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Bayesian-Based Ensemble Source Apportionment of PM_(2.5)

机译：基于贝叶斯的PM_（2.5）集合源分配

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摘要

A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties.Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM_(2.5)).The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model.The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM_(2.5) data set.For each day in a long-term PM_(2.5) data set,10 source profiles are sampled from these distributions and used in a CMB application,resulting in 10 SA results for each day.This formulation results in a distribution of daily source impacts rather than a single value.The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty,respectively.The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R~2 = 0.66) and water-soluble potassium (R~2 = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass.The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact.Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties.This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S.EPA).In addition to providing results that are more consistent with independent observations and known emission sources being present,the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results.
机译：提出了一种贝叶斯源分配（SA）方法来提供源影响估计和相关的不确定性。基于贝叶斯的多个模型的集合平均提供了新的源概况，可用于细颗粒物质的化学质量平衡（CMB）SA（PM_（2.5 ））。该方法通过短期应用四种单独的SA方法估算污染源影响及其不确定性：三种基于受体的模型和一种化学迁移模型，该方法用于估算所使用的两种源剖面的季节性分布在SA中获取长期PM_（2.5）数据集。对于长期PM_（2.5）数据集中的每一天，从这些分布中采样10个源配置文件并将其用于CMB应用程序，从而得出10个SA结果该公式得出的是每日源头影响的分布，而不是单个值。分布的平均值和标准偏差用作源头影响的最终估计和不确定性的量度基于贝叶斯的生物量燃烧源影响与观察到的左葡糖聚糖（R〜2 = 0.66）和水溶性钾（R〜2 = 0.63）的相关性比使用更传统方法估算的源影响更好，并且更接近贝叶斯方法还捕获了预期的生物质燃烧和次级影响的季节性变化，并导致零影响的源减少了几天的时间。敏感性分析发现，使用非信息性先验加权比使用基于方法衍生的加权要好这种方法可以应用于来自美国环境保护局（USEPA）物种形成网络站点的长期数据集。除了提供与独立观测和现有已知排放源更一致的结果外，分布源影响的数量可用于流行病学分析，以估计与SA结果相关的不确定性。

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  • 来源
    《Environmental Science & Technology》 |2013年第23期|13511-13518|共8页
  • 作者

    Sivaraman Balachandran; Howard H.Chang; Jorge E.Pachon; Heather A.Holmes; James A.Mulholland; Armistead G.Russell;

  • 作者单位

    School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,Georgia 30332,United States;

    Rollins School of Public Health,Emory University,Atlanta,Georgia 30322,United States;

    Programa de Ingenieria Ambiental,Universidad de La Salle,Bogota,Colombia;

    School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,Georgia 30332,United States;

    School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,Georgia 30332,United States;

    School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,Georgia 30332,United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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