Water Quality Assessment Method Research Articles

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model’s water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a “medium” level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Semi-analytical inversion modelling of Chlorophyll a variability in the U.S. Virgin Islands

Coral reef health in the U.S. Virgin Islands (USVI) is in decline due to land-based sources of pollution associated with watershed development and global climate change. Water quality is a good indicator of stress in these nearshore environments as it plays a key role in determining the health and distribution of coral reef communities. Conventional water quality assessment methods based on in situ measurements are both time consuming and costly, and they lack the spatial coverage and temporal resolution that can be achieved using satellite remote sensing techniques. Water quality parameters (WQPs) such as Chlorophyll a (Chl-a), can be studied remotely using models that account for the inherent optical properties (IOPs) of the water. In this study, empirical based standard ocean color algorithm (OC4) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, were evaluated in retrieving Chl-a in the nearshore waters of the USVI. GSM and GIOP were also evaluated for modeling inherent optical properties such as absorption coefficient of phytoplankton (aph (443)). Analysis of the results from each model using a field database from six cruises during May/June and December between 2016 and 2018, showed that the OC4 performed poorly with R2 of 0.14 and RMSE = 0.15. Effects of suspended particulates and benthic reflectance most likely contributed to the poor performance of the algorithm. GSM is a slightly better estimator for aph (443) and Chl-a (R2 = 0.55, RMSE = 0.04; R2 = 0.60, RMSE = 0.09) than GIOP (R2 = 0.52, RMSE = 0.05; R2 = 0.17, RMSE = 0.15). Performance of the semi-analytical models are limited in estimating particulate back scattering (bbp (443)) also due to the benthic albedo effects in the shallow waters. The calibrated GSM model was applied to Landsat 8 OLI satellite imagery spanning 2016–2018 to develop a time series of the spatial changes in Chl-a concentrations in the coastal waters of the USVI. The Landsat GSM Chl-a model produced promising results of R2 = 0.45, RMSE = 0.07, in an environment where signal-to-noise ratio is significantly low.

Evaluation and Prediction of Water Quality of Typical Wetlands in the Source Region of the Yangtze River

Wetlands play an important role in water storage and water conservation, but with global climate change, the degradation of wetland ecosystems is accelerating. In this study, we conducted research on the current situation and future prediction of water quality in typical wetlands in the source region of the Yangtze River to provide a scientific basis for the protection and restoration of wetlands in the source region of the Yangtze River. The Bayesian water quality assessment method and Yao Zhiqi evaluation method were used to evaluate the water quality of typical wetlands in the source region of the Yangtze River from 2016 to 2021 and based on the climate change scenarios of three RCPs (Representative Concentration Pathways) under the CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate model and SWAT (soil and water assessment tool) hydrological model, the wetland water quality in the source region of the Yangtze River from 2022 to 2100 was predicted. The results show that the inter-annual changes in CODMn, NH3-N, and TN in a typical wetland show a downward trend, while the temperature and DO concentration show an upward trend from 2016–2021. The changes in CODMn, temperature, and conductivity within the year are abundant season > flat season > dry season; and DO, NH3-A, TN, and TP concentrations within the year are opposite. The water quality of typical wetlands in the source region of the Yangtze River has reached Class II and above. From 2022 to 2100, under climate change in the future, TN, TP, CODMn, NH3-N, and temperature in the wetland water in the source region of the Yangtze River will continue to rise, and the concentration of DO will continue to decline. Therefore, the pressure on water resources in the source region of the Yangtze River is further aggravated, so it is urgent to strengthen water resources protection.

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.

A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment

Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessment methods usually do not consider the uncertainty of water quality data in the collection process, which limits the application of these methods. In order to overcome the above shortcomings, this study constructed a water quality assessment method by integrating Monte Carlo method (MC), CRITIC and VIKOR methods, and applied it to assess the quality of water in the Songhua River tributary. Results indicate that: (1) The water quality assessment of the two sampling points in the study area is level III, which is consistent with the actual situation; (2) This method can overcome the uncertainty caused by sampling error and improve the credibility of water quality evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) and ammonia nitrogen (NH3-N) are more evaluation factors related to the evaluation results. When the decision coefficient mechanism λ is taken [0.1–0.5], the outcomes are in line with the real quality of the water. In addition, we recommend that the distribution profile generated based on the measured data should obey the probability distribution density curve that decreases from the middle to the tail of both sides. The findings of this paper can provide a scientific basis for decision makers to carry out water quality restoration and management.

