River Water Quality Research Articles

Distributed enhanced multi-objective evolutionary algorithm based on decomposition for cluster analysis in wireless sensor network

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization problems in wireless sensor networks (WSNs). In MOEA/d, a multiobjective optimization problem decomposes into several scalar optimization subproblems, each focusing on a distinct objective. Each subproblem is expressed as a clustering problem that uses local data to perform distributed clustering. The proposed method has been extended to achieve improved accuracy in less time by using a smaller feature subset with less redundancy. The Distributed Enhanced MOEA/d (DE-MOEA/d) avoids local optima by achieving diversity in the population using fuzzy-based nearest neighbor selection, sparse population initialization, and evolved mutation operator. This integration improves the accuracy of the clustering process at WSN nodes, ensuring the attainment of well-balanced solutions across multiple optimization criteria in the distributed environment. Average Euclidean and total symmetrical deviations are the two cost functions used to minimize while clustering on the MOEA/d framework. Six real-life WSN datasets are used to assess the performance of the proposed technique: (1) the Delhi air pollution dataset, (2) the Canada weather station dataset, (3) the Thames River water quality dataset, (4) the Narragansett Bay water quality dataset, (5) the Cook Agricultural land dataset and 6) Gordon Soil dataset. The simulation results of both proposed algorithms are compared with Multiobjective distributed particle swarm optimization (DMOPSO) and Distributed K-means (DK-Means). The proposed algorithm DE-MOEA/d performs better in terms of the Silhouette index (SI), Dunn index (DI), Davies–Bouldin index (DBI), and Kruskal–Wallis (KW) statistical test.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3−-N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Geochemical Modeling and Quality Assessment of the Surface Waters from the Mouhoun River System, Burkina Faso: Implications of Water–Rock Interactions and Anthropogenic Activities

Major ion and heavy metal geochemistry was used to investigate the factors that control the Mouhoun River water quality in western Burkina Faso. Major cation concentrations was in the following decreasing order: Ca2+> Na+> K+> Mg2+, reflecting silicate weathering. However, relatively high HCO3- and SO42- concentrations could be related to dissolution of calcite and sulfide minerals, respectively. The average heavy metal concentrations in the riverine system were in the following order: FeT>Ag>Mn>Zn>Pb>Cu>Ni>AsT>Cr>Hg>Co>Cd with FeT and Ag exceeding the World Health Organization permissible limits. Concentrations of all heavy metals, except Cr, were higher in the Mouhoun system compared to the average world river concentrations. Adsorption and speciation models showed that Zn, Ag, Cd, Co, Cr and Ni are likely to remain in their free ionic forms, and thus posing serious threats to aquatic life and human health. Ag-Hg-Ni-Cd association around Poura and Tenado on the principal component plot suggested that the widespread artisanal gold mining in these areas may have contributed to these metals’ loading. In contrast, NH4+, Pb, Cu, Cr, AsT and Zn were clustered around a densely populated and industrialized Kou watershed, pointing to agricultural and industrial sources of these pollutants. Furthermore, the Kou River had the highest metal index followed by Tenado, Samendeni, Poura and Boromo. Hence, the chemistry of the Mouhoun River and its tributary appears to be mainly controlled by population density and various anthropogenic activities taking place in their drainage basins. The findings could provide avenues for sound and sustainable water resource management in a water scarce semi-arid environment.

WATER QUALITY ASSESSMENT OF THE SHUSHIC RIVER USING BENTHIC MACROINVERTEBRATES

The study of river waters and water flows of their tributaries is very important and necessary for the protection, use and improvement of their condition. According to the Water Framework Directive (WFD, 2000) the use of benthic macroinvertebrates in river water quality monitoring is considered an efficient method. This study was conducted during 2019 in the Shushicë river, where its purpose was to assess the quality of river waters using the biotic index ASPT (BMWP) and the biotic index - SWRC. The sampling method used was based on the Kick net technique with a mesh size of 500 microns. From the sampling taken in four stations, 481 individuals belonging to two types (Arthropoda and Annelida), one class (Insecta) and one subclass (N/K Oligochaeta) were collected. The unidentified species belong to 8 orders (Ephemeroptera, Plecoptera, Trichoptera, Diptera, Coleoptera, Haplotaxid, Odonata, Hoplonemertea) and 28 families. Based on the results obtained from the calculation of the ASPT (BMWP) biotic index, station I and III result in a "Clean" bio classification, station II and IV result in a "Partially clean" bio-classification. From the SWRC Biotic index calculations, the first station and the fourth station result in a bio classification of "Good", the second station results in a bio classification of "Clean", and the third station results in a bio classification of "Excellent" Based on the Biotic index ASPT (BMWP) and SWRC we can say that the Shushic river resultshas a good water quality, and partially with a slight organic pollution.

