Water Quality Model Research Articles

River water quality modelling in South Africa: Considerations, sourcing and accessing of input data

River water quality modelling relies on a good dataset for model input, calibration and validation to develop a reliable model. Determination of data requirements, and data sourcing towards this purpose in South Africa, are not elementary. Data availability is key throughout the modelling processes, making the sourcing of good data equally imperative. This paper provides starting considerations, and compiles a list of available data sources applicable to river water quality modelling in South Africa. It also provides a background on the included data sources and a general overview of the extent of the databases.

Statistical analysis and water quality modeling towards holistic health assessment of Himalayan and peninsular rivers of India

Water occurs naturally in a variety of forms. Liquid water is present in rivers, lakes, the ground, the sea, and precipitation. The primary objective of water quality monitoring is to ascertain whether the water quality being observed is appropriate for its designated purposes. An investigation was undertaken to assess the concentration levels of various water quality parameters, including total hardness, electric conductivity, temperature, Ca, Cl, K, Mg, Na, SO4, and HCO3. The research assessed the physicochemical parameters of ten (10) rivers, including the following: Brahmaputra (Arunachal Pradesh); Barak (Assam); Godavari (Maharastra); Brahmani (Odisha); Sabarmati (Gujarat); Tapi (Gujarat); Mahi (Gujarat); and Ganga (Uttar Pradesh). Water samples were obtained from the aforementioned rivers spanning the years 2013 to 2020. Descriptive Analysis, one-way ANOVA, Covariance, and Correlation were utilized to examine the impact of the rate of change on the interrelationships among the parameters. At the current level, the Narmada River cannot meet the daily demands that arise during and after the monsoon. The primary sources of pollution were inputs from households, businesses, and agriculture. The Gang River portions have been poisoned by sewerages, untreated industrial discharges, and chemical surges from agricultural fields. The basin is still being impacted by a number of anthropogenic pressures associated with an expanding human population, such as changes in land use, elevated water pollution, increased water demand, river channelization, and the Brahmanputra River's regulating flow. Anthropogenic heavy metals Cu, Cd, Cr, Pb, and Ni have not been shown to exhibit seasonal changes; however, geogenic heavy metals Mn, Fe, Mg, and Ca have been recorded to undergo seasonal oscillations in the Barak River and Still. The alkaline impacts of the Godavari water sample on the river are shown by the physical-chemical analysis of the samples. The amount of dissolved oxygen has decreased at various sites when pollutants are added to the water. A rise in BOD and turbidity levels is a result of the introduction of contaminants at different sites. The tests of TDS and F-coli showed an increase in value because of the sewage and burial procedures. Reduced flows below the dam and sudden increases resulted in a significant loss of organisms due to water discharge for hydropower generation. The Krishna River lost several of its characteristic characteristics as a result of dams being built both upstream and downstream. The Brahmani River's summertime dissolved oxygen (DO) levels are found to vary from 6.16 to 7.81 mg/L. The Brahman River's water quality index varied from 36.7 to 44.1, which is considered excellent water quality. Huge public awareness, strong implementing environmental policy decisions are some of the potential solutions to increase the health conditions of the Himalayan and Peninsular rivers of India. India's Himalayan and Peninsular rivers' health is measured via water quality modeling. This study evaluates river health using physiochemical and biological parameters. This study examines Himalayan and Peninsular India's water resource management, environmental preservation, and public health.

Sinuosity‐Driven Hyporheic Exchange: Hydrodynamics and Biogeochemical Potentials

AbstractHydrologic exchange processes are critical for ecosystem services along river corridors. Meandering contributes to this exchange by driving channel water, solutes, and energy through the surrounding alluvium, a process called sinuosity‐driven hyporheic exchange. This exchange is embedded within and modulated by the regional groundwater flow (RGF), which compresses the hyporheic zone and potentially diminishes its overall impact. Quantifying the role of sinuosity‐driven hyporheic exchange at the reach‐to‐watershed scale requires a mechanistic understanding of the interplay between drivers (meander planform) and modulators (RGF) and its implications for biogeochemical transformations. Here, we use a 2D, vertically integrated numerical model for flow, transport, and reaction to analyze sinuosity‐driven hyporheic exchange systematically. Using this model, we propose a dimensionless framework to explore the role of meander planform and RGF in hydrodynamics and how they constrain nitrogen cycling. Our results highlight the importance of meander topology for water flow and age. We demonstrate how the meander neck induces a shielding effect that protects the hyporheic zone against RGF, imposing a physical constraint on biogeochemical transformations. Furthermore, we explore the conditions when a meander acts as a net nitrogen source or sink. This transition in the net biogeochemical potential is described by a handful of dimensionless physical and biogeochemical parameters that can be measured or constrained from literature and remote sensing. This work provides a new physically based model that quantifies sinuosity‐driven hyporheic exchange and biogeochemical reactions, a critical step toward their representation in water quality models and the design and assessment of river restoration strategies.

