Darlan Daniel Alves

Land Use and Land Cover (LULC) properties give vital information about pollution signatures in rivers, and they help develop best management practices (BMPs) for effective water resource management. This work employs multivariate statistical methods, receptor modeling, connectivity analysis, and univariate trend analysis to investigate pollution sources across spatiotemporal scales in the Manawatu River, New Zealand. A positive matrix factorization (PMF) method was applied to interpret possible contamination sources. A 25-year dataset (1989–2014) comprising 12 water quality variables from three sites was used. Runoff connectivity analyses identified high-producing grassland (HG) as the most dominant pollution class in all sub-catchments. Univariate analyses revealed that nutrients and sediments were higher than in the initial monitoring years. The PMF analysis found possible pollutants causing impairment, which required attention from waste managers. PMF also showed that point, natural, and agricultural sources significantly contributed to pollution downstream of the river. In the midstream, the erosion, point, and agricultural sources were significant contributing factors. Agricultural pollution and soil erosion were the main contributors to the upstream sub-catchment area. This study suggests that BMPs with a high retention capacity are needed in specific locations in the catchment area to filter high concentrations of pollutants generated.

Climate change has become a worldwide theme discussed in the last decades, mainly about the various expected impacts. Some research claims that some of these impacts are already occurring, such as the change in the rainfall regime, causing more frequent droughts and floods, an increase in sea levels, among others. Based on this, monitoring water resources to analyze possible changes in hydrological behavior is fundamental for the proper planning and management of these resources. Therefore, the objective of this research was to evaluate the flow behavior in the Guaíba hydrographic region over a period of 33 years and disturb the series based on expected trends due to climate changes to assess how the flows can be affected. For this purpose, data on maximum monthly flows for the period from 1985 to 2017 were obtained from ten fluviometric stations in the Guaíba hydrographic region, southern Brazil. These data were evaluated using descriptive statistics and permanence curves. Afterwards, the series were disturbed and were evaluated again. Through the observation of the historical behavior of the flow it was noticed that many stations indicate changes in the temporal variation, with a tendency to increase the number of great magnitude flows, but, on the other hand, it is verified the temporal increase of minimum flows. The projected trends for the region indicate an increase in the volume of precipitation and in the flow of the rivers, however they tend to be more concentrated in short periods of time. Análise de vazões na Região Hidrográfica do Guaíba: Variabilidade atual e Tendências devido às Mudanças Climáticas RESUMO As mudanças climáticas tornaram-se um tema mundialmente discutido nas últimas décadas, principalmente levando-se em consideração os diversos impactos previstos decorrentes destas mudanças. Algumas pesquisas alegam que alguns destes impactos já estão ocorrendo, como a alteração no regime das chuvas, ocasionando secas e inundações mais frequentes, aumento nos níveis dos mares, dentre outros. Com base no exposto, percebe-se que realizar um monitoramento dos recursos hídricos para analisar possíveis alterações no comportamento hidrológico é fundamental para o adequado planejamento e gerenciamento destes recursos. Assim sendo, o objetivo desta pesquisa foi avaliar o comportamento das vazões na região hidrográfica do Guaíba em um período de 33 anos e, após, perturbar as séries com base nas tendências esperadas devido às mudanças climáticas para avaliar como as vazões desta região podem ser afetadas. Para tanto, foram obtidos dados de vazões máximas mensais para o período de 1985 a 2017 de dez estações fluviométricas da região hidrográfica do Guaíba, sul do Brasil. Estes dados foram avaliados utilizando-se estatística descritiva e curvas de permanência. Após, as séries foram perturbadas e foram novamente avaliadas. Através da observação do comportamento histórico da vazão percebeu-se que muitas estações indicam alterações na variação temporal, com tendência de aumento do número de vazões de grande magnitude, mas, por outro lado constata-se o aumento temporal de vazões mínimas. As tendências projetadas para a região indicam aumento no volume da precipitação e, consequentemente, na vazão dos rios, no entanto tendem a ser mais concentradas em curtos espaços de tempo. Palavras-chave: Bacias hidrográficas; curvas de permanência; mudanças climáticas; tendências; vazão

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 PM_2.5 and PM_2.5–10, and evaluate the toxicity of PM_2.5 and PM_2.5–10 water-soluble fractions using Lactuca sativa as bioindicator. The collection of PM_2.5 and PM_2.5–10 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 thePM_2.5 and PM_2.5–10. 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.

This study aimed to apply a methodology for evaluating raw water qualityand its relationship with land uses and occupations through multivariatestatistical analysis and Geographic Information System. Hydrogenic potential,water temperature, dissolved oxygen, biochemical oxygen demand,chemical oxygen demand, total nitrogen, total phosphorus, and E. coli weremonitored from August 2012 until March 2013. The geoprocessing toolenabled delimiting the contribution areas of each sampling site, as well asthe individual identification of land use of each area. Principal ComponentAnalysis resulted in: domestic sewage, domestic sewage/agriculture, andindustrial discharge. Significant correlations were identified between thevariable urban area and hydrogenionic potential (ρ = 0.446; p = 0.049),dissolved oxygen (ρ = -0.625; p = 0.003), total nitrogen (ρ = 0.649; p = 0.002),and E. coli (ρ = 0.932; p < 0.001). The methodology enabled to identify thecontribution of land use factors as to water quality.

Urbanization has led to a series of problems in medium and large cities, resulting from a lack of planning and technologies to support decision making. This work aims to contribute to the methodology of modeling by cellular automata, aiming the study of the processes that condition the expansion of the urban area. Having as a study case of the municipality of Novo Hamburgo, RS, Brazil. For this purpose, the urban areas of 1967, 1977, 1987, 1997, 2009 and 2015 were used, totalling a historical analysis of 48 years, besides the thematic maps: Declivities, Hypsometric, Land Use, Water Resources and Road System. The compiled data was inserted in the environment of the platform Dinamica EGO, which is a free software that uses the system of Cellular Automata. The software employs a set of input maps composed of an initial landscape, a final landscape and a compilation of thematic variables that are combined from the definition of weights of evidence to generate the transition probability maps. The results showed similarity indexes of 0.9 for the first three models, 1977, 1987 and 1997, as well as 0.83 for the simulation of 2015. It was also possible to analyze the predominant factors for each expansion process, besides the simulation of areas of prognosis for short and medium term, that is, 5, 10 and 15 years.Keywords: Urban expansion. Simulation of scenarios. Cellular automata.RESUMOA urbanização alavancou uma série de problemas nas cidades de médio e grande porte, resultado da falta de planejamento e de tecnologias para apoiar a tomada de decisões. Neste sentido, este trabalho tem como objetivo contribuir para a metodologia de modelagem por autômatos celulares, visando o estudo dos processos que condicionam a expansão da mancha urbana. Tendo como estudo de caso o município de Novo Hamburgo, RS. Para tanto foram utilizadas as manchas urbana de 1967, 1977, 1987, 1997, 2009 e 2015, totalizando uma análise histórica de 48 anos, além dos mapas temáticos: Declividades, Hipsométrico, Uso do Solo, Recursos Hídricos e Sistema Viário. Os dados compilados foram inseridos no ambiente da plataforma Dinamica EGO, que é um software livre que utiliza o sistema de Autômatos Celulares. O software, emprega um conjunto de mapas de entrada, composto por uma paisagem inicial, uma paisagem final e um compilado de variáveis temáticas que são combinadas a partir da definição de pesos de evidência visando gerar os mapas de probabilidade de transição. Os resultados apresentaram índices de similaridade superior 0,9 para as três primeiras modelagem 1977, 1987 e 1997, além de 0,83 para a simulação de 2015. Também foi possível analisar os fatores predominantes para cada processo de expansão, além da simulação de manchas de prognóstico para curto e médio prazo, ou seja 5, 10 e 15 anos. Palavras-chave: Expansão urbana. Simulação de cenários. Autômatos celulares.

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.

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.

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.