globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2018.03.057
Scopus记录号: 2-s2.0-85046098318
论文题名:
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
作者: Gonçalves K; D; S; , Winkler M; S; , Benchimol-Barbosa P; R; , de Hoogh K; , Artaxo P; E; , de Souza Hacon S; , Schindler C; , Künzli N
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 184
起始页码: 156
结束页码: 165
语种: 英语
英文关键词: Aerosol optical depth ; Air pollution ; Brazilian Amazon Region ; Forest fire ; Particulate matter ; Validation approach
Scopus学科分类: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
英文摘要: 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 R2 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. © 2018 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82843
Appears in Collections:气候变化事实与影响

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作者单位: Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; National School of Public Health Sergio Arouca, Oswaldo Cruz Foundation – ENSP/FIOCRUZ, Rio de Janeiro, Brazil; Clinical Coordination of Pedro Ernesto University Hospital, Rio de Janeiro State University – HUPE/UERJ, Rio de Janeiro, Brazil; Physics Institute, University of São Paulo – IFUSP/USP, São Paulo, Brazil

Recommended Citation:
Gonçalves K,D,S,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[J]. Atmospheric Environment,2018-01-01,184
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