A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> The study of stochastic processes has naturally led to the consideration of stochastic phenomena which are distributed in space of two or more dimensions. Such investigations are, for instance, of practical interest in connexion with problems concerning the distribution of soil fertility over a field or the relations between the velocities at different points in a turbulent fluid. A review of such work with many references has recently been given by Ghosh (1949) (see also Matern, 1947). In the present note I consider two problems arising in the twoand three-dimensional cases. <s> BIB001 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. These estimates of concentrations of NO2 will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO2 was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO2 and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO2 was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Indust... <s> BIB002 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> We modeled the intraurban distribution of nitrogen dioxide (NO2), a marker for traffic pollution, with land use regression, a promising new exposure classification technique. We deployed diffusion tubes to measure NO2 levels at 39 locations in the fall of 2003 in San Diego County, CA, USA. At each sample location, we constructed circular buffers in a geographic information system and captured information on roads, traffic flow, land use, population and housing. Using multiple linear regression, we were able to predict 79% of the variation in NO2 levels with four variables: traffic density within 40–300 m of the sampling location, traffic density within 300–1000 m, length of road within 40 m and distance to the Pacific coast. Applying this model to validation samples showed that the model predicted NO2 levels within, on average, 2.1 p.p.b for 12 training sites initially excluded from the model.Our evaluation of this land use regression model showed that this method had excellent prediction and robustness in a North American context. These models may be useful tools in evaluating health effects of long-term exposure to traffic-related pollution. <s> BIB003 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Land use regression (LUR) is a promising technique for predicting ambient air pollutant concentrations at high spatial resolution. We expand on previous work by modeling oxides of nitrogen and fine particulate matter in Vancouver, Canada, using two measures of traffic. Systematic review of historical data identified optimal sampling periods for NO and NO2. Integrated 14-day mean concentrations were measured with passive samplers at 116 sites in the spring and fall of 2003. Study estimates for annual mean NO and NO2 ranged from 5.4−98.7 and 4.8−28.0 ppb, respectively. Regulatory measurements ranged from 4.8−29.7 and 9.0−24.1 ppb and exhibited less spatial variability. Measurements of particle mass concentration (PM2.5) and light absorbance (ABS) were made at a subset of 25 sites during another campaign. Fifty-five variables describing each sampling site were generated in a Geographic Information System (GIS) and linear regression models for NO, NO2, PM2.5, and ABS were built with the most predictive covari... <s> BIB004 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods. Long-term exposure to nitrogen dioxide (NO2), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO2) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated. The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO2, NO, BS, and SO2 concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO2, NO, BS and SO2, respectively. We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations. <s> BIB005 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Land use regression (LUR) models have been used successfully for predicting local variation in traffic pollution, but few studies have explored this method for deriving fine particle exposure surfaces. The primary purpose of this method is to develop a LUR model for predicting fine particle or PM2.5 mass over the five county metropolitan statistical area (MSA) of Los Angeles. PM2.5 includes all particles with diameter less than or equal to 2.5 microns. In the Los Angeles MSA, 23 monitors of PM2.5 were available in the year 2000. This study uses GIS to integrate data regarding land use, transportation and physical geography to derive a PM2.5 dataset covering Los Angeles. Multiple linear regression was used to create the model for predicting the PM2.5 surface. Our parsimonious model explained 69% of the variance in PM2.5 with three predictors: (1) traffic density within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m of the monitoring site. These results suggest the LUR method can refine exposure models for epidemiologic studies in a North American context. <s> BIB006 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Particulate matter (PM) is a major family of atmospheric pollutants (National Center for