Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study
Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study
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DOI：
10.1016/j.envint.2021.107021
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发表时间：
2021-12-13
影响因子：
11.8
通讯作者：
Brauer, Michael
中科院分区：
文献类型：
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作者：
Shupler, Matthew;Hystad, Perry;Brauer, Michael
简介：使用污染性烹饪燃料会产生家庭空气污染（HAP），其中含有损害健康的细颗粒物（PM2.5）。许多全球流行病学研究依赖于HAP暴露的分类指标，这是测量的PM2.5水平的不良替代品。为了大规模地定量描述HAP水平，利用多国测量活动来开发家庭和个人PM2.5暴露模型。研究方法：前瞻性城市和农村流行病学（PURE）-空气研究包括48小时监测PM2.5厨房浓度（n = 2，365）和男性和/或女性PM2.5暴露监测（n = 910）在孟加拉国，智利，中国，哥伦比亚，印度，巴基斯坦，坦桑尼亚和津巴布韦的一个家庭子集。将PURE-AIR测量结果与烹饪环境特征的调查数据结合在分层贝叶斯对数线性回归模型中。使用留一交叉验证评估模型性能。将预测模型应用于较大的PURE队列（22，480户家庭; 33，554人）的调查数据，以定量估计PM2.5暴露。结果如下：最终模型解释了厨房PM2.5测量值（均方根误差（RMSE）（对数尺度）：2.22）和个人测量值（R2 = 48%; RMSE（对数尺度）：2.08）的一半（R2 = 54%）。主要烹饪燃料类型，加热燃料类型，国家和季节对PM2.5厨房浓度具有高度预测性。全国厨房PM2.5平均浓度在主要用燃气做饭的家庭中相差近3倍（20微克/立方米（智利）; 55微克/立方米（中国）），在主要用木材做饭的家庭中相差12倍（36微克/立方米（智利）; 427微克/立方米（巴基斯坦））。厨房平均PM2.5浓度、取暖燃料类型、季节和二手烟暴露是个人暴露的重要预测因素。在中上/高收入国家（印度、中国、哥伦比亚、智利），模拟的平均PM2.5女性暴露低于男性暴露。结论：使用调查数据来估计多国范围内的PM2.5暴露，可以经济有效地扩大疾病负担评估的定量HAP测量。模拟的PM2.5暴露可用于未来的流行病学研究，并为针对HAP减少的政策提供信息。
Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models. Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n = 2,365) and male and/or female PM2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM2.5 exposures. Results: The final models explained half (R2 = 54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2 = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 mu g/m3 (Chile); 55 mu g/m3 (China)) and 12-fold among households primarily cooking with wood (36 mu g/ m3 (Chile)); 427 mu g/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). Conclusion: Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.