PM have used at most 40 sampling sites, whereas NOx models have used as many as 120

locations. In their recent review, Hoek et al., suggest that 40-80 sampling locations are reasonable for LUR modeling, although requirements are likely to vary between locations (20). Even a protocol for (2-week average) PM measurements at 40 locations typically requires rotation of 10 samplers over a minimum of an 8-week period (12). In contrast, the method described here has the capacity to measure the same 40 locations in only 8 days. There are several obvious limitations of this approach. The first is that it relies on short-term measurements during a selected time of day over a few days and in a single season, rather than relying on integrated samples which may better characterize long-term average concentrations. This method, and LUR in general, is not suited toward characterization of short-term temporal variability. By conducting mobile monitoring at fixed times of day and by randomizing the measurement of specific sites across the 8-day sampling period we have partially decoupled the spatial and temporal components contributing to variability in ambient concentrations. However, given that exposure is determined by both temporal and spatial variability in pollutant concentrations (50), incorporation of temporality into LUR models should lead to more accurate characterization of true spatiotemporal variability in ambient concentrations. Depending on the VOL. 43, NO. 13, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4675

ultimate application of the LUR model, emphasis on spatial variability in annual or seasonal concentrations may be adequate. Temporality can also be externally incorporated into LUR model estimates for specific periods of interest by applying trend data from a limited number of fixed monitoring sites (51). Second, there is little theoretical-physical basis behind the application of LUR models, particularly the use of circular buffers to extract local covariates. Some attempts have been made to address both theoretical understanding of pollutant dispersion as well as temporal variability, for example the use of wind fields (52, 53) to more accurately characterize concentrations for locations downwind of major highways. We have not included meteorological variables in this analysis, in part due to the very short-term nature of the measurements. Even the simplest dispersion models break down when transport times (here on the order of minutes) are long compared with sampling times (on the order of 10 s) at a given location. Third, given that our measurements were focused on summer rush hour periods, the resulting LUR model is more precisely a model of black carbon concentrations during this period. Repeat sampling across seasons and within days would strengthen the generalization of this as a model of long-term average concentrations. Even with these limitations, our results suggest that the ability to measure distributions of short-term levels at a relatively large number of sampling locations, as well as the focus on periods of higher traffic emissions, results in σap models with a similar level of explained variance than those utilizing a smaller number of fixed location integrated samplers. Further, the σap models based on mobile monitoring had R2 values comparable to those developed for NOx in the same region (10) based on 2-week average measurements and many more sampling sites (N ) 116). Our measurements, while collected in-traffic, are also not completely comparable to fixed-site measurements which may be collected at varying distances from roadways. By repeatedly traversing the intersections that were sampled and averaging measurements across all traverses, we hoped to minimize the impact of extreme concentrations from individual vehicles on the characterization of each site. These results are encouraging and justify making repeated measures at both on and offpeak traffic hours within the same season and across two different seasons in order to examine the generality of our derived models. The simple LUR models that we developed from the mobile σap measurements suggest that nearby truck traffic is an important determinant of the upper end of the σap distribution surrounding a given intersection. This is reasonable and not surprising. Given that (A) elemental carbon emissions from diesel vehicles are higher than those from spark ignition vehicles (54), and (B) we found that truck traffic variables were more predictive of σap than automobile traffic variables, we conclude that these models provide some indication of the spatial variability of diesel exhaust PM in the study region. This contrasts with models for the lower end of the σap distribution (25th percentile, median) at these same locations which depend on larger scale geographic variables and total vehicle emissions, similar to models of mean NOx values in this area (10). Specifically, mean NO2 and NO models for the same 39 sites used in this analysis (see Supporting Information Table 3), had R2 values that were comparable to the 25th percentile and median models in this analysis. The measurement method we have described should be applicable to any urban area and will facilitate the development of LUR models for traffic-related particle characteristics, including σap. Use of other continuous measurement devices in a similar protocol to that described 4676

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 13, 2009

here may also provide useful information on other pollutants and on the spatial influence of other sources. For example, mobile measurements from a portable condensation nuclei counter (55) or a photoelectric aerosol sensor (56) could be used to develop LUR models for particle number or particle-bound PAHs, respectively.

