River Water Quality Assessment Research Articles

Klang River Water Quality Assessment and Its Effects on Human Health Using Chemometric Analysis

Introduction: River water pollution has been a significant hazard to human health and is associated with severe health risks. This study evaluates water quality and heavy metal levels in the Klang River, analyzing their health risks through chemometric analysis. Methods: Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to analyse the heavy metal contents in river water samples obtained from 9 sampling stations. Chemometric statistical techniques (principal component analysis (PCA) and hierarchical cluster analysis (HCA)) are employed to identify the sources of physicochemical properties and heavy metals. The human health risk was evaluated using statistical analysis, apart from hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR). Results and Discussion: Results showed that the physicochemical parameters were within acceptable limits. The concentration of heavy metals was found to follow a decreasing order of As > Ni below permissible levels, except at P9 and P8. PCA and HCA showed important connections among parameters, emphasizing that COD, NH3N, and TDS are key factors affecting Klang River water quality. Conclusion: The study assesses pollution risks in the Klang River, offering crucial insights for sustainable estuary management. It highlights significant changes in temperature, pH, TDS, BOD, DO, and NH3N levels, along with specific trends in heavy metal concentrations. The Health Risk Assessment indicates acceptable HQ and Target Cancer Risk values. However, the study's limited sample sites and focused timeframe might hinder understanding long-term patterns and regional differences. Extended data collection and additional information are necessary to improve water quality management and protect public health

Evaluation and monitoring of the water quality of an Argentinian urban river applying multivariate statistics.

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results.

Water quality assessment of river based on phytoplankton biological integrity index in rural areas of the upstream Citarum River, West Java, Indonesia

Abstract. Pratiwi D, Oktavia D, Sumiarsa D, Sunardi S. 2024. Water quality assessment of river based on phytoplankton biological integrity index in rural areas of the upstream Citarum River, West Java, Indonesia. Biodiversitas 25: 881-889. The deterioration of water quality in the upstream Citarum River is one of the consequences of agricultural practices characterized by fertilizer and pesticide runoff that disrupts the ecological balance of aquatic ecosystems. The research area was on agricultural land use in the Cihawuk segment, Kertasari, Bandung District. The present study attempts to community structure and assess the water quality using the biological indexes of rural areas in upstream Citarum River through phytoplankton studies. A purposive sampling technique was used to collect data on physico-chemical parameters and phytoplankton community structure from stations selected on the basis of agricultural land use. Furthermore, biological indexes such as pollution levels based on the Shannon Wienner diversity index (H'), Palmer algae index, and Diatomeae index are examined using the data community structure of phytoplankton. The findings show that the abundance of phytoplankton in rural areas is 4,105 cells/L. The community structure of phytoplankton was dominated by Coelastrum sp. and Trachellomonas sp. The Palmer algae index ranges from 23-26, indicating that the water was a highly organic pollutant. The pollution level of upstream Citarum River based on the diversity index shows moderately polluted water. Meanwhile, the diatomeae index showed less than 0.2, which sites water bodies were mesotrophic. Phytoplankton and their indexes serve as valuable tools for evaluating the quality of the water environment.

Spatio-temporal dynamics of ecological, bacteriological, and overall water quality of the Damodar River, India.

