Evaluation Of Water Quality Research Articles

Water quality assessment for aquacultivation in Quang Ngai city, Quang Ngai province

Aquatic product development and growth are significantly influenced by the water supply used for aquaculture. Evaluating the water quality available for this activity is vital since Quang Ngai city has been a major hub for the growth of aquaculture in Quang Ngai province. Twelve surface water quality parameters were monitored and analyzed at eight sampling points in the river basins of An Nghia and Tinh Khe communes from May to September 2023. The Water Quality Index (WQI) was calculated, and methods including Principal Component Analysis (PCA), calculation of environmental variable weights based on principal components (PC), and Pearson correlation were used to assess the correlation of the investigated factors. In the river basin of An Nghia and Tinh Khe communes, 12 criteria for the assessment of surface water quality were tracked and examined using 8 sampling locations between May and September in 2023. The degree of linkage was assessed using principal component analysis (PCA), weighting environmental factors based on principal components (PC), and Pearson correlation. The water quality index (WQI) was calculated between the factors of the research. The findings show that: (1) The parameters exceeded the permitted limit: DO, Alkalinity, NH4+, TP, Vibrio SPP, Coliform, TOC, and TN. The two metrics with the greatest levels of pollution among them are Vibrio SPP and NH4+. The WQI index ranges from 52-74 and is within the average water quality range (51-75); (2) PCA analysis reveals that only the first two principal components, PC1 and PC2 can account for 74.39% of the variance, with NH4+ (0.089) having the highest weight; (3) NH4+ and Vibrio SPP content have a strong correlation (r = 0.95), indicating that both of these parameters can be used to evaluate water quality in the study area in general.

Automated Water Quality Assessment Using Big Data Analytics

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest accuracy at 66.77% after significant data preparation and training, followed by Gradient Boosting at 66.01% and SVM at 62.80%. This shows that Big Data analytics and machine learning algorithms can interpret complex water quality data for public health and natural resource management. The Random Forest classifier and SVM in this study accurately calculate water potability. Prediction algorithms consider water cleanliness data and may aid public safety and water resource monitoring.

Water quality evaluation by WQI and ICOs for the rivers of Joya de los Sachas and Francisco de Orellana

A monitoring program enabled the evaluation of water quality and the simulation of the pollution dynamics of the rivers in the Joya de los Sachas and Francisco de Orellana cantons in Ecuador. For this assessment one used the WQI (water quality index) and the pollution indices ICOMO (organics contamination), ICOSUS (suspended solids), ICOMI (mineralization), ICOTRO (trophy system), and ICOpH (pH Index). The mathematical model operates with several input parameters, including dissolved oxygen, fecal coliforms, the biochemical oxygen demand (BOD5), turbidity, total solids, total phosphorus, pH, total nitrogen, suspended solids, alkalinity, conductivity, temperature, and the concentrations of nitrates, nitrites, and phosphates. WQI values were obtained from ICO pollution indices. The pollution of the Sacha area is significant, WQI = 0.292, which represents an advanced degree of pollution, since increased BOD values correspond to a high load of organic pollutants. They can originate from household, agricultural, industrial, and soil erosion waste. Likewise, the Sacha River has an ICOSUS = 0.16, which renders water treatment difficult, without impacting human health. Calculated WQI values reveal that the water quality of the Sacha, Basura and Coca rivers is low, being highly contaminated. In contrast, the Huamayacu, Jivino Rojo, Jivino Negro, and Jivino Blanco rivers are commonly polluted.

Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas.

