A review and evaluation of intraurban air pollution exposure models MICHAEL JERRETT,a ALTAF ARAIN,b PAVLOS KANAROGLOU,c BERNARDO BECKERMAN,d DIMITRI POTOGLOU,d TALAR SAHSUVAROGLUd, JASON MORRISONe AND CHRIS GIOVISd a Division of Biostatistics, Departments of Preventive Medicine and Geography, University of Southern California, 1540Alcazar Street, CHP-220, Los Angeles, California, USA bSchool of Geography and Geology, Hydrometeorology and Climatology Research Group, McMaster University, Hamilton, Ontario, Canada cSchool of Geography and Geology, McMaster Institute of Environment and Health, McMaster University, Hamilton, Ontario, Canada dSchool of Geography and Geology, McMaster University, Hamilton, Ontario, Canada eSchool of Computer Science, Carleton University, Ottawa, Ontario, Canada The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity���space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health. Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 185���204. doi:10.1038/sj.jea.7500388 Published online 4 August 2004 Keywords: air pollution, exposure assessment, intraurban scale, GIS, dispersion models, health effects assessment. Introduction The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research (Brunekreef and Holgate, 2002 Brauer et al., 2003). While surrogate measures, such as distance to roads, have been related to large health effects (Hoek et al., 2002), these may misclassify exposure because they are not directly estimated from monitored data. Potential alternatives to surrogate measures arise from geographic and dispersion exposure methods. These methods utilize geographic information systems (GIS) to combine available geographic data with short-term monitoring information to developexposure models capable of identifying small-area variations in pollution. Results from these models can then be overlaid on geo-referenced health data to assign exposure to individuals at their place of residence, work, or some combination of both. Interest in assessing exposure to ambient air pollution at the intraurban scale (i.e., within-city scale) has increased for a variety of reasons. First, the contribution of traffic pollution has grown, and most studies agree that the demand for transportation will exceed improvements to emission reduction technologies (Faiz, 1993 Delucchi, 2000). Re- gardless of regulatory interventions, higher exposure to traffic pollution with distinct intraurban gradients may be seen around major roads and highways (Gilbert et al., 2002). Recent exposure studies have shown that for some pollutants associated with traffic, such as nitrogen dioxide (NO2) and ultrafine particles, variation within cities may exceed varia- tions between cities (Briggs, 2000 Zhu et al., 2002). Some studies from the United Kingdom (UK) indicate two- to three-fold differences in NO2 within distances of 50m or less (Hewitt, 1991), while US studies suggest ultrafine particles are elevated above background concentrations until about 300m of highways (Zhu et al., 2002). Second, while results remain far from conclusive (English et al., 1999), sufficient number of studies have uncovered positive health effects to suggest that the exposure experience within cities may exert significant health effects. For example, a recent study from the Netherlands reported a doubling of Received 8 August 2003 accepted 3 April 2004 published online 4 August 2004 1. Address all correspondence to: Dr. Michael Jerrett, Division of Biostatistics, Departments of Preventive Medicine and Geography, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA. Tel.: �� 1-323-442-1260. Fax: �� 1-323-442-2349. E-mail: jerrett@usc.edu Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 185���204 r 2005 Nature Publishing GroupAll rights reserved 1053-4245/05/$30.00 www.nature.com/jea

cardiopulmonary mortality (relative risk (RR) �� 1.95, 95% CI 1.09���3.52) near major roads in a cohort of 5000 people, where extensive control was available for confounding factors. Urban background pollution interpolated from government monitoring sites also exerted an independent effect on mortality (Hoek et al., 2002). Yet, this study used the most basic type of exposure measurement (i.e., buffers), and a need exists to test similar relationships with more robust exposure metrics. Third, over the past 10 years, advances in GIS and associated statistical techniques have expanded into the field of exposure analysis (Collins, 1998 Melnick, 2002). These technological and methodological innovations have fuelled research on intraurban exposure because what would have been previously impossible or taken many years to accom- plish can now be done in weeks to months. Coupling of dispersion, atmospheric, and time-activity models with GIS capabilities has led to even more sophisticated attempts to characterize intraurban exposures (Kramer et al., 2000 Mukala et al., 2000). To date, there have been no published reviews of models for assessing intraurban exposure. In an effort to fill this gap in the literature, we have identified six classes of models for deriving intraurban exposure assignment, including: (i) proximity-based assessments (e.g., Venn et al., 2000) (ii) statistical interpolation (e.g., Jerrett et al., 2001a) (iii) land use regression models (e.g., Briggs, 2000 Hoek et al., 2001) (iv) line dispersion models (e.g., Bellander et al., 2001) (v) integrated emission- meteorological models (AMD and NOAA-EPA, 2003) and (vi) two classes of hybrid models, the first combining personal or household exposure monitoring with one of the preceding methods (Kramer et al., 2000 Zmirou et al., 2002) and the second combining two or more of the preceding methods with regional monitoring (Hoek et al., 2001). We have organized this paper into three main sections. First, we systematically review literature on models for intraurban exposure assessment under the typology of the models above. We also enrich this review with applied examples from Hamilton, Canada. Second, we present a qualitative evaluation of the models based on key criteria important to health effects assessment. The paper concludes with a discussion of priorities for future research. Methods The literature review discusses exposure models proceeding from the simple to the more complex. In most instances, progression from one type of model to another entails increased implementation costs in terms of research time, software, hardware and data requirements. These must be weighed against potential benefits in the accuracy of the results. We used the following inclusion criteria to guide our search: (a) recent publication in a peer-reviewed journal available in the PubMed database (1997���2002) (b) testing of an empirical model using real data inputs (as opposed to a conceptual treatise) (c) some connection to exposure assessment for health studies or potential to be used in these studies, instead of a model focused purely on meteorological, traffic, or land use processes and (d) some emphasis on intraurban or traffic-related pollution. The review is not intended to be exhaustive, but rather to identify representa- tive examples of model type and to highlight some of the empirical findings when subsequent modelled exposures are applied to health outcomes. Keywords beginning with ������air pollution������ followed by the terms: ������long-term������, ������traffic������, ������asthma������, ������health effects������, ������kriging������, ������monitoring������, and ������MM5������ were entered into PubMed. The search was performed for articles published in English from 1997 to August 2002. The estimated number of related articles was approximately 3100. Many of these were outside our inclusion criteria and were excluded. In a few instances, we relaxed the inclusion criteria to cover articles that were helpful in interpreting other studies or were published after 2002, but met other aspects of the inclusion criteria. Results This section summarizes the results of our review by providing, for each model type, an overview of the methods, a synopsis of the applied studies using the particular method, a discussion of the outcome from applied studies, and a brief evaluation. Proximity Models Overview Measuring the proximity of a subject to a pollution source represents the most basic approach in differentiating intraurban air pollution exposures. This method helps to identify relationships between air pollution and health outcomes based on the assumption that nearness to emission sources proxies for exposure in human populations. Figure 1 illustrates a typical road buffer that may be used to assign exposure to respondents from a respiratory health survey based on proximity to major roadways in Hamilton, Ontario, Canada. Respondents within a given distance would be assigned a ������1������, while respondents outside prespecified distance would receive a ������0������. Proximity estimates have been widely used to assess the exacerbation of asthma symptoms in children with the use of empirical models. Application Our review examined 12 peer-reviewed papers focused on the association between road proximity and respiratory disease, lung cancer and stroke mortality with most quantified within a buffer of some predefined extent. The majority of these studies were conducted in countries Air pollution exposure models Jerrett et al. 186 Journal of Exposure Analysis and Environmental Epidemiology (2005) 15(2)