Environmental Assessment 2004). Fine PM (PM with an aerodynamic diameter < 2.5 μm; PM2.5) and, perhaps to a greater extent, ultra-fine particles (PM < 0.1 μm) can penetrate the innermost region of the lungs, and a fraction of them can cross the lung epithelium and enter the blood circulation (Kreyling et al. 2002). Several epidemiologic studies have reported associations between PM levels—most often total suspended particles (TSP) and PM < 10 μm in aerodynamic diameter (PM10)—around the maternal home address during pregnancy with offspring birth weight (reviewed by Glinianaia et al. 2004; Sram et al. 2005). Few studies assessed exposure to PM2.5 (Basu et al. 2004; Bell et al. 2007; Dejmek et al. 2000; Jedrychowski et al. 2004; Parker et al. 2005). Four of these studies reported a decrease in term birth weight in relation to maternal exposure to PM2.5 during pregnancy; exposure was assessed using individual dosimeters carried 48 hr during pregnancy (Jedrychowski et al. 2004), from the pregnancy-average of the measurements of the air quality monitoring stations within an 8-km radius from the home address (Basu et al. 2004; Parker et al. 2005), or of all the measurement stations located in the county of residence of the woman (Bell et al. 2007). ::: ::: Fine particles are composed of nonorganic compounds (sulfate, nitrate, ammonium and hydrogen ions, certain transition metals), elemental carbon, organic species including poly-cyclic aromatic hydrocarbons (PAHs) and many other families (National Center for Environmental Assessment 2004; Schauer et al. 1999, 2002). Vehicular traffic is one of the major sources of fine particles. Nitrogen dioxide, PM2.5 mass concentration, and also PM2.5 absorbance are possible markers of traffic-related pollution (Janssen et al. 2001). More specifically, PM2.5 absorbance is a measure of the blackness of PM2.5, which strongly depends on the presence of elemental carbon in PM2.5 (Janssen et al. 2001; Kinney et al. 2000). Because elemental carbon represents a major fraction of diesel motor exhausts (Lloyd and Cackette 2001; Schauer et al. 1999), PM2.5 absorbance is considered a sensitive marker of air pollution due to diesel engines and truck traffic (Janssen et al. 2001; Kinney et al. 2000) and is probably a more sensitive marker of traffic-related pollution than PM2.5 (Cyrys et al. 2003; Kinney et al. 2000; Roemer and van Wijnen 2001). Diesel exhaust (Lloyd and Cackette 2001) has been shown in experimental animal studies to be a possible mutagenic agent, to cause allergic and nonallergic respiratory diseases (Krzyzanowski et al. 2005; Pope and Dockery 2006), to be a possible reprotoxicant, and to act as an endocrine disruptor (Takeda et al. 2004; Tsukue et al. 2002; Yoshida et al. 2006). No epidemiologic study has described the association between PM absorbance and birth weight. ::: ::: With a few exceptions (Choi et al. 2006; Jedrychowski et al. 2004; Wilhelm and Ritz 2003), most epidemiologic studies on the influence of PM or traffic-related pollutants on intrauterine growth restriction relied on birth weight certificates for the collection of birth weight and adjustment factors, whereas exposure was assessed from the background monitoring stations closest to the home address of the mother at the time of delivery. This design has several limitations: Factors known to strongly influence birth weight—such as maternal smoking, weight, or height, not always or accurately available in birth certificates—could not always be controlled for, not allowing researchers to discard confounding (Glinianaia et al. 2004). Exposure misclassification is also a concern: First, pregnancy is often a time to change address, so the exposure levels around the home address at the time of birth might not match exposure levels around the home address during pregnancy for a number of women. Second, all women living within a distance of up to several kilometers around a monitoring station are assumed to be exposed to the pollutants’ levels measured by the station. To limit exposure misclassification, one may prefer to exclude women living far away from monitoring stations (Wilhelm and Ritz 2005); however, monitoring stations are often located at places where population density is higher, and hence air pollution levels are higher. Therefore, if unmeasured environmental or social factors influencing birth weight also varied with distance from monitoring stations, selection bias might occur in studies restricted to subjects living close to monitoring stations. This dilemma between exposure mis-classification and possible selection bias could be avoided by using alternative approaches to model exposure, such as land-use regression or dispersion modeling, which allow modeling of fine spatial contrasts in pollution levels in an area considered as a whole, using information on sources of pollution (Nieuwenhuijsen et al. 2006). ::: ::: Within a cohort conducted in the Munich metropolitan area (Bavaria), we aimed to characterize the influence of maternal exposure to PM2.5, PM2.5 