The application of satellite data in ambient PM pollution monitoring - MISR as an example

Fine particulate matter (PM2.5) has been shown to be correlated with adverse health effects, and understanding the distribution of its concentrations in the atmosphere is of great importance to both health scientists and policy makers. Latest satellite sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite routinely measure various aerosol parameters including aerosol optical thickness (AOT), which is an indicator of aerosol abundance in the atmosphere. The objective of this study is to explore different ways to associate MISR AOT with ground level PM2.5 concentrations so that the complete spatial coverage of satellite data can be used to extend ground monitoring network and provide valuable information about the spatial distribution of PM2.5 pollution in regions where ground monitoring is not available.

As a first attempt, an empirical model based on the regression between daily PM2.5 concentrations and MISR AOT was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root mean square error (RMSE) of the model was 6.2 ug/m3 with a corresponding average PM2.5 concentration of 13.8 ug/m3. When PM2.5 concentrations greater than 40 ug/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks.

Then, we developed a simple approach to estimating PM2.5 concentrations by applying local scaling factors from a global atmospheric chemistry model (GEOS-CHEM with GOCART dust and sea salt data) to MISR AOT. The resulting MISR PM2.5 concentrations are compared with measurements from the EPA PM2.5 compliance network for the year 2001. Regression analyses show that the annual mean MISR PM2.5 concentration is strongly correlated with EPA PM2.5 concentration (correlation coefficient r = 0.81), with an estimated slope of 1.00 and an insignificant intercept, when three potential outliers from Southern California are excluded. The MISR PM2.5 concentrations have a root mean square error (RMSE) of 2.20 ug/m3, which corresponds to a relative error of approximately 20%. Using simulated aerosol vertical profiles generated by the global models helps to reduce the uncertainty in estimated PM2.5 concentrations due to the changing correlation between lower and upper tropospheric aerosols and therefore to improve the capability of MISR AOT in estimating surface-level PM2.5 concentrations. With improved MISR cloud screening algorithms and the dust simulation of global models, as well as a higher model spatial resolution, we expect that this approach will be able to make reliable estimation of seasonal average surface-level PM2.5 concentration at higher temporal and spatial resolution.