TECHNICAL PAPER
ISSN 1047-3289 J. Air & Waste Manage. Assoc. 56:1059 –1069 Copyright 2006 Air & Waste Management Association
A Land Use Regression Model for Predicting Ambient Concentrations of Nitrogen Dioxide in Hamilton, Ontario, Canada Talar Sahsuvaroglu, Altaf Arain, Pavlos Kanaroglou, Norm Finkelstein, and Bruce Newbold School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada Michael Jerrett School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada; and Division of Biostatistics, Department of Preventative Medicine, University of Southern California, Los Angeles, CA Bernardo Beckerman Division of Biostatistics, Department of Preventative Medicine, University of Southern California, Los Angeles, CA Jeffrey Brook Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada Murray Finkelstein Family Medicine Centre, Mt. Sinai Hospital, Toronto, Ontario, Canada Nicolas L. Gilbert Air Health Effects Division, Health Canada, Ottawa, Ontario, Canada
ABSTRACT This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method
IMPLICATIONS LUR modeling has emerged as a promising approach to estimating small-area variations in pollution exposures within cities. The method allows for the simultaneous consideration of traffic patterns, road patterns, and land use characteristics as predictors of pollution variability. These predictions can subsequently be used to visualize pollution and to refine exposure models used in epidemiologic investigations. In this paper, the authors improve upon existing LUR methodologies by including wind interactions with distance from highways and seasonal validation. Their findings also demonstrate the capacity of LUR modeling to predict well in industrialized settings. The higher spatial resolution of these pollution surfaces offers an important contribution as exposure inputs for future epidemiologic studies.
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can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at ⬎100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies. INTRODUCTION Many epidemiologic investigations involving exposure assessments of air pollution and health effects have relied on estimates from central outdoor monitoring sites. These studies have assumed that such estimates adequately represent individual exposures or at least capture some Journal of the Air & Waste Management Association 1059
Sahsuvaroglu et al. important facet of the exposure experience. Outdoor estimates, however, have shown to be poor predictors of true personal exposures in many studies.1–3 Such studies have documented that, although central monitors correlate well with average personal exposures for daily variations in acute studies,4,5 they do not account for spatial variation within cities,6,7 which is the key exposure contrast for chronic studies of health effects. This is especially accentuated for traffic-related pollutants, such as nitrogen dioxide (NO2) and ultrafine particles, because of their high spatial variability within small distances from emission sources.8,9 Assessing within-city or intraurban variability in air pollution is increasingly important for a variety of reasons, including: (1) the increase in traffic pollution10 and consequent intensification of clear exposure gradients around roads11; (2) positive associations between exposure to traffic pollution and health outcomes that have been discovered, although not conclusively12–14; and (3) advances in geographical information systems (GIS) and statistical techniques in the exposure analysis field that allow for reducing uncertainty.15 One promising method, the land use regression (LUR), predicts ambient concentrations from land use, population density, and transportation characteristics within the urban area. The goal of this study is to develop an LUR estimate for NO2 in Hamilton, Ontario, Canada. Background LUR models have been applied in ⬎9 countries and 14 cities across Europe,16 –18 and most recently in North America.19 –21 Similar to the earlier European studies, the North American studies have used NO2 as a proxy for traffic emissions, because it correlates well with traffic densities22 and is relatively simple and inexpensive to measure. In Europe, the LUR methods were initially developed as part of the Small Area Variation in Air pollution and Health study.16,17 Briggs et al.23 used a revised LUR model in four contrasting urban areas in the United Kingdom. Road traffic volume, elevation, and land-use type were used as independent variables to predict NO2 distribution. The determination coefficient (R2) for these variables, calibrated to local conditions, ranged from 0.58 to 0.76. Briggs et al.16 reported higher predictions for average annual NO2 with R2 of 0.79 – 0.87. The North American studies have produced similar results but are somewhat less predictive of ambient concentrations than those observed in the European settings. Jerrett et al.20 applied the LUR to Toronto, Ontario, Canada, and developed a model with an R2 of 0.69. Comparable analysis published previously in this journal was conducted in Montreal, Quebec, Canada, where the R2 was 0.54.19 Ross et al.21 tested the LUR model in Southern California and were able to predict 79% of the variation in NO2. The differences in LUR models of European and North American cities have been suggested to be because of different city configurations, population density patterns, vehicle fleets, and fuels used.19 As a comparative exercise, Jerrett et al.20 applied the LUR model in a Canadian context using coefficients derived from the Briggs et al.23 Amsterdam model. The derived surface was unable to 1060 Journal of the Air & Waste Management Association
identify the spatial variability within the city and overpredicted values in most areas, indicating that perhaps LUR models do not easily transfer between areas with such different land use patterns, further highlighting the need of locally calibrated models. It is important to note that the LUR model does not aim to replace or represent the complex atmospheric chemical interactions underlying air pollution distribution. Rather, the method is particularly useful because it draws on an extensive database that is measured with relatively small errors compared with the meteorological and other data that support dispersion models. In general, LUR models have performed as well as or better than more theoretically elegant, but empirically poor, dispersion models.24,25 The LUR offers an improved level of detail at which pollution variability is observed. Identifying these smallarea variations in air pollution is potentially important for increased accuracy when conducting epidemiologic and health impact studies. With less accurate exposure estimating methodologies, such as those based on ambient city-wide monitoring or distance to road calculations, increased exposure measurement error may bias models toward the null.26 For the LUR method to provide accurate exposure data in future epidemiologic investigations, there needs to be an improved evaluation of how well the model performs in North America more generally, as well as advancing the methodology to incorporate more aspects of pollution exposure. Specifically, cities with large industrial point sources have not yet been investigated. To address these research needs, the model was applied to Hamilton, a medium-sized industrial city in Central Canada. EXPERIMENTAL WORK Study Area Hamilton, situated at 43° 16⬘ N, 79° 54⬘ W, is the ninth largest city in Canada, with a population of ⬎660,000 in 2001 and an area of 1372 km.2,27 Its climate is classified as being in the “humid east” region of temperate North America.28 The city has some of the highest ambient air pollution exposures in Canada, exceeding government objectives on ⬃20 days per year. High exposures are experienced for a variety of reasons, including: (1) local point source emissions from one of the largest industrial areas in Canada, which houses two large steel manufacturing complexes; (2) increasing transportation emissions that result from automobile and truck traffic in and around the city; (3) topographic and meteorological conditions that often keep pollution close to ground level and drive it to heavily populated areas of the city from the industrial core; (4) proximity to the Ohio River Valley, where coal-fired generating stations emit pollutants that travel hundreds of kilometers to Hamilton; and (5) the Nanticoke coal-fired generating station located on the northern shore of Lake Erie, which also contributes considerably to local pollution.29 Recent studies based on spatial interpolations of air pollution data from the sparse government monitoring network in Hamilton confirm that spatial differences in air pollution levels exist within the city.7,14,30,31 The Volume 56 August 2006
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Figure 1. Map of Hamilton, highlighting important identification factors.
