Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations

Abstract

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include: combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or “hybrid” models for pollutant exposure estimates, and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared to use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g. EC, CO, and NOx). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develop pollutant-specific infiltration data (including for PM species), and improve existing data on human time-activity patterns, and exposure to local source (e.g. traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space, and investigating the impact of improved exposure estimates in chronic health studies.

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

“…In addition, for a time-series study health outcome is often aggregated at the county level, thus rendering the increased spatial resolution of the exposure estimate of little use. Nevertheless, analysis by SE Sarnat et al in a time-series study at the finer ZIP code level showed some impact on effect estimates for cardiovascular and respiratory outcomes from exposure to traffic-related pollutants (EC, NO x , CO) when dispersion/statistical models were used compared to CS measurements (Baxter et al 2013b;Sarnat et al 2013b). A case-crossover study of exposure to PM 2.5 , using estimates from ambient monitors and from a semiempirical air exchange rate (AER)/mass balance model, showed little to no difference in odds ratios (ORs) or in confidence intervals (CIs) for transmural myocardial infarction outcomes (Hodas et al 2013).…”

“…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). …”

“…In addition, the studies that have evaluated multiple exposure metrics have reported mixed findings, as the benefit of using a particular alternative exposure metric depends on the pollutant and health effect relationships being studied, the most appropriate type of exposure metric (e.g., a metric which evaluates ambient-generated exposures versus total personal exposure), and on the various study designs considered. Results from a group of studies investigating the impact of alternative exposure estimation approaches on health risk estimates associated with short-term exposure studies were summarized recently (Baxter et al 2013b). A survey of more recently available literature revealed a relatively small number of additional published studies beyond those presented earlier.…”

Characteristics and applications of small, portable gaseous air pollution monitors

“…every 1 or 5 s), which provides higher temporal resolution. The sample rate and sensor performance are both factors important for personal monitoring applications, as observations for medical and health audiences need to be highly accurate and sample more quickly depending on the movement speed of the subject (Baxter et al, 2013;Weichenthal et al, 2011;Williams et al, 2014a).…”

“…In order to advance environmental and human health studies, mobile air pollution monitors are increasingly used to obtain more accurate personal exposure estimates. Studies show that personal sampling of air pollution is preferable when attempting to accurately measure human exposure (Good et al, 2015;Steinle et al, 2013;Weichenthal et al, 2011;Zartarian et al, 2007), and that a high spatiotemporal resolution is required to correct for misinterpretation of actual exposure (Baxter et al, 2013;Kumar et al, 2013). Although there is a lack of concrete information on the effective use of such sensors by individuals and communities, specific vulnerable populations in urban areas could benefit from these sensor systems (e.g.…”