Assessment of suitability of the Dniester River's water for drinking water supply

The Dniester is a transboundary river between Ukraine and the Republic of Moldova. It is the second longest river in Ukraine and the ninth in Europe. From its source to a town of Staryi Sambir, the Dniester flows through the Carpathian Mountains, then across the flat territory of Ukraine and Moldova. The Dniester Basin extends across seven regions in southwestern Ukraine (Lviv, Ivano-Frankivsk, Ternopil, Khmelnytskyi, Vinnytsia, Chernivtsi and Odesa Regions). A small area of the river in its upper reaches is located in Poland. The basin's shape is a very elongated oval that is curved in the middle, about 700 km long with an average width of 120 km. As it flows into the Black Sea, the Dniester River forms the Dniester estuary in Odesa region (between Ovidiopol and Bilhorod-Dnistrovskyi districts). The estuary area of the Dniester River is a complex landscape system at the sea-river-land contact. Drinking water is an important factor determining a person's health. Due to the intensive development of industry, agriculture and utility services, the number of pollutants that enter the environment and worsen the quality of drinking water in Ukraine keeps increasing. This increases the risk of toxic and infectious diseases spread among the population that consumes such low-quality water. That is why solving the problem of drinking water monitoring and assessment of its quality is a pressing task requiring urgent action. The article examines the quality of water for drinking needs at the water intake site of the Dniester River, a town of Biliaivka, for 2018-2022. Water quality assessment was carried out according to DSTU 4808: 2007 based on average and worst values. The methodology covers 80 indicators and, in accordance with sanitary legislation, is divided into seven separate groups (blocks). According to hygienic standards, the quality of river water is considered suitable for use if it belongs to quality classes 1-3. In this research, the analysis was carried out according to 23 indicators included in three groups, so the assessment is considered as tentative: Group I - organoleptic indicators (chromaticity, turbidity); Group II - general sanitary indicators (dry residue (mineralization), sulfates, chlorides, magnesium, alkalinity, hydrogen index, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen, phosphate phosphorus, dissolved oxygen, bichromate oxidation (COD), BSK5); Group VII – toxicological parameters (total iron, manganese, chromium (VI), zinc, petroleum products, synthetic surface active agents (surfactants), volatile phenols). It was determined that, according to the average values, the water quality of the Dniester at the water intake site is characterized as "good", which means it has clean water of acceptable quality. As for the worst indicators, the water was assessed as transitional in quality from "good" (clean) to "satisfactory" (slightly polluted).

Spatiotemporal Changes in the Quantity and Quality of Water in the Xiao Bei Mainstream of the Yellow River and Characteristics of Pollutant Fluxes

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (CODMn), and 5-day biochemical oxygen demand (BOD5)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH3-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment.

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

Performance evaluation of a community-based water management organisation using integrated water quality monitoring

ABSTRACT Community-based water management organisations (CBWMOs) play an important role in providing water services worldwide. However, there is some debate as to their capacity to sustain a safe drinking water supply considering the current socioeconomic and climate scenarios. Here, we present an integrated water quality assessment of the supply system of a CBWMO in Concepción of San Ramon, Costa Rica. Major ions, trace metals, and coliforms were analysed in the households and water sources over a 1-year period. The spring risk assessments and the water quality of the main river in the catchment were also carried out. We found that although the supplied water meets adequate standards, spatial and temporal changes in the water quality of the sources exist. Springwater composition is mainly driven by rock–water interaction processes, but early signs of potential anthropogenic pollution were found. The springs showed concentrations of NO3- above natural levels and microbial contamination in 37 and 18% of the cases, respectively. River water quality showed a distinct composition, which is consistent with the anthropogenic pressures in the catchment. This study provides useful information on how water quality assessments can be used beyond regulatory processes to improve planning by CBWMOs to ensure water security.

