Comparison of GOES and MODIS Aerosol Optical Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM_2.5Mass at Bondville, Illinois

Abstract

Collocated Interagency Monitoring of Protected VisualEnvironments (IMPROVE) particulate matter (PM) less than 2.5 m in aerodynamic diameter (PM 2.5 ) chemically speciated data, mass of PM less than 10 m in aerodynamic diameter (PM 10 ), and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and size distribution at Bondville, IL, were compared with satellitederived AOD. This was done to evaluate the quality of the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data and their potential to predict surface PM 2.5 concentrations. MODIS AOD correlated better to AERONET AOD (r ϭ 0.835) than did GOES AOD (r ϭ 0.523). MODIS and GOES AOD compared better to AERONET AOD when the particle size distribution was dominated by fine mode. For all three AOD methods, correlation between AOD and PM 2.5 concentration was highest in autumn and lowest in winter. The AERONET AOD-PM 2.5 relationship was strongest with moderate relative humidity (RH). At low RH, AOD attributable to coarse mass degrades the relationship; at high RH, added AOD from water growth appears to mask the relationship. For locations such as many in the central and western United States with substantial coarse mass, coarse mass contributions to AOD may make predictions of PM 2.5 from AOD data problematic. Seasonal and diurnal variations in particle size distributions, RH, and seasonal changes in boundary layer height need to be accounted for to use satellite AOD to predict surface PM 2.5 .

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…(1) Model predictability: MLR was commonly used in early studies [17,20,21,[24][25][26][39][40][41]46,47,49,50,54,75], whereas MEM and CTM gradually became the dominant methods and replaced MLR after 2010. However, GWR has developed at a slower pace with a limited number of studies to data, and had moderate performance [32,74,125,126].…”

“…More recently, in order to improve model performance, some studies have explored covariate factors in the MLR model under different conditions [17,20,21,[24][25][26][39][40][41]46,47,49,50,54,75]. A few covariate factors, such as relative humidity and height of the boundary layer, were regarded as significant enough to affect and even invert the relationships between AOD and PM 2.5 .…”

Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

“…When using MODIS atmospheric products, which did not fully cover the GOCI observation times, we assumed that the daily variation in the atmospheric constituents from the MODIS atmospheric products was low. When comparing ground station particulate matter (PM2.5), we found that the overall root mean square error (RMSE) of the aerosol optical depth (AOD) was 0.123 [30]; it follows that the expected error in the surface reflectance using the MODIS daily AOD will be less than 3% in the 6S radiative transfer model. When MODIS products were unavailable (mainly due to cloud contamination), we substituted the aerosol optical thickness based on COMS MI [31] for the MODIS aerosol optical thickness.…”

A novel calibration approach of MODIS AOD data to predict PM<sub>2.5</sub> concentrations

“…To fully exploit the measurements of both satellites we primarily used Terra AOD data for our predictions, and for days with no Terra data, Aqua AOD measurement values were used to estimate the missing Terra values. This was accomplished by multiplying Aqua AOD measurements by an adjustment factor, which was necessary to account for diurnal variations (Green et al, 2009) and potential calibration differences in two satellite sensors. This factor was equal to the average Terra AOD/Aqua AOD ratio which was calculated for days where both Terra and Aqua data were available.…”