A climate-water quality assessment framework for quantifying the contributions of climate change and human activities to water quality variations

The water cycle of small catchments impacted with tailings mudflows: A study in the Ferro-Carvão watershed after the breakup of B1 dam in Brumadinho

The B1 tailings dam of Córrego do Feijão iron-ore mine owned by Vale, S.A. company collapsed in 25 January 2019 releasing to the Ferro-Carvão stream watershed (32.6 km2) as much as 11.7 Mm3 of mine waste. A major share (8.9 Mm3) has been deposited along the stream channel and margins forming a 2.7 km2 patch. The main purpose of this study was to question whether the tailings deposit impacted the local water cycle and how. Using the Soil and Water Assessment Tool (SWAT) hydrologic model, the water balance components of 36 hydrologic response units (HRU) were calculated for pre- (S1) and post- (S2) B1 dam rupture scenarios represented by appropriate soil, land use and tailings cover. The results revealed an increase of evapotranspiration from S1 to S2, related to the sudden removal of vegetation from the stream valley and replacement with a blanket of mud, which raised the exposure of Earth's surface to sunlight and hence soil evaporation. For 11 HRU (10.3 km2) located around the tailings deposit, a decrease in lateral flow was observed, accompanied by an increase in percolation and a slight increase in groundwater flow. In this case, the water balance changes observed between S1 and S2 reflected a barrier effect imposed to the lateral flows by the tailings, which shifted the flows towards the vertical direction (percolation). Thus, the water followed an easier vertical route until reaching the shallow aquifer and being converted into groundwater flow. As per the modelling outcomes, the hydrologic impacts of B1 dam rupture are relevant because they affected 1/3 of Ferro-Carvão stream watershed, and hence claim for the complete removal of the tailings.

The importance of integrating phycological research, teaching, outreach, and engagement in a changing world

The ecological, evolutionary, economic, and cultural importance of algae necessitates a continued integration of phycological research, education, outreach, and engagement. Here, we comment on several topics discussed during a networking workshop-Algae and the Environment-that brought together phycological researchers from a variety of institutions and career stages. We share some of our perspectives on the state of phycology by examining gaps in teaching and research. We identify action areas where we urge the phycological community to prepare itself to embrace the rapidly changing world. We emphasize the need for more trained taxonomists as well as integration with molecular techniques, which may be expensive and complicated but are important. An essential benefit of these integrative studies is the creation of high-quality algal reference barcoding libraries augmented with morphological, physiological, and ecological data that are important for studies of systematics and crucial for the accuracy of the metabarcoding bioassessment. We highlight different teaching approaches for engaging undergraduate students in algal studies and the importance of algal field courses, forays, and professional phycological societies in supporting the algal training of students, professionals, and citizen scientists.

A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (-32.2%) and SSP 4.5 (-88.8%) for 2024-2049, with further decreases expected for 2050-2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Effect of climate change on the water quality of Mediterranean rivers and alternatives to improve its status

Surface water (SW) quality is particularly vulnerable to increased concentrations of nutrients, and this issue may be exacerbated by climate change. Knowledge of the effects of temperature and rainfall on SW quality is required to take the necessary measures to achieve good SW status in the future. To address this, the aims of this study were threefold: (1) to assess how a changing climate may alter the nitrate, ammonium, phosphorus and biological oxygen demand status (BOD5) of SW; (2) assess the relationship between water quality and flow; and (3) simulate diffuse and point source pollution reduction scenarios in the Júcar River Basin District in the Mediterranean region. A regionalised long-term climate scenario was used following one Representative Concentration Pathway (RCP8.5) with the data incorporated into the coupling of hydrological and water quality models. According to these climate change scenarios, SW with poor nitrate, ammonium, phosphorus and BOD5 status are expected to increase in the future by factors of 1.3, 1.9, 4 and 4, respectively. Furthermore, median ammonium and phosphorus concentration may be doubled in months with low flows. Additional measures are required to maintain current status in the water bodies, and it is necessary to reduce at least 25% of diffuse nitrate pollution, and 50% of point loads of ammonium, phosphorus, and BOD5.

