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. (July 2018)

• Record Type: Journal Article
• Authors: Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino
• Is Part Of: Atmospheric environment. Volume 184(2018)
• Page Start: 156
• Page End: 165
• Publication Date: 2018-07
• Subjects: Aerosol optical depth -- Particulate matter -- Air pollution -- Forest fire -- Validation approach -- Brazilian Amazon Region; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.03.057
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5 ) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365, 25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R 2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region. Highlights: Non-linear model was applied for predicting PM2.5 from AOD with a good performance. The model can be applied to other sites if site-specific data are available. The lagged relative residual it is cautious strategy to further improve the model. It was the first Brazilian study predicting PM2.5 from high resolution AOD data.