major industrial zone creates higher levels of air pollutants in the northeast and comparatively lower levels of pollutants in the southern and western parts of the city. Thus, Hamilton presents a challenging location to study intraurban concentrations because of the complex factors that influence pollution distribution (Figure 1). NO2 Measurement In this study, Ogawa passive monitors were used to measure ambient NO2 (Ogawa and Co., USA). These two-sided monitors use triethanolamine-coated pads, yielding two measurements per monitor. Two monitors were deployed at 107 locations over a 2-week period in October 2002, yielding four readings per site. Monitors were installed at a height of between 1.5 and 2.5 m, depending on the availability of safe attachment locations, such as light or hydro poles, fences, and parking signs. Placing monitors at obstructed locations, such as alleys or on traffic signal posts, was avoided. Previous research from Europe23 and Canada20 suggests that using ⬃100 locations provides adequate spatial information to calibrate the LUR model, although there is no presently accepted method estimating sufficient sample size.32 The pads were analyzed in an Volume 56 August 2006
Environment Canada laboratory using ion chromatographic methods.11 Five additional monitors were exposed to ambient air while preparing the filters, then sealed, and sent to the analysis facilities to serve as blanks. Monitoring Site Locations The 107 locations used for measuring NO2 were identified based on a population-weighted location-allocation model. This model is described in detail elsewhere.33 Briefly, the method optimizes pollution monitor locations in areas where high spatial variability in both the pollutant and density of study population exist. Areas with large populations of elderly persons over the age of 65 were initially targeted, because the subsequent exposure surfaces were developed for use in studies with this age group. However, it was found that too many monitoring locations were clustered in local areas around oldage homes, so 20 of the locations were moved to capture variation in the pollution surface rather than underlying characteristics of the study population. Additional modifications were made to four monitoring locations by colocating them with Ontario Ministry of the Environment (MoE) continuous monitoring facilities. Journal of the Air & Waste Management Association 1061
Sahsuvaroglu et al. NO2 Monitor Reliability Monitors from 102 locations were successfully retrieved. Five locations had monitors that were missing, damaged, or vandalized. After subsequent laboratory analyses, resulting NO2 values from the filters were thoroughly checked for consistency and reliability. Accuracy was determined based on two screening criteria. First, each location had to have at least two valid measurements returned from the analysis facilities. Second, the coefficient of variation (CV ⫽ standard deviation/average) between the available readings per monitoring location had to be ⬍25%. If a location had a CV ⬎25%, then the most extreme observation was removed, and the screening criteria were applied again. This was continued until a CV ⬍25% was achieved with a minimum of two observations. If the CV remained ⬎25% for both observations, the whole location was discarded. The mean of all valid readings was then calculated for each monitoring location. After this screening process, only one location was discarded, and the final values for seven locations were averaged from two to three observations. All of other observations were based on four readings that had a CV ⬍25%. Data from 101 valid readings were used as the dependent variable in the LUR. Independent Variables In total, ⬎110 independent variables (and variations of variables) were tested for their association to the observed NO2 levels in Hamilton. The variables were grouped into five categories: (1) land use (area of different land uses within buffers of different radii); (2) physical geography (X and Y coordinates, elevation); (3) meteorology (wind direction in relation to major emission sources); (4) roads and traffic (lengths of different road types and traffic flow density within buffers of different radii); and (5) population (population density, density of dwelling units in terms of census tracts and enumeration areas, and dwelling count density). Within each category, numerous variables were derived using spatial overlays of buffers with various radii around the monitoring location. Specifically, land use types were categorized into commercial, governmental, industrial, open, and residential. Area of land use (in hectares) within buffers around the monitors with radii of 100, 200, 300, 500, 750, and 1000 m were tested. Road types consisted of minor, major, and highways, and transportation variables were calculated as length of road (in kilometers) within buffers of 50, 100, 200, 250, 500, 750, and 1000 m, as well as an annulus of 50 –200 m around the monitors. Traffic variables were derived from traffic counts as a kernel density estimate at distances of 300 and 500 m; thus, actual values provided an index rather than specific units. Population and dwelling densities were calculated from counts within density estimates of 750, 1000, 1250, 1500, 2000, and 2500 m radii. Distances to roadways or the lake were calculated in meters. Indicator variables were created for monitors located downwind of major roads, based on wind interpolations from government monitoring sites and for the presence of monitors within a 1000-m buffer around the downtown industrial core. Land use variable and road network data were acquired from DMTI Spatial Inc., a commercial source. Field 1062 Journal of the Air & Waste Management Association
