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Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

Karen dos Santos Gonçalves Mirko S Winklera; Paulo Roberto Benchimol-Barbosa; Keesde Hoogh; Paulo Eduardo Artaxo Netto; Sandra de Souza Hacon; Christian Schindler; Nino Künzli

Atmospheric Environment Amsterdam v. 184, n. 7, p. 156-165, 2018

Amsterdan 2018

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  • Título:
    Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region
  • Autor: Karen dos Santos Gonçalves
  • Mirko S Winklera; Paulo Roberto Benchimol-Barbosa; Keesde Hoogh; Paulo Eduardo Artaxo Netto; Sandra de Souza Hacon; Christian Schindler; Nino Künzli
  • Assuntos: FÍSICA ATMOSFÉRICA; AEROSSOL; POLUIÇÃO ATMOSFÉRICA; INCÊNDIOS FLORESTAIS -- AMAZÔNIA BRASILEIRA; Aerosol Optical Depth; Particulate Matter; Air Pollution; Forest Fire; Validation Approach; Brazilian Amazon Region
  • É parte de: Atmospheric Environment Amsterdam v. 184, n. 7, p. 156-165, 2018
  • Notas: Disponível em: . Acesso em: 09 nov. 2018
  • Descrição: Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) frommonitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5concentrations, and thus provides an alternative method for producing knowledge regarding the level of pol-lution and its health impact in areas where no ground PM2.5measurements are available. This is the case in theBrazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, weapplied a non-linear model for predicting PM2.5concentration from AOD retrievals using interaction termsbetween average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square ofthe lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2based on results from linear regression and non-linear regression for six different models. The regression resultsfor non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5con-centrations when considering the whole period studied. In the context of Amazonia, it was the first study pre-dicting PM2.5concentrations using the latest high-resolution AOD products also in combination with the testingof a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5re-lationship and set the basis for further investigations on air pollution impacts in the complex context of BrazilianAmazon Region.
  • Editor: Amsterdan
  • Data de criação/publicação: 2018
  • Formato: p. 156-165.
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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