High-resolution estimation of ambient sulfate concentration over Taiwan Island using a novel ensemble machine-learning model
High-resolution estimation of ambient sulfate concentration over Taiwan Island using a novel ensemble machine-learning model
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DOI：
10.1007/s11356-021-12418-7
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发表时间：
2021-01
影响因子：
5.8
通讯作者：
Lulu Cui;Qingwei Ma;Rui Li;Hongbo Fu;Ziyu Zhang;Liwu Zhang;Ying Chen
中科院分区：
文献类型：
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作者：
Lulu Cui;Qingwei Ma;Rui Li;Hongbo Fu;Ziyu Zhang;Liwu Zhang;Ying Chen
硫酸盐气溶胶的重载会引发雾霾，对台湾岛的人体健康造成极大危害。然而，由于监测点稀缺，台湾岛环境硫酸盐的高分辨率时空变化仍然未知。因此，我们开发了一种名为极端梯度增强与地理和时间加权回归相结合的新型集合模型（XGBoost-GTWR），以根据卫星数据、同化气象学和化学传输模型（CTM）的输出来预测高分辨率硫酸盐浓度（0.05°）。结果表明，XGBoost-GTWR 模型在预测硫酸盐浓度方面优于其他 5 个模型，具有最高的 R2 值（R2= 0.58）和最低的相对均方误差（RMSE = 1.96 μg/m3）。此外，还基于2019年地面硫酸盐数据验证了XGBoost-GTWR模型的可移植性。结果表明，外推方程的R2值（0.53）与10倍交叉验证结果（0.58）相比没有出现明显下降，表明该模型对硫酸盐浓度的预测具有稳健性。台湾岛环境硫酸盐浓度呈现出明显的空间变化特征，西南地区最高，东北地区最低。据推测，较高的人为排放加上不利的气象条件导致西南沿海地区硫酸盐含量较高。环境硫酸盐浓度呈现明显的季节变化，春季最高（5.65±0.84 μg/m3），其次为冬季（5.45±1.25 μg/m3）和秋季（4.60±0.80 μg/m3），夏季最低（3.80±0.65 μg/m3）。春季硫酸盐浓度较高主要是由于生物质燃烧密集和降雨量稀少造成的。本研究开发了一种新的模型来捕获高分辨率硫酸盐图，并为空气污染的有效监管和流行病学研究提供基础数据。
Heavy loadings of sulfate aerosol trigger haze formation and pose great damage to human health in Taiwan Island. Nevertheless, high-resolution spatiotemporal variation of ambient sulfate across Taiwan Island still remained unknown because of the scarce monitoring sites. Thus, we developed a novel ensemble model named extreme gradient boosting coupled with geographically and temporally weighted regression (XGBoost-GTWR) to predict the high-resolution sulfate concentration (0.05°) based on satellite data, assimilated meteorology, and the output of chemical transport models (CTMs). The result suggested that XGBoost-GTWR model outperformed other five models in predicting the sulfate concentration with the highestR2value (R2= 0.58) and the lowest relative mean square error (RMSE = 1.96 μg/m3). Besides, the transferability of the XGBoost-GTWR model was also validated based on the ground-level sulfate data in 2019. The result suggested that theR2value of the extrapolation equation (0.53) did not show notable decrease compared with the 10-fold cross-validation result (0.58), indicating that the model was robust to predict the sulfate concentration. The ambient sulfate concentration in Taiwan Island displayed featured spatial variation with the highest one in Southwest Taiwan and the lowest one in Northeast Taiwan, respectively. It was assumed that the higher anthropogenic emission combined with the adverse meteorological condition led to the higher sulfate level in the southwestern coastal region. The ambient sulfate concentration exhibited significantly seasonal variation with the highest value in spring (5.65 ± 0.84 μg/m3), followed by those in winter (5.45 ± 1.25 μg/m3) and autumn (4.60 ± 0.80 μg/m3), and the lowest one in summer (3.80 ± 0.65 μg/m3). The higher sulfate concentration in spring was mainly contributed by the dense biomass burning and scarce rainfall amount. The present study develops a novel model to capture the high-resolution sulfate map and provides basic data for effective regulations of air pollution and epidemiological studies.