Acknowledgments Nichole Garzia assisted with the collection of PSAP measurements. Elizabeth Matovinovic and Jelle Vlaanderen helped conduct initial pilot measurements and assisted, along with Darren Wilton, with the development of data processing procedures. Dave Covert provided the PSAP instrument and gave general advice and assistance in its use and operation.

Supporting Information Available Table 1: Sampling locations, dates and intersection travel time. Table 2: Summary statistics of 10-s average light absorption coefficient for each location (10-6m-1). Table 3: Land-use regression model for NO and NO2 [ppb] at 39 sites. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Roosli, M.; Braun-Fahrlander, C.; Kanzli, N.; Oglesby, L.; Theis, G.; Camenzind, M.; Mathys, P.; Staehelin, J. Spatial variability of different fractions of particulate matter within an urban Environment and between urban and rural sites. J Air Waste Manage Assoc. 2000, 50 (7), 1115–1124. (2) Janssen, N. A. H.; Van Mansom, D. F. M.; Van Der Jagt, K.; Harssema, H.; Hoek, G. Mass concentration and elemental composition of airborne particulate matter at street and background locations. Atmos. Environ. 1997, 31 (8), 1185–1193. (3) Hoek, G.; Meliefste, K.; Cyrys, J.; Lewne, M.; Bellander, T.; Brauer, M.; Fischer, P.; Gehring, U.; Heinrich, J.; van Vliet, P.; Brunekreef, B. Spatial variability of fine particle concentrations in three European areas. Atmos. Environ. 2002, 36 (25), 4077–4088. (4) Roemer, W. H.; van Wijnen, J. H. Differences among black smoke, PM10 and PM1 Levels at urban measurement sites. Environ Health Perspect. 2001, 109 (2), 151–154. (5) Cyrys, J.; Heinrich, J.; Brauer, M.; Wichmann, H. Spatial Variability of Acidic Aerosols, Sulfate and PM10 in Erfurt, Eastern Germany. J Exposure Anal. Environ. Epidemiol. 1998, 8 (4), 447– 464. (6) Zhu, Y.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C. Study of Ultrafine particles near a major highway with heavy-duty diesel traffic. Atmos. Environ. 2002, 36 (27), 4323–4335. (7) Larson, T.; Su, J.; Baribeau, A.-M.; Buzzelli, M.; Setton, E.; Brauer, M. A spatial model of urban winter woodsmoke concentrations. Environ. Sci. Technol. 2007, 41 (7), 2429–2436. (8) Su, J. G.; Buzzelli, M.; Brauer, M.; Gould, T.; Larson, T. Modeling spatial variability of airborne levoglucosan in Seattle, Washington. Atmos. Environ. 2008, 42 (22), 5519–5525. (9) Brauer, M.; Hoek, G.; Van Vliet, P.; Meliefste, K.; Fischer, P.; Gehring, U.; Heinrich, J.; Cyrys, J.; Bellander, T.; Lewne, M.; Brunekreef, B. Prediction of long term average particulate air pollution concentrations by traffic indicators for epidemiological studies. Epidemiology 2003, 14, 228–239. (10) Henderson, S. B.; Beckerman, B.; Jerrett, M.; Brauer, M. Application of land use regression to estimate ambient concentrations of traffic-related NOX and fine particulate matter. Environ. Sci. Technol. 2007, 41 (7), 2422–2428. (11) Ryan, P. H.; Lemasters, G. K.; Levin, L.; Burkle, J.; Biswas, P.; Hu, S.; Grinshpun, S.; Reponen, T. A land-use regression model for estimating microenvironmental diesel exposure given multiple addresses from birth through childhood. Sci. Total Environ. 2008, 404 (1), 139–47. (12) Hoek, G.; Meliefste, K.; Cyrys, J.; Lewne, M.; Bellander, T.; Brauer, M.; Fischer, P.; Gehring, U.; Heinrich, J.; van Vliet, P.; Brunekreef, B. Spatial variability of fine particle concentrations in three European areas. Atmos. Environ. 2002, 36 (25), 4077–4088. (13) Carr, D.; von Ehrenstein, O.; Weiland, S.; Wagner, C.; Wellie, O.; Nicolai, T.; von Mutius, E. Modeling annual benzene, toluene, NO2, and soot concentrations on the basis of road traffic characteristics. Environ. Res., A 2002, 90, 111–118.