absorbance, and NO2 during pregnancy on the birth weight of offspring at term, using a land-use regression exposure model and taking into account factors known to influence intrauterine growth. <s> BIB007 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Epidemiologic studies of air pollution require accurate exposure assessments at unmonitored locations in order to minimize exposure misclassification. One approach gaining considerable interest is the land-use regression (LUR) model. Generally, the LUR model has been utilized to characterize air pollution exposure and health effects for individuals residing within urban areas. The objective of this article is to briefly summarize the history and application of LUR models to date outlining similarities and differences of the variables included in the model, model development, and model validation. There were 6 studies available for a total of 12 LUR models. Our findings indicated that among these studies, the four primary classes of variables used were road type, traffic count, elevation, and land cover. Of these four, traffic count was generally the most important. The model R2 explaining the variability in the exposure estimates for these LUR models ranged from .54 to .81. The number of air sampling sites generating the exposure estimates, however, was not correlated with the model R2 suggesting that the locations of the sampling sites may be of greater importance than the total number of sites. The primary conclusion of this study is that LUR models are an important tool for integrating traffic and geographic information to characterize variability in exposures. <s> BIB008 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> BackgroundThere is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques.MethodsWe measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations.ResultsPM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56).ConclusionEach pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods. <s> BIB009 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe and North America. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Shizuoka, Japan. We used the annual-mean nitrogen dioxide (NO 2 ) and suspended particulate matter (SPM) concentrations between April 2000 and March 2006 in the study area. SPM accounts for particulate matter with an aerodynamic diameter less than 8 μm (PM 8 ). Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and physical component. Using geographical variables, we then constructed a model to predict the monitored levels of NO 2 and SPM. The mean concentrations of NO 2 and SPM were 35.75 μg/m 3 (standard deviation of 11.28) and 28.67 μg/m 3 (standard deviation of 4.73), respectively. The final regression model for the NO 2 concentration included five independent variables. R 2 for the NO 2 model was 0.54. On the other hand, the regression model for the SPM concentration included only one independent variable. R 2 for the SPM model was quite low ( R 2 = 0.11). The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO 2 moderately well. This result could encourage the wide use of LUR models in Asian countries. <s> BIB010 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. <s> BIB011 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM2.5, NOx, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability. <s> BIB012 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK. <s> BIB013 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO₂], fine particulate matter [PM₂.₅], and ozone [O₃]) in a population of susceptible subjects in Toronto. Local (NO₂) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM₂.₅, O₃) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412-414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO₂ was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM₂.₅ and O₃ were not associated with risk of IHD. <s> BIB014 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Land-use regression (LUR) models have been developed to estimate spatial distributions of traffic-related pollutants. Several studies have examined spatial autocorrelation among residuals in LUR models, but few utilized spatial residual information in model prediction, or examined the impact of modeling methods, monitoring site selection, or traffic data quality on LUR performance. This study aims to improve spatial models for traffic-related pollutants using generalized additive models (GAM) combined with cokriging of spatial residuals. Specifically, we developed spatial models for nitrogen dioxide (NO(2)) and nitrogen oxides (NO(x)) concentrations in Southern California separately for two seasons (summer and winter) based on over 240 sampling locations. Pollutant concentrations were disaggregated into three components: local means, spatial residuals, and normal random residuals. Local means were modeled by GAM. Spatial residuals were cokriged with global residuals at nearby sampling locations that were spatially auto-correlated. We compared this two-stage approach with four