MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Abstract

Much attention has been paid to develop methods to estimate particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) from satellite aerosol optical depth (AOD). One of fundamental limitation of these methods is lack of AOD and thereby PM2.5 cannot be derived from satellites when clouds are present or when surface conditions are not favorable. This would probably result in an inherent clear-sky biased estimate of PM2.5 for air quality assessment that requires continuous 24-h measurements at all-sky conditions. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 data in North China, a highly polluted area with large spatiotemporal variabilities of AOD and PM2.5 values, missing MODIS AOD retrievals and its potential effect on PM2.5 estimation are studied. The MODIS dark target (DT) algorithm produces very few AODs in winter, with a regional observation rate of 4%, which limits its statistical significance for PM2.5 air quality monitoring. This limitation applies to MODIS DT AOD products at 10-km and 3-km resolutions since they are derived from the same retrieval core (Remer et al., 2013). In contrast, The MODIS deep blue (DB) AOD product complements the MODIS DT AOD coverage, which is remarkable in winter. The MODIS DT and DB merged product has comparable accuracy to that of the DT and DB products but shows a larger sampling rate, therefore, it is more suitable for estimating surface PM2.5. While the regional mean PM2.5 values in the presence and absence of AOD retrievals in spring and summer are comparable, but the former is substantially lower than the latter in autumn by 11.2 μgm−3 and winter by 8.5 μgm−3 on average. The difference in some stations even exceeds 20 μg m−3. Methods to fill missing AOD values in North China are crucial to provide an unbiased sampling and estimate of PM2.5 concentration in all-sky conditions, likely by integrating satellite, surface and modeling data.