REVIEW PAPER ON PREDICTION OF WATER QUALITY PARAMETER USING MACHINE LEARNING

Surface water pollution become a nuisance for humankind as river water fulfill requirement of a major population and traditional method of water quality assessment and evaluation is inadequate in this era. So using advance method of machine learning in prediction of surface water proves to be helpful to prevent future water accident. As we seen many recent studies of water quality prediction and river water assessment using machine learning approach for better accuracy and less labor and to optimize its overall results. It’s become essential to review the recent studies which used Machine Learning algorithms for prediction, analysis, evaluation and assessment of river water quality and different models used in these studies for different environmental conditions. Machine learning models are superior to handle such complex and non linear data such as water quality parameters with greater accuracy, reliability, cost-effectiveness and efficiency as considered as great tool for surface Water Quality monitoring, prediction, future projects and help lawmakers in policy. In this report we reviewed around 17 research papers which uses machine learning approach from different journal and concise it to covers the structure of study, datasets used, methodology analysis, models performance, environment susceptibility, comparative analysis and assessments of Machine Learning models progress in river water quality research. For better management and control of surface water quality and its treatment, this study will help in understanding and analyzing the studies reviewed in this paper and its future application. We can conclude that research on Water Quality prediction using Machine Learning model are inadequate in the context future vulnerability, observing increasing pollution in recent years we require more research in this field. Finally, this study provides breakthrough in Surface Water Engineering and Management to give a new direction to fore coming studies and fortified it scope also gives a comparative approach for its implementation in new studies.

Water quality variation in the middle route of South-to-North Water Diversion Project, China

The South-to-North Water Division Middle Route Project (MRP) is currently the longest inter-basin water diversion project in the world. It benefits a large population and its water quality has attracted much attention. In this study, seasonal investigations on 11 sampling sites along the MRP were conducted from 2018 to 2019; water temperature, pH, turbidity, transparency, CODMn, dissolved oxygen, total phosphorus, phosphate, total nitrogen, ammonia, nitrate, and chlorophyll a were determined synchronously. Single leakage distance clustering analysis identified the spatio-seasonal heterogeneity of physiochemical parameters. The trophic level index (TLI) and the water quality status were assessed: TLI increased and WQI decreased from south to north; TLI and WQI had seasonal differences (p < 0.001), the best water quality was observed in autumn, and the lowest TLI was observed in winter. The trophic level was “oligotrophic to mesotrophic”; water quality status was “good.” The multiple linear stepwise regression analysis confirmed that total nitrogen (TN), Chl a, and CODMn were the driving factors in water quality. These factors were applied to build the simplified WQI model, which was confirmed as a reliable method of water quality assessment for the MRP and a fitting substitute for TLI and WQI. The results provided support for the water quality evaluation of the MRP.

Irrigation suitability and health risk assessment of groundwater resources in the Firozabad industrial area of north-central India: An integrated indexical, statistical, and geospatial approach

The recent global upsurge in anthropogenic activities has resulted in a decline in the quality of water. This by extension has resulted in increased ubiquity of water pollution in terms of sources. The application of traditional water quality assessment methods usually involves the use of conventional water quality parameters and guideline values. This may be associated with bias and errors during the computation of various sub-indices. Hence, to overcome this limitation, it is critical to have a visual appraisal of the water quality in terms of source and human health risks exposure for sustainable water resource management and informed decision-making. Therefore, the present study has integrated multiple water quality assessment indices, spatio-temporal, and statistical models to assess the suitability of fifty groundwater samples (n = 50) within the Firozabad industrial area for irrigation and drinking; as well as the likely health risks from oral intake and dermal contact by inhabitants. Electrical conductivity (mean = 1,576.6 μs/cm), total hardness (mean = 230.9 mg/L), dissolved sodium (mean = 305.1 mg/L) chloride (mean = 306.1 mg/L) and fluoride (mean = 1.52 mg/L) occurred in the water at concentrations above the recommended standards; attributed influxes from agricultural and industrial wastewater. The pollution index of groundwater and water quality index revealed that 100% of the groundwater samples are extremely polluted; this was also supported by the joint multivariate statistical analyses. The majority of the irrigational water quality indices (sodium adsorption ratio, Kelly’s Ratio, permeability index, percent sodium) revealed that the long-term use of the groundwater for irrigation in the area will result in reduced crop yield unless remedial measures are put in place. Higher Hazard index (HI > 1) for nitrate and fluoride ingestion was recorded in water for the children population compared to adult; an indication that the children population is more predisposed to health risks from the oral intake of water. Generally, risk levels from ingestion appear to increase in the western and north-eastern parts of the study area. From the findings of this study, it is highly recommended that adequate agricultural practices, land use, and water treatment regulatory strategies be put in place for water quality sustainability for enhanced agricultural production and human health protection.

