Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks

Optimizing surface water quality parameters in monitoring networks in a developing sub-tropical region with high anthropogenic pressure (São Paulo State Brazil)

Efficient water quality monitoring is a central aspect of water resources management, especially in developing countries, where water quality is under high anthropogenic pressure and resources for monitoring are usually limited. Here, we evaluated an alternative to optimize water quality parameters (WQPs) in the water quality monitoring network (WQMN) of the most populous state in Brazil (São Paulo State). We focused on the monitoring goal of identifying water quality temporal trends, selecting WQPs with high statistical explanatory power and those that were particularly sensitive to natural and anthropogenic perturbations. We considered 12 initial WQPs (dissolved copper, total zinc, total lead, total chromium, total mercury, total nickel, total cadmium, total iron, total manganese, total aluminum, total copper, and surfactant) with data from 2004 to 2018 for 56 monitoring sites distributed across four major watersheds with contrasting land uses in the state. We performed principal component analysis, followed by objective criteria to refine WQPs recommendation for the WQMN. Our results indicated the opportunity of reducing at least one parameter from the initial set of WQPs in all watersheds. Total iron, total manganese, and total aluminum were the most relevant initial WQPs, since their maintenance in monitoring were indicated in all the analyzed cases. Natural watershed conditions (e.g., geomorphology and water geochemistry) potentially governed their concentrations in surface water. On the other hand, total mercury, total chromium, and dissolved copper had the maintenance indicated in only one watershed, especially due to concentrations consistently below the respective limits of quantification (LoQs). Future investigations can complement our recommendations for these parameters, since changes in LoQs could throw another light on water quality spatial and temporal variations and the need for reference areas for assessing baseline conditions can also be relevant. Moreover, we argue that depending on the monitoring goals of the WQMN, additional sampling of biota and sediments could be useful as many of the studied WQPs' bioconcentrate. Our results illustrated an alternative approach towards adaptive monitoring in São Paulo state in accordance with the intended monitoring goal (i.e., water quality temporal trends), converging with the more flexible monitoring adopted in well-structured networks worldwide. While we did not cover other monitoring goals in our study (as the control of illegal discharge of effluents or industrial spills, for example), we expect our methodology can contribute to establishing technical guidelines for reviewing the existing WQMNs in Brazil and other developing countries with similar challenges.

Water availability and extreme events under climate change scenarios in an experimental watershed of the Brazilian Atlantic Forest

Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest. In this study, extreme climate change scenarios were developed using a wide array of global climate models acquired from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Reports (AR6). Two extreme climate change scenarios, DryHot and WetCool, were integrated into the Soil and Water Assessment Tools (SWAT) hydrological model to evaluate their impacts on the hydrological dynamics in the watershed. The baseline SWAT model was first developed and evaluated using different model performance evaluation metrics such as coefficient of determination (R2), Nash-Sutcliffe (NSC), and Kling-Gupta efficiency coefficient (KGE). The model results illustrated an excellent model performance with metric values reaching 0.89 and 0.64 for monthly and daily time steps respectively in the calibration (2008 to 2017) and validation (2018 to 2023) periods. The findings of future climate change impacts assessment underscored an increase in temperature and shifts in precipitation patterns. In terms of streamflow, high-flow events may experience a 47.3 % increase, while low-flows could see an 76.6 % reduction. In the DryHot scenario, annual precipitation declines from 1657 to 1420 mm, with evapotranspiration reaching 54 % of precipitation, marking a 9 % rise compared to the baseline. Such changes could induce water stress in plants and lead to modifications on structural attributes of the ecosystem recognized as the Atlantic rainforest. This study established boundaries concerning the effects of climate change and highlighted the need for proactive adaptation strategies and mitigation measures to minimize the potential adverse impacts in the study watershed.

Identification of water pollution sources and analysis of pollution trigger conditions in Jiuqu River, Luxian County, China

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

Anthropogenic factors affecting the Moskva River water quality: levels and sources of nutrients and potentially toxic elements in Moscow metropolitan area

This study aims to identify the main patterns of distribution and sources of pollutants in the Moskva River and their influence on river water quality under different levels of anthropogenic stress caused by the largest megacity in Europe - Moscow. For this study, we determined concentrations of 18 trace elements, nutrient elements and major ions, chemical and biochemical oxygen demand, and physical parameters of water at 45 stations on the Moskva River and 20 stations on its tributaries during spring flood and low water of 2019 and 2020 to identify the extent and mechanisms of urban impact on its water chemistry. Chemical elements concentrations have been determined using ICP-MS and ICP-AES methods. Mn, Al, Cu, Sr, Zn, B, Mo, and inorganic nitrogen were outlined as key pollutants according to various drinking water and environmental guidelines. Using correlation and factor analysis, five groups of elements were identified, corresponding to different drivers controlling their unequal distribution within the watershed: mineral sources (Sr, Li, B, Mo, Ca), sewage and road runoff (TN, TP, Sb, Ni, N-NO₂, BOD₅, COD, V, Zn), impact of acidic wetlands (Al, COD, Zr, Bi), groundwater and landfills leachate (V, As, Pb, U, Sb), and industrial activities (Zn, Cu). Water quality in the Moskva Basin on the whole is good according to the CCME Water Quality Index. Local deterioration of water quality to marginal and even fair (depending on the reference water quality guideline) is confined to the Moskva River part downstream from the Kuryanovo aeration station to the Moskva mouth and to the mouths of several heavily contaminated tributaries.

