Abstract 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 planing and evaluation of environmental risk assessment by stakeholders.

中文翻译：

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对大城市周围两个重要大西洋雨林热点的环境气溶胶源的评估

摘要 2010 年至 2015 年期间，对里约热内卢大都会区 (MRRJ) 周围大西洋雨林的两个独特片段进行了环境气溶胶源评估。在 Serra dos Orgaos 国家公园（西经 43° 04 ' 42.1 英寸和南纬 22° 29 ' 16.9 英寸）和马里奥泽维尔国家森林（西经 43° 42 ' 21.8 英寸和南纬 22° 43 ' 21.7 英寸）收集了空气中的颗粒物样品）。在前一个地点收集了 PM 10 样本，而在后一个地点收集了 TSP 样本，因为他们对保护濒临灭绝的特有树蛙（Physalaemus soaresi）物种特别感兴趣。元素组成，分析了无机和有机水溶性化合物以及当地气象变量，以便为颗粒物预测及其潜在来源提供最相关的变量。对于TSP，主要预测因子是NO 3 − >Mn >Rad（全球辐射）>Ca 2 + >降水>Mg 2 + 。对于 PM 10 ，主要预测因子是 Gust（阵风风速）>NO 3 − >Ca 2 + >Zn >Cu >Ti。此外，考虑到盛行风和源评估是间歇性还是连续性，使用条件双变量概率函数 (CBPF) 分析了颗粒物的趋势。随着 CBPF 的使用，最近开发的机器学习算法（条件推理树 - CIT 和使用条件推理框架的随机森林），