(14) Beelen, R.; Hoek, G.; Fischer, P.; van den Brandt, P. A.; Brunekreef, B. Estimated long-term outdoor air pollution concentrations in a cohort study. Atmos. Environ. 2007, 41, 1343–1358. (15) Hochadel, M.; Heinrich, J.; Gehring, U.; Morgenstern, V.; Kuhlbusch, T.; Link, E.; Wichmann, H. E.; Kramer, U. Predicting long-term average concentrations of traffic-related air pollutants using GIS based information. Atmos. Environ. 2006, 40, 542– 553. (16) Morgenstern, V.; Zutavern, A.; Cyrys, J.; Brockow, I.; Gehring, U.; Koletzko, S.; Bauer, C. P.; Reinhardt, D.; Wichmann, H. E.; Heinrich, J. Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children. Occup. Environ. Med. 2007, 64, 8–16. (17) Ross, Z.; Jerrett, M.; Ito, K.; Tempalski, B.; Thurston, G. D. A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos. Environ. 2007, 41, 2255–2269. (18) Moore, D. K.; Jerrett, M.; Mack, W. J.; Kunzli, N. A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA. J. Environ. Monit. 2007, 9, 246–252. (19) Briggs, D. J.; de Hoogh, C.; Gulliver, J. A comparison of techniques for modelling exposure to traffic-related air pollution in contrasting urban environments. Epidemiology 2002, 13, S98-S98. (20) Hoek, G.; Beelen, R.; de Hoogh, K.; Vienneau, D.; Gulliver, J.; Fischer, P.; Briggs, D. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 2008, 42, 7561–7578. (21) Pakkanen, T.; Makela, T.; Hillamo, R. E.; Virtanen, A.; Ronkko, T.; Keskinen, J.; Pirjola, L.; Parviainen, H.; Hussein, T.; Hameri, K. Monitoring of Black Carbon and size-segregated particle number concentrations at 9-m and 65-m distances from a major road in Helsinki. Boreal Environ Res 2006, 11, 295–309. (22) Zereini, F.; Alt, F.; Messerschmidt, J.; Wiseman, C.; Fledmann, I.; von Bohlen, A.; Muller, J.; Liebl, K.; Puttmann, W. Concentration and distribution of heavy metals in urban airborne particulate matter in Frankfurt am Main, Germany. Environ. Sci. Technol. 2005, 39, 2983–2989. (23) Weckwerth, G. Verificiation of traffic emitted aerosol components in the ambient air of Cologne (Germany). Atmos. Environ. 2001, 35, 5525–5536. (24) Briggs, D. J.; Collins, S.; Elliot, P.; Fischer, P.; Kingham, S.; Lebret, E.; Pryl, K.; van Reeuwijk, H.; Smallbone, K.; van der Veen, A. Mapping urban air pollution using GIS: a regression-based approach. Int. J. Geographic Inf. Sci. 1997, 11 (7), 699–718. (25) Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models. J. Expo. Anal. Environ. Epidemiol 2005, 15 (2), 185–204. (26) Ryan, P. H.; LeMasters, G. K. A review of land-use regression models for characterizing intraurban air pollution exposure. Inhalation Toxicol. 2007, 19 (Suppl 1), 127–133. (27) Gonzales, M.; Qualls, C.; Hudgens, E.; Neas, L. Characterization of a spatial gradient of nitrogen dioxide across a United StatesMexico border city during winter. Sci. Total Environ. 2005, 337 (1-3), 163–173. (28) Ross, Z.; English, P. B.; Scalf, R.; Gunier, R.; Smorodinsky, S.; Wall, S.; Jerrett, M. Nitrogen dioxide prediction in Southern California using land use regression modeling: Potential for environmental health analyses. J. Expo. Anal. Environ. Epidemiol. 2006, 16 (2), 106–114. (29) Jerrett, M.; Arain, M.; Kanaroglou, P.; Beckerman, B.; Crouse, D.; Gilbert, N.; Brook, J.; Finkelstein, N.; Finkelstein, M. Modelling the intra-urban variability of ambient traffic pollution in Toronto, Canada. J. Toxicol. Environ. Health A. 2007, 70 (3-4), 200–212. (30) Mavko, M. E.; Tang, B.; George, L. A. A sub-neighborhood scale land use regression model for predicting NO(2). Sci. Total Environ. 2008, 398 (1-3), 68–75. (31) Briggs, D. J.; de Hoogh, C.; Gulliver, J.; Wills, J.; Elliott, P.; Kingham, S.; Smallbone, K. A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci. Total Environ. 2000, 253 (1-3), 151–167. (32) Gilbert, N. L.; Goldberg, M. S.; Beckerman, B.; Brook, J. R.; Jerrett, M. Assessing spatial variability of ambient nitrogen dioxide in Montreal, Canada, with a land-use regression model. J. Air Waste Manage. Assoc. 2005, 55, 1059–1063. (33) Sahsuvaroglu, T.; Arain, A.; Beckerman, B.; Kanaroglou, P.; Brook, J. R.; Finkelstein, N.; Finkelstein, M. M.; Gilbert, N. L.; Newbold,

