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Increased dust and air pollution in the Middle East over the past two decades has caused many problems, including human health risks and environmental hazards. Concentrations of surface Particulate Matter (PM2.5 and PM10) have always been considered as important indicators in assessing air pollution. It is necessary to have models that accurately estimate and predict the concentration of particulate matter to measure and reduce air pollution. In order to estimate the surface PM2.5 and PM10 concentrations, nine different linear and nonlinear multivariable regression models were provided in this study. Aerosol Optical Depth (AOD) data along with several effective meteorological variables such as temperature, relative humidity, wind speed, wind direction, horizontal visibility, and K-index were used to estimate PM2.5 and PM10 concentration. AOD data obtained from Medium Resolution Imaging Spectroscopy (MODIS) and the meteorological parameters were used to generate several statistical models, including linear and nonlinear multivariable regression models during ten years over Ahvaz. The highest correlation was observed between the observed and estimated PM2.5 and PM10 concentrations in the nonlinear equation. The forecast accuracy of the PM2.5 and PM10 concentrations using the nonlinear equation was 70% and 64%, respectively, which are the best predictions in the nine models obtained in this study.