Retrieve water quality parameters of urban rivers from hyperspectral images through feature interaction based two-stage method fused with spectral unmixing and spatial relatedness

Monitoring water quality through efficient quantification of water quality parameters (WQPs) is of paramount importance to environmental management of urban rivers. Unmanned aerial vehicle (UAV) remote sensing techniques posed a great opportunity for visualizing spatial distributions of WQPs concentrations with higher flexibility and monitoring frequency compared to satellite remote sensing techniques, assisting to trace potential contamination sources and prevent water quality from degradation. However, current methods of water quality monitoring usually involved large masses of water samples as training data to keep calculation accuracy every time their study area changed, increasing financial cost and incurring time delay in monitoring and evaluating water quality. This study proposed a UAV-based two-stage multidirectional fusion with probabilistic matrix factorization (TSMF-PMF) method in unified framework, to effectively quantify WQPs including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity from UAV hyperspectral images. TSMF-PMF established feature interaction and outlier feedback module, ensuring relatively high calculation stability with less training samples. The experimental results showed that TSMF-PMF achieved good performance on predicting concentrations of WQPs with coefficient of determination (R2) and mean absolute percentage error (MAPE) ranging from 0.82 to 0.91 and from 5.35 % to 10.23 %. This study established theoretical and technical foundation to optimize environmental management scheme of urban rivers and provided an application demonstration.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Inisiasi Pengolahan Air Embung dengan Teknologi Multimedia Filter (MMF) sebagai Solusi Air Bersih di Kabupaten Bojonegoro

Clean water and proper sanitation are fundamental human needs. Nganti Village, located in Ngraho Subdistrict, Bojonegoro Regency, faces significant drought and clean water shortages, particularly during the dry season. The water quality remains subpar despite the presence of springs and water reservoirs. Consequently, processing raw water from the village's reservoir can offer a viable solution. This service activity aims to harness water reservoirs as a clean water source through multimedia filter (MMF) technology. The activities, conducted from 2022 to the end of 2023, encompass four stages: socializing the program plans, constructing multimedia filters (MMF), installing the water treatment unit on-site, and evaluating water quality, followed by necessary follow-ups. The primary activities involve installing water treatment units with MMF technology in collaboration with village officials and the community. The treated water is then tested for quality at the IPB University laboratory. Test results indicate that MMF technology significantly improves water quality, reducing parameters such as Magnesium, Manganese, Calcium, Iron, and Phosphate by 25.8‒97.8%, thus meeting quality standards. However, some parameters, such as TSS, COD, and Zinc, still exceed quality standards, necessitating further processing. This advanced water treatment requires involvement from the local government, particularly in funding through community empowerment programs, to install advanced treatment units and provide operational support for these units.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Evaluating water quality of rock glacier outflows in the Western Alps, Italy: a regional perspective.

Intact rock glaciers (RG) are considered valuable water storage because containing permafrost ice volumes. The hydrological relevance of RG is forecasted to increase with respect to glaciers under climate change scenarios, as well as RG's role as water resources in alpine basins for multiple uses. Besides the assessment of water amount stored in intact rock glaciers, the evaluation of water quality is of primary importance. Here, we present the results of a chemical survey performed on five outflows from intact RG in 2020-2023 in the Piedmont region, Western Alps, Italy, along a latitudinal gradient and in different geological settings. The survey aimed to assess the water quality of RG outflows based on chemical indicators (major ions, nutrients, trace metals). Sampling and analyses were performed according to standard methods for freshwater samples, paying specific attention to the analytical quality and consistency of the data. We considered seasonal and interannual variability of the main chemical variables and the possible effects of RG outflows on the chemistry of lakes and ponds located in proximity to the RG. All the investigated sites were characterized by low to moderate ion content, low nutrients, and trace metals close to or below the detection limit, indicating a good water quality status. Results suggested lithology as the main factor affecting the chemical composition of RG outflows. The results of this study indicate it is advisable to develop shared protocols and joint monitoring programs for data collecting at RG outflow sites all over the Alps, possibly integrating chemical and biological indicators, with the final aim of monitoring the water quality of these valuable resources and its temporal evolution under climate change.

