Mohammad Reza Atabaki; Mohammad Sakhaei; Hassan hoveidi; Mohammad Pooteh rigi; Ehsan Karimimanesh
Abstract
Introduction: suspended particles has numerous negative effects on human health and plants.it plays an extremely important role in global climate change as well. Objective:In this survey, variations and influence of meteorological parameters on the concentration of PM10 concentrations were studied. Methods: ...
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Introduction: suspended particles has numerous negative effects on human health and plants.it plays an extremely important role in global climate change as well. Objective:In this survey, variations and influence of meteorological parameters on the concentration of PM10 concentrations were studied. Methods: In this study, first, daily, monthly and seasonal concentrations variation of PM10 were investigated. Then, the degree of correlation between PM10 and meteorological parameters were analyzed by Pearson correlation. Also regression model was used to predict PM10 concentration. Findings: Daily average PM10 concentration during the study period indicates that the highest concentration was in the 22nd August (1077 µg/m3) and the lowest in the 8th march (42 µg/m3). It also shows the monthly average concentration was in August (301/06 µg/m3), While the lowest concentration is accounted November (152/16 µg/m3). Seasonal concentration showed that the highest concentrations are in the summer (272/76 µg/m3). Pearson correlation coefficient analysis shows that particulate matter has a direct correlation with temperature and wind speed, while reverse correlation with precipitation and atmospheric pressure. conclusion: Based on the results of Pearson correlation, it was found that rainfall and relative humidity have adverse effects but the temperature and wind speed have a direct impact on the concentrations of PM10. So that the increased rainfall will reduce the concentration of PM10. While the temperature and the wind speed increases the concentration of suspended particles. Also, the coefficient of determination in the regression model Suggests that 13, 25 and 6 percent of PM10 changes in spring, summer and fall are explained by meteorological parameters used in the model. Based on these results we can say that adverse meteorological conditions may lead to increased concentrations of PM10.