River Water Quality Management Research Articles

Estimation of Groundwater River Availability in Leang Lonrong Cave Using ARIMA Model and Econophysics Valuation Approach

This quantitative descriptive study uses the ARIMA model approach to predict groundwater river water availability. It assesses its valuation with an econophysics orientation in the Leang Lonrong Cave area of TN Bantimurung Bulusaraung. Secondary data from 2010-2023, including Water Discharge and Water Level data from the River Basin Center of Pompengan Jeneberang (BBWS) and primary data on the Valuation of Willingness to Pay, were utilized. The analysis reveals that water discharge predictions (2024-2030) in the Gua Leang Lonrong area indicate consistent annual water availability, with the highest discharge projected in December 2030 (5.08 m3/s) and the lowest in September 2024 (1.12 m3/s). Valuation of utilizing Leang Lonrong River water through willingness to pay showed residents willing to pay Rp 42,142.85/month for clean water management. At the same time, visitors are willing to contribute Rp 21,596.77/visit to enhance water quality management. Elasticity analysis modeling water discharge with willingness to pay suggests a relatively elastic relationship, with a minor impact, indicating unresponsiveness to changes in water discharge percentages. Statistical and economic methods were employed to analyze daily water discharge patterns and evaluate residents' willingness to pay for underground river water utilization. The ARIMA model (0,1,1) forecasted future water discharge patterns with a mean absolute percentage error (MAPE) of 33.7%. The study surveyed 62 respondents, finding an average willingness to pay approximately IDR 21,596.77 per visit to maintain Leang Lonrong River water quality management.

Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing

Abstract. The Chlorophyll-a (Chla) concentration is an important parameter characterizing the water quality of rivers and lakes. Satellite remote sensing provides new opportunities for the quantitative monitoring of Chla concentration in large-scale water bodies. In this study, Sentinel-2 satellite remote sensing data integrated on the Google Earth Engine (GEE) remote sensing big data platform are employed, along with hourly measured water quality site data, to establish a quantitative inversion model for Chla concentration water quality parameters in Jiujiang City within Jiangxi Province, China. The Chla concentration is estimated for each quarter from 2020 to 2022, and the spatial distribution is analyzed, revealing the changing trend of Chla concentration over the past three years. The key findings are as follows: (1) The quantitative inversion model for Chla concentration has been validated with measured data, achieving a model accuracy of 0.53; (2) The spatial inversion results of Chla concentration exhibit an increasing trend that is consistent with actual measurement site results; (3) Owing to the influence of human activities and the peak-low water level of rivers and lakes, Chla concentration shows a discernible seasonal variation pattern. This methodology offers a new perspective for analyzing the seasonal variation characteristics of Chla concentration in rivers and lakes, providing valuable insights for the sustainable management of river and lake water quality.

Clustering evaluation of water quality for various classes of in-flow rivers in connected brackish lakes.

Brackish waters and estuaries at the lower reaches of rivers accumulate organic matter and nutrients from various sources in the watershed. Sufficient light and shallow water depth stimulate phytoplankton growth, resulting in a more diversified ecosystem with higher trophic levels. For effective watershed management, it is crucial to characterize the water quality of all rivers, including small and medium-sized ones. Our field survey assessed water quality parameters in 26 inflow rivers surrounding Lakes Shinji and Nakaumi, two consolidated brackish lakes in Japan. The parameters included water temperature, salinity, chlorophyll-a, and nutrients. The study used hierarchical clustering. The Silhouette Index was used to assess clustering outcomes and identify any difficulties in dispersion across clusters. The 26 rivers surrounding Lakes Shinji and Nakaumi were classified into six groups based on their water quality characteristics. This classification distinguishes itself from earlier subjective methods that relied on geographical factors. The new approach identifies a need for improved management of river water quality. The results of the cluster analysis provide valuable insights for future management initiatives. It is important to consider these findings alongside established watershed criteria.

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Design of Non-Structural Practices for Sustainable Water Quality Improvement in an Urban River: A Case Study of South Korea

Urban rivers exhibit characteristics of low flow and significant water quality fluctuations, making them susceptible to pollution from various sources such as untreated sewage, non-point pollution within the urban area, and unknown inflows. To address water quality management in urban rivers, precise investigations into background water quality, pollution levels, and the characteristics of pollution sources are essential. Following the identification of pollution sources, sustainable river management strategies, incorporating both structural and non-structural measures, are crucial. This study aims to develop continuous and long-term river management strategies, considering the characteristics of urban river basins, through citizen participation governance and non-structural approaches. Citizen networks were formed for each target urban river, and activities for water quality improvement were proposed and implemented. This study provides phased approaches to citizen participation governance, and activities include citizen-led water quality monitoring, the purification and monitoring of riverbank pollution sources, and water-related education. It emphasizes the importance of local residents’ interest in urban river water quality improvement and underscores the need for sustained activities through local citizen networks. Additionally, active participation and investments from the local government, government agencies, and various experts are deemed essential.

