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 PM 2.5 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-PM 2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MA-IAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM 2.5 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-PM 2.5 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-PM 2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM 2.5 . 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 PM 2.5 spatial pattern prediction based on satellite observations.

Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2 5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R 2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM 2 5 ground concentrations. Finally, we studied the relationship between PM 2 5 and AOD at the intra-urban scale ( 10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM 2 5 relationship does not depend on relative humidity and air temperatures below~7°C. The correlation improves for temperatures above 7 -16°C. We found no dependence on the boundary layer height except when the former was in the range 250-500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM 2 5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM 2 5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM 2 5 mass concentrations are highly correlated with the actual observations (out-of-sample R 2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM 2 5 relationship provides a means for obtaining spatially-resolved PM 2 5 concentrations.

Emission trend analysis provides crucial information for evaluating and enhancing the efficacies of emission control strategies as well as studying air pollution associated health risks. In this study, the patterns and trends of year-round and seasonal PM emission over the Northeast United States are presented at a spatial resolution of 1 km × 1 km for the period of 2002-2012.

We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

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 PM_2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard supersite during 2002–2008 was investigated and also compared to the AOD/PM_2.5 relationship using conventional MODIS 10 km AOD retrieval (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 PM_2.5 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/PM_2.5 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 vs PM_2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data is more capable in capturing spatial patterns of PM_2.5. Importantly, the performance of MAIAC AOD retrievals remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to significantly increase the number of days for PM_2.5 spatial pattern prediction based on satellite observations