Huang Ran, Li Zongrun, Ivey Cesunica E, Zhai Xinxin, Shi Guoliang, Mulholland James A, Devlin Robert, Russell Armistead G
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, California, USA.
Atmos Environ (1994). 2022 May 1;276. doi: 10.1016/j.atmosenv.2022.119031. Epub 2022 Feb 26.
A number of studies have found differing associations of disease outcomes with PM components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO, NO, O, and SO), total PM mass, and speciated PM (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.
多项研究发现，疾病结果与颗粒物成分（或种类）及来源（如生物质燃烧、柴油车和汽油车）之间存在不同的关联。在此，一种将日常化学传输模型（社区多尺度空气质量模型）结果与观测数据相融合的独特方法，被用于生成2002 - 2010年北卡罗来纳州主要气态污染物（一氧化碳、一氧化氮、二氧化氮、臭氧和二氧化硫）、总颗粒物质量以及特定颗粒物（包括地壳元素）浓度的时空分布。然后，将融合后的结果用于化学质量平衡源解析模型CMBGC - 迭代，该模型利用气体约束和颗粒物浓度来量化源的影响。应用于北卡罗来纳州的这种方法，量化了十个源类别的影响，并提供了源对颗粒物浓度贡献量的估计。这十个源类别包括一次源（柴油车、汽油车、扬尘、生物质燃烧、燃煤电厂和海盐）以及二次成分（硫酸铵、硫酸氢铵、硝酸铵和二次有机碳）。结果表明，人为源的影响呈稳步下降趋势，尤其是柴油车和燃煤电厂的影响。二次污染物成分约占颗粒物质量的70%。本研究展示了一种能力，即利用与观测数据融合的化学传输模型，结合基于受体的源解析方法，来提供颗粒物成分和源影响的时空分布，从而生成多种污染物的时空分布。
