Estimation of surface particulate matter (PM2.5 and PM10) mass concentration by multivariable linear and nonlinear models using remote sensing data and meteorological variables over Ahvaz, Iran

Abstract

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 (PM 2.5 and PM 10 ) 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 PM 2.5 and PM 10 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 PM 2.5 and PM 10 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 PM 2.5 and PM 10 concentrations in the nonlinear equation. The forecast accuracy of the PM 2.5 and PM 10 concentrations using the nonlinear equation was 70% and 64%, respectively, which are the best predictions in the nine models obtained in this study. • Daily and monthly PM 2.5 and PM 10 estimated using the MODIS AOD data and ground-based meteorological parameters. • Multivariable linear and nonlinear regression analyses were used to estimate the concentration of PM 10 and PM 2.5 over Ahvaz. • Multivariable nonlinear regression models performed well with the forecast accuracy of 70% and 64% for PM 2.5 and PM 10