Stability and unweighted L1-gain analysis for switched time-delay positive systems with stable and unstable subsystems

This article investigates the stability and unweighted L1-gain analysis of switched time-delay positive systems (STDPSs) with stable and unstable subsystems. The persistent dwell time (PDT) switching is considered for the first time, which is more general compared with the typical dwell time (DT) or average dwell time (ADT) techniques. Under the PDT switching, the activation time of an unstable subsystem is less than an upper bound instead of greater than a given constant, which makes it easier to achieve stability in practice. Considering the stable and unstable subsystems, a piecewise multiple co-positive Lyapunov–Krasovskii functional is constructed to obtain the stability criterion of the STDPSs. Subsequently, the unweighted L1-gain for STDPSs is explored, which has explicit physical meaning compared with the weighted results and can be calculated by solving the linear programming problem. Finally, a water-quality model and a numerical example are provided to indicate the validity of the proposed methods.

Calibration, validation and uncertainty analysis of a SWAT water quality model

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.

Water Quality Modelling with Industrial and Domestic Point Source Pollution : a Study Case of Cikakembang River, Majalaya District

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River’s water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the resultsof water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability.

Numerical simulation model of water quality evolution in urban rivers considering adsorption–desorption process of nitrogen and phosphorus in sediments

In order to more realistically simulate the river channel water quality considering the characteristics of sediment release, it is necessary to couple the simulation model of sediment release to the hydrology-hydrodynamics-water quality model to quantify the complexities involved in the nitrogen and phosphorus cycle in river channel sediments. However, in the existing studies, most of the water–sediment models constructed in the analysis of single pollutants such as NH3-N or BOD, but fail to consider the interaction between multiple pollutants, and the model software related to water quality simulation fails to establish complex river sediment pollution simulations, there are still challenges in the application of the existing water quality module of the MIKE simulation software to the nitrogen and phosphorus cycle of sediments. In this study, based on the new sediment nitrogen and phosphorus release mechanism, we rewrite the water quality module ECOLab of the MIKE 21 model. The laboratory test results and the measured data are applied to the calibration of the model parameters. The new water quality module considering the adsorption–desorption process of sediments was embedded into the MIKE 21 model. The new module couples the MIKE model and the SWWM model to establish a 2D water quality model of river channel hydrology-hydrodynamics-water quality-sediment release coupling. This model targeted applies in urban river channels with sediment releasion as the main pollution. The results show that the new model has significantly improved the simulation accuracy of BOD and NH3-N, and has an optimized effect on the simulation of TP. The R, NSE, and RMSE values of the model for NH3-N simulation are 0.904, 0.845, and 0.612. This model is more suitable for numerical simulation of urban river water quality where sediment release is the main cause of endogenous pollution.

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures.The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators.However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications.Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.

The Collaborative Policy Modeling Paradox: Perceptions of water quality modeling in the Chesapeake Bay Watershed

The Chesapeake Assessment Scenario Tool (CAST) serves multiple key functions in meeting nutrient reduction targets across the Chesapeake Bay Watershed (CBW) and is embedded in the water quality governance system. To investigate contested perspectives regarding the model, we interviewed 59 stakeholders engaged in model governance across the CBW. We recorded statements regarding the accuracy, legitimacy, and credibility of the model, influences on its use, and on challenges and opportunities. We found skepticism regarding the legitimacy of CAST, including suggestions its role facilitates a “paper process” of policy design and that past experience has greater influence on policy decisions than model predictions. However, despite its perceived shortcomings, CAST has been central in helping stakeholders in prioritizing mitigative activities. With respect to credibility, most respondents believe the model underestimates the effects of nutrient-reduction practices, thereby underestimating progress toward TMDL-related goals. Respondents also identified opportunities for model improvement, emphasizing co-benefits of conservation practices over and above nutrient reduction. Overall, our analysis demonstrates a Collaborative Policy Modeling Paradox: collaborative model development is necessary for effective policy modeling, but the political processes of collaborative model development can negatively impact perceptions of salience, credibility, and legitimacy. Although it is important to recognize this paradox, as it is linked to dissatisfaction with the models, our findings also point to areas where improvement has occurred and to future opportunities for development.