checks of the accuracy of the GIS road and land use coverages were conducted with GARMIN eTrex Vista global positioning system (GPS) tracker (GARMIN International). GPS field observations were also taken during the deployment of the monitors to mark their locations and to validate land use accuracy. The land use data from DMTI were found to be highly accurate, with ⬍2% of the classifications incorrect compared with field validations. Traffic data, provided by the City of Hamilton, Department of Transportation, was a 24-hr average count of vehicles per day. A majority of the traffic data was based on values collected in 2000; however, updated data was provided, where available, to the end of 2003. The level of information contained in this data set included detailed measures of traffic on highways and major and most minor roads. Population variables were collected from the Statistics Canada 1996 Census of Population (the most recent iteration available at the initiation of this study). Elevation values were taken from a 5-m resolution digital elevation network provided by the Ontario Ministry of Natural Resources and compiled through ArcGIS 8.2. Meteorological data were derived from a network of 27 surface observation stations. Seventeen-day average hourly zonal (east) and meridional (west) orthogonal components of wind were calculated for each station. Wind fields were constructed from the wind direction vectors by interpolation using the radial basis functions multiquadric method, this being a method that has been used successfully for wind field construction in meteorology and related fields.34,35 After an interaction calculation between both wind component vectors, a final variable was derived from both data sets that indicated whether a monitor was upwind or downwind from an expressway (for a detailed explanation, see ref 20) Location-specific variables, such as monitor placement above or below the Niagara Escarpment, a natural feature that runs through the city dividing it into “upper” and “lower” Hamilton (see Figure 1), were also tested to assess potential inversion effects known to affect the area below the escarpment. Model Development A manual forward selection method was used to find the most significant combination of independent variables that explained the dependent variable of monitored NO2. Each independent variable was tested against the dependent variable by using a bivariate regression model, using SPSS 11.1. After identification of the most significant bivariate relationship, trivariate regressions were used to find the best variable combinations (e.g., highest and next highest t values included). Variables kept in the regression had to be significant at the 95% level and have low collinearity with other variables (variance inflation factor [VIF] ⬍ 2). “Best” models were identified as having a combination of variables with the highest R2. This process involved numerous iterations of the different variables in an attempt to find the model with the most predictive power. This work was limited by high collinearity among the variables that resulted in some interchangeability of variables when particular combinations were tested. Volume 56 August 2006
Sahsuvaroglu et al. Table 1. Summary of regression model predicting NO2. Variable Ln(NO2) Constant Traffic density at 300 m 50 m from highways 1500 m downwind of Hwy 403 Open land use within 500 m Industrial land use within 200 m Downtown industrial core within 1000 m Distance to the lake
Coefficient
Standard Error
t
Prob > t
VIF
2.72000 0.00148 0.00133 0.00017 ⫺0.00345 0.01675 0.09452 ⫺0.00002
0.003 0.000 0.000 0.000 0.001 0.008 0.044 0.001
95.985 5.473 3.080 2.740 ⫺6.299 2.145 2.135 ⫺4.977
0.000 0.000 0.003 0.007 0.000 0.035 0.035 0.000
1.118 1.046 1.056 1.281 1.456 1.569 1.396
Notes: R2 ⫽ 0.764.
After a potential model was identified, standard regression diagnostics were conducted, including identification of outliers and leverage values. If the chosen combination of variables did not fulfill all of these criteria, the steps were retraced, the “next best” model was found, and the regression analysis was rerun. Residuals were then tested for spatial autocorrelation to avoid biased and inefficient estimates.36 The final seven-variable parsimonious model presented below resulted from this selection process. RESULTS The NO2 values from the final 101 monitors ranged from 8 to 28.1 ppb, with an arithmetic mean of 14.6 ppb and standard deviation of 3.7 ppb (geometric mean ⫽ 14.2 ppb). The blank filters had an average detection limit of 0.27 ppb (range ⫽ 0.21 – 0.42 ppb) in ambient air, with all of the samples measuring above this amount. These levels are all below the Canadian National Ambient Air Quality Objectives annual maximum desirable limit of 32 ppb.37 Because of a slight right skew, natural log transformation of the NO2 variable was used for further analysis. The final regression model included variables that incorporated open space and industrial land use, traffic density, location downwind from the northwest corridor of Highway 403 (see Figure 1 for placement), distance from a highway, and distance from Lake Ontario. Each variable took the expected sign. Traffic density within a 300-m buffer resulted in higher NO2 levels. Industrial land use resulted in higher NO2 concentrations, whereas open space land use resulted in lower NO2 concentrations. Locations ⱕ1500 m downwind of Highway 403 had
higher NO2 values, as did locations within 50 m from all of the highways. The distance-to-lake variable had a negative association, such that the greater the distance from the lake, the lower the NO2 concentration, a finding consistent with local lake-induced currents and higher wind speeds.7 In addition, the Hamilton lakeshore is the primary location of the large industries; thus, this variable could also be acting as a proxy for a measure of distance from major industrial locations. The coefficients of the variables and the resultant regression equation are shown in Table 1. Table 2 shows the correlation coefficients for the significant variables retained in the model. Two-way interaction effects for all possible combinations of the significant variables in the final model were also tested. Only the interaction between length of highway within 50 m and open land use within 500 m of a monitor was significant when tested in the model (t ⫽ ⫺2.14). Because length of highway within 50 m was positively associated with NO2, the interaction suggests that highways have less impact on NO2 concentrations when surrounded by open space. The interaction also implies that the negative effect of open space becomes more pronounced when there are nearby highways. Because the coefficient induced high collinearity in the model and added only 1.5% to the R2, this interaction was not included in the final prediction surface. The model performed well in the standard residual diagnostics for leverage values and heteroskedasticity. One case was identified in nearly all of the competing models as a multivariate outlier. On closer examination, it was discovered that it had unusual land use and traffic characteristics. The monitor was located just over 2 km