A stage-based approach to allocating water quality monitoring stations based on the WorldQual model: The Jubba River as a case study

Ensuring adequate freshwater quality is an important aspect of integrated environmental management and sustainable development. One contribution towards this end is to monitor the water quality of river basins. An important issue in constructing a water quality monitoring network is how to allocate the stations. This is usually done by using in situ measurements of pollutants together with other information. A stage-based optimization approach has been developed to find the optimal sites to allocate the monitoring stations. The proposed approach constructs a network in a sequence of stages without the need for in situ pollution measurements. Instead, it uses pollutant estimates from the WorldQual model together with other social and hydrological criteria. The approach is computationally efficient and provides an ordered list of stations that can be used to initialize or augment a water quality network. This is especially relevant for consideration by developing countries since, with this approach, they can get an overview of their river basins, and then prioritize the initial distributions of the networks. The approach was applied successfully to the 741,751 km² of the Jubba River basin, but it is applicable to river basins of any size.

From Monitoring and Modeling to Management: How to Improve Water Quality in Brazilian Rivers? A Case Study: Piabanha River Watershed

Water quality has been a global concern, as evidenced by UN Sustainable Development Goals. The current paper has focused on the Piabanha River rehabilitation as a case study which can be generalized to other similar watersheds. A monitoring program during a hydrological year was carried out, and different databases were used to calibrate and validate the QUAL-UFMG water quality model. Sanitation is the major problem in the watershed, notably in its headwater catchments, which concentrate the most urbanized regions where water quality is worse in the dry season due to low river flows. Thus, simulations of the river water quality have been performed through computational modeling suggesting organic load reductions in some sub-basins. In conclusion, some strategies to improve water quality have been discussed: (i) The water quality rehabilitation must consider progressive goals of pollution reduction starting with an initial implementation in a reduced area. The monitoring should be based on a few parameters relevant and simple to monitor. (ii) Pollution reduction ought to be carried out strategically with deadlines and intermediate goals that must be agreed upon between the stakeholders in the watershed. (iii) Watershed committees should supervise projects to improve water quality in partnership with the State Prosecutor’s Office.

Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI_NSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil

The objective of the present study was to evaluate the water quality data in the Minas Gerais portion of the Doce River basin in order to analyze the current monitoring network by identifying the main variables to be maintained in the network, their possible sources of pollution, and the best sampling frequency. Multivariate statistical techniques (factor analysis/principal components analysis, FA/PCA and cluster analysis, CA) complemented by the analysis of violation of the framing classes were used for this purpose. Water quality variables common to 64 monitoring sites were analyzed for the base period from 2010 to 2017. The water quality variables were analyzed considering the different monitoring campaigns: (a) partial campaigns; (b) total campaigns; and (c) monthly campaigns. It was identified from the FA/PCA results, that, when the partial campaign data were analyzed, the variables selected represent the high susceptibility that the basin presents to erosion and the release of domestic effluents in its water bodies. When the data of total campaigns were evaluated, representative variables of the contamination by heavy metals from industrial and mining activities were included. Therefore, the analysis of violation of the framing classes made possible to identify five critical variables: thermotolerant coliforms, dissolved iron, total phosphorus, and total manganese, which reinforced the results obtained in FA/PCA. Based on the results of the analyses, it was recommended to include variables associated with heavy metal contamination in the partial campaigns, prioritizing the dissolved iron and total manganese, as well as total chloride sampling only for the total campaigns. The evaluated data from the monthly campaigns, the CA showed that although the quarterly monitoring frequency is satisfactory, the monthly monitoring is more appropriate for the monitoring of water quality in the Minas Gerais portion of the Doce River basin.

In situ generation of hydroxyl radical for efficient degradation of 2,4-dichlorophenol from aqueous solutions

Since 2,4-dichlorophenol (2,4-DCP) as a priority pollutant is used in numerous industrial processes, its removal from the aqueous environment is of utmost importance and desire. Herein, the authors describe an electrochemical treatment process for efficient removal of 2,4-DCP from aqueous solutions using electro-Fenton (EF) process. Response surface methodology (RSM) was applied to optimize the operating parameters. Analysis of variance (ANOVA) confirmed the significance of the predicted model. The effect of independent variables on the removal of 2,4-DCP was investigated and the best removal efficiency of 98.28% achieved under the optimal experimental condition including initial pH of 3, H₂O₂ dosage of 80 μL, initial 2,4-DCP concentration of 3.25 mg L^-1, current density of 3.32 mA cm^-2, and inter-electrode distance of 5.04 cm. The predicted removal efficiency was in satisfactory agreement with the obtained experimental removal efficiency of 99.21%. According to the obtained polynomial model, H₂O₂ dosage revealed the most significant effect on degradation process. The kinetic investigation revealed that the first-order model with the correlation coefficient of 0.9907 and rate constant (K_app) of 0.831 min^-1 best fitted with the experimental results. Generation of the hydroxyl radicals throughout the EF process controlled the degradation process.