Impact of reclamation projects on water quality in jinmeng bay, China

Since 2015, green tides have recurred annually in Jinmeng Bay (JMB), China. Reclamation projects have been considered an important anthropogenic perturbation in JMB for green tides. A coupled hydrodynamic and water quality model was used to simulate hydrodynamic and transport processes for different artificial structure scenarios. Numerical results prove that reclamation projects exert a significant impact on the water environment in JMB, and nutrient distributions are mainly influenced by Conch Island, which decreases pollutant dilution and diffusion in coastal waters. Moreover, the average residence time increases by about 35% after the construction of the two artificial islands. Areas with long residence time coincide with the major sources of the green tide blooms, which declares that the weak water exchange capacity affected by artificial islands is responsible for the water quality degradation. The increased nutrient levels and hydraulic residence time also suggest the high possibility of green algal blooms in JMB.

Water quality prediction based on sparse dataset using enhanced machine learning

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

An extensive spatiotemporal water quality dataset covering four decades (1980–2022) in China

Abstract. Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at https://doi.org/10.6084/m9.figshare.22584742 (Lin et al., 2023b).

Improving soil water storage with no‐till cover cropping in the Mississippi River Alluvial Basin

AbstractExcessive tillage reduces soil water storage and increases surface runoff. Cover crops can modify the effect of tillage on soil water dynamics, but limited information is available for the Mississippi River Alluvial Basin. This study was conducted to determine whether soil–water dynamics could be manipulated through conservation production systems affecting surface residue management. Effects of tillage (conventional tillage [CT] vs. no‐tillage [NT]) and cover cropping (no cover crop and Austrian pea [Pisum sativum L.] cover crop [CC]) on soil water balance during the CC season in sorghum (Sorghum bicolor L.) and cotton (Gossypium hirsutum L.) crops were modeled using the root zone water quality model for a site near Stoneville, MS, on “Commerce” silty‐loam soil. Capacitance sensors measured soil volumetric water content to 120 cm during CC growth in 2019 and 2020. Field calibration of sensors reduced root mean square error from 0.05 to 0.03 m3 m−3. Pooled across years, NT increased volumetric water content in the 0–40 and 40–120 soil layers by 8.5% and 6.8%, respectively, compared to CT. Regardless of the CC treatment and cropping system, NT reduced surface runoff by 24.5% over its conventional counterpart, while soil water storage increased from 2.35 to 5.23 cm within the 1.2‐m profile, resulting in a 17% increase in consumptive use of water (evapotranspiration). CC treatments did not impact soil water storage and consumptive water use. The results demonstrate that NT systems can affect water dynamics by increasing infiltration and soil water storage in the humid subtropical region of the United States.

A comprehensive review of water quality control on lentic ecosystem through the water quality, IoT and aeration models

Eutrophication is the term used to describe the phenomenon in which a body of water becomes excessively loaded with nutrients, leading to a population of basic plant life. Excessive growth of algae and planktonic organism’s manifests as blooms on the water surface. Eutrophication gets worse in a lentic ecosystem when there is no outflow or water movement, resulting in the accumulation of pollutants on the surface. Water quality measurements, including temperature, pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD), exhibit correlations with one another. Therefore, this article provides a comprehensive review of the water quality model, aeration system, and IoT technology. The water quality model enables the development of forecasts on future water quality. Therefore, effective water quality management can be strategized based on the specific attributes of the water. In addition, an aeration system is a technology used to improve water quality by enhancing oxygen levels and decreasing nutrient levels. Furthermore, it has been shown that real-time surveillance with IoT technology can enhance precision, effectiveness, and reduce time consumption. Therefore, it is important to priorities the implementation of water quality model, aeration, and IoT technology since they offer significant benefits in controlling water quality.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Factors Influencing the Spatio-Temporal Distribution of Chlorophyll-a in Jinmeng Bay, China

Field observations were combined with a coupled hydrodynamic and water quality model to investigate the spatial and temporal variation in Chlorophyll-a (Chl-a) in Jinmeng Bay, China. The relatively high Chl-a values were distributed in the inshore waters, mainly due to the abundant nutrient inflow from the Tanghe River. The model’s results indicate that the Chl-a concentration was much higher in seaweed beds surrounded by artificial islands and reefs under the southeasterly wind, largely due to the fact that pollutants are prone to accumulate in coastal areas where flow is attenuated by the presence of natural and artificial marine structures. It was also found that the southwesterly winds suppress the inflow of nutrients from the Tanghe River to the coastal areas, and, therefore, lower the Chl-a levels. River input and wind forcing are the major factors that influence Chl-a concentrations in the anthropogenically influenced bay. This finding provides useful guidance for the prediction and mitigation of green tides in Jinmeng Bay in the future.

