Coal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified. We offer a novel statistical evaluation of some of these presumed causal relationships, integrating the formality of causal inference methods with repurposed tools from atmospheric science to accommodate the central challenge of long‐range pollution transport of emissions from power plants to exposed populations. The statistical approach follows recent work on Bayesian methods for deploying principal stratification and causal mediation analysis in tandem to characterize the extent to which decreased sulphur dioxide emissions from 410 power plants across the USA impact mortality and hospitalization outcomes across Medicare beneficiaries residing across 12370 locations in a manner that is mediated through reductions of ambient fine particulate pollution. The result is the first epidemiological investigation integrating causal inference methodology with direct measurements of coal emissions, pollution transport, ambient pollution and human health in a single analysis, indicating the potential for data science at the intersection of statistics, epidemiology and atmospheric science.

中文翻译：

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周围环境空气质量对电厂排放物的健康影响

燃煤发电厂是监管计划的经常目标，因为它们排放的化学物质是形成环境微粒空气污染的前体。燃煤电厂排放物对健康的影响评估通常依赖于排放量，环境污染和健康之间的假定因果关系，其中许多因果关系从未经过经验验证。我们对其中某些因果关系进行了新颖的统​​计评估，将因果推断方法的形式与大气科学的重新设计工具进行了整合，以应对电厂向受污染人群的排放物进行长距离污染运输的主要挑战。统计方法是继贝叶斯方法最近开展的工作之后进行的，该方法用于同时进行主要分层和因果调解分析，以表征美国410家发电厂的二氧化硫排放量减少在多大程度上影响了遍布美国12370个地区的Medicare受益人的死亡率和住院结果通过减少环境细颗粒物污染来介导的方式。结果是第一次流行病学调查，将因果推论方法与直接测量煤炭排放，污染传输，环境污染和人体健康的分析整合在一起，从而表明了数据科学在统计，流行病学和大气科学交叉领域的潜力。