A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine.

Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and does not take into account the weighting of the indicators. Therefore, this paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) andparticle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. Firstly, the combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP)AHP and Criteria Importance Though Intercrieria Correlation (CRITIC)CRITIC, and then the correlation between water quality indicators was calculated for feature selection. Secondly, the PSO-MSVM model was established using the water quality indicators obtained by ACGRA as input parameters for water environment quality assessment. The river water environment assessment methods of ACGRA and PSO-MSVM were applied to the evaluation of water environment quality in different watersheds in the country. Accuracy, precision, recall and root mean square errorRMSE were also introduced as model evaluation criteria. The results show that the river water environment assessment methods based on ACGRA and PSO-MSVM can evaluate the water environment quality more accurately.

Using integrative samplers to estimate the removal of pharmaceuticals and personal care products in a WWTP and by soil aquifer treatment enhanced with a reactive barrier

The need and availability of freshwater is a major environmental issue, aggravated by climate change. It is necessary to find alternative sources of freshwater. Wastewater could represent a valid option but requires extensive treatment to remove wastewater-borne contaminants, such as contaminants of emerging concern (CECs). It is urgent to develop not only sustainable and effective wastewater treatment techniques, but also water quality assessment methods. In this study, we used polar organic chemical integrative samplers (POCIS) to investigate the presence and abatement of contaminants in an urban wastewater treatment plant (WWTP) and in soil aquifer treatment (SAT) systems (a conventional one and one enhanced with a reactive barrier). This approach allowed us to overcome inter-day and intraday variability of the wastewater composition. Passive sampler extracts were analyzed to investigate contamination from 56 pharmaceuticals and personal care products (PPCPs). Data from the POCIS were used to estimate PPCPs' removal efficiency along the WWTP and the SAT systems. A total of 31 compounds, out of the 56 investigated, were detected in the WWTP influent. Removal rates along WWTP were highly variable (16-100 %), with benzophenone-3, benzophenone-1, parabens, ciprofloxacin, ibuprofen, and acetaminophen as the most effectively removed chemicals. The two SAT systems yielded much higher elimination rates than those achieved through the primary and secondary treatments together. The SAT system that integrated a reactive barrier, based on sustainable materials to promote enhanced elimination of CECs, was significantly more efficient than the conventional one. The removal of the recalcitrant carbamazepine and its epoxy- metabolite was especially remarkable in this SAT, with removal rates between 69–81 % and 63–70 %, respectively.

The 2nd method Parfait-Hounsinou used to determine the overall water quality

The 2nd method Parfait-Hounsinou makes it possible to note the physico-chemical quality and the microbiological quality of water by two alphabetical letters representing respectively the Chemical Water Quality Index (CWQI) and the Microbiological Water Quality Index (MWQI). The steps of using this method are the measurement of the physico-chemical and microbiological parameters of the water samples, the calculation of the CWQI and MWQI, the deduction of the overall quality of the water samples and the construction and then the study of the 2nd Parfait-Hounsinou diagram which is 2 particulars diagrams of Spie chart use to show the detailed chemical quality of the water samples. We applied this method to the groundwater of the municipality of Abomey-Calavi in Benin and then we compared this method to the water quality assessment methods that are often the most used. The novelty of the 2nd method Parfait-Hounsinou is that it makes it possible to evaluate the global quality of water in a uniform way all over the world, in particular whatever the influence of temperature on the pH of the water.•The 2nd method Parfait-Hounsinou makes it possible to attribute to a water sample a score which characterizes all its physico-chemical and microbiological parameters.•Researchers, WHO and governments around the world can use the 2nd method Parfait-Hounsinou to monitor changes in the quality of water resources around the world.•The meanings of the scores assigned to water samples using the 2nd method Parfait-Hounsinou are easily understood and useful by journalists and the general public.

ANALYSIS OF THE USE OF INDEXING METHODS IN WATER QUALITY ASSESSMENT

This paper presents the terminological definitions of indexing methods and the possibilities of their use in different scientific areas. Additionally, the paper identifies the most relevant indexing methods for water quality assessment with an overview of how they are applied in Serbia. Serbia has adopted a National List of Environmental Protection Indicators in order to establish, operate, develop, coordinate, and maintain a unified information system for environmental protection. National indicators of a country are used throughout that country and have a general and mandatory character. The paper highlights the importance of indicators and helps to better understand the importance and the purpose of using indexing methods in the assessment of environmental quality and safety parameters and in the field of water protection. The aim of the paper is to analyze the most relevant indicators and indices used in water management in Serbia. The paper specifically focuses on the importance of using the water pollution index and its correlation with other water protection indices.