Assessing river water quality is crucial for human and ecological needs because of the deterioration of the river and escalated water pollution under the threats of anthropogenic activities. In order to assess river water quality, the Damodar River water was evaluated from the perspectives of spatio-temporal dynamics of ecological (organic pollution index or OPI and eutrophication index or EI), bacteriological (coliform count and comprehensive bathing water quality index or CBWQI), and overall water quality assessments (water quality index or WQI and comprehensive pollution index or CPI). The OPI reveals that 44.66% of water samples have heavy organic pollution; however, EI depicts that almost all water samples of Damodar River have severe eutrophication, especially in the pre- and post-monsoon seasons. Moreover, the fecal coliform count and CBWQI indicate the unsuitability of river water for bathing. The overall WQI portrays that 21.56%, 33.59%, and 22.47% of water samples have heavy pollution in pre-monsoon, monsoon, and post-monsoon, respectively. Moreover, 73.39% of water samples have low CPI indicating slight comprehensive pollution. This study also reveals that the pollution level in the Damodar River downstream of the Durgapur barrage is high among the other stations. The major reasons behind the severe contamination of Damodar River water are urban-industrial and agricultural effluents mixing into the river that lead to higher concentrations of BOD, DO, fecal coliform, COD, fluoride TSS, and turbidity in the river water. Thus, this study carries appreciated information on policy recommendations for the different stakeholders of the Damodar River basin including regional planners, agri-engineers, and ecological river engineers for sustainable river management.

Fungal communities as dual indicators of river biodiversity and water quality assessment

Given their ecological importance, bioindicators are used for the assessment of the health of river ecosystems. This study explored the fungal compositions and the potential of fungal taxa as bioindicators for indicating the water quality of the Mekong River, as the use of fungal indicators of the Mekong River was not previously well characterized. The Mekong River exhibited dynamic variations in both physicochemical/hydrochemical properties and fungal communities according to seasons and locations. The results revealed the dominance of alkaline earth metal ions and weak acids in the water. The magnesium-bicarbonate water type was found in the dry season, but the water became the chloride-calcium type or mixed type of magnesium-bicarbonate and chloride-calcium in the rainy season at downstream sites. Fungal composition analysis revealed the dominance of Chytridiomycota in the dry season and intermediate periods, and Ascomycota and Basidiomycota in the rainy season. The fungal communities were influenced by stochastic and deterministic assembly processes, mainly homogeneous selection, heterogeneous selection, and dispersal limitation. The extent of environmental filtering implied that some fungal taxa were affected by environmental conditions, suggesting the possibility of identifying certain fungal taxa suitable for being bioindicators of water quality. Subsequently, machine learning with recursive feature elimination identified specific fungal bins mostly consisting of Agaricomycetes (mainly Polyporales, Agaricales, and Auriculariales), Dothideomycetes (mainly Pleosporales), Saccharomycetes (mainly Saccharomycetales), Chytridiomycota, and Rozellomycota as bioindicators that could predict ambient and irrigation water quality with high selectivity and sensitivity. These results thus promote the use of fungal indicators to assess the health of the river.

Assessment of coastal river water quality in Bangladesh: Implications for drinking and irrigation purposes.

Saltwater intrusion in the coastal areas of Bangladesh is a prevalent phenomenon. However, it is not conducive to activities such as irrigation, navigation, fish spawning and shelter, and industrial usage. The present study analyzed 45 water samples collected from 15 locations in coastal areas during three seasons: monsoon, pre-monsoon, and post-monsoon. The aim was to comprehend the seasonal variation in physicochemical parameters, including water temperature, pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), hardness, and concentrations of Na+, K+, Mg2+, Ca2+, Fe2+, HCO3-, PO43-, SO42-, and Cl-. Additionally, parameters essential for agriculture, such as soluble sodium percentage (SSP), sodium absorption ratio (SAR), magnesium absorption ratio (MAR), residual sodium carbonate (RSC), Kelly's ratio (KR), and permeability index (PI), were examined. Their respective values were found to be 63%, 16.83 mg/L, 34.92 mg/L, 145.44 mg/L, 1.28 mg/L, and 89.29%. The integrated water quality index was determined using entropy theory and principal component analysis (PCA). The resulting entropy water quality index (EWQI) and SAR of 49.56% and 63%, respectively, indicated that the samples are suitable for drinking but unsuitable for irrigation. These findings can assist policymakers in implementing the Bangladesh Deltaplan-2100, focusing on sustainable land management, fish cultivation, agricultural production, environmental preservation, water resource management, and environmental protection in the deltaic areas of Bangladesh. This research contributes to a deeper understanding of seasonal variations in the hydrochemistry and water quality of coastal rivers, aiding in the comprehension of salinity intrusion origins, mechanisms, and causes.

