Air pollution exposure prediction approaches used in air pollution epidemiology studies

Abstract

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Exposure to particulate matter and ozone of outdoor origin in Singapore

“…in tropical climates should be better characterized. Modeling of human exposure to regionally homogeneous pollutants, like PM and O 3 , may be improved by improved understanding of factors that affect residential concentrations [19]. Best estimates of building use reported here illustrate that despite the prevalence of air-conditioner ownership, considerable time is spent in naturally ventilated environments.…”

“…As a result, exposure to air pollutants, even those of outdoor origin, largely occurs in indoor environments. A growing body of research highlights the importance of the built environment's impact on human exposure to air pollution [19,20]. For many pollutants, buildings attenuate indoor concentrations relative to those in the nearby outdoor air.…”

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

“…The statistical methodologies applied when CS measurements are used are more prone to estimation biases and/or higher imprecision in these multi-geographic epidemiologic applications, if the spatial distribution of pollutants or household characteristics (e.g., AER, infiltration rates, other exposure predictors) vary between the study sites and exhibit a different pattern from the ambient pollution concentrations over time and space. These types of studies often benefit from using more refined exposure metrics than the traditionally used ambient monitoring data (Baxter et al 2013b;Dai et al 2014;Hodas et al 2013;Özkaynak et al 2013). …”

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

“…When considering long-term health effects on an individual basis, however, the spatial and spatio-temporal variations are of great importance given that outdoor air pollution concentrations vary on a small spatial scale, e.g., within 100 m of a busy road [3]. More recent epidemiological studies have, thus, approached such small-scale intra-urban variation of air pollution concentrations by using different types of models, such as Land Use Regression (LUR) models, Dispersion Models (DM), chemistry Transport Model Models (CTM), a combination of DM+CTM (DCTM), hybrid models, or other alternatives [4,5].…”

“…So far, most studies on the comparison of different modeling strategies focused on the residential agreement of estimated exposure concentrations, disregarding the potential reasons for the disagreement between different modelling approaches, as well as respective strengths and limitations. Although all exposure metrics are equally used as a surrogate of personal exposure in epidemiological studies, exposure modeling is strongly influenced by the spatial and temporal variation of exposure and exposure sources [5]. Furthermore, aims, application, input data but also the complexity of models might differ, yielding not only different exposure estimates but consequently different health effect estimates in terms of magnitude and/or statistical significance [5,13].…”