Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil.

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.

Source apportionment of metallic elements in urban atmospheric particulate matter and assessment of its water-soluble fraction toxicity.

Clean air is essential for the maintenance of human life and environmental balance. The atmospheric particulate matter (PM) is one of the main air pollutants and is characterized by the heterogeneity of its composition, being able to accumulate numerous components, such as metallic elements, which contribute to increasing its toxicity. The objectives of this study were to assess of the air quality in two urban environments, to carry out the source apportionment of the metallic elements Al, Ba, Cd, Pb, Cu, Cr, Fe, Mn, Ni, and Zn in the PM2.5 and PM2.5-10, and evaluate the toxicity of PM2.5 and PM2.5-10 water-soluble fractions using Lactuca sativa as bioindicator. The collection of PM2.5 and PM2.5-10 was performed using a dichotomous stacked filter unit (SFU) sampler. The source apportionment was carried out using the EPA PMF 5.0 receptor model and the toxicity tests followed the EPA Ecological Effects Test Guidelines OPPTS 850.4200: Seed Germination/Root Elongation Toxicity Test. The source apportionment demonstrated that vehicular and industrial emissions are the main anthropogenic sources contributing to the concentration of metallic elements to thePM2.5 and PM2.5-10. The studied sites did not show statistically significant differences in terms of phytotoxicity to the Lactuca sativa seeds. Cd and Cu were identified as the main metallic elements which able to cause negative effects on seed germination and root elongation, respectively. The presence of cadmium and copper in the atmospheric particulate matter is one of the main causes of the phytotoxicity affecting the Lactuca sativa seed germination and root elongation.

Chemical composition of rainwater in the Sinos River Basin, Southern Brazil: a source apportionment study.

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 NH4+, Na+, K+, Ca2+, Mg2+, F-, Cl-, NO3-, SO42-, and pH were analyzed, as well as identify the main emission sources. The most abundant cations and anions were Ca2+, Na+, Cl-, and SO42-, 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 Ca2+ (NF = 1.36) and NH4+ (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 SO42-), (b) marine contribution (Na+ and Cl-), (c) crustal contribution (K+, Ca2+, and NO3-), and (d) agriculture/livestock (NH4+). Therefore, this study allows a more appropriate understanding of factors that contribute to rainwater chemical composition and also to possible changes in air quality.

Integrated assessment of air pollution by metals and source apportionment using ryegrass (Lolium multiflorum Lam.) in southern Brazil.

One of the biggest environmental problems existing today is air pollution, which is characterized by the presence of toxic gases and metal pollutants, the latter of which is generally associated with emissions of particulate matter (PM) from industries or automotive vehicles. Biomonitoring is a method that can be used to assess air pollution levels because it makes it possible to determine what effects these air pollutants cause in living organisms and their responses. The species Lolium multiflorum Lam., known as ryegrass, is considered a good bioindicator of metals, since it accumulates these substances during exposure. This study proposes to conduct an integrated assessment of air quality using two different monitoring methodologies: biomonitoring with L. multiflorum and active monitoring in areas with different levels of urbanization and industrialization. Concentrations found in ryegrass plants revealed high levels of Pb, Cr, Zn, and Cu, indicating that vehicular and industrial emissions were the main sources of pollution. Analysis of PM also revealed soot and biogenic particles, which can transport metals. Therefore, with the proposed method, the anthropogenic impact on air pollution in the investigated area could be clearly demonstrated.