Pennington Elyse A, Wang Yuan, Schulze Benjamin C, Seltzer Karl M, Yang Jiani, Zhao Bin, Jiang Zhe, Shi Hongru, Venecek Melissa, Chau Daniel, Murphy Benjamin N, Kenseth Christopher M, Ward Ryan X, Pye Havala O T, Seinfeld John H
Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA.
Atmos Chem Phys. 2024 Feb 23;24(4):2345-2363. doi: 10.5194/acp-24-2345-2024.
This study describes a modeling framework, model evaluation, and source apportionment to understand the causes of Los Angeles (LA) air pollution. A few major updates are applied to the Community Multiscale Air Quality (CMAQ) model with a high spatial resolution (1 km × 1 km). The updates include dynamic traffic emissions based on real-time, on-road information and recent emission factors and secondary organic aerosol (SOA) schemes to represent volatile chemical products (VCPs). Meteorology is well predicted compared to ground-based observations, and the emission rates from multiple sources (i.e., on-road, volatile chemical products, area, point, biogenic, and sea spray) are quantified. Evaluation of the CMAQ model shows that ozone is well predicted despite inaccuracies in nitrogen oxide (NO ) predictions. Particle matter (PM) is underpredicted compared to concurrent measurements made with an aerosol mass spectrometer (AMS) in Pasadena. Inorganic aerosol is well predicted, while SOA is underpredicted. Modeled SOA consists of mostly organic nitrates and products from oxidation of alkane-like intermediate volatility organic compounds (IVOCs) and has missing components that behave like less-oxidized oxygenated organic aerosol (LO-OOA). Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NO -saturated (VOC-sensitive), with the largest sensitivity of O to changes in VOCs in the urban core. Differing oxidative capacities in different regions impact the nonlinear chemistry leading to PM and SOA formation, which is quantified in this study.
本研究描述了一个用于理解洛杉矶空气污染成因的建模框架、模型评估和源解析。对具有高空间分辨率（1千米×1千米）的社区多尺度空气质量（CMAQ）模型进行了一些重大更新。这些更新包括基于实时道路信息和最新排放因子的动态交通排放以及用于表示挥发性化学产品（VCPs）的二次有机气溶胶（SOA）方案。与地面观测相比，气象条件得到了较好的预测，并且对多个源（即道路、挥发性化学产品、面源、点源、生物源和海沫）的排放率进行了量化。CMAQ模型的评估表明，尽管氮氧化物（NO ）预测存在不准确之处，但臭氧得到了较好的预测。与帕萨迪纳用气溶胶质谱仪（AMS）进行的同步测量相比，颗粒物（PM）预测值偏低。无机气溶胶得到了较好的预测，而SOA预测值偏低。模拟的SOA主要由有机硝酸盐以及类似烷烃的中等挥发性有机化合物（IVOCs）氧化产物组成，并且缺少一些表现得像氧化程度较低的含氧有机气溶胶（LO - OOA）的成分。源解析表明，洛杉矶盆地及其附近的城市地区是NO 饱和（对挥发性有机物敏感）的，城市核心区域中O对挥发性有机物变化的敏感性最大。不同区域不同的氧化能力影响导致PM和SOA形成的非线性化学过程，本研究对其进行了量化。