Table 2. Pearson correlation coefficients between variables significant in the final LUR model. Variables Ln(NO2) Traffic density at 300 m 50 m from highways Open land use within 500 m Industrial land use within 200 m Distance to the lake 1500 m downwind of Hwy 403 Downtown industrial core within 1000 m
Ln(NO2)
Traffic
Highway
Open
Industrial
Distance to Lake
Downwind
Industrial Core
1
0.565a 1
0.180 0.131 1
⫺0.628a ⫺0.238b 0.029 1
0.403a 0.184 ⫺0.066 ⫺0.182 1
⫺0.649a ⫺0.282a ⫺0.055 0.434a ⫺0.300a 1
0.221a 0.095 ⫺0.044 ⫺0.086 ⫺0.007 ⫺0.133 1
0.447a 0.265a ⫺0.074 ⫺0.259a 0.540a ⫺0.310a ⫺0.093 1
Notes: aP ⬍ 0.01; bP ⬍ 0.05. Volume 56 August 2006
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Figure 3. Comparison of NO2 measured by Ogawa monitors and colocated MoE continuously monitored locations.
Figure 2. Observed vs. predicted NO2 (natural log scale).
south of a municipal highway to the west of Central Hamilton on a rural route or side road, surrounded by mostly farmed open land use. There were no likely sources of NO2 in the neighboring area that could have contributed to the high concentrations measured by this monitor in comparison to the NO2 levels measured by the surrounding monitors located in similar settings. Because this combination was not found elsewhere in the study area, it was not deemed representative of the NO2 pollution within the city. The outlier also depleted predictive power by ⬃10%. This sampling location was excluded from further analysis. Figure 2 shows the plot of observed against predicted NO2 for this model. The authors also tested for spatial autocorrelation in the residuals of the model. If autocorrelation existed, it would imply that assumptions of error independence were violated, potentially because of either the omission of some source of variation or that the model was functionally incorrect.38 Global autocorrelation was tested in the residuals with Moran’s I based on a first-order neighborhood structure. There were no significant spatial autocorrelations based on this connectivity matrix. Additional exploration included both the mapping of residuals and calculating local Moran’s I. These findings suggest that the model produces efficient, unbiased estimates. Cross-Validation In the four locations where the Ogawa samplers were colocated with existing MoE sites, the Ogawa samplers measured lower concentrations. Whereas four locations are not enough to draw definitive conclusions, Figure 3 suggests that there was good agreement between the colocated samplers (R2 ⫽ 0.72). Detailed examination of the MoE data indicated that the higher averages were driven by a few extreme days, which may indicate that the passive samplers are less sensitive to detecting these wider variations. Unpublished data in similar analysis conducted in Montreal indicate a R2 closer to 0.86. Mukerjee et al.39 attempted to compare Ogawa and continuous monitors but were not able to make firm conclusions because of technical issues with the continuous monitors available during their study period. 1064 Journal of the Air & Waste Management Association
To cross-validate the model estimates, some of the locations were removed and the models were rerun to compare estimated to measured values.40 Because there is no “gold standard” to the number of samples that should be removed, the model was tested in three alternative situations: (1) a random selection of 85% of the sample, using the regression coefficients to predict the remaining 15%; (2) a random selection of 90% of the sample to predict 10%; and (3) a systematic selection of 90% to predict 10% of the data (e.g., first, 11th, and 21st observation). The average difference for all three of the methods ranged between ⫺0.5 and 0.94 ppb, equivalent to an average of 1–7% difference. A second cross-validation approach involved the application of the Chow test. The Chow test is a statistical method commonly applied for comparing group differences between regressions.41 Fifty random cases were selected from the whole sample and applied the Chow test to the separate halves in three separate trials. The coefficients were not significantly different from each other. The test was then applied to assess how the models performed if samples were restricted to different areas of Hamilton. Both above and below the escarpment was tested because of the possibility that the escarpment would trap pollutants at lower elevations. The urbanized old and new metropolitan City of Hamilton was also tested. The results indicated that the coefficients of the model applied to these different areas were also not significantly different from each other, thereby adding to the stability of this model. To investigate the seasonal stability of this model in a local context, additional fieldwork was conducted as part of another study. Thirty colocated monitors were set up for a 2-week period in May 2004. Comparing the values for monitors at the same locations in 2002 and 2004 revealed good direct correlation (R2 ⫽ 0.58). To test the ability of the LUR model to predict at these 30 locations in a different season, the model was run with the same variables in the original model, but the 30 were added as independent observations using a dummy indicator variable for season. The model had an R2 of 0.86, with all of the variables retaining significance. Plotting predicted against observed NO2 values for the 130 variables revealed a correlation of 0.808 (see Figure 4). The same model and Volume 56 August 2006
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Figure 4. Observed vs. predicted NO2 for the 130 observations (ppb).