Investigation of Deoxygenation Rate Determination in Cikakembang River, West Java, Indonesia

Cikakembang River, a tributary of Citarum River, is situated in the densely populated Majalaya District, renowned for textile production. Direct discharges of domestic and industrial pollutants into the river contribute to substantial pollution, making it crucial to manage pollution levels. This implies that controlling pollution is crucial, as it significantly impacts the condition of Citarum River, already infamous as one of the world most polluted rivers. A key indicator for assessing river water quality is Biological Oxygen Demand (BOD), representing the oxygen required for microorganism-mediated decomposition. This parameter is influenced by deoxygenation rate, denoted as kd. Therefore, this study aimed to analyze the most suitable kd value for Cikakembang River using various empirical methods, including Simple, Fujimoto, Sawyer, Thomas, Fair, and Hydroscience. The result showed that Thomas method provided the most accurate prediction for BOD concentration of the river. In rainy season, Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) values were 0.542, 0.035, and 0.981, respectively, and in dry season, the values were 0.117, 0.009, and 0.999. Additionally, kd value effectively simulated the river water quality using HEC-RAS, yielding satisfactory results. RMSE, MAPE, and R2 values for BOD concentration were 3.551, 0.162, and 0.331 in rainy season and 1.071, 0.100, and 0.812 in dry season. Finally, the modeling result showed that Cikakembang River did not meet the Class 2 Water Quality Standard during both rainy and dry seasons. This finding is critical, as it underscores the severity of the pollution problem in the river and the urgent need for comprehensive and effective management strategies to improve its water quality.

Water quality assessment of Elgo river in Ethiopia using CCME, WQI and IWQI for domestic and agricultural usage

The increasing demand for water due to the escalation in population and aggressive agricultural activities for drinking and irrigation purposes in the rural areas of Ethiopia has put tremendous stress on water requirements. The Elgo River in southern Ethiopia is deteriorating due to sedimentation, soil erosion, stormwater runoff, and anthropogenic activities. Elgo village faces water shortages and a lack of safe drinking water. The purpose of this research was to identify the extent of pollution in Elgo River water using the Canadian Council of Ministers of the Environment (CCME), Water Quality Index (WQI), and Irrigation Water Quality Index (IWQI). A total of 12 water samples were collected from 3 river sampling sites for the dry and wet seasons to test the physicochemical and biological parameters. Results obtained were: turbidity (46.5–156) NTU, colour (103.65–606.5) TCU, EC (182–268) μS/cm, TDS (192.5–275.5) mg/l, TSS (680–2774) mg/l, Ca2+ (22–45) mg/l, Mg2+ (19.5–23.5) mg/l, Cl- (10.5–16.65) mg/l, and SO42- (17.18–47) mg/l for both the dry and wet seasons, respectively. The CCME WQI revealed that the overall results were 38.38 for the dry season and 36.6 for the wet season for drinking water parameters. The CCME WQI categorization indicates that the Elgo River water is classified as poor, with results ranging from 0 to 44. For irrigation purposes 10, parameters such as SAR, PS, PI, MAR, KI, RSC, EC, SSP, TH, and %Na were examined to compute indices using the IWQI model. The overall result of water quality indicated that IWQIs of 81.4 and 62.14 are good for the dry season and poor for the wet season, respectively. This research provides a thorough analysis through modelling to determine the suitability of water for different purposes for the tribal and backward communities of the area.