Modeling of Water Quality in West Ukrainian Rivers Based on Fluctuating Asymmetry of the Fish Population

Increased concentrations of chemicals in surface waters affect the development of fish and the state of water bodies in general. In turn, the human consumption of fish that have accumulated heavy metals can cause toxicological hazards and endanger health. The importance of this area and the lack of water quality assessment methods in Ukraine based on the fluctuating asymmetry level of fish and the chemical parameters of water informed the object and aim of the current research. The object of this study was the use of fish populations as a bioindicator of water quality. The study had three purposes: (1) the determination of the dominant fish species and a comparison of their fluctuating asymmetry in the studied rivers; (2) the evaluation of the sensitivity/tolerance of the selected fish populations for assessing water quality; and (3) the creation of a model for assessing the water quality of the studied rivers based on the determined fluctuating asymmetry of the typical fish populations. Each of the studied fish populations had different frequency of fluctuating asymmetry (FFA) levels: the common roach had the highest value, and the silver crucian carp had the lowest. The final stage of the study was building an artificial neural network (ANN) model for predicting water quality based on the FFA of meristic features. Optimal results were obtained for the ANN model with the ReLU activation function and SGD optimization algorithm (MAPE = 6.7%; R2 = 0.97187). Such values for the MAPE and R2 indicators demonstrated that the level of agreement between the target and forecast data was satisfactory. The novelty of this research lay in the development of a model for assessing water quality based on the comparison of the fluctuating asymmetry values of the typical fish populations in the studied rivers.

Study of the Influence of Physicochemical Parameters on the Water Quality Index (WQI) in the Maranhão Amazon, Brazil

Water quality is mainly assessed using traditional water quality assessment methods that measure chemical parameters against established standards. The water quality index is used worldwide for water quality assessment. The main parameters evaluated include the total dissolved solids, electrical conductivity, nitrite, and nitrate. In this study, the WQI combined with microbiological analyses was used to assess the water quality of two rivers, Munim and Iguará. Data obtained in this study were then correlated using multivariate statistical analysis. Principal component analysis grouped the monitored sampling points into three clusters and identified temperature, Escherichia coli, and turbidity, as features correlated to the rainy season, while phosphorus, total dissolved solids, and biochemical oxygen demand are associated with the dry season. Four principal components explained 81.20% of the data variance during the studied seasons. The evaluated correlations indicated that in the rainy season, E. coli (~443.63 CFU/100 mL) and turbidity (~36.51 NTU) levels were the highest. However, in the dry season, the levels of phosphorus (~4.25 mg·L−1), total dissolved solids (145.46 mg·L−1), and dissolved oxygen (~9.89 mg·L−1) were the highest.

Adversarially robust water quality assessment associated with power plants

Power production is responsible for the introduction of eutrophication into surface water bodies via multiple routes. Therefore, water quality assessment is quite necessary for the resource protection of surface water bodies near power plants. At present there is an urgent need to develop automated methods for fast and accurate water quality assessment. In this paper three CNN-based classifiers are constructed aiming at the classification of surface water images. With respect to the threat of adversarial attacks, three typical attacking methods and a JPEG compression-based countermeasure are chosen for observing the impact of adversarial attacks. The evaluation of edge inference is made on two edge inference engines (platform-independent and platform-dependent) respectively. The experimental results show that the classifiers own excellent performance all with accuracy above 98%. They are able to be conveniently deployed on edge devices without performance degradation for at most 80 FPS. It is also shown that adversarial attacks will reduce classification accuracy markedly while JPEG compression can mitigate the threat as a simple and practical mechanism.

Comparative analysis of molecular and conventional methods for bacteriological water quality assessment in drinking water resources around Chennai

Abstract Coliforms and Escherichia coli represent the fecal contamination in drinking water and hence its potability. The present study estimates uidA and lacZ genes as distinctive biomarkers for rapid and efficient detection of contaminated water resources. In total, 39 environmental water samples were collected in and around the metropolitan city of Chennai, India. The results were compared with culture-based method using selective medium. The study shows that the performance and amplification efficiency of uidA and lacZ are 99.6% and 103.4% respectively. The sensitivity of the qPCR method in selectively identifying E. coli was 71.4% in a total of 92.3% coliform contamination. The conventional method showed that 10 samples were positive for E. coli and 12 samples positive for coliforms from a total of 39 samples. A lower positive predictive value of the biomarkers observed is due to the insignificant association between the two methods and is determined to be 59.0% and 66.6% for E. coli and coliforms respectively. The study reports 50 and 70% of the overhead tank (OHT) samples were contaminated with E. coli and coliform respectively, which indicates sanitation measures through these water supplies are not adequately taken care of. High throughput molecular detection technique is reported here, which can be used for monitoring environmental samples more quickly.