(34)

(35)

(36)

(37)

(38)

(39)

(40)

(41)

(42)

(43) (44)

(45)

(46) (47)

(48)

(49) (50)

(51)

(52)

B.; Jerrett, M. A land-use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Canada. J. Air Waste Manage. Assoc. 2006, 56 (8), 1059–1069. Aguilera, I.; Sunyer, J.; Ferna´ndez-Patier, R.; Hoek, G.; AguirreAlfaro, A.; Meliefste, K.; Bomboi-Mingarro, M. T.; Nieuwenhuijsen, M. J.; Herce-Garraleta, D.; Brunekreef, B. Estimation of outdoor NO(x), NO(2), and BTEX exposure in a cohort of pregnant women using land use regression modeling. Environ. Sci. Technol. 2008, 42 (3), 815–821. Clougherty, J. E.; Wright, R. J.; Baxter, L. K.; Levy, J. I. Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants. Environ Health. 2008, 7, 17. Beelen, R.; Hoek, G.; van den Brandt, P. A.; Goldbohm, R. A.; Fischer, P.; Schouten, L. J.; Armstrong, B.; Brunekreef, B. Longterm exposure to traffic-related air pollution and lung cancer risk. Epidemiology. 2008, 19 (5), 702–710. Beelen, R.; Hoek, G.; van den Brandt, P. A.; Goldbohm, R. A.; Fischer, P.; Schouten, L. J.; Jerrett, M.; Hughes, E.; Armstrong, B.; Brunekreef, B. Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR study). Environ Health Perspect. 2008, 116 (2), 196–202. Rosenlund, M.; Picciotto, S.; Forastiere, F.; Stafoggia, M.; Perucci, C. A. Traffic-related air pollution in relation to incidence and prognosis of coronary heart disease. Epidemiology. 2008, 19 (1), 121–128. Slama, R.; Morgenstern, V.; Cyrys, J.; Zutavern, A.; Herbarth, O.; Wichmann, H. E.; Heinrich, J. LISA Study Group; Traffic-related atmospheric pollutants levels during pregnancy and offspring’s term birth weight: a study relying on a land-use regression exposure model. Environ Health Perspect. 2007, 115 (9), 1283– 1292. Brauer, M.; Hoek, G.; Smit, H. A.; de Jongste, J. C.; Gerritsen, J.; Postma, D. S.; Kerkhof, M.; Brunekreef, B. Air pollution and development of asthma, allergy and infections in a birth cohort. Eur. Respir. J. 2007, 29 (5), 879–888. Wheeler, A. J.; Smith-Doiron, M.; Xu, X.; Gilbert, N. L.; Brook, J. R. Intra-urban variability of air pollution in Windsor, OntariosMeasurement and modeling for human exposure assessment. Environ Res. 2008, 106 (1), 7–16. Atari, D. O.; Luginaah, I.; Xu, X.; Fung, K. Spatial variability of ambient nitrogen dioxide and sulfur dioxide in Sarnia, “Chemical Valley,” Ontario, Canada. J. Toxicol. Environ. Health A 2008, 71 (24), 1572–1581. Palmes, E.; Gunnison, A. Personal monitoring device for gaseous contaminants. Am. Ind. Hyg. Assoc. J. 1973, 34 (2), 78–81. Suglia, S. F.; Gryparis, A.; Wright, R. O.; Schwartz, J.; Wright, R. J. Association of Black Carbon with cognition among children in a prospective birth cohort study. Am J Epidemiol. 