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Estimating high-resolution PM1 concentration from Himawari-8 combining extreme gradient boosting-geographically and temporally weighted regression (XGBoost-GTWR)
Abstract As a much finer particle, particulate matter less than 1 μm (PM1) plays an important role on the haze formation and human health. However, the capability of mapping PM1 concentration is severely impaired by coarse temporal resolution and low estimation accuracy, largely due to the neglect of spatial or temporal autocorrelation of PM1. In order to improve the estimation of high-resolution PM1, here we developed a novel spatiotemporal model named extreme gradient boosting (XGBoost)-geographically and temporally weighted regression (GTWR) using Himawari-8 aerosol optical depth (AOD), meteorological factors, and geographical covariates. The estimation of PM1 over Zhejiang province showed that XGBoost-GTWR method was characterized by greater predictive ability (10-fold cross-validation R2 = 0.83, root mean squared error (RMSE) = 10.72 μg/m3) compared with other 11 models. Additionally, the extrapolation test was performed to validate the robustness of the hybrid model and the result demonstrated that XGBoost-GTWR can accurately predict the out-of-band PM1 concentration (R2 = 0.75 (0.60), RMSE = 12.71 (12.58) μg/m3). The PM1 concentration displayed pronounced spatial heterogeneity, with the highest value in Quzhou (34.72 ± 1.77 μg/m3) and the lowest in Zhoushan (26.39 ± 1.56 μg/m3), respectively. In terms of the seasonality, the highest PM1 concentration was observed in winter (39.06 ± 3.08 μg/m3), followed by those in spring (32.54 ± 3.09 μg/m3) and autumn (30.97 ± 4.50 μg/m3), and the lowest one in summer (25.57 ± 5.22 μg/m3). The high aerosol emission and adverse meteorological conditions (e.g., low boundary layer height and lack of precipitation) were key factors accounting for the peak PM1 concentration observed in winter. Also, the PM1 concentration exhibited significant diurnal variation, peaking at 1500 local solar time (LST) but reaching the lowest value at 1000 LST. This method enhances our capability of estimating hourly PM1 from space, and lays a solid data foundation for improving the assessment of the fine particle-related health effect.
Highlights XGBoost-GTWR model outperformed other 11 models in predicting PM1 level. The higher PM1 level focused on the northern and central region of Zhejiang province. The PM1 level peaked at 15:00 LST, but showed the lowest value at 10:00 LST.
Estimating high-resolution PM1 concentration from Himawari-8 combining extreme gradient boosting-geographically and temporally weighted regression (XGBoost-GTWR)
Abstract As a much finer particle, particulate matter less than 1 μm (PM1) plays an important role on the haze formation and human health. However, the capability of mapping PM1 concentration is severely impaired by coarse temporal resolution and low estimation accuracy, largely due to the neglect of spatial or temporal autocorrelation of PM1. In order to improve the estimation of high-resolution PM1, here we developed a novel spatiotemporal model named extreme gradient boosting (XGBoost)-geographically and temporally weighted regression (GTWR) using Himawari-8 aerosol optical depth (AOD), meteorological factors, and geographical covariates. The estimation of PM1 over Zhejiang province showed that XGBoost-GTWR method was characterized by greater predictive ability (10-fold cross-validation R2 = 0.83, root mean squared error (RMSE) = 10.72 μg/m3) compared with other 11 models. Additionally, the extrapolation test was performed to validate the robustness of the hybrid model and the result demonstrated that XGBoost-GTWR can accurately predict the out-of-band PM1 concentration (R2 = 0.75 (0.60), RMSE = 12.71 (12.58) μg/m3). The PM1 concentration displayed pronounced spatial heterogeneity, with the highest value in Quzhou (34.72 ± 1.77 μg/m3) and the lowest in Zhoushan (26.39 ± 1.56 μg/m3), respectively. In terms of the seasonality, the highest PM1 concentration was observed in winter (39.06 ± 3.08 μg/m3), followed by those in spring (32.54 ± 3.09 μg/m3) and autumn (30.97 ± 4.50 μg/m3), and the lowest one in summer (25.57 ± 5.22 μg/m3). The high aerosol emission and adverse meteorological conditions (e.g., low boundary layer height and lack of precipitation) were key factors accounting for the peak PM1 concentration observed in winter. Also, the PM1 concentration exhibited significant diurnal variation, peaking at 1500 local solar time (LST) but reaching the lowest value at 1000 LST. This method enhances our capability of estimating hourly PM1 from space, and lays a solid data foundation for improving the assessment of the fine particle-related health effect.
Highlights XGBoost-GTWR model outperformed other 11 models in predicting PM1 level. The higher PM1 level focused on the northern and central region of Zhejiang province. The PM1 level peaked at 15:00 LST, but showed the lowest value at 10:00 LST.
Estimating high-resolution PM1 concentration from Himawari-8 combining extreme gradient boosting-geographically and temporally weighted regression (XGBoost-GTWR)
Li, Rui (author) / Cui, Lulu (author) / Fu, Hongbo (author) / Meng, Ya (author) / Li, Junlin (author) / Guo, Jianping (author)
Atmospheric Environment ; 229
2020-03-21
Article (Journal)
Electronic Resource
English
AOD , PM<inf>1</inf> , XGBoost , GTWR , Zhejiang
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