Integrating fecal pollution markers and fluorescence analysis for water quality assessment of urban river

Human fecal contamination in urban rivers poses significant health risks, but their potential connections with other substances like dissolved organic matter (DOM) remain underexplored. In this study, five fecal pollution markers related to fecal Bacteroides or human fecal contamination (AllBac, HF183, BacH, Hum2, and Hum163) and DOM along an urban river were analyzed using quantitative polymerase chain reaction (qPCR) and three-dimensional excitation-emission (3D EEM) fluorescence spectrometry. All five markers were detected with average absolute abundance ranging from 2.51 to 6.28 lg gene copies/100 mL, showing a progressive increase along the river (R2 = 0.29–0.92, p < 0.05). Parallel factor analysis identified three dominant DOM components (humic acid-like, fulvic acid-like, and protein-like), with strong positive correlations between protein-like components and all fecal markers (R2 = 0.59–0.66, p < 0.001). Both fecal and DOM distributions consistently showed significant differences between upstream and downstream areas (p < 0.001), suggesting their complementary assessment. While DOM was more sensitive to environmental variables such as rainfall, rubber dam, and tidal dynamic, the combination of fecal pollution markers and 3D EEM analysis allowed a more comprehensive assessment of contamination levels, mitigating potential biases caused by the influence of multiple factors on a single method. Furthermore, due to the strong correlation between protein-like and fecal markers in the DOM, 3D EEM can be used as a pre-detection means for qPCR detection, reducing testing time and costs.

Assessment of River Water Quality during Summer Season in Prayagraj, Uttar Pradesh, India

The present study is focused on assessment of river water quality during summer season in Prayagraj, Uttar Pradesh was experimentally investigated. The purpose of this study was to analyzed the physio-chemical properties of the river Ganga and Yamuna in Prayagraj. Water parameters such as pH, EC, DO, BOD, Alkalinity, Chloride, TDS and Hardness were analyzed. The results reveals that water was suitable for irrigation on purpose as the values were found to be within the permissible limit except DO whose values were found to be slightly less than the permissible limit.

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

AbstractThis study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin.

Mapping and assessment of river water quality under varying hydro-climatic and pollution scenarios by integrating QUAL2K, GEFC, and GIS

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

Comprehensive Water Quality and Heavy Metal Pollution Assessment of the Lom River in Bekao Gold Mining Sites (Adamawa-Cameroon) Using the Pollution Indices and Multivariate Statistical Approach

Comprehensive Water Quality and Heavy Metal Pollution Assessment of the Lom River in Bekao Gold Mining Sites (Adamawa-Cameroon) Using the Pollution Indices and Multivariate Statistical Approach

Using bacteria and benthic macroinvertebrates as water quality parameters in Mat River, Albania

Abstract This research examines the quality of Mat River water to control and reduce the level of environmental pollution in accordance with national rules and regulations. The focus of this study was the assessment of river water quality by using bacteria and benthic macroinvertebrates, as biological markers of stream water quality. The research was conducted from June 2018 to June 2019 at five sampling sites along the Mat River, with monthly sampling for chemical-physical and microbiological parameters and seasonal sampling for benthic macroinvertebrates. The investigated data for the studied parameters were statistically analysed using MINITAB 19 software. The variation in spatial and temporal trends of the investigated parameters showed differences in water quality among different sampling sites. The bacterial load was found to be higher near urban areas, and the pollution increased with the river course. The same tendency in water quality showed up even in the benthic macroinvertebrates population. Pearson correlation coefficients (p &amp;gt; 0.05) between the water quality data revealed the similarity and the associations between parameters. Cluster analysis of the investigated parameters revealed the classification of Mat River water quality and the possibility of using microbiological parameters and/or benthic macroinvertebrates for the assessment of water quality.

