Marmur Amit, Unal Alper, Mulholland James A, Russell Armistead G
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0512, USA.
Environ Sci Technol. 2005 May 1;39(9):3245-54. doi: 10.1021/es0490121.
A modified approach to PM2.5 source apportionment is developed, using source indicative SO2/PM2.5, CO/PM2.5, and NOx/PM2.5 ratios as constraints, in addition to the commonly used particulate-phase source profiles. Additional information from using gas-to-particle ratios assists in reducing collinearity between source profiles, a problem that often limits the source-identification capabilities and accuracy of traditional receptor models. This is especially true in the absence of speciated organic carbon measurements. In the approach presented here, the solution is based on a global optimization mechanism, minimizing the weighted error between apportioned and ambient levels of PM2.5 components, while introducing constraints on calculated source contributions that ensure that the ambient gas-phase pollutants (SO2, CO, and NOy) are reasonable. This technique was applied to a 25-month dataset of daily PM2.5 measurements (total mass and composition) at the Atlanta Jefferson Street SEARCH site. Results indicate that this technique was able to split the contributions of mobile sources (gasoline and diesel vehicles) more accurately than particulate-phase source apportionment methods. Furthermore, this technique was able to better quantify the direct contribution (primary PM2.5) of coal-fired power plants to ambient PM2.5 levels.
开发了一种改进的细颗粒物（PM2.5）源解析方法，除了常用的颗粒物相源谱外，还使用源指示性的二氧化硫/PM2.5、一氧化碳/PM2.5和氮氧化物/PM2.5比值作为约束条件。使用气粒比的额外信息有助于减少源谱之间的共线性，这一问题常常限制传统受体模型的源识别能力和准确性。在没有特定有机碳测量值的情况下尤其如此。在此提出的方法中，解决方案基于全局优化机制，使PM2.5组分的分配水平与环境水平之间的加权误差最小化，同时对计算出的源贡献引入约束条件，以确保环境气相污染物（二氧化硫、一氧化碳和氧化氮）合理。该技术应用于亚特兰大杰斐逊街SEARCH站点的25个月每日PM2.5测量数据集（总质量和成分）。结果表明，该技术比颗粒物相源解析方法能够更准确地划分移动源（汽油和柴油车辆）的贡献。此外，该技术能够更好地量化燃煤电厂对环境PM2.5水平的直接贡献（一次PM2.5）。
