Satellite constraints on surface concentrations of particulate matter
Date
2015
Authors
Ford Hotmann, Bonne, author
Heald, Colette L., advisor
Kreidenweis, Sonia M., committee member
Peel, Jennifer L., committee member
Collett, Jeffrey L., committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Because of the increasing evidence of the widespread adverse effects on human health from exposure to poor air quality and the recommendations of the World Health Organization to significantly reduce PM2.5 in order to reduce these risks, better estimates of surface air quality globally are required. However, surface measurements useful for monitoring particulate exposure are scarce, especially in developing countries which often experience the worst air pollution. Therefore, other methods are necessary to augment estimates in regions with limited surface observations. The prospect of using satellite observations to infer surface air quality is attractive; however, it requires knowledge of the complicated relationship between satellite-observed aerosol optical depth (AOD) and surface concentrations. This dissertation explores how satellite observations can be used in conjunction with a chemical transport model (GEOS-Chem) to better understand this relationship. First, we investigate the seasonality in aerosols over the Southeastern United States using observations from several satellite instruments (MODIS, MISR, CALIOP) and surface network sites (IMPROVE, SEARCH, AERONET). We find that the strong summertime enhancement in satellite-observed aerosol optical depth (factor 2-3 enhancement over wintertime AOD) is not present in surface mass concentrations (25-55% summertime enhancement). Goldstein et al. [2009] previously attributed this seasonality in AOD to biogenic organic aerosol; however, surface observations show that organic aerosol only accounts for ~35% of PM2.5 mass and exhibits similar seasonality to total surface PM2.5. The GEOS-Chem model generally reproduces these surface aerosol measurements, but under represents the AOD seasonality observed by satellites. We show that seasonal differences in water uptake cannot sufficiently explain the magnitude of AOD increase. As CALIOP profiles indicate the presence of additional aerosol in the lower troposphere (below 700 hPa), which cannot be explained by vertical mixing; we conclude that the discrepancy is due to a missing source of aerosols above the surface layer in summer. Next, we examine the usefulness of deriving premature mortality estimates from "satellite-based" PM2.5 concentrations. In particular, we examine how uncertainties in the model AOD-to-surface-PM2.5 relationship, satellite retrieved AOD, and particulars of the concentration-response function can impact these mortality estimates. We find that the satellite-based estimates suggest premature mortality due to chronic PM2.5 exposure is 2-16% higher in the U.S. and 4-13% lower in China compared to model-based estimates. However, this difference is overshadowed by the uncertainty in the methodology, which we quantify to be on order of 20% for the model-to- surface-PM2.5 relationship, 10% for the satellite AOD and 30-60% or greater with regards to the application of concentration response functions. Because there is a desire for acute exposure estimates, especially with regards to extreme events, we also examine how premature mortality due to acute exposure can be estimated from global models and satellite-observations. We find similar differences between model and satellite-based mortality estimates as with chronic exposure. However the range of uncertainty is much larger on these shorter timescales. This work suggests that although satellites can be useful for constraining model estimates of PM2.5, national mortality estimates from the two methods are not significantly different. In order to improve the efficacy of satellite-based PM2.5 mortality estimates, future work will need to focus on improving the model representation of the regional AOD-to-surface-PM2.5 relationship, reducing biases in satellite-retrieved AOD and advancing our understanding of personal and population-level responses to PM2.5 exposure.