Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran)

Abstract

The PM_2.5 as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM_2.5 concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM_2.5 concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM_2.5 concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM_2.5 concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM_2.5 concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM_2.5 concentration than summer season (40%). In winter, PM_2.5 concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM_2.5 concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM_2.5 concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM_2.5 concentration in summer. In this season, the air temperature parameter showed a high correlation with PM_2.5 concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer.

Keywords: Air pollution; Geographically weighted regression; MODIS; Meteorological parameters; NDVI; PM2.5.