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 with 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 NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves 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.

Figure 1

Seasonal distributions of 24 hour Tier 2A (SHEDS) exposure-concentration ratio (ratio of Tier 2A estimates over Tier concentrations) by monitoring area (solid line = median; boxes = 25^th and 75^th percentiles; whiskers = 10^th and 90^th percentiles; dots = 5^th and 95^th percentiles)

Figure 2

Seasonal distributions of 24 hour Tier 2B (APP) exposure-concentration ratio by monitoring area (solid line = median; boxes = 25^th and 75^th percentiles; whiskers = 10^th and 90^th percentiles; dots = 5^th and 95^th percentiles)

Figure 3

Seasonal distributions of 24 hour Tier 3 (Hybrid) exposure-concentration ratio by monitoring area (solid line = median; boxes = 25^th and 75^th percentiles; whiskers = 10^th and 90^th percentiles; dots = 5^th and 95^th percentiles)

Figure 4

Seasonal distributions of estimated 24 hour air exchange rates by monitoring area used in Tier 2B (APP) and 3 (Hybrid) exposure estimates (solid line = median; boxes = 25^th and 75^th percentiles; whiskers = 10^th and 90^th percentiles; dots = 5^th and 95^th percentiles)

Figure 5

Summer differences between a) Tier 2A (SHEDS model) zip code-specific daily PM_2.5 exposure estimates and overall average (all zip codes) daily PM_2.5 exposure estimates, b) Tier 2B (LBNL APP and Infiltration model) zip code-specific daily PM_2.5 exposure estimates and overall average (all zip codes) daily PM_2.5 exposure estimates, c) Tier 3 (Hybrid model) zip code-specific daily PM_2.5 exposure estimates and overall average (all zip codes) daily PM_2.5 exposure estimates, and d) zip code-specific daily air exchange rates and overall average (all zip codes) air exchange rates used in Tier 2B and 3 exposure estimates in Elizabeth, NJ (solid line = median; boxes = 25^th and 75^th percentiles; whiskers = 10^th and 90^th percentiles; dots = 5^th and 95^th percentiles)