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Estimate Ground-based PM2.5 Concentrations with Merra-2 Aerosol Components in Tehran, Iran: Merra-2 PM2.5 Concentrations Verification and Meteorological Dependence

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY(2024)

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摘要
The effect of fine particulates matter (PM2.5) as the main source of air pollution on human health and the environment is one of the major problems in Tehran. Due to the limited number of ground-level monitoring stations, PM2.5 concentrations are not widely monitored in Tehran. This present study aimed to estimate ground-based PM2.5 concentrations with Merra-2 aerosol components and compare them with Merra-2 PM2.5. In the first, ground-based PM2.5 concentrations from 23 stations of the Air Quality Control Company and Merra-2 PM2.5 components concentrations, Merra-2 PM2.5 concentrations, and aerosol optical thickness from the satellite reanalysis model were recorded over Tehran from 2012 to 2021. Then, we have analyzed the quantified impacts of PM2.5 components (including organic carbon, black carbon, sulfate, sea salt, and dust), meteorological variables (including temperature, wind speed, relative humidity, precipitation, boundary layer height, and cloud cover), and aerosol optical thickness on ground-based and Merra-2 PM2.5 concentrations. Our analysis shows that the Merra-2 PM2.5 concentrations were recorded much lower than the ground-based PM2.5. Also, the annual trend of PM2.5 components decreased except for dust. A strong linear correlation is obtained between Merra-2 PM2.5 concentrations and dust. Boundary layer height among the meteorological variables influencing PM2.5 plays a major role in PM2.5 concentrations. There is a relatively good correlation between ground-based and Merra-2 PM2.5 with aerosol optical thickness. Black carbon has the most significant anti-correlation with boundary layer height. Also, black carbon and relative humidity have a very good correlation. Finally, we validate the estimation of ground-based PM2.5 concentrations with aerosol components using a nonlinear multivariate power regression model ( R^2= 0.970, RMSE=5.942 ). Overall, this study underlines the impact of meteorological variables and anthropogenic emissions on ground-based PM2.5 growth.
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关键词
PM2.5,PM2.5 components,Aerosol optical thickness (AOT),Meteorological variables,Merra-2,Ground measurements
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