Modeling spatial non-stationarity of multiple industrial point source pollution emissions impact on regional cancer prevalence in China
文献类型：期刊论文
| 作者 | Xu, Yuan3,4; Lei, Mei3,4; Ju, Tienan3,4; Qiu, Rongliang5; Wang, Shaobin3; Zeng, Xiaowen1; Kang, Liang2 |
| 刊名 | APPLIED GEOGRAPHY
 |
| 出版日期 | 2026 |
| 卷号 | 186页码:103823 |
| 关键词 | Industrial pollution Exposure assessment Multiscale geographically weighted regression Spatial non-stationarity Colorectal cancer |
| ISSN号 | 0143-6228 |
| DOI | 10.1016/j.apgeog.2025.103823 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Understanding the spatial non-stationarity of industrial pollution's impact on cancer prevalence is crucial for targeted surveillance. This study examines the spatial non-stationarity of localized industrial point source emissions on regional colorectal cancer (CRC) patterns, utilizing a novel spatial coupling framework that integrates an exposure population-weighted assessment model (EPAM) with multiscale geographically weighted regression (MGWR). The key findings are as follows: First, we demonstrate that the association between metal surface treatment industry (MSTI) emissions and CRC is most accurately captured at a fine, localized scale of population exposure, a dimension obscured by conventional regional-aggregate or proximity-based exposure proxies. Further, our analysis reveals significant spatial non-stationarity, wherein the influence of MSTI emissions on CRC is concentrated in specific high-risk clusters, which primarily industrialized cities along China's southeastern coast. This spatial non-stationarity arises from the convergence of large-scale industrial pollution emissions, terrain favorable to pollutant dispersion, and high population density. Crucially, this EPAM-MGWR coupled framework quantifies localized exposure with a small-scale bandwidth, outperforming conventional medium-to-large-scale exposure proxies by enhancing the explained variance in CRC spatial patterns by 22 %-83 % compared to traditional Geographically Weighted Regression. In sum, these findings indicate that the carcinogenic impact of industrial pollution is a localized process, whose accurate detection at the regional level requires an analytical framework that reconciles the fine-grained emission dispersion with the multiscale health determinants. The coupling framework developed in this study offers a broadly applicable technical approach for examining the spatial associations between industrial point source pollution and various cancer types. |
| URL标识 | 查看原文 |
| WOS关键词 | COLORECTAL-CANCER ; THYROID-CANCER ; RISK-FACTORS ; URBANIZATION ; MORTALITY |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001609517300001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217757]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Lei, Mei; Wang, Shaobin |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Publ Hlth, Dept Prevent Med, Guangzhou 510080, Peoples R China; 2.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Gen Surg Colorectal Surg, Guangzhou, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.South China Agr Univ, Coll Nat Resources & Environm, Guangdong Lab Lingnan Modern Agr, Guangdong Prov Key Lab Agr & Rural Pollut Abatemen, Guangzhou 510642, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Xu, Yuan,Lei, Mei,Ju, Tienan,et al. Modeling spatial non-stationarity of multiple industrial point source pollution emissions impact on regional cancer prevalence in China[J]. APPLIED GEOGRAPHY,2026,186:103823. |
| APA | Xu, Yuan.,Lei, Mei.,Ju, Tienan.,Qiu, Rongliang.,Wang, Shaobin.,...&Kang, Liang.(2026).Modeling spatial non-stationarity of multiple industrial point source pollution emissions impact on regional cancer prevalence in China.APPLIED GEOGRAPHY,186,103823. |
| MLA | Xu, Yuan,et al."Modeling spatial non-stationarity of multiple industrial point source pollution emissions impact on regional cancer prevalence in China".APPLIED GEOGRAPHY 186(2026):103823. |
入库方式： OAI收割
来源：地理科学与资源研究所
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