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陈玉敏、硕士生谭黄元的论文在 ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 刊出
发布时间:2021-09-01 10:57:31     发布者:易真     浏览次数:

标题: Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China

作者: Tan, HY (Tan, Huangyuan); Chen, YM (Chen, Yumin); Wilson, JP (Wilson, John P.); Zhou, AN (Zhou, Annan); Chu, TY (Chu, Tianyou)

来源出版物: ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH DOI: 10.1007/s11356-021-15196-4 提前访问日期: JUL 2021

摘要: PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R-2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.

入藏号: WOS:000673836400016

PubMed ID: 34268695

语言: English

文献类型: Article; Early Access

作者关键词: PM2.5; Eigenvector spatial filtering; Stochastic site indicators; Geographically weighted regression; Genetic algorithm; Yangtze River Delta region

地址: [Tan, Huangyuan; Chen, Yumin; Zhou, Annan; Chu, Tianyou] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

通讯作者地址: Chen, YM (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: tanhuangyuan@whu.edu.cn; ymchen@whu.edu.cn; jpwilson@usc.edu; 2016301110183@whu.edu.cn; chutianyou@whu.edu.cn

影响因子:4.223


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