College of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA.
College of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.
Environ Pollut. 2018 Jul;238:39-51. doi: 10.1016/j.envpol.2018.02.091. Epub 2018 Mar 10.
The molecular marker-based chemical mass balance (MM-CMB) method performs well in the source apportionment of organic carbon (OC) but has some difficulty with contributions from primary sources to inorganic secondary ions when apportioning PM (particles with aerodynamic diameter of 2.5 μm or less) sources. Positive matrix factorization (PMF) with the input of inorganic and organic tracers can properly estimate the contributions of primary and secondary sources to inorganic secondary ions; however, PMF is unable to apportion several PM sources with large fractions of organic carbon and few elemental compositions. In this study regarding data collected in 2011 and 2012 at three sites in Wuhan, China, the MM-CMB model was used to apportion OC in the PM and the PMF model was used to apportion the inorganic ions (sulfate, nitrate, and ammonia), dust, and EC. The source contributions of PM were estimated by reconstructing masses of bulk chemical components that had been apportioned to real-world sources using suitable source apportionment methods. Good performance of this hybrid source apportionment strategy was observed with ten resolved factors, explaining 70-80% of measured PM mass on average. The hybrid strategy takes the advantages of both models in PM source apportionment and yields unique source apportionment results for PM bulk chemical components, which could provide new information for optimizing air quality regulations for the emission abatement of target PM mass and compositions for countries around the world.
基于分子标志物的化学质量平衡 (MM-CMB) 方法在有机碳 (OC) 的源解析方面表现出色，但在解析 PM (空气动力学直径为 2.5μm 或以下的颗粒) 源时，对于来自原生源的无机二次离子的贡献存在一些困难。输入无机和有机示踪剂的正定矩阵因子分解 (PMF) 可以正确估计原生源和次生源对无机二次离子的贡献；然而，PMF 无法分配几个有机碳分数较大且元素组成较少的 PM 源。在这项针对 2011 年和 2012 年在中国武汉三个地点收集的数据的研究中，使用 MM-CMB 模型来分配 PM 中的 OC，使用 PMF 模型来分配无机离子（硫酸盐、硝酸盐和氨）、灰尘和 EC。通过使用合适的源分配方法，将已分配给实际源的大量化学物质的质量进行重构，从而估算 PM 的源贡献。使用十个解析因子的混合源分配策略表现出良好的性能，平均解释了 70-80%的实测 PM 质量。该混合策略在 PM 源解析中利用了两种模型的优势，并为 PM 总体化学成分的源解析提供了独特的结果，这可为优化空气质量法规提供新信息，以减少目标 PM 质量和组成的排放，从而造福世界各地的国家。
