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This study aimed to assess the chemical composition of the rainwater in three areas of different environmental impact gradients in Southern Brazil using the receptor model EPA Positive Matrix Factorization (EPA PMF 5.0). The samples were collected in a bulk sampler, from October 2012 to August 2014, in three sampling sites along with the Sinos River Basin: Caraá, Taquara, and Campo Bom. The major ions NH, Na, K, Ca, Mg, F, Cl, NO, SO, and pH were analyzed, as well as identify the main emission sources. The most abundant cations and anions were Ca, Na, Cl, and SO, respectively. The mean pH value in the Sinos River Basin during the study period was 6.07 ± 0.49 (5.13-7.05), which suggests inputs of alkaline species into the atmosphere. The most important neutralizing agents of sulfuric and nitric acids in the Sinos River Basin are Ca (NF = 1.36) and NH (NF = 0.57). The source apportionment provided by the EPA PMF 5.0 resulted in four factors, which demonstrate the influence of anthropogenic and natural sources, in the form of (a) industry/combustion of fossil fuels (F and SO), (b) marine contribution (Na and Cl), (c) crustal contribution (K, Ca, and NO), and (d) agriculture/livestock (NH). Therefore, this study allows a more appropriate understanding of factors that contribute to rainwater chemical composition and also to possible changes in air quality.

Assessment of surface water quality is an issue of currently high importance, especially in polluted rivers which provide water for treatment and distribution as drinking water, as is the case of the Sinos River, southern Brazil. Multivariate statistical techniques allow a better understanding of the seasonal variations in water quality, as well as the source identification and source apportionment of water pollution. In this study, the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA), and positive matrix factorization (PMF) were used, along with the Kruskal-Wallis test and Spearman's correlation analysis in order to interpret a water quality data set resulting from a monitoring program conducted over a period of almost two years (May 2013 to April 2015). The water samples were collected from the raw water inlet of the municipal water treatment plant (WTP) operated by the Water and Sewage Services of Novo Hamburgo (COMUSA). CA allowed the data to be grouped into three periods (autumn and summer (AUT-SUM); winter (WIN); spring (SPR)). Through the PCA, it was possible to identify that the most important parameters in contribution to water quality variations are total coliforms (TCOLI) in SUM-AUT, water level (WL), water temperature (WT), and electrical conductivity (EC) in WIN and color (COLOR) and turbidity (TURB) in SPR. PMF was applied to the complete data set and enabled the source apportionment water pollution through three factors, which are related to anthropogenic sources, such as the discharge of domestic sewage (mostly represented by Escherichia coli (ECOLI)), industrial wastewaters, and agriculture runoff. The results provided by this study demonstrate the contribution provided by the use of integrated statistical techniques in the interpretation and understanding of large data sets of water quality, showing also that this approach can be used as an efficient methodology to optimize indicators for water quality assessment.

This research aimed to evaluate the air quality, by determining the concentrations of PM 2.5-10 , PM 2.5 and the metallic elements Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn and Hg in the leaf part of ryegrass (Lolium multiflorum) in an area close to Schmidt Stream, at the lower section of Sinos River Basin (SRB), in a research campaign of six months, from October 2013 to March 2014. The particles collected in the PM sampling were analyzed by Scanning Electron Microscopy (SEM) combined with Energy Dispersive X-ray Spectrometry (EDS), in order to study their morphology and chemical composition. The mean concentration of PM 2.5-10 was 9.1 µg m . Concentrations of metallic elements, especially Pb, Cr and Zn, were classified as Class 4 (very high pollution levels), according to the classification proposed by Klumpp et al. (2004). Chemical and morphological analysis of PM revealed the presence of particles of biological origin, soot (Cr, Fe, Ni, Zn, Cd, Hg and Pb), salts (KCl) and soil resuspension (Al and Si). The integrated study methodology, employing environmental variables, such as PM and ryegrass, can be of help in the preparation of wide-ranging environmental diagnoses, in addition providing information needed to develop precautionary measures designed to minimize the effects of atmospheric pollution that takes into consideration the environment's supportive capacity and environmental quality.Keywords: air pollution, particulate matter, biomonitoring, lolium multiflorum, mev/eds. Klumpp et al. (2004). A caracterização química e morfológica das partículas coletadas revelou a presença de fuligem (Cr, Fe, Ni, Zn, Cd, Hg e Pb), materiais biológicos, cristais salinos (KCl) e partículas ressuspensas de poeira do solo (Al e Si). Estudos integrados empregando variáveis ambientais como o MP e o biomonitoramento com azevém podem auxiliar na elaboração de diagnósticos ambientais robustos, além de fornecer informações para o desenvolvimento de medidas de precaução que considerem a capacidade de suporte do meio ambiente e que visem à minimização dos efeitos prejudiciais da poluição atmosférica. Concentrações de MPPalavras-chave: poluição atmosférica, material particulado, biomonitoramento, lolium multiflorum, mev/eds.

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