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Gizem Tuna Tuygun*, Tolga Elbir

Aerosol optical depth (AOD) is a significant predictor that can support PM concentrations in a region without ground-based monitoring. Estimation of intra-daily ground-level PM₁₀ concentrations based on satellite AOD products has been widely used over large areas with the help of machine learning models. This study was the first attempt in the north-western part of Turkey to estimate daily PM₁₀ concentrations based on Collection 6.1 (C6.1) Moderate Resolution Imaging Spectroradiometer (MODIS) DTB AOD products from Terra and Aqua satellites. A space-time random forest (STRF) model outperformed most models with strong predictive power to evaluate the differences between the time windows representing the mean PM₁₀ around the overpass times of the satellites with the synergetic use of space-time information over Turkey was developed in this study. Several spatiotemporal parameters such as MODIS AOD, meteorological, and land-related data were used to improve the overall accuracy of PM₁₀ estimation for 2008-2019. The most significant 13 variables were used to estimate PM₁₀ in the final STRF model. The STRF model performed moderately well, with a moderate correlation coefficient (R) of 0.71 (0.73) for Terra (Aqua) satellite overpass time. The model better estimated PM₁₀ concentrations at Aqua overpass time (R~0.73 and RMSE ~27.3 µg/m³). Moreover, the STRF model developed in this study is the first step to construct a high-quality PM₁₀ dataset over the region with poor air quality due to industrialization and the rapid growth of the regional population.

Keywords: MODIS; AOD; PM₁₀ estimation; Random Forest; Turkey

Proscience vol. 8

Pp 20-29

DOI: 10.14644/dust2021.003