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Abstract：
Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models\' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.
摘要：
许多研究已经使用空气质量模型通过使用不同的输入和假设来估计圣保罗都市区（MASP）的污染物浓度。我们的目标是总结这些研究，比较他们的表现，配置，和输入，并推荐进一步研究的领域。我们检查了29项空气质量建模研究，这些研究集中在MASP上进行的臭氧（O3）和细颗粒物（PM2.5）。2001年至2023年出版。加州理工学院的空中模型（CIT）是最常用的离线模型，而天气研究和预报模型与化学（WRF-Chem）耦合是最常用的在线模型。因为MASP空气污染的主要来源是车队，它通常被用作唯一的人为输入排放。模拟时期通常是冬季结束和春季，O3和PM2.5浓度较高的季节。每小时臭氧的模型性能良好，其中一半的研究Pearson相关性高于0.6，均方根误差（RMSE）范围为7.7至27.1ppb。对PM2.5建模的研究较少，其性能不如臭氧估计值。缺乏关于排放源的信息，污染物测量，和城市气象参数是进行空气质量建模的主要限制。然而，研究人员使用测量活动数据来更新排放因子，估计时间排放剖面，并估计挥发性有机化合物(VOC)和气溶胶形态。他们还测试了不同的排放空间分解方法，并以更高的空间分辨率过渡到全球气象重新分析。需要探索的研究领域是进一步评估模型的物理和化学构型，气候变化对空气质量的影响，利用卫星数据，数据同化技术，并在健康影响研究中使用模型结果。这项工作概述了MASP中空气质量建模的进展，并提供了在数据有限的情况下对其他南美城市的空气质量进行建模的实用方法。特别是那些受到车辆排放严重影响的。