Water quality modeling-based assessment for the scope of wastewater treatment of the urban reach of River Yamuna at Delhi, India.

River water quality management is a tedious job as it comprises multiple variables. The River Yamuna, Delhi's urban reach, is highly contaminated with very low or zero freshwater flow during the non-monsoon periods. The river quality has been appraised for pre- and post-monsoon periods of 10 years of data collected from the Delhi Pollution Control Council (DPCC). A sharp declination of dissolved oxygen and acceleration of BOD have been observed after the outfalling of drain 1. The wastewater treatment plants data for 2020-2022 have been analyzed, and the removal efficiencies of BOD and COD were found between 65 and 94%. The BIOFORE technology has shown maximum removal efficiencies, around 94% and 89% for BOD and COD, respectively. The level of treatment has been evaluated by developing a water quality model with the existing QUAL2kw framework. Twelve strategies have been generated for four levels of treatment and three different flow conditions. The severely polluted reach of Yamuna has low self-purification capacity; flow augmentation has little effect on the existing load. Hence, the level of treatment required to increase. After the introduction of outfalling drain D13-D16, a sag of DO has been observed; to improve the DO concentration, external aeration is suggested before wastewater disposal into the river. The study is a novel effort to manage the river quality by developing scenarios, including the level of treatments and flow augmentation, and keeping up the desired DO concentration assigned for this river reach.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Environmental Risk Assessment of the Harbin Section of the Songhua River Basin Based on Multi-Source Data Fusion

Surface water is a vital resource for human survival. However, economic and social development has resulted in significant pollutants from human activities, causing environmental pollution in watersheds. This pollution has had a profound impact on the surface water environment. However, limited studies have been conducted on the environmental risk evaluation of the watershed. In this study, we accounted for agricultural, industrial, and domestic source discharges in the districts and counties of the Harbin section of the Songhua River Basin for 2021. Data were collected from Statistical Yearbooks and governmental departments, and the characteristics of pollutant discharges in Harbin’s districts and counties were analyzed. Subsequently, we employed the Back Propagation neural network optimization method, combining remote sensing data, accounting data, pollution discharge data from each district and county, and economic and social data from the Statistical Yearbook and literature. This fusion of multiple data sources facilitated the construction of a watershed environmental risk evaluation system. The analysis considered four levels: economic and social, resource load, environmental infrastructure, and pollution discharge. Via this comprehensive evaluation, we identified the reasons for environmental risks in the water environment of the Harbin section of the Songhua River Basin. The evaluation results indicate that Nangang District, Xiangfang District, and Pingfang District face a higher risk to the water environment. Consequently, recommendations for mitigating water environment risks in these areas and across Harbin City are presented. The research methods and findings in this paper contribute valuable insights for developing control strategies to manage water quality in critically polluted areas of the Harbin section of the Songhua River Basin, providing a scientific foundation for regional river water quality management studies.

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.

Social imaginaries methods and socio-engineering competences in sustainable river management (a case study in the living lab Upper Citarum)

The Living Lab approach has become popular and developed in the past decade. It could provide a configuration to pursue a shared vision of integrated water resources management of the Citarum River in West Java - Indonesia. The multi-stakeholder situation and the growing recognition of interdependencies among stakeholders foster the complexity of addressing sustainable river management for the Upper Citarum River. To gain insights on essential competencies and adaptations in higher education curricula, the Environmental Engineering Department of the Faculty of Civil and Environmental Engineering-ITB, Telkom University Indonesia, and Van Hall Larenstein University of Applied Sciences, Netherlands, joined hands in a collaborative research project. This study aims to develop a socio-engineering aspect for sustainable river water quality management in the Environmental Engineering Field and Curricula. The methods used are social imaginaries of Participatory Mapping and a Poetry Route that allowed the involved river bank communities to activate their role and take positions in the living lab. Institutional stakeholders, acting in a facilitating role, learned to gain and share information from and with the community. The result concludes that social imaginaries methods enable a new perspective in developing community-based programs and advocate further exploring the socio-engineering competencies of environmental professionals.

