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 (2018)
- Authors:
- Autor USP: NETTO, PAULO EDUARDO ARTAXO - IF
- Unidade: IF
- DOI: 10.1016/j.atmosenv.2018.03.057
- Subjects: FÍSICA ATMOSFÉRICA; AEROSSOL; POLUIÇÃO ATMOSFÉRICA; INCÊNDIOS FLORESTAIS
- Keywords: AEROSOL OPTICAL DEPTH; PARTICULATE MATTER; AIR POLLUTION; FOREST FIRE; VALIDATION APPROACH; BRAZILIAN AMAZON REGION
- Agências de fomento:
- Language: Inglês
- Abstract: 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.
- Imprenta:
- Source:
- Título: Atmospheric Environment
- ISSN: 1352-2310
- Volume/Número/Paginação/Ano: v. 184, n. 7, p. 156-165, 2018
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GONÇALVES, Karen dos Santos et al. 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. Atmospheric Environment, v. 184, n. 7, p. 156-165, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.atmosenv.2018.03.057. Acesso em: 20 jan. 2026. -
APA
Gonçalves, K. dos S., Winklera, M. S., Benchimol-Barbosa, P. R., Hoogh, K., Artaxo Netto, P. E., Hacon, S. de S., et al. (2018). 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. Atmospheric Environment, 184( 7), 156-165. doi:10.1016/j.atmosenv.2018.03.057 -
NLM
Gonçalves K dos S, Winklera MS, Benchimol-Barbosa PR, Hoogh K, Artaxo Netto PE, Hacon S de S, Schindler C, Künzli N. 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 [Internet]. Atmospheric Environment. 2018 ; 184( 7): 156-165.[citado 2026 jan. 20 ] Available from: https://doi.org/10.1016/j.atmosenv.2018.03.057 -
Vancouver
Gonçalves K dos S, Winklera MS, Benchimol-Barbosa PR, Hoogh K, Artaxo Netto PE, Hacon S de S, Schindler C, Künzli N. 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 [Internet]. Atmospheric Environment. 2018 ; 184( 7): 156-165.[citado 2026 jan. 20 ] Available from: https://doi.org/10.1016/j.atmosenv.2018.03.057 - Aerosol number fluxes over the Amazon rain forest during the wet season
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Informações sobre o DOI: 10.1016/j.atmosenv.2018.03.057 (Fonte: oaDOI API)
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