commonly-used spatial models: universal kriging, multiple linear LUR and GAM with and without a spatial smoothing term. Leave-one-out cross validation was conducted for model validation and comparison purposes. The results show that our GAM plus cokriging models predicted summer and winter NO(2) and NO(x) concentration surfaces well, with cross validation R(2) values ranging from 0.88 to 0.92. While local covariates accounted for partial variance of the measured NO(2) and NO(x) concentrations, spatial autocorrelation accounted for about 20% of the variance. Our spatial GAM model improved R(2) considerably compared to the other four approaches. Conclusively, our two-stage model captured summer and winter differences in NO(2) and NO(x) spatial distributions in Southern California well. When sampling location selection cannot be optimized for the intended model and fewer covariates are available as predictors for the model, the two-stage model is more robust compared to multiple linear regression models. <s> BIB015 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract Background Land-use regression (LUR) is a cost-effective approach for predicting spatial variability in ambient air pollutant concentrations with high resolution. Models have been widely used in epidemiological studies and are often applied to time periods before or after the period of air quality monitoring used in model development. However, it is unclear how well such models perform when extrapolated over time. Objective The objective of this study was to assess the temporal stability of LUR models over a period of 7 years in Metro Vancouver, Canada. Methods A set of NO and NO 2 LUR models based on 116 measurements were developed in 2003. In 2010, we made 116 measurements again, of which 73 were made at the exact same location as in 2003. We then developed 2010 models using updated data for the same predictor variables used in 2003, and also explored additional variables. Four methods were used to derive model predictions over 7 years, and predictions were compared with measurements to assess the temporal stability of LUR models. Results The correlation between 2003 NO and 2010 NO measurements was 0.87 with a mean (sd) decrease of 11.3 (9.9) ppb. For NO 2 , the correlation was 0.74, with a mean (sd) decrease of 2.4 (3.2) ppb. 2003 and 2010 LUR models explained similar amounts of spatial variation ( R 2 = 0.59 and R 2 = 0.58 for NO; R 2 = 0.52 and R 2 = 0.63 for NO 2 , in 2003 and in 2010 respectively). The 2003 models explained more variability in the 2010 measurements ( R 2 = 0.58–0.60 for NO; R 2 = 0.52–0.61 for NO 2 ) than the 2010 models explained in the 2003 measurements ( R 2 = 0.50–0.55 for NO; R 2 = 0.44–0.49 for NO 2 ), and the 2003 models explained as much variability in the 2010 measurements as they did in the 2003 measurements. Conclusion LUR models are able to provide reliable estimates over a period of 7 years in Metro Vancouver. When concentrations and their variability are decreasing over time, the predictive power of LUR models is likely to remain the same or to improve in forecasting scenarios, but to decrease in hind-casting scenarios. <s> BIB016 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR mo ... <s> BIB017 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> RATIONALE ::: Although substantial scientific evidence suggests that chronic exposure to ambient air pollution contributes to premature mortality, uncertainties exist in the size and consistency of this association. Uncertainty may arise from inaccurate exposure assessment. ::: ::: ::: OBJECTIVES ::: To assess the associations of three types of air pollutants (fine particulate matter, ozone [O3], and nitrogen dioxide [NO2]) with the risk of mortality in a large cohort of California adults using individualized exposure assessments. ::: ::: ::: METHODS ::: For fine particulate matter and NO2, we used land use regression models to derive predicted individualized exposure at the home address. For O3, we estimated exposure with an inverse distance weighting interpolation. Standard and multilevel Cox survival models were used to assess the association between air pollution and mortality. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Data for 73,711 subjects who resided in California were abstracted from the American Cancer Society Cancer Prevention II Study cohort, with baseline ascertainment of individual characteristics in 1982 and follow-up of vital status through to 2000. Exposure data were derived from government monitors. Exposure to fine particulate matter, O3, and NO2 was positively associated with ischemic heart disease mortality. NO2 (a marker for traffic pollution) and fine particulate matter were also associated with mortality from all causes combined. Only NO2 had significant positive association with lung cancer mortality. ::: ::: ::: CONCLUSIONS ::: Using the first individualized exposure assignments in this important cohort, we found positive associations of fine particulate matter, O3, and NO2 with