Hopke Philip K, Ito Kazuhiko, Mar Therese, Christensen William F, Eatough Delbert J, Henry Ronald C, Kim Eugene, Laden Francine, Lall Ramona, Larson Timothy V, Liu Hao, Neas Lucas, Pinto Joseph, Stölzel Matthias, Suh Helen, Paatero Pentti, Thurston George D
Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA.
J Expo Sci Environ Epidemiol. 2006 May;16(3):275-86. doi: 10.1038/sj.jea.7500458.
During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE=7% and 11% for traffic; in Phoenix, secondary sulfate SE=17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM(2.5) mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM(2.5) health effects assessments.
在过去三十年中，受体模型已被用于识别环境浓度并将其分配到各个来源。许多团队正在使用这些方法为空气质量管控规划提供输入信息。一场研讨会探讨了在健康影响模型中使用已解析的源贡献情况。多个团队分析了来自华盛顿特区和亚利桑那州凤凰城的颗粒物成分数据集。研究人员使用不同的因子分析方法从这些数据集中提取了相似的源特征。在主要的已解析源类型之间存在良好的一致性。地壳（土壤）、硫酸盐、石油和盐是最明确识别出的来源（通常在各站点间相关性最高）。交通源和植被燃烧源在结果中表现出相当大的变异性，不同方法在区分汽油车和柴油车的机动车贡献能力上存在差异。然而，如果估算机动车的总贡献，结果之间具有良好的一致性。对于较大的质量贡献源（例如，在华盛顿，交通源的二次硫酸盐分别占7%和11%；在凤凰城，交通源的二次硫酸盐分别占17%和7%），不同分析的源影响尤其相似。对于时间序列健康影响评估特别重要的是，发现主要成分的源特定影响在不同分析方法/研究人员之间高度相关（例如，凤凰城交通源和二次硫酸盐以及华盛顿交通源和二次硝酸盐的分析间平均相关性，r>0.9。华盛顿的硫酸盐平均r值>0.75）。总体而言，尽管这些相互比较表明了需要进一步研究的领域（例如，更好地划分柴油车和汽油车的交通排放），但它们支持了这样的观点，即PM(2.5)质量源分配结果在不同用户和方法之间是一致的，并且当今的源分配方法足够稳健，可应用于PM(2.5)健康影响评估。
