A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions
- 1Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
- 2Department of Geography and Human Environment, Tel Aviv University, Tel Aviv, Israel
- 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
- 4Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD 21228, USA
Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002–2008 was investigated and also compared to the AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD–PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50–70% in the number of collocated AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM2.5 spatial pattern prediction based on satellite observations.