mortality. The positive associations of NO2 suggest that traffic pollution relates to premature death. <s> BIB018 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts. <s> BIB019 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> In this paper, we develop a land-use regression model for sulfur dioxide air pollution concentrations. We make use of mobile monitoring data collected in Hamilton, Ontario, Canada, between 2005 and 2010 inclusive. The observed SO2 concentrations are regressed against a comprehensive set of land use and transportation variables. Land use and transportation variables are assessed as the amount of each characteristic within buffers of 50, 100, 200, 400, 800, and 1600 m around pollution observation locations. In the first instance of regression modeling, we apply ordinary least-squares regression. The OLS model R2 for training data was 0.38, and an R2 of 0.3 for a 50% held out cross-validation data set. The residuals are spatially correlated with the OLS model as determined with Moran's I. We thus applied a simultaneous autoregressive model, specifically the spatial error model. The resulting model slightly improved fit as determined by a pseudo R2 = 0.4, improved log-likelihood, and reduced MSE, RMSE, and MAE. The spatial error model residuals were not spatially auto-correlated, resulting in a valid model. SAR modeling is a natural extension to OLS regression models and solves the issue of spatial autocorrelation in model residuals with a one-stage model. <s> BIB020 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> BACKGROUND ::: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. ::: ::: ::: OBJECTIVES ::: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. ::: ::: ::: METHODS ::: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. ::: ::: ::: RESULTS ::: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). ::: ::: ::: CONCLUSIONS ::: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. <s> BIB021 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> This study is conducted to characterize the intra-urban distribution of NOx and NO2; develop land use regression (LUR) models to assess outdoor NOx and NO2 concentrations, using the ESCAPE modeling approach with locally specific land use data; and compare NOx and NO2 exposures for children in the Taipei Metropolis by the LUR models, the nearest monitoring station, and kriging methods based on data collected at the measurement sites. NOx and NO2 were measured for 2 weeks during 3 seasons at 40 sampling sites by Ogawa passive badges to represent their concentrations at urban backgrounds and streets from October 2009 to September 2010. Land use data and traffic-related information in different buffer zones were combined with measured concentrations to derive LUR models using supervised forward stepwise multiple regressions. The annual average concentrations of NOx and NO2 in Taipei were 72.4 ± 22.5 and 48.9 ± 12.2 μg/m(3), respectively, which were at the high end of all 36 European areas in the ESCAPE project. Spatial contrasts in Taipei were lower than those of the European areas in the ESCAPE project. The NOx LUR model included 6 land use variables, which were lengths of major roads within 25 m, 25-50 m, and 50-500 m, urban green areas within 300 m and 300-5,000 m, and semi-natural and forested areas within 500 m, with R(2)=0.81. The NO2 LUR model included 4 land use variables, which were lengths of major roads within 25 m, urban green areas within 100 m, semi-natural and forested areas within 500 m, and low-density residential area within 500 m, with R(2)=0.74. The LUR models gave a wider variation in estimating NOx and NO2 exposures than either the ordinary kriging method or the nearest measurement site did for the children of Taiwan Birth Cohort Study (TBCS) in Taipei. <s> BIB022 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR model for ozone at a fine spatial scale. Ozone concentrations were measured with passive samplers at 90 locations across the Netherlands (19 regional background, 36 urban background, 35 traffic). All sites were measured simultaneously during four 2-weekly campaigns spread over the seasons. LUR models were developed for the summer average as the primary exposure and annual average using predictor variables obtained with Geographic Information Systems. Summer average ozone concentrations varied between 32 and 61 µg/m(3). Ozone concentrations at traffic sites were on average 9 µg/m(3) lower compared to regional background sites. Ozone correlated highly negatively with nitrogen dioxide and moderately with fine particles. A LUR model including small-scale traffic, large-scale address density, urban green and a region indicator explained 71% of the spatial variation in summer average ozone concentrations. Land use regression modeling is a promising method to assess ozone spatial variation, but the high correlation with NO2 limits application in epidemiology. <s> BIB023 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract When measurements or other