Assessment of the Al-Abbasiyah River (Iraq) Water Quality for Drinking Purposes Using the Water Quality Index and GIS Software

Assessment of the Al-Abbasiyah River (Iraq) Water Quality for Drinking Purposes Using the Water Quality Index and GIS Software

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3−-N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

Evaluation of Disturbance Zonation of Bagmati River System using Benthic Macroinvertebrates in the Kathmandu Valley, Nepal

The existence of Nepal's holy river, Bagmati, which flows through the core of Kathmandu Valley has been menaced by many anthropogenic threats. It is necessary to identify how vulnerable it has been. This research focuses on the evaluation of disturbance zonation to analyze the Bagmati River System’s spatial biological health. Benthic macroinvertebrates (BMIs) were used as biological indicators and were sampled from upstream to downstream using a multi-habitat sampling approach during the post-monsoon period in 2021. The Ganga River System Biotic Score/Average Score per Taxa (GRSBIOS/ASPT) was used to assess river water quality. From the sampling of 21 sites, a total of 5839 individual BMIs from 51 Families and 11 Orders were recorded. Upstream accounted for more than 30% of all the families, making upstream rich in taxonomic preferences, which steadily decreased from midstream to downstream. Facultative taxa were widely distributed in both upstream and midstream, but sensitive taxa were limited to upstream only. There are no signs that facultative and sensitive taxa existed downstream and were fully dominated by pollution-tolerant species. According to classification, the upstream river within Shivapuri Nagarjun National Park of the Bagmati River System was clean and was categorized as Class I, whereas rivers from the boundaries of the protected area to downstream were categorized as Class IV-V with few sites as Class II and Class III, indicating that this stretch of the river was extremely polluted. Water resource managers should utilize the study's findings to assess and restore the water's quality using biological indicators.

Quality Assessment and Monitoring of River Water Using IoT Infrastructure

The quality assessment of water is a challenging task due to extensive experimental requirements. However, the process of water quality monitoring can be automated with the help of Internet of Things (IoT) devices using sensor probes. This paper presents the IoT infrastructure-based river water quality monitoring and assessment. Experiments were performed to assess the water quality during different months and seasons for the Ganga river and Sangam (confluence of Ganga and Yamuna rivers) at Prayagraj, Uttar Pradesh, India. The data samples were collected for 15 months continuously using the Libelium smart water kit. The Smart Water IoT (SWIoT) kit was equipped with sensors to assess specific parameters like pH, dissolved oxygen, temperature, conductivity, and oxidation-reduction potential. An algorithm is also presented that harnesses PCA and factor analysis for feature selection and weight assignment for river water quality assessment. Further water quality is quantified using the water quality index that helps to categorize the water quality for different usages. The results corroborate that the water quality of the Ganga River was found to be better than the Sangam site most of the time, owing to the higher level of pollution in Yamuna River. Additionally, the water quality of both rivers were found to be suitable for irrigation and fisheries but not for drinking purposes, considering the average oxygen levels.

Selection of an adequate functional diversity index for stream assessment based on biological traits of macroinvertebrates

Functional diversity (FD) is useful for the evaluation of freshwater ecosystems. The FD of macroinvertebrate families for river water quality (WQ) assessment in the Paute River Basin (PRB), Ecuador, was investigated. Macroinvertebrate samples and data about 26 physical, chemical, microbiological and hydro-geomorphological variables were available. Literature-based biological traits were allocated as scores to the macroinvertebrates data through fuzzy coding. The Generalised Additive Mixed Model (GAMM) was used to assess the performance of six FD indices using the referred 26 WQ descriptive variables. The best performing GAMM led to selecting the index based on functional dendrograms including the species community pool (wFDc) as the most suitable to characterise FD in the PRB. The sub-basins of the PRB were grouped in three classes applying Average Linkage Clustering (ALC) and using wFDc. The Random Forest (RF) algorithm was used with a global efficiency of 89% to assess the ALC clusters consistency and pre-identify the significant WQ descriptive variables, explaining most of the FD variability. The Kruskal-Wallis test was then applied to refine the outcomes of the previous analysis. Twelve WQ descriptive variables were finally identified as the best discriminant predictors for FD, including the riparian vegetation, electric conductivity, dissolved oxygen, total hardness, faecal coliforms and pH. It is believed that the implemented approach successfully assessed the stream WQ status of the PRB upon selecting a suitable macroinvertebrate FD index; as such, it could be applied to other tropical basins for WQ 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.