Water quality variation and driving factors quantitatively evaluation of urban lakes during quick socioeconomic development

Rapid urbanisation has caused a significant impact on the ecological environment of urban lakes in the world. To maintain the harmonious development of urban progress and water quality, it is essential to evaluate water quality variation and explore the driving factors quantitatively. A comprehensive evaluation method with cluster analysis and Kriging interpolation was used to explore the spatiotemporal variation in a typical urban lake in China, Chaohu Lake, from 2011 to 2020. The correlation between water quality and socioeconomic factors was evaluated by Pearson correlation analysis. Results indicated that: total phosphorus (TP) and total nitrogen (TN) were the key pollution parameters of Chaohu Lake. The pollution situation was gradually improving, however, and the improvement in chemical oxygen demand (COD) is more evident due to anthropogenic control. The spatial heterogeneity of water quality in Chaohu Lake is remarkable, and the water quality is poor in the west but better in the east. Natural attributes of lakes and external load were the main reasons for the spatial heterogeneity. The western residential areas of Chaohu Lake Basin (CLB) are concentrated, and a large amount of industrial and domestic sewage exacerbates water pollution in the west of tributaries. In contrast, the implementation of water environmental governance policies in recent years has alleviated water pollution. From 2011 to 2020, water quality has improved by 23%-35% in the west and 7%-14% in the east. This study provided a framework for quantitatively assessing water quality variation and its driving forces in urban lakes.

Carbon reduction behavior of waste power battery recycling enterprises considering learning effects

The carbon reduction behavior of waste power battery recycling (WPBR) enterprises is essential for promoting resource conservation and environmental protection. Introducing the learning effects of carbon reduction research and development (R&D) investment, this study constructs an evolutionary game model between local governments and WPBR enterprises to study the behavior choice of carbon reduction. The paper explores the evolutionary process and factors affecting carbon reduction behavior choices of WPBR enterprises from internal R&D motivation and external regulation perspectives. The critical results reveal that the existence of learning effects significantly reduces the probability of environmental regulation by local governments while effectively increasing the probability of WPBR enterprises implementing carbon reduction. The learning rate index positively correlates with the likelihood of enterprises implementing carbon emissions reduction. In addition, carbon reduction subsidies considerably maintain considerably negative relation with the probability of enterprise carbon reduction behavior. The following conclusions are drawn: (1) The learning effect of carbon reduction R&D investment is the intrinsic driving force for WPBR enterprises' carbon reduction behavior, which can promote enterprises to proactively implement carbon reduction under fewer constraints of government environmental regulation; (2) Pollution fines and carbon trade prices in environmental regulation can promote enterprises carbon reduction, while carbon reduction subsidies inhibit their reduction behavior; (3) There exists an evolutionarily stable strategy between government-enterprise game only under the dynamic mechanism. The research provides insights for decision-making on enterprises' carbon reduction R&D investment and local government environmental regulation policy under carbon reduction targets.

Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995-2020

Although physical models at present have made important achievements in the assessment of non-point source pollution (NPSP), the requirement for large volumes of data and their accuracy limit their application. Therefore, constructing a scientific evaluation model of NPS nitrogen (N) and phosphorus (P) output is of great significance for the identification of N and P sources as well as pollution prevention and control in the basin. We considered runoff, leaching and landscape interception conditions, and constructed an input-migration-output (IMO) model based on the classic export coefficient model (ECM), and identified the main driving factors of NPSP using geographical detector (GD) in Three Gorges Reservoir area (TGRA). The results showed that, compared with the traditional export coefficient model, the prediction accuracy of the improved model for total nitrogen (TN) and total phosphorus (TP) increased by 15.46 % and 20.17 % respectively, and the error rates with the measured data were 9.43 % and 10.62 %. It was found that the total input volume of TN in the TGRA had declined from 58.16 × 10⁴ t to 48.37 × 10⁴ t, while the TP input volume increased from 2.76 × 10⁴ t to 4.11 × 10⁴ t, and then decreased to 4.01 × 10⁴ t. In addition Pengxi River, Huangjin River and the northern part of Qi River were high value areas of NPSP input and output, but the range of high value areas of migration factors has narrowed. Pig breeding, rural population and dry land area were the main driving factors of N and P export. The IMO model can effectively improve prediction accuracy, and has significant implications for the prevention and control of NPSP.