基于风险评价识别我国空气污染健康损伤的高危人群

项目名称： 基于风险评价识别我国空气污染健康损伤的高危人群

项目编号： No.41701591

项目类型： 青年科学基金项目

立项/批准年度： 2018

项目学科： 地质学

项目作者： 薛涛

作者单位： 清华大学

项目金额： 8万元

中文摘要： 空气污染，尤其是细颗粒物（PM2.5）已成为损害我国公共健康的头号风险因素之一。为保护人民健康，我国施行《大气污染防治行动计划》等相关政策,对污染减排和空气质量改善提出明确规划。国际经验表明空气污染治理通常需要持续数十年。识别并有侧重地保护分摊了多数空气污染疾病负担的少数高危人群，有助于在此期间合理分配我国的污染减排潜力。高危人群识别依赖于精确的、分人群的空气污染风险评价，因此其成为本研究的核心问题。具体来说，本研究拟从两个方面对现行的空气污染风险评价方法进行优化：1.融合卫星遥感数据和空气质量模式数据，开发时空连续的PM2.5浓度，提高暴露评价精度；2.综合已有流行病学证据，筛选影响PM2.5健康效应敏感性的因子，开发分人群的剂量—效应关系。通过解析PM2.5风险分布识别高危人群，并进一步识别其特征因子和空间分布。本研究结果将有助于决策者针对重点人群及其聚居区域的空气污染健康干预政策。

中文关键词： 环境健康影响；空气污染；遥感监测；高危人群；疾病负担

英文摘要： Air pollutants, especially fine particles（PM2.5）have been ranked as top risk factors of diseases burden in China. To protect public health, China implemented a series of regulatory policies, such as Air Pollution Prevention and Control Action, which committed the targets of emission reductions and air quality improvements. According to international experiences, it may take tens of years to meet the safety standards of air quality in China. Identifying susceptible subpopulations to disease burden attributed to PM2.5 plays a key role in optimizing the potential capacities of emission reductions. Characterizing susceptible subpopulations depends on accurate and personally specific risk assessments of PM2.5. Briefly, we optimize current methodology of risk assessment in following two aspects: (1) improving accuracy of exposure assessment through developing spatiotemporally contiguous estimator of PM2.5 based on satellite remote sensing and air quality modeling data, and (2) deriving individual-level dose-response curves from previous epidemiological studies about modifying effects on health risks of PM2.5. We will further identify major socioeconomic drivers towards higher risks in different susceptible subpopulations, and their spatial distributions in China. Our study will support the public interventions of disease burdens caused by air pollution in the regions clustered by susceptible individuals.

英文关键词： Environmental impact assessment;Air pollution;Remote sensing;Susceptible population;disease burden