Analyzing spatio-temporal dynamics of dissolved oxygen for the River Thames using superstatistical methods and machine learning

By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames (UK). The indicators analyzed include dissolved oxygen, temperature, electrical conductivity, pH, ammonium, turbidity, and rainfall, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using q-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the q-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer, a state-of-the-art machine learning model, to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest Mean Absolute Error (0.15) and Symmetric Mean Absolute Percentage Error (21.96%) with the 192 historical time steps that we used. This performance is attributed to the Informer’s ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to critical periods such as morning to early afternoon, late evening to early morning, and key intervals between the 16th and 26th quarter-hours of the previous half-day. Our findings provide valuable insights for policymakers involved in ecological health assessments, aiding in accurate predictions of river water quality and the maintenance of healthy aquatic ecosystems.

Prediction of river water quality based on Artificial Neural Network

Prediction of river water quality based on Artificial Neural Network

Water Quality Evaluation and Spatial-temporal Distribution Characteristics of the East Route of the South-to-North Water Diversion Project in Jiangsu Province Based on WQI

The east route of the South-to-North Water Diversion Project in Jiangsu Province was used as an example to investigate the characteristics of changes in water quality in the area affected by significant water diversion projects. Based on the comprehensive assessment method of the water quality index （WQI）, the M-K trend test, and the geographic information system （GIS）, the spatial and temporal variation characteristics of water quality in the 13 national assessment sections of the east route of the South-to-North Water Diversion Project in Jiangsu Province were evaluated and analyzed from 2013 to 2022. The results showed that the water quality assessment grades in the study area were mainly "medium" and "good." The overall mean value was 74.03, indicating "good" water quality. DO, BOD5, and NH4+-N were the primary indicators of changes in river water quality in the region. Over time, the water quality in the basin had significantly improved from 2013 to 2022. However, there was a rebound in 2021, but this phenomenon was effectively controlled in 2022. Water quality was better during the non-flood season compared to that during the flood season. From a spatial perspective, the water quality in the southern part of the region was superior to that in the north. Furthermore, the rate of improvement was faster in the south. This could be attributed to factors such as precipitation, population density, and the scale of agricultural and livestock activities. However, the water quality of the S11 （Sanduoxi Bridge） section was generally lower than that of other sections and should be given further attention.

Investigation of the Effects of Erzurum Province Wastewater Treatment Plant on the Karasu River Water Quality

This study was carried out to investigate the effects of the wastewater treatment plant located in the Aziziye District of Erzurum Province on the Karasu River. For this purpose, water samples were taken from 3 stations for a year between 2022-2023. The mean water temperature was measured in situ during sampling as 13.8 ˚C, the mean pH value was 7.7, the mean electrical conductivity was 403 µs/cm, and the mean dissolved oxygen value was 6 mg/l. The mean total suspended solid (TSS) amount was determined as 46.7 mg/l, the mean chemical oxygen demand (COD) concentration was 28.9 mg/l, the mean biological oxygen demand (BOD) concentration was 18.5 mg/l, the mean total nitrogen (TN) concentration was 4.47 mg/l, the mean nitrate (NO3) concentration was 5.35 mg/l, the mean nitrate nitrogen (NO3-N) concentration was 1.21 mg/l, the mean total hardness (TH) was 155 mg/l, the mean ammonium (NH4) concentration was 2.75 mg/l, and the mean ammonium nitrogen (NH4-N) concentration was 2.16 mg/L. As a result, it was determined that the Wastewater Treatment Plant positively affected some water quality parameter values of the Karasu River.

Effect of Land Use Land Cover on the Hydrology of Rwizi River Catchment located in Southwestern Uganda

Changes in land use and land cover usually affect the hydrological processes of river flows, sediment transport, and water quality on a global scale. Hence, the objective of this paper was to investigate the effect of Land Use Land Cover (LULCC) on the Hydrology of Rwizi River Catchment located in South-western Uganda using appropriate standard techniques and procedures including Soil and Water Assessment Tool (SWAT). Model calibration and validation demonstrated good agreement (R² = 0.76-0.86, NSE = 0.75-0.78). LULC analysis revealed significant increases in built-up areas and forestland, with declines in grazing land and wetlands. These changes were driven by socio-economic factors and hydro-climatic influences. The study found strong correlations between LULC types and sediment yield, highlighting the implications for water quality and erosion control. The results show that urban growth raises surface runoff and peak flows as a result of a rise in impervious surfaces, whereas agricultural intensification raises water demand and lowers base flow, especially during dry spells. Deforestation has led to increased sedimentation rates, degrading water quality. These findings underscore the significant anthropogenic and climatic impacts on LULCC dynamics and hydrological processes in the catchment. The study emphasizes the critical need for integrated watershed management strategies to mitigate these impacts and ensure sustainable water management practices. By advancing understanding in hydrological sciences, this research informs policy and management decisions for sustainable development in tropical river basins globally.