Assessment of Climate Change Impacts on Hydrology Using an Integrated Water Quality Index

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Water Quality Index (IWQI) was developed by combining water quality parameters and flow rate, providing a more comprehensive assessment under various flow conditions. Compared to traditional indices, the IWQI showed slightly lower correlations in individual parameter performance, but it performed well in evaluating water quality changes associated with flow variations. Parameters such as Total Phosphorus (TP), Total Coliforms (TC), and Fecal Coliforms (FC), which are prevalent pollutants in the Cheongmi River, significantly influenced IWQI scores. River water quality was evaluated using input data simulated under a climate change scenario. When precipitation was abundant, the IWQI score remained relatively stable even with reduced flow rates. However, during periods of insufficient rainfall, water quality deteriorated sharply. While general water quality parameters exhibited approximately a 10% change as flow decreased, TC and FC showed rapid deterioration, with change rates ranging from 20% to 60%. These findings underscore the importance of managing TC and FC, particularly when insufficient rainfall is predicted, as they are major sources of pollution.

Irrigation suitability assessment of urban wastewater for sustainable agriculture: a case study of Musi river, India

In urban areas with dense populations and high water demands, effective management of water resources is crucial for sustainable development. It includes optimizing water usage and recycling wastewater to balance supply and demand. Urban rivers, which now carry sewage year-round due to increased urban water consumption, are increasingly used for irrigation in peri-urban regions. This study aims to assess the suitability of an urban stream for irrigation compared to catchment groundwater, using hydrochemical parameters to evaluate water quality indices. Analysis of major ion distribution from upstream to downstream reveals a significant increase of TDS (r2 = 0.65; p < 0.001) in the surface water and a decrease (r2 = 0.93; p < 0.001) in groundwater samples. The calculated Weighted Arithmetic-Water Quality Index (WA-WQI) indicates that around 85% and 75% of surface water and groundwater samples from the Musi river are classified as very poor to unsuitable for any use, highlighting critical pollution levels. Based on irrigation suitability indices, only 16% of the surface water samples and 60% of the groundwater samples are suitable for irrigation. The nitrate concentration increased from less than 10 mg l−1 in the upstream region to over 100 mg l−1 in the urban stretch. Nitrogen-Phosphate-Potassium (NPK) ratios across the basin are exceptionally higher, representing crops irrigated using the Musi river water might not require fertilizer applications. The adoption of fertilizer application concerning supplied water hydrochemistry, particularly the NPK ratio, might limit the excess usage of fertilizer and benefit farmers. Additionally, this practice may also limits the excess input of nitrate to the groundwater in the Musi river basin.

Python-Based Water Quality Evaluation of Tranza and Albarika Wells

Introduction: The increase in waterborne diseases in certain remote areas of Benin led the study to conduct surveys on the different waters used by these inhabitants. Aims: This study is conducted to assess the probable risks related to the physicochemical and bacteriological quality of well water consumed by the population of the Tranza and Albarika neighborhoods. Place and Duration of Study: Water samples were collected from 20 wells (open and closed in Albarika and Tranza districts) in the Parakou commune and analyzed at the LaKReCA laboratory between April and July 2024. Methodology: A two-stage methodology was used to approach this study. Thus, a survey was conducted with the aim of determining the actual fraction of the population using well water as drinking water. The physicochemical and bacteriological parameters were determined according to standard normalization in order to assess water quality using Python software for the statistical study. Results: The results show that 26.66% of the inhabitants of the Tranza and Albarika districts are subscribers to SONEB against 20.01% who use well water as drinking water. In addition, the waters are colored (16.16±5.86 Pt/Co), cloudy (69±3.22 NTU), loaded (678.58±6.07 µS/cm), rich in magnesium (132.42±7.87mg/L), calcium (52.73±1.68mg/L), chlorine (88.23±1.96mg/L) and have high alkalinity contents (194.49±16.57mg/L). They are hard (185.09±5.09 mg/L) and can cause waterborne diseases depending on their total coliform (23 CFU) and E. coli (5 CFU) contents. The correlation between the parameters shows that a magnesium content equal to 67 mg/L leads to a high value of total coliform and calcium in well water. Conclusion: Ultimately, the origin of the pollution can be attributed to sanitation, household waste, the transfer of pollutants from surface layers, the conditions of drawing water and the structure of the wells. In the long term, the use of well water could constitute a significant health risk for the majority of the inhabitants of Tranza and Albarika in the city of Parakou.

