Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity

《Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity》

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作者: Vinícius L. Mateus;Adriana Gioda;Helga R. Marinho;Rafael C.C. Rocha;Thiago V. Valles;Ana Clara I. Prohmann;Larissa C. dos Santos;Tatiane B. Oliveira;Fernanda M. Melo;Tatiana D. Saint’Pierre;Luiz Francisco P.G. Maia

来源: URBAN FORESTRY & URBAN GREENING,Vol.57,Issue1,Article 126858

语言: 英文

关键字: Atmospheric pollution;Atlantic Rain Forest;Source apportionment;Diagnostic ratios;Conditional bivariate probability function;Conditional inference trees

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of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity","shouldShowAmLink":false,"onAbstractAllowlist":true,"renderingMode":"Abstract","isAbstract":true,"isContentVisible":false,"ajaxLinks":{"citingArticles":true,"referredToBy":true,"toc":true,"recommendations":true}},"authors":{"content":[{"#name":"author-group","$":{"id":"aug0005"},"$$":[{"#name":"author","$":{"id":"aut0005","author-id":"S1618866720306750-146fa150d984ffd885ca35bc1da5d914"},"$$":[{"#name":"given-name","_":"Vinícius L.;Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Department of Chemistry, Rio de Janeiro, Brazil;Federal University of Rio de Janeiro (UFRJ), Department of Meteorology, Rio de Janeiro, Brazil;Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Department of Chemistry, Rio de Janeiro, Brazil;Federal University of Rio de Janeiro (UFRJ), Department of Meteorology, Rio de Janeiro, Brazil

摘要: Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park (43°04′42.1″W and 22°29′16.9″S) and Mário Xavier National Forest (43°42′21.8″W and 22°43′21.7″S). At the former site, PM10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO3− >Mn >Rad (Global radiation) >Ca2+ >Precipitation >Mg 2+. For PM 10, the main predictors were Gust (Gust wind speed) >NO 3− >Ca2+ >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders.