Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Abstract

Fine particulate matter (PM_2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM_2.5 exposures. This review article surveys publicly available exposure datasets for surface PM_2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM_2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM_2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM_2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM_2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM_2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM_2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM_2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM_2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Figure 1.

County-level maps of annual mean PM_2.5 in 2011 using: (a) CDC WONDER, (b) EPHTN, (c) Dalhousie data (V4.NA.02), and (d) EPA AQS and IMPROVE fused data. White spots on the map represent “no data available”.

Figure 2.

Scatter plots of publicly available surface PM_2.5 datasets – (a) CDC WONDER, (b) EPHTN, and (c) Dalhousie (V4.NA.02) versus AQS+IMPROVE fused data. All data represent county-average 2011 annual mean. Two linear regressions are calculated: one for all data (top text box, red solid line showing this fit) and one for PM_2.5 < 15 μg m-3 only (bottom text box). Black solid line stands for 1:1 line. The value of a and b represent intercept and slope of the linear regression, respectively. The ±1σ stands for ± one standard deviation. The number of samples used for linear regression in (a), (b) and (c) are 543, 544 and 544, respectively.

Figure 3.

(a) Frequency distributions of the county-average 2011 annual mean PM_2.5 mass concentrations for the four data sets shown in Figure 1. (b) Mean (triangles), median (horizontal bar in the middle), 25 and 75 percentiles (bottom and top of the box, respectively) for the four data sets. Black dots represent outliers.

Figure 4.

An example of forecasting smoke conditions in local communities, using Smoke Outlook, for Ranch Fire and River Fire in Sacramento Valley area, California in August 9–10, 2018.