variables were also applied to only the 30 sites, and despite the loss of significance of the distance-from-the-lake variable, the model predicted 88% of the variability in NO2 for the spring season. Creating the Prediction Surface Once the model was finalized and tested intensively, a predicted pollution surface was created to map and visualize the distribution of NO2 pollution within Hamilton. Using the format of the regression equation, a separate raster surface was created for each variable. The variables were multiplied with their coefficients and then added using the Raster calculator function in ArcGIS 8. Figure 5 shows the surface derived from the LUR model. Much of Hamilton remains largely rural with areas of peripheral low-density agricultural land use. Figure 6 provides a closer view of more urbanized Hamilton emphasizing the detail of variation identified by the application of the LUR model. DISCUSSION AND CONCLUSIONS This paper documents the development of an LUR model that predicts ambient concentrations of NO2 within Hamilton. A combination of seven land use variables accounted for 76% of the variation in NO2 calculated from field measurements. Open and industrial land use, traffic, distance from the lake, being 50 m from a highway, and being downwind from a highway were significant factors in explaining the variation of NO2. This model predicted slightly less of the variation in ambient NO2 than the European estimates; however, the present work achieved better predictions than those developed for other Canadian locations. Jerrett et al.20 suggest that the difference Volume 56 August 2006
between European and North American models could be because of variation in the land use between the continents, attributed possibly to their differing characteristics of urban sprawl. The LUR model has been applied in two other Canadian locations: Toronto and Montreal. Both models had seven variables that explained NO2 variation. The Toronto LUR20 had an R2 ⫽ 0.69, whereas the Montreal LUR model had a lower R2 of 0.54.19 Table 3 compares the significant variables in these previous studies to the current study. All three had a traffic indicator that was significantly associated with NO2 and proximity to a highway. Toronto and Montreal also had major and minor roads appearing as significant contributors to the variation in NO2. With regard to land use type, the Hamilton and Montreal models found open space to be negatively associated with NO2, whereas the Hamilton and Toronto models were affected by industrial land use. Furthermore, geographic location measures (distance to lake, expressway, and x-coordinate location) were important considerations for explaining the variation in NO2 for all three of the cities. Population density did not have a significant relationship with NO2 in the Hamilton model, unlike the other cities. Being downwind of major highways was significant in Hamilton and Toronto but was not tested in Montreal. The Hamilton model is based on field data collected from a single 2-week sampling season in October 2002. This particular monitoring period was chosen because it was the closest to the average annual weather conditions for Hamilton, rather than for the extremes of winter or summer conditions. The other North American models Journal of the Air & Waste Management Association 1065
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Figure 5. Final predicted NO2 surface for Hamilton.
also based their models on one 2-week monitoring session. European models incorporated between four and six separate 2-week periods throughout a year and calculated annual average concentrations on which the LUR models were based. This may have led to more stable monitor results, thereby increasing predictive capabilities of the European models. Lebret et al.17 conducted four seasonal 2-week rounds of passive NO2 monitoring in four European cities, in addition to continuous monitoring in a select number of monitoring locations during the course of the study year. However, their research showed that geographic pollution patterns were stable throughout the year, because areas of high pollution tended to stay in the same locations. They suggest that even as little as one short-term monitoring session could potentially adequately estimate mean annual concentrations of NO2. Overall the LUR model performed well in the crossvalidation analyses, demonstrating good predictions for sites not used in model calibration and stable coefficients when assessed with the Chow test. Seasonality had not been considered in any of the previous North American and in only a few European LUR models. Our seasonal analysis based on 30 colocated sites suggest that the model is capable of predicting spatial variation within the 1066 Journal of the Air & Waste Management Association
city for different seasons, probably because of spatial patterns of pollution that remain stable over time. The Hamilton LUR offers several improvements on the previously published Canadian model by Gilbert et al.,19 thereby further advancing this methodology. An assessment of wind effects was incorporated within the city by identifying upwind and downwind patterns from the highways. Traffic densities were developed and incorporated directly into the model. Introducing and improving these variables increased the explanatory power of the model. Approximately 40 more monitors were located within the city, resulting in a more detailed and representative surface capturing more variability. In addition, the seasonal measurements were not previously accounted for in any of the North American LUR studies. Thus, the 30 additional seasonal observations in the present study were the first to test the temporal variation of pollution within the North American context. Finally, the capacity of the LUR model to predict ambient concentrations was demonstrated in a highly industrial location. Estimating outdoor ambient levels of pollution does not accurately estimate the levels of human exposures.42 Nerriere et al.43 observed that ambient measurements and personal exposures of NO2 vary according to city as well Volume 56 August 2006
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Figure 6. Final predicted NO2 surface within central Hamilton.