Evaluation of water quality and heavy metal contamination in Cauvery River: Tamil Nadu region India

Abstract Comprehensive water quality control is a fundamental requirement for environmental preservation and the sustainable development of communities around the globe. To showcase the importance of local quality controls in identifying the sources of pollution, a case study was conducted to analyze the quality of drinking water from different locations along the Cauvery River from Mettur to Trichy (200 km) in Tamil Nadu, India. The quality of water samples from different locations was indexed and compared with the World Health Organization and Indian Standards of water quality. The results indicate some high local values of TDS, hardness, and chloride content. These high values may be due to effluents from industries, dying factories, and sewage from the urban areas on the banks of the Cauvery River. This is most prevalent near Mohanur, where industrial waste and effluents were directly linked into the river. The results emphasize the importance of local quality control for accurately pinpointing the factors affecting the environment.

A new virtual water qualimeter with a changeable broken linear pointer based on a quadrant circular constellation graph

ABSTRACT As water is a very important natural resource for humans and its quality is closely related to human health, water quality evaluation (WQE) is very important for the protection of human health, livelihoods, environment, and management of water. Although various WQE methods have been developed and used, a common drawback is that the methods lack visualization of the WQE results and require additional complicated graphical techniques. The Pointer meter is an analogue measuring device with a pointer indicating the measured value with its location. A constellation graph (CG) enables to map multi-dimensional data on a two-dimensional plane. By introducing the pointer meter and CG for WQE, this paper proposes a new visualized pointer meter-type virtual water qualimeter (VWQ-meter) based on a quadrant circular constellation graph (QCCG). It has a changeable broken linear pointer with variable lengths and shapes, while the traditional pointer meters have straight-line pointers with a fixed length and a straight-line shape. It evaluates the water quality not only quantitatively but also intuitively as in the pointer meters. To demonstrate its effectiveness, it is applied to one example of WQE. It could be widely applied to not only WQE but also various multi-criteria decision-making (MCDM) problems arising in water pollution evaluation, water resource management, water supply, and wastewater treatment.

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Monitoring Salinity in Inner Mongolian Lakes Based on Sentinel-2 Images and Machine Learning

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%–3.54%) characterized by NO3-N, phytoplankton source (38.43%–50.05%) indicated by chl and PC, agricultural cultivation (16.1%–17.63%) marked by TP and CODMn, industrial wastewater (17.64%–25.1%) primarily associated with TN, and natural source (10.32%–13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China

Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed.

Comparative Water Quality Evaluation of Fu River Using Multiple Methods

Comparative Water Quality Evaluation of Fu River Using Multiple Methods

Risk assessment for the surface water quality evaluation of a hydrological basin

This paper proposes a risk assessment methodology for evaluating the surface water quality of hydrological basins based on physico-chemical parameter concentrations. Considering the Douro River basin in Portugal and monthly recorded dissolved oxygen and conductivity parameter measurements in 18 water sampling stations from January 2002 to December 2013, the work intends to answer the research question of identifying the riskiest periods for water pollution in the year and classifying the water sampling stations in terms of risk for water pollution. The methodology consists first in determining the pollution risk implied by the physico-chemical parameters, based on the monthly water station measurements, using six different risk measures, namely mean, variance, loss probability, entropy, mean excess loss and value at risk. The risk values are ordered according to each risk measure and a final ranking is established through a ranking aggregation method. The final ranking permitted identifying the high risk period as ranging from May to October and the low risk period from November to April. Furthermore, July was classified as riskiest month concerning the dissolved oxygen concentration, and August as riskiest month regarding the conductivity levels. On the other hand, the ranking allowed classifying the water sampling stations, previously grouped in clusters, in terms of similar risk for water pollution: six sampling stations in the west of the basin formed the riskiest cluster in the dry period considering the dissolved oxygen concentrations, and four of those stations formed also the riskiest cluster concerning the conductivity levels.