exposure models are unavailable, air pollution concentrations could be estimated by transferring land-use regression (LUR) models from other areas. No studies have looked at transferability of LUR models from regions to cities. We investigated model transferability issues. We developed a LUR model for 2010 using annual average nitrogen dioxide (NO2) concentrations retrieved from 47 regulatory stations of the Veneto region, Northern Italy. We applied this model to 40 independent sites in Verona, a city inside the region, where NO2 had been monitored in the European Study of Cohorts for Air Pollution Effects (ESCAPE) during 2010. We also used this model to estimate average NO2 concentrations at the regulatory network in 2008, 2009 and 2011. Of 33 predictor variables offered, five were retained in the LUR model (R2 = 0.75). The number of buildings in 5000 m buffers, industry surface area in 1000 m buffers and altitude, mainly representing large-scale air pollution dispersion patterns, explained most of the spatial variability in NO2 concentrations (R2 = 0.68), while two local traffic proxy indicators explained little of the variability (R2 = 0.07). The performance of this model transferred to urban sites was poor overall (R2 = 0.18), but it improved when only predicting inner-city background concentrations (R2 = 0.52). Recalibration of LUR coefficients improved model performance when predicting NO2 concentrations at the regulatory sites in 2008, 2009 and 2011 (R2 between 0.67 and 0.80). Models developed for a region using NO2 regulatory data are unable to capture small-scale variability in NO2 concentrations in urban traffic areas. Our study documents limitations in transferring a regional model to a city, even if it is nested within that region. <s> BIB024 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Land-Use Regression Models <s> Abstract Important health relevance has been suggested for ultrafine particles (UFP) and ozone, but studies on long-term effects are scarce, mainly due to the lack of appropriate spatial exposure models. We designed a measurement campaign to develop land use regression (LUR) models to predict the spatial variability focusing on particle number concentration (PNC) as indicator for UFP, ozone and several other air pollutants in the Augsburg region, Southern Germany. Three bi-weekly measurements of PNC, ozone, particulate matter (PM 10 , PM 2.5 ), soot (PM 2.5 abs) and nitrogen oxides (NO x , NO 2 ) were performed at 20 sites in 2014/15. Annual average concentration were calculated and temporally adjusted by measurements from a continuous background station. As geographic predictors we offered several traffic and land use variables, altitude, population and building density. Models were validated using leave-one-out cross-validation. Adjusted model explained variance (R 2 ) was high for PNC and ozone (0.89 and 0.88). Cross-validation adjusted R 2 was slightly lower (0.82 and 0.81) but still indicated a very good fit. LUR models for other pollutants performed well with adjusted R 2 between 0.68 (PM coarse ) and 0.94 (NO 2 ). Contrary to previous studies, ozone showed a moderate correlation with NO 2 (Pearson's r = − 0.26). PNC was moderately correlated with ozone and PM 2.5 , but highly correlated with NO x (r = 0.91). For PNC and NO x , LUR models comprised similar predictors and future epidemiological analyses evaluating health effects need to consider these similarities. <s> BIB025
Land-Use Regression (LUR) models are based on the principle that the pollutant concentrations at any location depend on the environmental characteristics of the surrounding area. The models are developed through construction of multiple regression equations describing the relationship between the pollutant measurements at the monitoring stations and the predictor variables usually obtained through Geographic Information Systems (GIS), such as traffic intensity, road length, distance to the major road, road type, population density, land cover, wind speed, etc. The model function is presented below:ŷ where x 1 , . . . x n represent the predictor variables,ŷ is the predicted value of the pollution level, and β 0 , . . . β n represent the coefficients of the predictor variables that indicate the influence of the predictor variables on the pollution level. Linear regression aims to find estimated values of the coefficients β 0 , . . . β n that will provide the "best" fit in the data points-for instance, a line that minimizes the sum of squared residuals (differences between the observed and predicted values of the pollution level) for ordinary least squares regression and a line that minimizes the penalized residual sum for ridge regression. The resulting models are then used to predict pollutant concentrations for the target locations, utilizing the given predictor variables at the target locations  . Briggs et al. introduced an LUR approach for air pollution mapping in the SAVIAH (Small Area Variations In Air quality and Health) study  . Results showed good predictions