Tracing fluorescent dissolved organic matter (fDOM) characteristics and water quality parameters: Insights from an urban industrial river to marine zone.

This study aims to identify continuous water quality changes and identify fluorescence properties from urban rivers to marine zones. Various types of natural and anthropogenic sources derived dissolved organic matter (DOM) have been identified in this study. These include soil-derived DOM, plant remnants, and soluble particles produced when organic material partially decomposes and is released by microorganisms, such as bacteria, algae, and plants. DOM was characterized using a three-dimensional excitation-emission matrix (3DEEM), parallel factor analysis (PARAFAC), and water quality parameters from the Buriganga River, Dhaka to Patenga Seaport, Chittagong, along with the Shitalakshya River, a small portion of the Padma River, and the Meghna River. To better understand the data analysis, the study area was divided into three central regions: urban industrial rivers, industrial estuarine rivers, and marine zones. In the urban industrial river, 3DEEM and PARAFAC identified five fluorophores (peaks: A, C, M, T, and Tuv) with five components: detergent-like, fulvic-like, tryptophan or protein-like, fulvic-like (C-type), and protein-like, which might originate from the industrial activities and sewage pollution. In the industrial estuarine river zone, three fluorophores have been identified (peaks: A, C, Tuv) with two known components, namely, fulvic acid (A-type) and fulvic acid (C-type), with an unknown photoproduct at Ex/Em = 295/368 (peak Tuv). Components in the industrial river zone may originate from terrestrial sources, indicating vegetation along the river. In the marine zone, four fluorophores have been identified (peaks: Tuv, A, T, C) with two components, that is, protein- or tryptophan-like and humic acid-like from coral origin. The intensities of both fulvic-like and protein-like substances were high in urban industrial river water owing to industrial activity and sewage pollution. SUVA254 suggests high aromaticity in all three regions, whereas the optical properties suggest that terrestrial and microbial components are present in the urban industrial and estuarine rivers. This further indicates that urban industrial river water quality is highly polluted. The lowest degradation potential index (DPI) in the marine zone might result from the presence of the highest number of dissolved solids in the water, and the highest DPI of industrial estuarine rivers explains the comparatively high presence of terrestrial-derived humic (A)- and humic (C)-like components in the ratio to the unknown photoproduct of mid-wavelength. PRACTITIONER POINTS: This study's uniqueness is a 220-km cruise from an urban river to a coastal seaport to analyze fluorescence properties. The study found that most water parameters were within the DoE standards, except for DO, which was consistently low. 3DEEM-PARAFAC identified five fluorophores linked to detergent, fulvic, and protein-like substances from sewage and industrial sources. Our study concludes that microbial and terrestrial sources dominate dissolved organic matter in urban, estuarine, and marine regions.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Unfulfilled water impact forecasts, plans and projects

The article presents a retrospective review of some forecasts, plans and projects of anthropogenic impact on water resources that did not materialize, were not implemented by the planned date or were incomplete. Values of full (water intake) and irrevocable water consumption for 2000, predicted in the 1960s and 1970s by well-known domestic and foreign researchers, are compared with the actual water consumption in 2000 in the world, the USA, and our country. It is shown that most of water consumption forecast parameters turned out to be significantly higher than the actual one, which gives reason to consider these forecasts were not borne out. In the Water Strategy of the Russian Federation, developed in 2009 for the period up to 2020, these values were also significantly overestimated (by tens of percent). Incomplete implementation of various programs did not lead to the expected significant improvement in the water quality of rivers, including the Volga, and reservoirs in Russia. Such failed projects as NAWAPA in the USA and Canada, projects of interzonal redistribution of water resources in the USSR, projects of Nizhneobskaya and Turukhanskaya (Evenkijskaya) hydroelectric power plants, partially implemented projects of Cheboksary and Nizhnekamsk hydroelectric power plants, as well as a number of others are considered. Among the main reasons for unsuccessful forecasts, unfulfilled plans and projects are the lack of reliable data, incomplete knowledge about laws of nature and society development, financial problems, environmental demands, and the dramatically changed economic and political situation.

