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Many studies have been carried out for the validation of Aerosol Optical Depth (AOD) retrieved using MODerate resolution Imaging Spectroradiometer (MODIS) satellite data at the spatial resolution of several to several tens of km. Most of the validation efforts have focused on urban areas with fewer studies focused on deserts with high amounts of dust. On the other hand, calculating AOD at a high spatial resolution over desert surfaces is of high interest to identify local and accurate dust source regions. In this study, two different high-resolution MODIS AOD retrieval algorithms were evaluated to investigate the nature of high dust loading events over the Middle East region. These algorithms include the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution based on the MOD09 Level 2 daily (original SARAMOD09) and MCD43A4 16 day (modified SARAMCD43) surface reflectance products. These algorithms were validated using AERONET AOD observations at Kuwait University, and accuracy was evaluated using descriptive statistics and Expected Error (EE) over land. Also, the results of these algorithms were evaluated with the Collection 6 (C6) Deep Blue algorithm at 10 km spatial resolution (DB10km). Results show that the SARAMCD43 and SARAMOD09 AOD retrievals are well correlated (r) with AERONET data (SARAMCD43 = 0.89 and SARAMOD09 = 0.84), with a larger number of retrievals falling within the Expected Error (SARAMCD43 = 86% and SARAMOD09 = 77%) than the DB (DB10km = 60%) AOD product. The SARAMCD43 AOD exhibited good agreement with DB10km AOD over desert surfaces areas during high aerosol loading events, with an average correlation coefficient of 0.92, RMSE of 0.09, and mean absolute error (MAE) of 0.08. While SARAMOD09 AOD over background desert areas is similar to SARAMCD43, significant differences were observed over high aerosol loading areas and SARAMOD09 dramatically underestimates AOD. The results demonstrate that: (i) the SARA algorithm (regardless of surface reflectance data used) is more robust than the MODIS AOD products (both DT and DB), and is better able to represent spatial dust aerosol conditions with an accurate AOD over desert areas over a range of low to high dust pollutant levels; (ii) SARAMCD43 is well correlated with the DB AOD observations and provides AOD with much greater accuracy and reliability than SARAMOD09 over desert areas during severe dust loading events.

The lack of reference ground-based PM2.5 observation leads to large gaps in air quality information, particularly in many areas of the developing world. This study investigated a new solution for urban air-quality monitoring in regions with limited reference ground-based monitoring. We developed an observation-based method by combining satellite remote-sensing techniques and a newly established low-cost sensor network to estimate long-term PM2.5 concentrations over Krasnoyarsk, a highly industrialized Siberian city. First, a physical model was developed to estimate PM2.5 concentrations using satellite remote-sensing with the aid of ground-based meteorological and radiosonde observations. Observations from the ground-based sensor network were then used to calibrate the deviations in the satellite-derived PM2.5 concentrations. The results show that the satellite-based PM2.5 concentrations obtained by our physical model were in good agreement with the sensor observations (R = 0.78 on the monthly scale). The deviation in satellite-derived annual PM2.5 concentrations resulted from data restrictions that occurred at noon and data loss in winter were identified as 20% and 30%, respectively. The regional transport of smoke from forest wildfires increased PM2.5 concentration to 150 μg/m3 in the summer 2018. The average PM2.5 concentrations in the urban districts could reach 35 μg/m3, which far exceeded the World Health Organization air quality guideline. These results underscore the good ability of our new method to determine PM2.5 concentrations in regions with limited reference ground-based monitoring. Use of sensor and meteorological observations greatly improved satellite detection of PM2.5 concentration. In addition, our method has the potential for global application to improve determination of PM2.5 concentrations, especially in sparsely monitored regions.