环境科学
空气污染
空间分析
污染
随机森林
自相关
效果修正
环境卫生
地理
医学
统计
生态学
遥感
数学
机器学习
计算机科学
生物
置信区间
化学
有机化学
内科学
标识
DOI:10.1016/j.scitotenv.2024.172387
摘要
Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.
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