THE PREDICTOR-CORRECTOR BACKWARD DIFFERENCE FORMULAS TO ESTIMATE THE TREND OF RIVER WATER QUALITY

The time-series trend analysis is a mathematical technique that attempts to estimate future data movement based on observed previous and current data. This can include analysing long-terms changes in river water quality in order to emphasise river management in the future. Therefore, in this study, the backward difference formula (BDF) with forward and central difference approximations is derived to estimate a of 20-year trend in water quality index (WQI) data at the Selangor River basin in Malaysia. As an adaptation of the conventional BDF and predictor-corrector scheme, the Euler’s method with a central difference formula is introduced as a predictor formula to forecast future values. Subsequently, BDF with central and forward difference formulas are specifically designed as corrector formulas to adjust the predicted future values. The performance of the method is evaluated with absolute error and average error accuracy measure indicators. The numerical results validated the proposed method and proved its reliability in estimating the time series trend analysis owing to its reasonable accuracy. In conclusion, the outcomes of this study can provide the authorities with a technical reference for monitoring, maintaining and improving the river water quality in Selangor and other states.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36mg/L and 44.72mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Comparison of novel physics-guided machine learning models with empirical equations for predicting longitudinal dispersion coefficient in diverse natural river systems

Accurate calculation of the longitudinal dispersion coefficient (LDC) is crucial for assessing river fate and transport processes. Traditional methods, including experimental, analytical, and empirical equations, each have their challenges, such as complexity and susceptibility to errors. This study introduces and evaluates advanced physics-guided machine learning (ML) models, including hybrid hydrodynamic-particle simulation (HHPS), physics-enhanced ML (PEML), and physics-regularized regression trees (PRRT). Our dataset includes hydraulic and geometric parameters collected from 50 diverse rivers across the US and the UK, encompassing variables such as river width (W), flow depth (H), average flow velocity (U), flow shear velocity (U*), concentration of the solute (μgL) and LDC (ε). Input parameters were dimensioned using the UU∗ and WHratios, while the dimensionless LDC (εHU∗) served as the model output. By embedding core hydrodynamic principles such as continuity, mass and momentum conservation, and Navier-Stokes equations into the ML algorithms, these models achieved high predictive accuracy. Specifically, the HHPS model reached a coefficient of determination of 0.997, and both PEML and PRRT demonstrated Nash-Sutcliffe efficiencies exceeding 0.950, significantly outperforming traditional empirical equations. Additionally, SHapley Additive exPlanations (SHAP) sensitivity analysis revealed the substantial influence of channel width and flow conditions on LDC predictions. Our study highlights the superior performance of physics-guided ML models in providing accurate and reliable tools for river water quality management, demonstrating their substantial improvements over traditional methods and their potential broader applications in environmental studies.

Tracing nitrogen and phosphorus pollution in urban runoff: Insights from isotopic tracers and SWMM modeling

Water contamination in certain rivers during rainfall necessitates the evaluation of pollutant levels within pipelines and the identification of their sources to ensure effective pollution control. In this study, we assess pollutant concentrations during runoff from surfaces, pipelines, and channels, identify pollutant sources within pipelines, and analyze sewage leakage contributions to channel pollution in residential areas in Wuxi City, China. The results showed that the event mean concentrations of total nitrogen (TN), total phosphorus (TP), ammonium (NH4+-N), and nitrate (NO3−-N) in the drainage outlet runoff were 3.4, 1.1, 8.8, and 14.9 times higher than in impervious surface runoff. The predominant forms of nitrogen and phosphorus in runoff shift from organic to inorganic as they move from the surface to pipelines. The pipeline load and contribution were further estimated by combining water quantity calculations from the calibrated and verified Storm Water Management Model (SWMM) with measured nitrogen and phosphorus concentrations in the runoff. TN, NH4+-N, NO3−-N, and PO43--P within the pipelines accounted for 59%, 83%, 90%, and 77% of the discharge loads, respectively. The application of the Stable Isotope Analysis in R (SIAR) package (MixSIAR) revealed that the domestic sewage and soil organic N were the predominant sources of pollutants within drainage pipelines, contributing 23.7% and 32.6% of NO3−-N, respectively. Additionally, the sewage leakage accounted for 33.7% and 66.9% of NO3−-N in drainage runoff and channels, respectively. Unlike existing methods, this study quantitatively highlights the significant impact of pipeline pollutants and sewage leakage on urban river water quality, providing essential insights for developing effective strategies to mitigate non-point source water pollution.