Quality Assessment of Drinking Water Sources in the Upper Jhelum Basin of Kashmir Himalaya, J&K, India

Water is the most significant natural resource for all living beings on the planet. The quality of water is considered the main factor linked to the status of health and disease in living organisms, including humans. The present study aims to assess the quality of water in the upper Jhelum basin of the Kashmir Himalayas. A multi-stage sampling technique has been used. To achieve the measured values for quality assessment, a total of 60 water samples were collected from various drinking water sources. The data was analyzed using suitable statistical techniques and relevant methods of water quality assessment. The results were compared with standard norms of the World Health Organization (WHO) and the Bureau of Indian Standards (BIS). The inferences revealed that most of the sampling sites had either excellent or good water quality, except for the sites encoded as RWS1, RWS4, RWS5, RWS6, RWS13 and RWS14, which depicted poor water quality. The findings revealed that water quality fluctuations are caused by anthropogenic activities such as domestic waste, agriculture and horticulture, as well as natural processes such as weathering and erosion. In general, the inferential statistics revealed that water quality varies from excellent to moderately polluted. Thus, proper purification and timely monitoring of water bodies must be ensured. The purpose of the study is to suggest remedial measures to ease the deterioration and related repercussions in the future.

Water quality evolution mechanism modeling and health risk assessment based on stochastic hybrid dynamic systems

Water quality assessment analysis is an important technical means for water pollution prevention and control. In this research area, mechanism models and real observation data of water quality evolution are always used to perform water quality assessment. However, the existing water quality evolution mechanism modeling researches commonly use a single time-invariant model to model the water quality evolution process. It is inappropriate to directly describe the complex behavior of long-term water quality evolution with the existing models, since the evolution process contain different feature states, and the water quality evolution characteristics under these states are different. In addition, the existing water quality assessment methods are mostly methods for directly processing and calculating the observation data of water quality. This makes the existing methods difficult to effectively compensate for the contingency and randomness in the water quality evolution process, which leads to deviations and errors in performing the water quality assessment. Considering these deficiencies, this paper proposes a water quality evolution mechanism modeling and health risk assessment method based on stochastic hybrid dynamic systems (SHDS). Firstly, a hybrid water quality evolution mechanism (H-WQEM) model is established based on SHDS, and a hybrid improved fruit fly optimization algorithm (H-IFFOA) is proposed to identify the unknown parameters of the H-WQEM model. Then, an improved interacting multiple model extended Kalman filter algorithm (IIMM-EKF) is employed to estimate the probability distribution of the hybrid state of the H-WQEM model, including the probability distribution of different feature states of water bodies and the probability distribution of water quality indexes under these states. Finally, the health degree of water quality is proposed as an indicator to achieve a quantitative assessment of the water health risk status. Real observation data from a monitoring station at Baiyangwan in China is used to validate the effectiveness of the proposed method. The results show that the method can effectively describe the complex water quality evolution process, and reasonably assess the water quality health risk status.

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

Application of fuzzy evaluation technique and grey clustering method for water quality assessment of the coastal and estuaries of selected rivers in Sarawak

BackgroundEstuarine and marine water quality has remarkable importance because these water resources are used for multiple reasons for instance: transportation, tourism, recreation, and other human or economic ways to use water. The objective of the study was to assess the water quality of the coastal and estuaries of the Rambungan, Sibu, Salak, and Santubong rivers in Sarawak, Malaysia. Water samples were collected from 10 locations and analyzed by employing standard techniques. A fuzzy comprehensive evaluation, grey clustering evaluation methods, Thailand Marine Water Classification System, and the Malaysian Marine Water Quality Index (MMWQI) and its classification system were applied to compute the index of each water quality parameter.ResultsThe results showed that all the analyzed water quality parameters were within the allowable threshold levels. The results obtained by the application of fuzzy comprehensive evaluation and grey clustering evaluation methods proved that the coastal and the estuaries waters were clean with exception of coastal location CZ9 and the estuary of Salak river which showed slight pollution. Based on the Malaysian Marine Water Quality Index, it was observed that all the locations were in the classification group of moderate (i.e. 50–79%). This suggests that the estuaries of selected rivers can be used for natural resource conservation, while the coastal regions are good for fish farming.ConclusionIt can be deduced that the suggested techniques were workable and logical. The method developed and the information in this study can serve as a reference and decision support for scientists and policymakers of concern.