Assessment of the spatial association between multiple pollutants of surface water and digestive cancer incidence in China: A novel application of spatial machine learning

• Published in: Ecological indicators Vol. 154; p. 110897
• Main Authors: Gu, Wentao, Xue, Fang, Han, Wei, Wang, Zixing, Zhao, Jing, Zhang, Luwen, Yang, Cuihong, Jiang, Jingmei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023; Elsevier
• Subjects: China; Digestive cancer; Spatial association; Spatial machine learning; Surface water pollutant; Digestive cancer; Spatial machine learning; Surface water pollutant; China; Spatial association
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2023.110897

[Display omitted] •A nationwide spatial analysis on surface water pollutants and cancers integrating datasets was conducted.•Geographically weighted random forest addressed spatial heterogeneity for surface water pollutants.•Multiple pollutants of surface water spatially associated with digestive cancer incidence in China. Surface water pollution and digestive cancers are concerning environmental health issues. Multiple pollutants of surface water coexist within unique geospatial heterogeneous contexts of surface water flow, which can lead to complex patterns of impact on cancer incidence. This complexity has posed challenges to comprehensive assessment. In this study, we integrated nationwide surface water data of 21 pollutants collected from 3632 sections of nine large river basins and cancer incidence data for six digestive cancers covering 381.6 million people in China. Local Moran's I method was used to assess the patterns of spatial aggregation of each surface water pollutant separately to observe the local spatial nonstationarity and heterogeneity of the pollutant concentrations in different spatial areas. A geographically weighted random forest (GWRF) model, a spatial machine learning approach, was applied to address the spatial heterogeneity of the pollutants and assess their association with cancer incidence. Goodness-of-fit and overall performance of the GWRF model were assessed by the local statistics in the out-of-bag set, such as the mean local pseudo-R2, increase in mean squared error, and local residuals in each local model. Compared with the aspatial random forest model, the GWRF model had better performance (mean local pseudo-R2 ranged from 0.16 (gallbladder cancer) to 0.60 (pancreatic cancer)). Surface water pollution was associated with digestive cancer incidence mainly in the Songhua and Liaohe, Haihe, and Huaihe River Basins. By focusing on these areas, we identified key pollutants specific to different cancer types. Incidence of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancer was associated with common pollutants such as fluoride and arsenic. Our assessment provides targets for the government and environmental health specialists to take tailored actions to control pollution and effectively prevent cancer incidence.