Daniela Montanari Migliavacca Osório

Concentrations of PM2.5-10 and PM2.5 and metallic elements around the Schmidt Stream area, in the Sinos River Basin, southern Brazil

This research aimed to evaluate the air quality, by determining the concentrations of PM2.5-10, PM2.5 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 PM2.5-10 was 9.1 µg m(-3), with a range of 2.2 µg m(-3) to 15.4 µg m(-3) and the mean concentration of PM2.5 was 4.7 µg m(-3), with a range of 1.9 µg m(-3) to 8.2 µg m(-3). 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 environments supportive capacity and environmental 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.

Germination and sporophytic development of Regnellidium diphyllum Lindm. (Marsileaceae) in the presence of copper

Regnellidium diphyllum Lindm. is a heterosporous fern growing in wetlands and humid soils that are being converted to agricultural activities. Many products that are used in agriculture contain copper, resulting in surface and groundwater contamination. Germination and initial development tests were performed using Meyers solution containing copper sulphate at concentrations of 0 (control), 1, 5, 10, 50 and 100 mg L-1. The experiment was conducted in a growth chamber at 25 ± 1oC for 28 days, with a 12/12-hour light/dark cycle and a photon flux density of 100 µmol m-2 s-1. The lowest germination rate (6%) was observed at 100 mg L-1. Primary root growth was significantly reduced at > 10 mg L-1. Secondary leaves of sporophytes grown in concentrations > 5 mg L-1 were progressively shorter than were those formed by the control plants. We conclude that the release of pollutants containing copper into the natural habitats of R. diphyllum can cause phytotoxicity, threatening the establishment of populations and worsening the already vulnerable conservation status of this species.

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.

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.