PHYSICO-CHEMICAL PARAMETERS AND CLIMATE CHANGE IMPACT ON ALGAL GROWTH

Identifying the biological processes that influence algal growth is essential for ecosystem management. Algae are little aquatic plants that exist either as solitary cells or in regions of diverse dimensions. Zooplankton rely on them as a crucial component of the aquatic food chain, since they provide as their primary source of nutrition. Algae, via the process of photosynthesis, produce oxygen that is subsequently released into the water, serving as a vital resource for fish and other aquatic organisms. This work presents a methodology for estimating the biological characteristics of algae, which play a crucial role in the regulation of eutrophication, via the use of modelling and exploration methodologies. The estimation of algae growth and respiration rates was conducted by using a one-dimensional water quality model and two-dimensional regionally distributed water quality data taken from Lower Manair Dam of Karimnagar District, Telangana. A total of 24 algae were detected in the sample. The most abundant types of algae include Green algae, Flagellate algae, Cyanobacteria, and Diatoms. In the summer season, 17 types of algae were found, while in the spring season, 20 types were detected. The estimation of biological characteristics of algae in natural freshwater environments may be achieved by a physico-chemical technique, which offers an alternate option to sampling and in situ testing. KEYWORDS: Algal growth, biological parameters, Lower Manair Dam, eutrophication, in situ testing

Selecting effluent outfall location in small rivers using proper water quality models based on effluent and local characteristics

ABSTRACT This study presents a methodology to determine the appropriate location for effluent disposal in a low-flow river using numerical modelling, via a case study of a river located in Southern Brazil, considering disposal of landfill leachate with known characteristics. There are four key steps: 1) a principal component analysis (PCA) for a set of representative parameters; 2) a decision flowchart for proper water quality models; 3) a modified Water Quality Index (WQI) using the resulting concentrations and 4) simulation on 1D and 2D models and WQI calculation and evaluation. The PCA highlights eight relevant parameters: ammonia (NH3), chloride (Cl−), biochemical oxygen demand (BOD), total phosp horus, nitrite (NO2 −), nitrate (NO3 −), dissolved oxygen (DO) and total dissolved solids. Two models were considered suitable: HEC-RAS (1D) and SisBaHiA (2DH). After simulating the parameters in both models, a WQI was calculated for each location. Both HEC-RAS and SisBaHiA lead to the same effluent outfall location, showing that spatial discretisation does not affect the results.

A triple increase in global river basins with water scarcity due to future pollution

Water security is at stake today. While climate changes influence water availability, urbanization and agricultural activities have led to increasing water demand as well as pollution, limiting safe water use. We conducted a global assessment of future clean-water scarcity for 2050s by adding the water pollution aspect to the classical water quantity-induced scarcity assessments. This was done for >10,000 sub-basins focusing on nitrogen pollution in rivers by integrating land-system, hydrological and water quality models. We found that water pollution aggravates water scarcity in >2000 sub-basins worldwide. The number of sub-basins with water scarcity triples due to future nitrogen pollution worldwide. In 2010, 984 sub-basins are classified as water scarce when considering only quantity-induced scarcity, while 2517 sub-basins are affected by quantity & quality-induced scarcity. This number even increases to 3061 sub-basins in the worst case scenario in 2050. This aggravation means an extra 40 million km2 of basin area and 3 billion more people that may potentially face water scarcity in 2050. Our results stress the urgent need to address water quality in future water management policies for the Sustainable Development Goals.

Joint Optimization of Urban Water Quantity and Quality Allocation in the Plain River Network Area

Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and quality is proposed. The model considers regional water resources, economic, social, and environmental requirements and uses the NSGA-II genetic algorithm for model solution. Furthermore, to evaluate and analyze the degree of spatial equilibrium of regional water resources and how it relates to economic factors, the study uses the spatial equilibrium theory of water resources and the Gini coefficient of water resources. Jingjiang, a city in Jiangsu Province characterized by a typical plain river network area, was selected as the study area. The results of the optimal allocation of water resources in Jingjiang City show that: (1) total water consumption and chemical oxygen demand (COD) emissions for the current planning period are within their respective limits. In addition, the implementation of the water conservation program has resulted in a 5% reduction in total water shortages and a reduction of COD emissions by 1276 tons, (2) the structure of the water supply in Jingjiang City has been optimized; more than 90% of Ⅳ~V surface water is used for agriculture, and the domestic water supply is mainly from transit water, which effectively ensures that high-quality water is used in the domestic water supply, (3) the spatial equilibrium coefficient of water resources per sub-area is between 0.33 and 0.74, indicating an unbalanced or almost unbalanced level. The application of a water conservation program has resulted in the improvement of the spatial equilibrium level of water resources in each sub-area, with an overall spatial equilibrium of 0.64, indicating a more balanced level; the degree of matching of water resources with population, GDP, and land area is at the matching level, (4) according to the Gini coefficient of the distribution of water resources, the plains river network area displays a better match between water resources and economic and social factors of each water receiving area, thanks to its unique geographical location and natural conditions. This study can serve as a decision-making reference for addressing the urban water quality water shortage problem in the plain river network area.