of the mean annual concentration of NO 2 in Amsterdam (Netherlands), Huddersfield (England) and Prague (Czech Republic). Since then, LUR models have generally been applied to model annual mean concentrations of air pollutants in different settings, especially in European and North American cities. Gilbert et al. developed a LUR model to estimate the NO 2 concentrations in Montreal, QC, Canada BIB002 . Their study suggested positive associations between NO 2 and the following land-use variables: traffic count on nearest highway, nearby major road length and local population density. Wang et al. applied LUR models to estimate the NO and NO 2 concentrations in Metro Vancouver, BC, Canada BIB016 . They built LUR models with the same predictor variables using measurements made at similar locations in 2003 and 2010, respectively. This study proved the temporal stability of LUR models over a period of seven years. Beelen et al. developed LUR models for the ESCAPE project to estimate the NO 2 and NO x concentrations in 36 areas in Europe BIB017 . Their results showed the importance of the accuracy of local traffic intensity as predictor variables in the model development. Kashima et al. introduced LUR models to NO 2 estimation in Shizuoka, Japan BIB010 . Except NO x BIB004 BIB014 BIB005 , LUR models have also been applied successfully for predicting other traffic-related pollutants such as fine PM and benzene BIB012 BIB009 BIB011 BIB006 BIB007 BIB003  . Besides the commonly used predictor variables such as traffic intensity, road length, population, Chen et al. added meteorological variables into their LUR models, including wind index, temperature, humidity and wind speed  . BIB018 BIB019 . This study showed a strong association between NO 2 and lung cancer. Jerrett and his colleagues later estimated the O 3 exposure in Quebec, QC, Canada using a variety of models, including a Land-Use mixed-effects Regression (LUR) model, a mixture of a Bayesian Maximum Entropy (BME) model and the land-use mixed model as well as a kriging method model BIB021 . The experimental results proved the superiority of the mixture model (BME-LUR). Beckerman et al. also applied different techniques on different pollutants BIB014 . Regional exposures to PM 2.5 and O 3 were modeled using inverse distance weighted interpolation, while local NO 2 exposures were modeled using LUR. Their results suggested that NO 2 was significantly associated with ischemic heart disease. Although researchers chose different predictor variables according to local environments in their LUR models, most of them used linear regression to model the relationship between the pollutant level and the predictor variables BIB004 BIB012 BIB009   BIB002 BIB016 BIB017 BIB010 BIB006 BIB007 BIB003   BIB019 BIB021 BIB022 BIB023  BIB024  BIB025 . One of the assumptions for linear regression is that the observations should be independent of each other. The regression residuals are therefore supposed to be independent. However, the observations in air pollution monitoring tend to be similar at nearby locations. This violates the independence assumption for the linear models. Few researchers have proposed methods to deal with the spatial correlation of the observations. Mercer et al. proposed a two-step approach to predict the NO x concentrations in Los Angeles, CA, United States BIB013 . This approach combined the prediction of local means by standard linear regression with universal kriging of the regression residuals to handle the spatial structure in the model residuals. Their experimental results showed that their spatial LUR model performed as well or better than non-spatial LUR models, in terms of Cross-Validated (CV) R 2 . Li et al. proposed a two-stage model combining a Generalized Additive Model (GAM) with cokriging of spatial residuals BIB015 . This spatial model predicted NO x and NO 2 concentration surfaces well with high CV R 2 values. Kanaroglou et al. first applied a linear regression model on their entire data set BIB020 . They then used a spatial autoregressive model to remove the spatial autocorrelation in the linear regression residuals if any exists BIB001 . Hoek et al. reviewed different components of LUR models in 25 studies  , including how to choose the number and distribution of the monitoring stations, the monitoring periods, significant predictor variables (road length, traffic intensity, distance and emission), and the performance of the LUR models for different pollutants (PM 2.5 , NO, NO x and VOC). They concluded that the LUR method typically performs better than, or equivalent to, the geo-statistical methods such as interpolation and dispersion models. Ryan and LeMasters summarized four important predictor variables used in 12 LUR models from six studies: road type, traffic count, elevation and land cover. They concluded an LUR model as an important tool of incorporating traffic and geographic information in air pollution exposure assessment BIB008 . The general overview of LUR studies is given in Table 2 .