Intercomparison of air quality models in a megacity: Towards an operational ensemble forecasting system for São Paulo

• Main Author: Deroubaix, Adrien
• Publication Date: 2022
• Other Authors: Hoelzemann, Judith J., Ynoue, Rita Yuri, Bouarar, Idir, Duarte, Edicle, Albuquerque, Taciana Toledo de Almeida, Alves, Rafaela Cruz, Andrade, Maria de Fatima, Andreão, Willian Lemker, Elbern, Hendrik, Franke, Philipp, Lange, Anne Caroline, Lichtig, Pablo, Lugon, Lya, Martins, Leila D., Moreira, Gregori de Arruda, Pedruzzi, Rizzieri, Rosario, Nilton, Brasseur, Guy
• Format: Article
• Language: por
• Source: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Download full: http://hdl.handle.net/10174/35188; https://doi.org/10.1002/essoar.10512847.2
• topic: Air quality; Modelling; Air pollution

An intercomparison of four air quality models is performed in the tropical megacity of Sao Paulo with the perspective of developing an air quality forecasting system based on a regional model ensemble. During three contrasting periods marked by different types of pollution events, we analyze the concentrations of the main regulated pollutants (Ozone, CO, SO2, NOx, PM2.5 and PM10) compared to observations of a dense air quality monitoring network. The modeled concentrations of CO, PM and NOx are in good agreement with the observations for the temporal variability and the range of variation. However, the transport of pollutants due to biomass burning pollution events can strongly affect the air quality in the metropolitan area of Sao Paulo with increases of CO, PM2.5 and PM10, and is associated with an important inter-model variability. Our results show that each model has periods and pollutants for which it has the best agreement. The observed day-to-day variability of ozone concentration is well reproduced by the models, as well as the average diurnal cycle in terms of timing. Overall the performance for ozone of the median of the regional model ensemble is the best in terms of time and magnitude because it takes advantage of the capabilities of each model. Therefore, an ensemble prediction of regional models is promising for an operational air quality forecasting system for the megacity of Sao Paulo.