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Sci Total Environ ; 682: 541-552, 2019 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-31129542


A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO3- are the most significant. The correlation coefficient between PM2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 µg·m-3, which respectively compared to those without DA.

Sci Total Environ ; 575: 1219-1227, 2017 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-27693145


The aerosol optical depth (AOD) is widely used to predict surface PM2.5 (particles with an aerodynamic diameter smaller than 2.5µm) concentrations using regression methods, as a way to supplement observations from sparse ground PM2.5 monitoring networks. Several meteorological parameters, such as surface humidity, temperature and height of planetary boundary layer (HPBL), are usually combined with AOD to improve the accuracy of the regression model in predicting PM2.5 concentrations. In this paper, we investigate the role of the temperature inversion layer in the prediction of PM2.5 concentrations by the optimal subset regression method. The result indicates that the optimal subset regression model with the parameters of the depth and temperature difference of the inversion can significantly improve the accuracy of the predictions of surface PM2.5 concentrations, compared with the original regression model with one factor of AOD. The determination coefficient (R2) increases from 0.51 to 0.63, and the Root Mean Square Error (RMSE) decreases from 40.38 to 35.45µg/m3. The optimal subset regressions were also built for each season. The temperature difference of the inversion is introduced into the autumn and winter optimal subset regression, and the depth of the inversion is introduced into the spring optimal subset regression. The contribution of the inversion parameters to the regression model is affected by the different type of the temperature inversion layer present in each season.

Environ Sci Pollut Res Int ; 23(9): 8327-38, 2016 May.
Artigo em Inglês | MEDLINE | ID: mdl-26780051


Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 µg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 µg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.

Poluentes Atmosféricos/análise , Poluição do Ar/estatística & dados numéricos , Monitoramento Ambiental/métodos , Material Particulado/análise , Imagens de Satélites , Regressão Espacial , Aerossóis/análise , Calibragem , China , Humanos , Modelos Teóricos
Sci Total Environ ; 505: 1156-65, 2015 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-25466686


Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R(2) and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R(2)=0.83) with an APE of 19%, whereas it was relatively poor in spring (R(2)=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.

Aerossóis/análise , Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Poluição do Ar/estatística & dados numéricos , China , Imagens de Satélites