Abstract
In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM2.5 concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (R2) between the measured and predicted PM2.5 concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 µg/m3. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient R2 of the measured and predicted results was 0.784, and the RMSE was 20.104 µg/m3, which is only 8 µg/m3 higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM2.5 concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.
Highlights
Real-time monitoring of ground-level fine particulate matter (PM2.5) concentrations is essential for the early warning of extreme weather events such as dust and haze, and the prediction of health exposure risks [1]
This paper describes the algorithm for retrieving the hourly PM2.5 concentrations over central and eastern China using aerosol optical depth (AOD) data from the geostationary satellite Himawari-8
This study focused on estimation of the hourly PM2.5 data over central and eastern China
Summary
Real-time monitoring of ground-level fine particulate matter (PM2.5) concentrations is essential for the early warning of extreme weather events such as dust and haze, and the prediction of health exposure risks [1]. There are some studies on the estimation of the hourly PM values using Himawari-8 aerosol property data, for example, studies on the estimation of the hourly PM2.5 for the Beijing–Tianjin–Hebei region using a linear mixed-effect model [20] and the hourly PM2.5 concentrations in Hebei using a vertical-humidity correction method [21]. As the spatial and temporal variations should be considered simultaneously in the estimation of hourly PM2.5 concentration data, a GTWR model is adopted in this study. In this study, the hourly PM2.5 data for central and eastern China, where extreme weather events such as haze and dust storms are frequent, were estimated. This paper describes the algorithm for retrieving the hourly PM2.5 concentrations over central and eastern China using AOD data from the geostationary satellite Himawari-8. The selection of input parameters, calculation and setting of the key coefficients of the model, model structure composition, model performance, temporal and spatial distribution of hourly PM2.5, and model testing under extreme weather conditions are discussed
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