■空气污染风险评估通常不会使用多污染物风险估计来量化健康影响,而是使用单污染物或共污染物模型的结果。多污染物流行病学模型考虑了污染物的相互作用和共同影响,但计算复杂且数据密集。因此,多污染物研究的风险估计在量化健康影响方面具有挑战性。
■我们的目标是使用环境效益制图和分析计划社区版(BenMAP-CE)的开发多污染物版本进行案例研究,以评估与多种空气污染物变化相关的健康影响使用单一和多污染物方法。
■BenMAP-CE用于估算2011年至2025年间亚特兰大因空气污染的模拟变化而导致的小儿哮喘急诊科(ED)就诊次数的变化,格鲁吉亚,应用来自流行病学研究的风险估计,该研究检查了短期单污染物和多污染物(有和没有一级相互作用)暴露。分析了单个污染物(即,臭氧,细颗粒物,一氧化碳,二氧化氮(NO2),二氧化硫,和颗粒物成分)以及这些污染物的组合,旨在代表共享属性或预定义来源(即,氧化剂气体,二次污染物,交通,发电厂,和标准污染物)。对构成各个污染物组的单个污染物的多污染物健康影响函数(HIF)和单污染物HIF之和进行了比较。
■光化学模型预测,根据行业具体情况,2011年至2025年期间,大多数检查污染物浓度将大幅下降(即,基于来源的)对增长和预期控制的估计。使用包含相互作用项的模型的结果与不包含相互作用项的模型的结果相比,归因于任何给定多污染物组的避免哮喘ED就诊的估计数量通常更高。我们估计,对于包括NO2在内的污染物组,避免小儿哮喘ED就诊的次数最多(i。e.,标准污染物,氧化剂,和交通污染物)。在考虑相互作用的模型中,包括NO2的污染物组的全年估计值为27.1[95%置信区间(CI):1.6,52.7;交通污染物]至55.4(95%CI:41.8,69.0;氧化剂),避免了小儿哮喘ED访视.使用具有相互作用的多污染物风险估计的全年结果与对应于大多数多污染物组的单污染物结果之和相当[例如,氧化剂为52.9(95%CI:43.6,62.2)],但明显低于某些污染物组的单污染物结果之和[例如,交通污染物为77.5(95%CI:66.0,89.0)]。
■进行多污染物健康影响评估在技术上是可行的,但计算复杂。它需要时间,资源,以及空气污染流行病学研究中不常见的详细输入参数。使用单污染物模型总和估算的结果与使用多污染物模型量化的结果相当。虽然仅限于单一的研究和地点,评估多污染物和单污染物方法之间的权衡是有必要的。https://doi.org/10.1289/EHP12969.
UNASSIGNED: Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts.
UNASSIGNED: Our objective was to conduct a
case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach.
UNASSIGNED: BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO2), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups.
UNASSIGNED: Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO2 (i. e., criteria pollutants,
oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0;
oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for
oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants].
UNASSIGNED: Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.