Abstract
The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs.
Highlights
Air pollution seriously affects the environment and endangers human health
The percentage of the stable boundary layer is highest for winter with 61.67% that is almost two times as large as the percentage of the convective boundary layer (38.33%)
The results indicate that BLHSBI but not BLHSta
Summary
Air pollution seriously affects the environment and endangers human health. Atmosphere 2017, 8, 104 lung cancer [1,2,3,4]. Most aerosols are emitted into the atmospheric boundary layer and result in serious pollution near the ground. The space coverage of PM2.5 monitoring stations is sparse, especially in the suburban environment. Aerosol optical depth (AOD) data from satellites or ground-based sun photometer spectral observations can play an auxiliary role in air quality monitoring. AOD is the integral of the aerosol extinction due to scattering and absorption in the vertical plane, and surface PM2.5 concentrations are related to the aerosol extinction. There is a wide body of literature that shows that there are strong correlations between AOD and surface PM2.5 concentrations [5,6,7]
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