across Europe (van Vliet et al., 1997 Ciccone et al., 1998 Wilkinson et al., 1999 Venn et al., 2000, 2001 Wyler et al., 2000 Janssen et al., 2001 Hoek et al., 2002), one in San Diego, California (English et al., 1999), one in Los Angeles (Langholz et al., 2002), one in England and Wales (Maheswaran and Elliot, 2003) and one in Hamilton, Canada (Jerrett et al., 2002). All studies focused on the intra-urban scale, where traffic counts and distance to roads were the two main indicators of pollution exposure estimates. Nine analyses involve school children, but three surveyed adults. Researchers often combine proximity measures with measures of road type or traffic density to differentially classify exposure based on both potential emissions and distance from source. Between studies, the exact implementa- tion of the buffering analysis varies. Janssen et al. (2001) measured particulate matter less than 2.5 mm (PM2.5), NO2, and benzene. The measurements were taken inside and outside a groupof 24 schools located within 400m of major traffic routes. They found significant positive associations between the pollution concentration and decreasing distances to schools from major automotive routes. Wyler et al. (2000) matched traffic inventory, which included data on the average number of cars and trucks passing per hour at each participant���s home address. Venn et al. (2000) used a traffic activity index. Vehicle flows were measured on roads in the vicinity of the study schools as a continuous measure of traffic density for those 1-km2 grid cells containing a school. Respondents surveyed in the study conducted in 10 Italian cities were asked to answer questions about the level of traffic density and frequency of passing buses with the classification never, seldom, sometimes in a day or often in a day. Questions were limited to those living in houses with windows facing the street (Ciccone et al., 1998). Venn et al. (2001) used another method that proxies for traffic-related air pollution using continuous distance from the child���s home to the nearest main road as the exposure proxy. Jerrett et al. (2002) used buffers at different distances from major roads to assess distance decay (i.e., 0���50, 51���100, and 101���150 m). English et al. (1999) implemented a traffic emissions model and combined this with circular buffers around the subjects��� homes. The Langholz et al. (2002) leukemia study assigned traffic exposures to their case���control study using a Gaussian weighted traffic density assignment (Pearson and Fitzgerald, 2001). Maheswaran and Elliot (2003) measured the distance from the centroids of the respective census enumeration district to the closest major road and used this value as a proxy for exposure. Links to Health Effects Research findings suggest that higher traffic counts or emissions near the residence may exacerbate asthma symptoms (van Vliet et al., 1997 Ciccone et al., 1998 Venn et al., 2000, 2001), yet little evidence supports a link between asthma onset and intraurban exposure. For example, after controlling for confounding effects such as age, sex, and race, English et al. (1999) found no evidence of increased risk of asthma in children under 14 with an increase in traffic counts. Yet among children with asthma, the number of medical care visits escalated with higher traffic counts. Conversely, Wilkinson et al. (1999) found no association between children, ages 5���14 years, within 150m of a main road and the number of hospital admissions for treatment of asthma. For a study on adult asthma in Hamilton by Jerrett et al. (2002), women, aged 20���44 years, within 50 m of a major road were associated with a 50% increased risk of reporting asthma symptoms, but no significant association was found for males. None of the asthma studies used a prospective cohort design to assess the question on asthma formation. Consequently, these results must be viewed with this limitation in mind (cf. McConnell et al. (2002) for a cohort approach at the interurban scale). With respect to other outcomes, the study by Langholz et al. (2002) found no association between leukaemia and air pollution. Maheswaran and Elliot (2003) reported a significant positive association between air pollution and stroke mortality. Evaluation While the proximity method provides a straightforward application for the analysis of long-term exposure classification, it has considerable limitations. First, studies use a restricted number of covariates that could possibly confound the relationship between air pollution and health. Most studies of this type ignore population exposure to traffic exhaust at locations other than the place of residence, school or work (English et al., 1999), potentially leading to misclassification and biased risk estimates. Neglect of time���activity patterns runs through most of the exposure models we examined, but seems particularly problematic for Figure 1. Example of binary classification within a buffering scheme for proximity models. Air pollution exposure models Jerrett et al. Journal of Exposure Analysis and Environmental Epidemiology (2005) 15(2) 187