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Atmosphere 2017, 8(6), 104; doi:10.3390/atmos8060104

Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

1
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
2
Public Weather Service Center, China Meteorological administration, Beijing 100081, China
3
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Prashant Kumar
Received: 13 April 2017 / Revised: 4 June 2017 / Accepted: 6 June 2017 / Published: 12 June 2017
(This article belongs to the Section Air Quality)
View Full-Text   |   Download PDF [5162 KB, uploaded 12 June 2017]   |  

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. View Full-Text
Keywords: aerosol optical depth; PM2.5; boundary layer height; stepwise regression aerosol optical depth; PM2.5; boundary layer height; stepwise regression
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Zang, Z.; Wang, W.; Cheng, X.; Yang, B.; Pan, X.; You, W. Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods. Atmosphere 2017, 8, 104.

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