as source and type of outdoor environment. Other researchers report ambient outdoor NO2 concentrations to be poor indicators of personal exposure to this pollutant,1,44 yet NO2 does seem to proxy well for ambient traffic pollution exposure. Calculated traffic indices have also been associated with increases in personal NO2 concentrations.3 Both Palin et al.45 and Harrison et al.46 found that personal exposures of NO2 were associated significantly with distances to busy streets. Rijnders et al.47 demonstrated that degree of urbanization, traffic
density, and distance from a highway all influenced personal NO2 concentrations. Moreover, researchers have demonstrated that exposure to NO2 concentrations estimated at outdoor levels also results in adverse health outcomes, such as childhood asthma.48 Therefore, the relationship between modeled ambient estimates from this new generation of intraurban models and personal exposure depends on many factors and warrants further investigation. As part of the larger research program, the relationships of indoor, outdoor, and personal exposure
Table 3. Summary table of all significant variables in Hamilton, Toronto, and Montreal LURs. Variables R-2 Roads
Land use categories
Population density Geographic location
Wind Traffic
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Hamilton
Toronto
Montreal
0.76 Highways 50 m — — Industry 200 m Open 500 m Downtown industrial core 1000 m — Distance to lake — — Downwind of 403 Traffic density 300 m
0.69 Highways 200 m Major roads 50 m — Industry 750 m — — Dwelling density 2000 m — X (longitude) — Downwind of all highways 1500 m Traffic density 500 m
0.54 Highways 100 m Major roads 100 m Minor roads 500 m — Open 100 m — Population density 2000 m — — Distance to highway Not tested Traffic count on nearest highway
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Sahsuvaroglu et al. to the modeled estimates presented here are being investigated. These findings will inform the epidemiologic investigation of three major health outcomes in relation to intraurban exposures: mortality, childhood asthma, and pneumonia. In this empirical study, it has been demonstrated that the LUR can produce accurate predictions of traffic pollution for places with large industrial sources, as well as complex topographic and meteorological conditions. This model was calibrated based on one monitoring season at ⬎100 locations and conducted seasonal sensitivity analysis on 30 colocated sites. Based on this information and on similar studies in Europe,17 the geographic pattern of exposure appears to be stable, making short-term estimates of spatial exposure gradients amenable to chronic health effects studies. As demonstrated by recent studies,49 –51 improved prediction of intraurban variation in air pollution exposure appears to contribute to the detection of larger health effects. Thus, LUR and other intraurban models appear increasingly important for the accurate assessment of the health effects from ambient air pollution exposure. ACKNOWLEDGMENTS The authors thank Chris Giovis, Natalia Restrepo, Kelly Williams, Michael Heffernan, and Shuhua Yi for their assistance in preparing and deploying the monitors within Hamilton; Sandy Benetti for carrying out the NO2 filter analysis; and Rick Burnett for reviewing and providing comments on an earlier version of the paper. They also acknowledge the Ontario Ministry of Transportation for providing approval to access their highways and the Department of Transportation for the City of Hamilton for providing traffic data. This research was funded by the Canadian Institutes of Health Research, Health Canada, National Institute of Environmental Health Sciences (grants 5P30 ES07048 and 5PO1ES011627), and the Verna Richter Chair in Cancer Research. REFERENCES 1. Kramer, U.; Koch, T.; Ranft, U.; Ring, J.; Behrendt, H. Traffic-Related Air Pollution Is Associated with Atopy in Children Living in Urban Areas; Epidemiol. 2000, 11, 64-70. 2. Linaker, C.H.; Chauhan, A.J.; Inskip, H.M.; Holgate, S.T.; Coggon, D. Personal Exposures of Children to Nitrogen Dioxide Relative to Concentrations in Outdoor Air; Occupat. Environ. Med. 2000, 57, 472-476. 3. Gauvin, S.; Le Moullec, Y.; Bremont, F.; Momas, I.; Balducci, F.; Ciognard, F.; Poilve, M.P.; Zmirou, D. Relationships between Nitrogen Dioxide Personal Exposure and Ambient Air Monitoring Measurements among Children in Three French Metropolitan Areas: VESTA Study; Arch. Environ. Health 2001, 56, 336-341. 4. Schwartz, J.; Spix, C.; Touloumi, G.; Bacharova, L.; Barumamdzadeh, T.; Le Tertre, A.; Piekarksi, T.; Ponce de Leon, A.; Ponka, A.; Rossi, G.; Saez, M.; Schouten, J. Methodological Issues in Studies of Air Pollution and Daily Counts of Deaths or Hospital Admissions; J. Epidemiol. Commun. Health 1996, 50, S3-S11. 5. Mage, D.; Wilson, W.; Hasselblad, V.; Grant, L. Assessment of Human Exposure to Ambient Particulate Matter; J. Air & Waste Manage. Assoc. 1999, 49, 1280-1291. 6. Fischer, P.; Hoek, G.; Van Reeuwijk, H.; Briggs, D.; Lebret, E.; van Wijnen, J.; Kingham, S.; Elliot, P. Traffic-Related Difference in Outdoor and Indoor Concentrations of Particles and Volatile Organic Compounds in Amsterdam; Atmos. Environ. 2000, 34, 3713-3722. 7. Jerrett, M.; Burnett, R.T.; Kanaroglou, P.; Eyles, J.; Finkelstein, N.; Giovis, C.; Brook, J.R. A GIS–Environmental Justice Analysis of Particulate Air Pollution in Hamilton, Canada; Environ. Plann. A 2001, 33, 955-973. 8. Hewitt, C.N. Spatial Variations in Nitrogen Dioxide Concentrations in an Urban Area; Atmos. Environ. 1991, 25B, 429-434. 1068 Journal of the Air & Waste Management Association
