Department of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta, Georgia, United States.
Atmospheric Sciences Program, Department of Physics, University of Nevada Reno , Reno, Nevada, United States.
Environ Sci Technol. 2017 Dec 5;51(23):13788-13796. doi: 10.1021/acs.est.7b03781. Epub 2017 Nov 16.
Laboratory-based or in situ PM source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.
基于实验室的或原地 PM 源谱可能无法代表不同研究地点的源的污染物组成，因为存在空间和时间变化的特征，例如燃料或地壳元素组成，或者由于在环境条件下与实验室条件下的排放行为的差异。在这项工作中，使用一种新的优化方法，根据观测浓度和化学传输模型（CTM）的源影响来估算 20 个源的 PM 源谱，以捕捉当地污染物的特征。非线性优化用于最小化源谱、CTM 源影响和观测值之间的误差。在 2006 年美国的一项应用中，与参考谱相比，煤炭燃烧、粉尘、火灾、金属加工和其他源谱的空间和季节性变化明显，物种分数的变化超过 400%（粉尘中的钙），而同一物种的平均贡献。修订后的谱图改善了几种痕量金属物种（包括 Na、Al、Ca、Mn、Cu、As、Se、Br 和 Pb）的模型浓度的空间和时间偏差。在对两个美国城市的 CMB-迭代模型的应用中，与以前的研究相比，修订后的谱图估计夏季生物质燃烧和粉尘的影响更高。对于感兴趣的地点，源谱优化对于源分配研究很有用，因为这些研究的源谱数据有限。
