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Application of an Ensemble-Trained Source Apportionment Approach at a Site Impacted by Multiple Point Sources

机译：集成训练的源分配方法在受多点源影响的站点上的应用

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摘要

Four receptor models and a chemical transport model were used to quantify PM_(2.5) source impacts at the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models used two semi-independent data sets, with the first including ions and trace elements and the second including 1-in-6 day particle-bound organics. Since each source apportionment (SA) technique has limitations, this work compares results from the five different SA approaches to better understand the biases and limitations of each. The source impacts calculated by these models were then integrated into a constrained, ensemble-trained SA approach. The ensemble method offers several improvements over the five individual SA techniques at the STL-SS. Primarily, the ensemble method calculates source impacts on days when individual models either do not converge to a solution or do not have adequate input data to develop source impact estimates. When compared with a chemical mass balance approach using measurement-based source profiles, the ensemble method improves fit statistics, reducing chi-squared values and improving PM_(2.5) mass reconstruction. Compared to other receptor models, the ensemble method also calculates zero or negative impacts from major emissions sources (e.g., secondary organic carbon (SOC) and diesel vehicles) for fewer days. One limitation of this analysis was that a composite metals profile was used in the ensemble analysis. Although STL-SS is impacted by multiple metals processing point sources, several of the initial SA methods could not resolve individual metals processing impacts. The results of this analysis also reveal some of the subjectivities associated with applying specific SA models at the STL-SS. For instance, Positive Matrix Factorization results are very sensitive to both the fitting species and number of factors selected by the user. Conversely, Chemical Mass Balance results are sensitive to the source profiles used to represent local metals processing emissions. Additionally, the different SA approaches predict different impacts for the same source on a given day, with correlation coefficients ranging from 0.034 to 0.65 for gasoline vehicles, -0.54-0.48 for diesel vehicles, -0.29-0.81 for dust, -0.34-0.89 for biomass burning, 0.38-0.49 for metals processing, and -0.25-0.51 for SOC. These issues emphasize the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by using an ensemble-based technique.
机译：在2001年6月至2003年5月之间，使用四个受体模型和一个化学迁移模型来量化圣路易斯超级站点（STL-SS）的PM_（2.5）源影响。该受体模型使用了两个半独立的数据集，第一个是包括离子和微量元素，第二个包含六合一颗粒结合的有机物。由于每种源分配（SA）技术都有局限性，因此本工作比较了五种不同SA方法的结果，以更好地理解每种方法的偏见和局限性。然后将这些模型计算出的源头影响整合到约束的，整体训练的SA方法中。集成方法对STL-SS的五种独立SA技术进行了一些改进。首先，当单个模型不收敛到一个解决方案或没有足够的输入数据来开发源影响估算时，集成方法计算源影响。与使用基于测量的源轮廓的化学物质平衡方法进行比较时，集成方法可改善拟合统计量，减少卡方值并改善PM_（2.5）质量重建。与其他接收器模型相比，集成方法还可以在短时间内计算出主要排放源（例如，次级有机碳（SOC）和柴油车辆）的零或负面影响。该分析的局限性在于在集成分析中使用了复合金属轮廓。尽管STL-SS受多个金属加工点源的影响，但是一些初始的SA方法无法解决单个金属加工的影响。分析结果还揭示了与在STL-SS上应用特定SA模型相关的一些主观性。例如，正矩阵分解结果对用户选择的拟合种类和因子数量非常敏感。相反，“化学物质平衡”结果对用来表示本地金属加工排放物的源曲线很敏感。此外，不同的SA方法在给定的一天中会预测相同来源的不同影响，相关系数的范围从汽油车的0.034至0.65，柴油车的-0.54-0.48，粉尘的-0.29-0.81，-0.34-0.89生物质燃烧，金属加工为0.38-0.49，SOC为-0.25-0.51。这些问题强调了通过比较不同模型预测的来源影响或使用基于集合的技术，在给定的受体位点使用几种不同的SA技术的价值。

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  • 来源
    《Environmental Science & Technology》 |2013年第8期|3743-3751|共9页
  • 作者

    Marissa L. Maier; Sivaraman Balachandran; Stefanie E. Sarnat; Jay R. Turner; 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, 820 North Pollard Street, Apt. 208, Arlington, VA 22203;

    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;

    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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