Nitrogen and Phosphorus Loading Characteristics of Agricultural Non-Point Sources in the Tuojiang River Basin

Agricultural non-point source (ANPS) pollution has emerged as a significant factor influencing water quality within watersheds. Understanding the spatial distribution and composition of ANPS is crucial for effective river water quality management. Based on the statistical data of 28 districts and counties in the Tuojiang River Basin (TJRB), the load distribution characteristics of total nitrogen (TN) and total phosphorus (TP) from ANPS were studied in this work by using the pollutant discharge coefficient method. In 2018, ANPS contributed 60,888.92 tons of TN and 20,085.98 tons of TP to the TJRB. By 2019, the TN load had decreased to 57,155.44 tons, while the TP load increased to 21,659.91 tons. Spatially, TN and TP loads follow a pattern of being lowest in the upstream, intermediate in the downstream, and highest in the middle reaches. Planting sources emerged as the primary contributors to TN and TP loads from ANPS in the TJRB, accounting for 61.43% and 77.39%, respectively. Rural living sources made a lesser contribution, at 20.23% for TN and 9.15% for TP, while poultry and livestock farming sources accounted for 18.34% of TN and 13.46% of TP loads. The analysis of grey water footprint (GWF) and water pollution level (WPL) revealed that TN and TP loads continued to exert significant pressure on the TJRB’s water environment throughout the study period. These findings offer valuable insights for enhancing water quality management in the TJRB.

Estimating the impact of temperature and streamflow change on river nitrogen pollution using the land-river integrated modeling system

The high nitrogen load in most rivers across the globe causes various environmental issues. Non-point source (NPS) pollution within a watershed has a significant impact on water quality management and prediction accuracy in rivers. Temperature and streamflow, as the most important environmental indicators, significantly affect water pollution management. This study takes the Rappahannock River Basins (RRB) as a case and adopts the method of coupling land and river models to improve the simulation accuracy of river pollution, outputs and analyzes the temporal and spatial distributions of nitrogen pollution in RRB, assesses the impact of temperature and streamflow changes on nitrogen pollution in water bodies. The results show that NPS pollution is mainly affected by topography and land use, and the annual output is more correlated with the watershed area. Temperature is negatively correlated with NH4-N and NO3-N in the Rappahannock River. Nitrogen is more susceptible to streamflow in winter; but in summer, it was stable and even showed the opposite changing tendency to winter. Changes in temperature of 1.5 ∼ 2℃ and streamflow of 20 ∼ 50% have similar effects on NH4-N, while increasing streamflow can significantly increase the concentration of NO3-N, and most of these changes are very evident. NH4-N and NO3-N concentrations will increase in RRB when the summer temperature is below the long-term average. NH4-N concentration will increase when upstream streamflow (such as the amount of mountain snowmelt) decreases in winter, while NO3-N concentration will decrease. The establishment of the integrated model can provide the nitrogen pollution load of each sub-watershed and each segment, which can be used to develop the TMDL for the Rappahannock River.

Assessment of Water Quality Parameters in a Rivers Using Qual2Kw: A Study in Bangkalan River

Increased human activity, shifts in land use and the increasingly diverse lifestyles of people in Bangkalan City have resulted in an increase in domestic waste and made the pollutant load in the Bangkalan River even greater over time. The Bangkalan River segmentation from the Tunjung Dam to the Bancaran River has indicated that the quality of river water has decreased. The purpose of this study was to determine the pollution level of the Bangkalan River, the condition of the Bangkalan River's water quality, and to obtain recommendations for treatment strategies for river water quality management in the future. This research approach uses descriptive, quantitative, and qualitative. The method used is calculating the Pollutant Index and Water Quality Index, simulates parameters in a scenario using QUAL2Kw modeling. In the results of this study it is known that the pollution level of the Bangkalan River is categorized as lightly polluted and the quality of the Bangkalan River is categorized as moderate. The results of the river water quality test in the simulation scenario with QUAL2Kw show that the water quality of the Bangkalan River is classified as class II water quality and is classified as medium water quality in the segment of the Tunjung Dam to the Bancaran River.

Application of best subset method for river water quality modeling: A study on Godavari River, India

AbstractThe utilization of water quality analysis to inform optimal decision‐making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post‐monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R2, F value, adjusted R2a, AICc, and RSS. The final best subset model across all parameters exhibits R2 values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision‐making and in the management of river water quality for its sustainable use in the study area.

Assessment of Phytoplankton Diversity in Downstream Region of Meenachil River,kerala, During the Pre-monsoon and Monsoon Season

The present study aimed to assess the Phytoplankton diversity in the downstream of Meenachil River ,Kerala,during pre-monsoon and monsoon seasons. A total of 39 species belonging to five taxonomic divisions were identified, namely Chlorophyceae, Bacillariophyceae, Cyanophyceae, Euglenophyceae, and Xanthophyceae. The results revealed that Chlorophyceae was the dominant group, followed by Bacillariophyceae, Cyanophyceae, Euglenophyceae, and Xanthophyceae. The season-wise distribution of phytoplankton showed a higher abundance in the pre-monsoon season compared to the monsoon season. This study provides valuable insights into the phytoplankton diversity of Meenachil River, Kerala, and highlights the significance of monitoring seasonal changes in phytoplankton abundance for effective water resource management. The findings of this study can be useful for further research on water quality monitoring and management in rivers

