Article, 2024

Air quality modeling in the metropolitan area of São Paulo, Brazil: A review

Atmospheric Environment, ISSN 1352-2310, 1873-2844, Volume 319, Page 120301, 10.1016/j.atmosenv.2023.120301

Contributors

Gavidia-Calderón, Mario Eduardo 0000-0002-7371-1116 (Corresponding author) [1] Schuch, Daniel Andrade 0000-0001-5977-4519 [2] Vara-Vela, Angel Liduvino 0000-0002-4972-4486 [3] Inoue, Rita [1] Freitas, Edmilson Dias De [1] De A Albuquerque, Taciana Toledo 0000-0002-6611-0283 [4] Zhang, Yang [2] De Fátima Andrade, Maria [1] Bell, Michelle Lee 0000-0002-3965-1359 [5]

Affiliations

  1. [1] Universidade de São Paulo
    2. [NORA names: Brazil; America, South];
  2. [2] Northeastern University
    3. [NORA names: United States; America, North; OECD];
  3. [3] Aarhus University
    4. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD];
  4. [4] Universidade Federal de Minas Gerais
    5. [NORA names: Brazil; America, South];
  5. [5] Yale University
    6. [NORA names: United States; America, North; OECD]

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.

Keywords

Airshed Model, American cities, Brazil, CIT, California, California Institute, Metropolitan, Metropolitan Area of Sao Paulo, Paulo, Pearson, Pearson correlation, Sao Paulo, South American cities, Vehicular, Weather Research and Forecasting model, advances, aerosol, aerosol speciation, air, air pollution, air quality, air quality modeling studies, air quality models, approach, area, area of research, assimilation techniques, assumptions, campaign data, changes, chemical, chemical configuration, chemistry, city, climate change, compounds, concentration, configuration, correlation, data, data assimilation techniques, disaggregation approach, emission, emission factors, emission profiles, emission sources, error, estimate pollutant concentrations, estimates volatile organic compound, estimation, evaluation, factors, fine particulate matter, fleet, forecasting model, health, health impact studies, high O<sub>3</sub>, hourly ozone, impact, impact studies, impacts of climate change, information, input, input emissions, institutions, lack, lack of information, limitations, matter, measurement campaign data, measurements, meteorological parameters, meteorological reanalysis, metropolitan area, model, model performance, model physics, model results, modeling air quality, modeling studies, offline model, online model, organic compounds, overview, overview of advances, ozone, ozone estimates, parameters, particulate matter, performance, period, physics, pollutant concentrations, pollution, pollution measurements, practical approach, profile, quality, quality model, reanalysis, research, resolution, results, review, root, root mean square error, satellite, satellite data, season, simulation, simulation period, source, sources of air pollution, spatial disaggregation approach, spatial resolution, speciation, spring, square error, study, technique, technology, temporal emission profiles, vehicle, vehicle emissions, vehicular fleet, volatile organic compounds, weather, winter

Funders

  • São Paulo Research Foundation
  • Wellcome Trust
  • National Council for Scientific and Technological Development

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