2008, 167 (3), 280–286. de Castro, B. R.; Wang, L.; Mihalic, J. N.; Breysse, P. N.; Geyh, A. S.; Buckley, T. J. The longitudinal dependence of black carbon concentration on traffic volume in an urban envrionment. J. Air Waste Manage. Assoc. 2008, 58, 928–939. Rich, K. Air pollution and patients with implanted cardiac defibrillators: An epidemiological analysis and assessment of exposure. MSc. University of British Columbia, Vancouver, 2003. Cyrys, J.; Heinrich, J.; Hoek, G.; Meliefste, K.; Lewne´, M.; Gehring, U.; Bellander, T.; Fischer, T.; van Vliet, P.; Brauer, M.; Wichmann, E.; Brunekreef, B. Comparison between different traffic-related particle indicators: Elemental carbon (EC), PM2.5 mass, and absorbance. J. Exposure Anal. Environ. Epidemiol. 2003, 13 (2), 134–143. Noullett, M.; Jackson, P.; Brauer, M. Winter measurements of children’s personal exposure and ambient fine particle mass, sulphate and light-absorbing components in a northern community. Atmos. Environ. 2006, 40, 1971–1990. Schauer, J. Evaluation of elemental carbon as a marker for diesel particulate matter. J. Exposure Anal. Environ. Epidemiol. 2003, 13, 443–453. Nethery, E.; Leckie, S.; Teschke, K.; Brauer, M. From measures to models: An evaluation of air pollution exposure assessment for epidemiologic studies of pregnant women. Occup. Environ. Med. 2008, 65, 579–586. Brauer, M.; Lencar, C.; Tamburic, L.; Koehoorn, M.; Demers, P.; Karr, C. A cohort study of traffic-related air pollution impacts on birth outcomes. Environ. Health Perspect. 2008, 116, 680– 686. Arain, M. A.; Blair, R.; Finkelstein, N.; Brook, J. R.; Sahsuvaroglu, T.; Beckerman, B.; Zhang, L.; Jerrett, M. The use of wind fields to improve empirical chronic air pollution exposure models. Atmos. Environ. 2007, 41, 3453–3464.

VOL. 43, NO. 13, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4677

(53) Su, J. G.; Brauer, M.; Ainslie, B.; Steyn, D.; Larson, T. V.; Buzzelli, M. An innovative land use regression model incorporating meteorology for exposure analysis. Sci. Total Environ. 2008, 390, 520–529. (54) Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P.; Sioutas, C. Mobile platform measurements of Ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles. Atmos. Environ. 2005, 39, 3597–3610. (55) Jiang, M.; Marr, L. C.; Dunlea, E. J.; Herndon, S. C.; Jayne, J. T.; Kolb, C. E.; Knighton, W. B.; Rogers, T. M.; Zavala, M.; Molina,

4678

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 13, 2009

L. T.; Molina, M. J. Vehicle fleet emissions of black carbon, polycyclic aromatic hydrocarbons, and other pollutants measured by a mobile laboratory in Mexico City. Atmos. Chem. Phys. 2005, 5, 3377–3387. (56) Gray, A.; Cass, G. Source contributions to atmospheric fine carbon particle concentrations. Atmos. Environ. 1998, 32 (22), 3805–3825.

ES803068E