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硕士研究生苏恒,陈玉敏的论文在REMOTE SENSING刊出
发布时间:2022-10-17 12:59:16     发布者:易真     浏览次数:

标题: Estimating Regional PM2.5 Concentrations in China Using a Global-Local Regression Model Considering Global Spatial Autocorrelation and Local Spatial Heterogeneity

作者: Su, H (Su, Heng); Chen, YM (Chen, Yumin); Tan, HY (Tan, Huangyuan); Zhou, AN (Zhou, Annan); Chen, GD (Chen, Guodong); Chen, YJ (Chen, Yuejun)

来源出版物: REMOTE SENSING : 14 : 18 文献号: 4545 DOI: 10.3390/rs14184545 出版年: SEP 2022

摘要: Linear regression models are commonly used for estimating ground PM2.5 concentrations, but the global spatial autocorrelation and local spatial heterogeneity of PM2.5 distribution are either ignored or only partially considered in commonly used models for estimating PM2.5 concentrations. Therefore, taking both global spatial autocorrelation and local spatial heterogeneity into consideration, a global-local regression (GLR) model is proposed for estimating ground PM2.5 concentrations in the Yangtze River Delta (YRD) and in the Beijing, Tianjin, Hebei (BTH) regions of China based on the aerosol optical depth data, meteorological data, remote sensing data, and pollution source data. Considering the global spatial autocorrelation, the GLR model extracts global factors by the eigenvector spatial filtering (ESF) method, and combines the fraction of them that passes further filtering with the geographically weighted regression (GWR) method to address the local spatial heterogeneity. Comprehensive results show that the GLR model outperforms the ordinary GWR and ESF models, and the GLR model has the best performance at the monthly, seasonal, and annual levels. The average adjusted R-2 of the monthly GLR model in the YRD region (the BTH region) is 0.620 (0.853), which is 8.0% and 7.4% (6.8% and 7.0%) higher than that of the monthly ESF and GWR models, respectively. The average cross-validation root mean square error of the monthly GLR model is 7.024 mu g/m(3) in the YRD region, and 9.499 mu g/m(3) in the BTH region, which is lower than that of the ESF and GWR models. The GLR model can effectively address the spatial autocorrelation and spatial heterogeneity, and overcome the shortcoming of the ordinary GWR model that overfocuses on local features and the disadvantage of the poor local performance of the ordinary ESF model. Overall, the GLR model with good spatial and temporal applicability is a promising method for estimating PM2.5 concentrations.

入藏号: WOS:000856946000001

语言: English

文献类型: Article

作者关键词: PM2 5; global-local regression; geographically weighted regression; eigenvector spatial filtering; YRD region; BTH region

地址: [Su, Heng; Chen, Yumin; Tan, Huangyuan; Zhou, Annan; Chen, Guodong; Chen, Yuejun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址: Chen, YM (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: ymchen@whu.edu.cn

影响因子:5.349

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