Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity
| Autor: | Tatiane B. Oliveira, Larissa C. dos Santos, Tatiana D. Saint'Pierre, Rafael Christian Chávez Rocha, Luiz Francisco Pires Guimarães Maia, Thiago V. Valles, Vinícius L. Mateus, Helga R. Marinho, Adriana Gioda, Ana Clara I. Prohmann, Fernanda M. Melo |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Ecology National park Soil Science Forestry Rainforest 010501 environmental sciences Particulates Atmospheric sciences 010603 evolutionary biology 01 natural sciences Wind speed Aerosol Prevailing winds Environmental science Precipitation Air quality index 0105 earth and related environmental sciences |
| Zdroj: | Urban Forestry & Urban Greening. 56:126858 |
| ISSN: | 1618-8667 |
| Popis: | 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 Orgaos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mario Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 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 NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >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. |
| Databáze: | OpenAIRE |
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