Revista Ambiente & Água - An Interdisciplinary Journal of Applied Science

1-16

Authors: Luis Otávio Miranda Peixoto; Luana Mayumi Takahasi Marques; Alinne Mizukawa; Julio Cesar Rodrigues de Azevedo

Caffeine is used worldwide as a chemical tracer to identify anthropic pressures on urban water resources. Nevertheless, its quantification demands great financial investments. This research created a model that would indirectly determine a range of possible caffeine concentrations along an urban river, without the need for extensive laboratory work. The model is based on Canonical Correlation Analysis (CCA), which can correlate two sets of different-sized independent and dependent variables in order to generate a single empirical equation. This equation takes as input the concentrations of ammonia nitrogen and orthophosphate, as well as the total population and the population inhabiting irregular housing areas. From the model’s results, it was possible to elaborate a spectrum of possible concentrations of caffeine along the Atuba River (Curitiba-Brazil). The tendency of water quality degradation of this river was also predicted. This model could become a useful preliminary analysis for water resource managers and researchers alike.

1-23

Authors: Ana Paula Dias Turetta; Elaine Cristina Cardoso Fidalgo ; Rachel Bardy Prado ; Azeneth Eufrasino Schuler ; Heitor Luiz da Costa Coutinho (in memorian)

The cascade model is a commonly applied framework to evaluate ecosystem services provision, highlighting their benefits to society and assigning non-monetary and monetary values to these services. Adapting this model, we present a methodology to establish the most suitable indicators for monitoring Payment for Ecosystem Services (PES) programs related to water resources in Brazil. Through a participatory process, a set of ecosystem functions indicators were assigned for each ecosystem service (ES) considered in the study. The indicators were then classified following these criteria: clarity, viability, sensitivity, and relevance. The indicators were organized by their final score according to each evaluated criterion. The results demonstrated that “clarity” and “relevance” criteria were those most important for the experts to choose an indicator. In general, we could also observe a preference for analytical and well-established indicators in the literature for all ES evaluated. The indicators list presented can support the PES program monitoring in Brazil. Additionally, the methodology developed can be easily applied in other areas and provide the definition of the most suitable indicators to monitor water-based PES in different Brazilian contexts.

1-27

Authors: Michel Trarbach Bleidorn; Wanderson de Paula Pinto ; Isamara Maria Schmidt; Antonio Sergio Ferreira Mendonça ; José Antonio Tosta dos Reis

Missing data is one of the main difficulties in working with fluviometric records. Database gaps may result from fluviometric stations components problems, monitoring interruptions and lack of observers. Incomplete series analysis generates uncertain results, negatively impacting water resources management. Thus, proper missing data consideration is very important to ensure better information quality. This work aims to analyze, comparatively, missing data imputation methodologies in monthly river-flow time series, considering, as a case study, the Doce River, located in Southeast Brazil. Missing data were simulated in 5%, 10%, 15%, 25% and 40% proportions following a random distribution pattern, ignoring the missing data generation mechanisms. Ten missing data imputation methodologies were used: arithmetic mean, median, simple and multiple linear regression, regional weighting, spline and Stineman interpolation, Kalman smoothing, multiple imputation and maximum likelihood. Their performances were compared through bias, root mean square error, absolute mean percentage error, determination coefficient and concordance index. Results indicate that for 5% missing data, any methodology for imputing can be considered, recommending caution for arithmetic mean method application. However, as the missing data proportion increases, it is recommended to use multiple imputation and maximum likelihood methodologies when there are support stations for imputation, and the Stineman interpolation and Kalman Smoothing methods when only the studied series is available.

1-9

Authors: Fabio Leandro da Silva; Ângela Terumi Fushita; Marcela Bianchessi da Cunha-Santino; Irineu Bianchini Júnior

Human activities are affecting reservoir water quality; consequently, methods are necessary to verify those impacts. Mathematical modeling improves the understanding of the anthropic impact on water quality, changes in limnological data, and helps formulate management strategies. However, it is necessary to consider the (dis)advantages as well as the methods used for water-quality assessment in reservoirs. This study conducted a systematic review in four databases: (i) PubMed/Medline; (ii) Scopus; (iii) Web of Science; and (iv) Wiley Online Library. We combined Boolean operators and words aiming to identify papers linked to the scope. Rayyan software allowed the initial screening of the found papers. Peer-reviewed papers and the use of mathematical models to assess reservoir water quality were the inclusion criteria. Exclusion criteria included articles in languages other than English, grey literature, and inaccessible articles. Our research found 169 articles, of which 39 were selected and only 13 were included in the review. Mathematical modeling has many benefits related to real-world problems, but the main disadvantages are process simplification, specific rules of the model, and lack of information or data monitoring. Kinetic equations, regression models, Monte Carlo analysis, finite segment models, modeling tools, zero-order rate equations, partial differential algebraic equations, and predictive analysis are the methods observed in mathematical modeling. This review provides information for unfamiliar managers who intend to use mathematical models to assess the water quality of reservoirs.

1-16

Authors: Enio Sebastião Vicente de Jesus ; Letícia Schönhofen Longoni; Luciano Kayser Vargas; Anelise Beneduzi

This study evaluated the water and sediment quality of samples collected from different points in organic (O1, O2, O3, and O4) and conventional (C1, C2, and C3) pre-germinated rice fields located in Viamão/RS. Quality indicators such as phosphorus, dissolved oxygen, manganese, iron, turbidity, and BOD5 reduced water quality beyond Classes 3 and 4, as defined by CONAMA Resolution 357/2005. Based on the aluminum levels, the water samples collected from all the points were categorized as Class 4; furthermore, the IQA classified the quality of water samples from Points O4 and C1 as “bad.” The COD/BOD ratio was high, demonstrating that the biodegradable fraction was considerably low. The conductivity of water at points O4, C1, C2, and C3, exceeded 100 µS/cm, as defined by CETESB, indicating impacted environments. The levels of zinc in C1 and nickel in O2, C2, and C3 in the sediment exceeded the quality reference values established by the FEPAM Ordinance 85/2014. In general, the lowest water quality was observed in the samples collected from Points O2 and O4, and the lowest sediment quality was observed in the samples collected from all points in the conventional rice fields and from Point O2 in the organic rice field.