A review and evaluation of intraurban air pollution exposure models

Abstract

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.

Repeated hospital encounters for asthma in children and exposure to traffic-related air pollution near the home

“…Nevertheless, fresh traffic-generated air pollution contributes greatly to the spatial heterogeneity of ambient pollution. 6 Table 3 indicates the significant increased risks of repeated hospital encounters of 7% to 10% per interquartile range increase in traffic-related NO x and CO exposures. Associations for NO 2 are approximately half that for NO x and do not reach significance at P Ͻ .05.…”

“…23 Wind patterns affect the general direction and dispersion of pollutants, leading to different exposures for individuals on the upwind vs downwind side of traffic sources. 6 Average diurnal and day-of-week freeway and nonfreeway traffic variations were included. Emission factors were obtained from the California Air Resources Board's EMFAC2007 (v2.3) vehicle emissions model.…”

“…The present results support the use of dispersion modeling for evaluating traffic-related exposures, but other methods involving direct home or neighborhood air pollutant measurements have been proposed to further limit exposure error. 6 We did not include background pollutant concentrations because variability is likely low within the 13-km study radius. Dispersion-modeled gases are considered surrogates of other more toxic gases and particles emitted from nearby diesel trucks and automobiles, including ultrafine particles that have been found at notably higher concentrations near roadways along with black carbon, particle number, and CO. 29 The stronger associations for NO x compared with NO 2 support this because NO 2 is strongly affected by photochemical reactions that can occur across time away from roadways, whereas NO x is expected to capture traffic emissions more generally.…”

“…Spatial heterogeneity of potentially toxic pollutant components are not well represented by data from ambient air monitoring sites, which have provided the bulk of exposure data in previous studies. 6,7 A major contributor to air pollution exposure in urban areas is from mobile transportation sources. In southern California, for example, on-road emission sources alone contribute approximately 45% of volatile organic compounds, 63% of nitrogen oxide (NO x ), and 76% of carbon monoxide (CO) in the air.…”

A participant-based approach to indoor/outdoor air monitoring in community health studies

“…Estimates of pollution concentrations and personal exposures can also be obtained through physical or mechanistic modeling-based approaches, including atmospheric, indoor/outdoor/personal exposure, and hybrid models (Boothe et al, 2005;Jerrett et al, 2005;Isakov et al, 2006;McConnell et al, 2006;Isakov and Ö zkaynak, 2007;Ö zkaynak et al, 2008); spatial interpolation techniques, for example, kriging or splining methods (Jerrett et al, 2005); and empirical statistical models, such as land-use regression models (Brauer et al, 2002;Jerrett et al, 2005;Ross et al, 2006;Smith et al, 2006). However, these methods can be resource intensive and may impose a significant burden on study participants.…”