A high-resolution comprehensive water quality model based on GPU acceleration techniques

The study of the temporal and spatial evolution of pollutants in modern water bodies and river water quality management largely depend on river water quality models. These models are usually constructed based on detailed hydrodynamic models but cannot satisfy the high precision and efficiency requirement of model simulations in high-resolution terrain. To solve this problem, we developed a high-resolution comprehensive water quality model based on the graphics processing unit (GPU) acceleration techniques in which hydrodynamics and accompanying contaminants were perfectly coupled. The performance and functionality of the model were improved by using the Compute Unified Device Architecture (CUDA) parallel computing architecture and robust model algorithms. A simulation function of the transport attenuation and interaction among multicomponent pollutants was stablished by solving mass conservation, momentum and chemical concentration equations of the flow process. The simulation results of the pure pollutant reaction process and the transport and reaction process were validated against an analytical solution and the results of a two-dimensional steady water quality model by using the Nash-Sutcliffe efficiency (NSE) coefficient. The coefficients were 0.996 and 0.984, respectively. The water quality of the Xidagou River in Yinchuan was evaluated and analyzed by using the validated high-resolution comprehensive water quality model, and the numerical results of two-dimensional pollutant transport were explored. On the adopted RTX3070d computer, only 0.12 h were needed to simulate the hydrodynamic and water quality evolution process involving 350.000 cells with 5-h model settings. The model achieved a high operation speed and high calculation accuracy and could effectively simulate, predict and evaluate various complex water environment problems. This study provides a reliable tool for further research on the water environment and water ecology and the treatment of black and odorous water bodies.

Integrated water-quality management indicators from river to sea: A case study of the Bohai Sea, China

Considering the interrelatedness of river and bay ecosystems, river and bay water quality management is shifting to integrated management across coastlines. Here, an integrated management indicator for the coordinated and efficient nitrogen abatement of the Bohai Sea and its basin was proposed. The terrigenous total nitrogen (TN) allocated load was optimized under the dual water quality constraints for both river and bay using a simulation–optimization method. The contributing jurisdictions were identified by their TN overload rates, and their responsibility apportionment rate for specific nitrogen-polluted segment was quantified. Integrated TN reduction scheme resulted in a 29 % greater reduction in bay and river nitrogen pollution than the equal proportion reduction approach. In 18 % of the watersheds in the Bohai basin, the water quality standards of the river were more restrictive than the standards of the bay. Integrated management scheme has higher coordination of river and sea management objectives.

Integrated river water quality and quantity management based on undesirability and cost‐effectiveness

AbstractIntegrated water resources management aims to simultaneously solve water shortage and contamination problems by considering economic, social, and environmental criteria. To that view, this study has developed a two‐objective optimization model regarding water allocation (WA) and wasteload allocation (WLA) in the Zayandehrood river basin, the central part of Iran. The model aimed to supply the users' water demand considering the downstream environmental flow to fulfill the social‐environmental requirements. Besides, satisfying the river water quality standards was regarded as the environmental‐economic criteria. The WA objective function minimizes the undesirability caused by reduced allocated water and downstream environmental flow. The QUAL2K model was used to simulate the wasteload discharge impacts on the river water quality. The water quality parameters included biochemical oxygen demand (BOD), TP, and TN for the urban and industrial wasteload and nitrate for the agricultural drainage. The results showed that a joint WA ‐ WLA optimization reduced the total average WA by 15% and increased the downstream flow by three times the base level. Also, the water quality violations of the standard limits were reduced by 18, 4, and 15 times for BOD, NO3, and TP, respectively.

Prediction of Surface Water Quality of Upper Ganga in Uttar Pradesh

Abstract: With an increase in population and accelerated pace of industrialization, water quality is going to degrade day-by-day. The main source of water in India is from rivers. The Ganga River Basin is the world’s most populated and is home to half of India’s population, including two-thirds of the nation’s poor. This paper highlights the utility of statistical techniques for evaluating, interpreting complex data sets and recognizing spatial differences in water quality for effective management of river water quality. The Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. 6 water quality parameters Dissolved Oxygen, BOD, pH, Temperature, Electrical Conductivity and Total Coliform are analysed and predicted. In this work 4 monitoring station is taken for the prediction analysis and data is taken from the CPCB. In this work ARIMA model is giving the better prediction of temperature, total coliform and conductivity in compare of other water quality parameter pH, BOD and DO. The max value for correlation coefficient for Dissolved Oxygen, BOD, pH, Temperature, Electrical Conductivity and Total Coliform are respectively 0.73, 0.76, 0.79, 0.83, 0.84 and 0.85.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET′) reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.