Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

Background and methodology: measurements by satellite remote sensing were combined with ground-basedmeteorological measurements to estimate ground-level pm10. aerosol optical depth (aod) by both modis andmisr were utilized to develop several statistical models including linear and non-linear multi-regression models.these models were examined for estimating pm10 measured at the air quality stations in tehran, iran, during2009–2010. significant issues are associated with airborne particulate matter in this city. moreover, the performancesof the constructed models during the middle eastern dust intrusions were examined.results: in general, non-linear multi-regression models outperformed the linear models. the developed modelsusing misr aod generally resulted in better estimate of ground-level pm10 compared to models using modis aod.consequently, among all the constructed models, results of non-linear multi-regression models utilizing misr aodacquired the highest correlation with ground level measurements (r2 of up to 0.55). the possibility of developing asingle model over all the stations was examined. as expected, the results were depreciated, while nonlinear misrmodel repeatedly showed the best performance being able to explain up to 38% of the pm10 variability.conclusions: generally, the models didn’t competently reflect wide temporal concentration variations, particularlydue to the elevated levels during the dust episodes. overall, using non-linear multi-regression model incorporatingboth remote sensing and ground-based meteorological measurements showed a rather optimistic prospective inestimating ground-level pm for the studied area. however, more studies by applying other statistical models andutilizing more parameters are required to increase the model accuracies.