9. Zhu, Y.; Hinds, W.; Kim, S.; Shen, S.; Sioutas, C. Study of Ultrafine Particles near a Major Highway with Heavy-Duty Diesel Traffic; Atmos. Environ. 2002, 36, 4323-4335. 10. Ferguson, E.; Maheswaran, R.; Daly, M. Road-Traffic Pollution and Asthma Using Modelled Exposure Assessment for Routine Public Health Surveillance; Int. J. Health Geog. 2004, 3, 24. 11. Gilbert, N.L.; Woodhouse, S.; Stieb, D.M.; Brook, J.R. Ambient Nitrogen Dioxide and Distance from a Major Highway; Sci. Total Environ. 2003, 312, 43-46. 12. English, P.; Neutra, R.; Scalf, R.; Sullivan, M.; Waller, L.; Zhu, L. Examining Associations between Childhood Asthma and Traffic Flow Using a Geographic Information System; Environ. Health Perspect. 1999, 107, 761-767. 13. Hoek, G.; Brunekreef, B.; Goldbohm, S.; Fischer, P.; van den Brandt, P.A. Association between Mortality and Indicators of Traffic-Related Air Pollution in the Netherlands: a Cohort Study; Lancet 2002, 360, 1203-1209. 14. Finkelstein, M.M.; Jerrett, M.; Sears, M.R. Traffic Air Pollution and Mortality Rate Advancement Periods; Am. J. Epidemiol. 2004, 160, 173-177. 15. Melnick, A. Introduction to Geographic Information Systems in Public Health; Aspen Publishers: New York, NY, 2002. 16. Briggs, D.; Collins, S.; Elliott, 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. Geog. Info. Sci. 1997, 11, 699-718. 17. Lebret, E.; Briggs, D.; Van Reeuwijk, H.; Fischer, P.; Smallbone, K.; Harssema, H.; Kriz, B.; Gorynski, P.; Elliott, P. Small Area Variations in Ambient NO2 Concentrations in Four European Areas. Atmos. Environ. 2000, 34, 177-185. 18. Brauer, M.; Hoek, G.; van Vliet, P.; Meliefste, K.; Fischer, P.; Gehring, U.; Heinrich, J.; Cyrys, J.; Bellander, T.; Lewne, M.; Brunekreef, B. Estimating Long-Term Average Particulate Air Pollution Concentrations: Application of Traffic Indicators and Geographic Information Systems. Epidemiol. 2003, 14, 228-239. 19. Gilbert, N.L.; Goldberg, M.S.; Beckerman, B.; Brook, J.; 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. 20. Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Crouse, D.; Gilbert, N.L.; Brook, J.R.; Finkelstein, N. Modelling the Intra-Urban Variability of Ambient Traffic Pollution in Toronto, Canada; J. Toxicol. Environ. Health 2006, in press. 21. Ross, Z.; English, P.; 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. 2005, 15, 1-9. 22. Brunekreef, B.; Janssen, N. A.; de Hartog, J.; Harssema, H.; Knape, M.; van Vliet, P. Air Pollution from Truck Traffic and Lung Function in Children Living near Motorways; Epidemiol. 1997, 8, 298-303. 23. Briggs, D.; 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, 151-167. 24. Van Atten, C.; Brauer, M.; Funk, T.; Gilbert, N.L.; Graham, L.; Kaden, D.; Miller, P.J.; Bracho, L.R.; Wheeler, A.; Whitee, R.H. Assessing Population Exposures to Motor Vehicle Exhaust; Rev. Environ. Health 2005, 20, 195-214. 25. Cyrys, J.; Hochadel, M.; Gehring, U.; Hoek, G.; Diegmann, V.; Brunekreef, B.; Heinrich, J. GIS-Based Estimation of Exposure to Particulate Matter and NO2 in an Urban Area: Stochastic Versus Dispersion Modeling; Environ. Health Perspect. 2005, 113, 987-992. 26. Thomas, D.; Stram, D. Dwyer, J. Exposure Measurement Error: Influence on Exposure-Disease Relationships and Methods of Correction; Annu. Rev. Public Health 1993, 14, 69-93. 27. Statistics Canada. Population and Dwelling Counts for Canada, Provinces and Territories, Census Metropolitan Areas and Census Agglomerations, 2001 and 1996 Censuses-100%; available on the Statistics Canada web site, http://www12.statcan.ca/english/census01/products/standard/ popdwell/Table-CMA-P.cfm?PR⫽35 (accessed April 2006). 28. Getis, A.; Getis, J. The United States and Canada: The Land and the People; Brown Publishers: Dubuque, IA, 1995. 29. Sahsuvaroglu, T.; Jerrett, M. A Public Health Assessment of Mortality and Hospital Admissions Attributable to Air Pollution in Hamilton; Hamilton Air Quality Initiative: Hamilton, Canada, 2003. 30. Buzzelli, M.; Jerrett, M.; Burnett, R.; Finklestein, N. Spatiotemporal Perspectives on Air Pollution and Environmental Justice in Hamilton, Canada, 1985–1996; Ann. Assoc. Am. Geogr. 2003, 93, 557-573. 31. Finkelstein, M.M.; Jerrett, M.; DeLuca, P.; Finkelstein, N.; Verma, D.K.; Chapman, K.; Sears, M.R. Relation between Income, Air Pollution and Mortality: a Cohort Study; Can. Med. Assoc. J. 2003, 169, 397-402. 32. Kanaroglou, P.S.; Jerrett, M.; Morrison, J.; Beckerman, B.; Arain, M.A.; Gilbert, N.L.; Brook, J.R. Establishing an Air Pollution Monitoring Network for Intra-Urban Population Exposure Assessment: a LocationAllocation Approach; Atmos. Environ. 2005, 39, 2399-2409. Volume 56 August 2006
Sahsuvaroglu et al. 33. Kanaroglou, P.; Jerrett, M.; Morrison, J.; Beckerman, B.; Arain, A.; Gilbert, N.L.; Brook, J. Establishing an Air Pollution Monitoring Network for Intra-Urban Population Exposure Assessment; In Proceedings of the Transport and Air Pollution Conference: Avignon, France, 2003. 34. Lynn, P.P. Rainfall Interpolation Using Multiquadratic Surfaces; Comput. Appl. Nat. Soc. Sci. 1975, 2, 321-334. 35. Hubbe, J.; Doran, J.; Liljegren, J.; Shaw, W. Observations of Spatial Variation of Boundary Layer Structure over the Southern Great Plains Cloud and Radiation Testbed; J. Appl. Meteorol. 1997, 66, 1221-1231. 36. Griffith, D. Spatial Autocorrelation: a Primer; Association of American Geographers: Washington, DC, 1987. 37. Health Canada. Regulations Related to Health and Air Quality; available on the Health Canada web site, http://www.hc-sc.gc.ca/ewh-semt/air/ out-ext/reg_e.html#3 (accessed 2006). 38. Odland, J. Spatial Autocorrelation; Sage Publications: New Delhi, India, 1988. 39. Mukerjee, S.; Smith, L.A.; Norris, G.A.; Morandi, M.T.; Gonzales, M.; Noble, C.A.; Neas, L.M.; Ozkaynak, A.H. Field Method Comparison between Passive Air Samplers and Continuous Monitors for VOCs and NO2 in El Paso, Texas; J. Air & Waste Manage. Assoc. 2004, 54, 307-319. 40. Isaaks, E.; Srivastava, R. An Introduction to Applied Geostatistics; Oxford University Press: New York, NY, 1989. 41. Chow, G. Tests of Equality between Sets of Coefficients in Two Linear Regressions; Econometrica. 1960, 28, 591-605. 42. Monn, C. Exposure Assessment of Air Pollutants: a Review on Spatial Heterogeneity and Indoor/Outdoor/Personal Exposure to Suspended Particulate Matter, Nitrogen Dioxide and Ozone; Atmos. Environ. 2001, 35, 1-32. 43. Nerriere, E.; Zmirou-Navier, D.; Blanchard, O.; Momas, I.; Ladner, J.; Le Moullec, Y.; Personnaz, M.B.; Lameloise, P.; Delmas, W.; Target, A.; Desqueyroux, H. Can We Use Fixed Ambient Air Monitors to Estimate Population Long-Term Exposure to Air Pollutants? The Case of Spatial Variability in the Genotox ER Study. Environ. Res. 2005, 97, 32-42. 44. Mukala, K.; Alm, S.; Tiittanen, P.; Salonen, R.O.; Jantunen, M.; Pekkanen, J. Nitrogen Dioxide Exposure Assessment and Cough among Preschool Children; Arch. Environ. Health. 2000, 55, 431-438. 45. Palin, L. A.; Binotti, M.; Bona, G.; Panella, M. Personal Exposure of Children to Nitrogen Dioxide; Occup. Environ. Med. 2001, 58, 682-682. 46. Harrison, R.; Thornton, C.; Lawrence, R.; Mark, D.; Kinnersley, R.; Ayres, J. Personal Exposure Monitorings of Particulate Matter, Nitrogen Dioxide, and Carbon Monoxide, Including Susceptible Groups; Occup. Environ. Med. 2002, 59, 671-679. 47. Rijnders, E.; Janssen, N.A.H.; van Vliet, P.H.N.; Brunekreef, B. Personal and Outdoor Nitrogen Dioxide Concentrations in Relation to Degree of Urbanization and Traffic Density; Environ. Health Perspect. 2001, 109, 411-417. 48. Gauderman, W.J.; Avol, E.; Gilliland, F.; Vora, H.; Thomas, D.; Berhane, K.; McConnell, R.; Kuenzli, N.; Lurmann, F.; Rappaport, E.;
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Margolis, H.; Bates, D.; Peters, J. The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age; N. Engl. J. Med. 2004, 351, 1057-1067. 49. Gauderman, W.J.; Avol, E.; Lurmann, F.; Kunzli, N.; Gilliland, F.; Peters, J.M.; McConnell, R. Childhood Asthma, and Exposure to Traffic and Nitrogen Dioxide. Epidemiol. 2005, 16, 737-743. 50. Jerrett, M.; Burnett, R.T.; Ma, R.J.; Pope, C.A.; Krewski, D.; Newbold, K.B.; Thurston, G.; Shi, Y.L.; Finkelstein, N.; Calle, E.E.; Thun, M.J. Spatial Analysis of Air Pollution and Mortality in Los Angeles; Epidemiol. 2005, 16, 727-736. 51. Nafstad, P.; Haheim, L.L.; Wisloff, T.; Gram, F.; Oftedal, B.; Holme, I.; Hjermann, I.; Leren, P. Urban Air Pollution and Mortality in a Cohort of Norwegian Men; Environ. Health Perspect. 2004, 112, 610-615.
About the Authors Talar Sahsuvaroglu is a Ph.D. candidate with the School of Geography and Earth Sciences at McMaster University. Altaf Arain and Bruce Newbold are associate professors, and Pavlos Kanaroglou is a professor with the same department. Bernardo Beckerman is a geographic information systems analyst with the Division of Biostatistics, Department of Preventative Medicine, University of Southern California. Jeffrey Brook is a research scientist with the Meteorological Service of Canada. Norm Finkelstein is a researcher at the School of Geography and Earth Sciences, McMaster University. Nicolas Gilbert is a senior biologist with the Air Health Effects Division, Health Canada. Michael Jerrett is an associate professor with the Division of Biostatistics, Department of Preventative Medicine, University of Southern California. Address correspondence to: Talar Sahsuvaroglu, School of Geography and Earth Sciences, McMaster University, 1280 Main St. West, Hamilton, Ontario, Canada L8S 4K1; phone: ⫹1-905-525-9140, ext 24815; fax: ⫹1-905-546-0463; e-mail:
[email protected].
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