Water Quality Assessment Research Articles

Water chemical formation mechanism and water quality analysis of Shichuan River under the influence of human activities

ABSTRACT It is necessary to analyze the characteristics of the water environment and factors influencing the basin in order to determine its current ecological status. This study aims to investigate the chemical properties in the Shichuan River Basin using Gibbs diagram, Piper triangle diagram, correlation analysis, factor analysis, and main ion ratio method. The enhanced Nemerow comprehensive index method and fuzzy comprehensive evaluation method are employed for river water quality assessment. The findings reveal that runoff water has a weak alkaline nature with low salinity. The hydrochemical type of river water includes Ca2++Mg2++Na+-SO4 2-, Ca2++Mg2+-SO4 2-+Cl-+HCO3 -, Ca2++Na+-SO4 2-+Cl-+HCO3 -. Silicite and dolomite weathering are identified as primary influencing factors, while evaporite weathering also contributes to variations in chemical composition within the basin's waters. Ca2+, Mg2+, SO4 2- and HCO3 - originate from carbonate rock and silicate rock weathering processes, whereas Na+, K+ and Cl-, are primarily derived from evaporite weathering influenced by domestic sewage and agricultural activities. Agricultural production practices along with discharge of domestic sewage have an impact on Na+, K+, Cl- and NO3 -concentrations in aquatic systems. Water quality at all three monitoring sections falls under level IV classification. Phosphorus emerges as a key limiting factor for eutrophication within this basin's watersheds.

HERisk and statistical clustering integrated for health risk modelling of PTEs in natural water resources for drinking and sanitary uses

Potentially toxic elements (PTEs) are well-known for exposing living organisms and humans to different levels of risk. The present study aimed to evaluate the extent of exposure to health risks sustained by the inhabitants of different suburbs across Southeastern Nigeria as a result of contaminated water sources. There are existing literatures on human health risk assessment in the study region. However, this is the first study to report a detailed breakdown of the risks faced by nine age groups in the study area. This was achieved by integrating a novel code (HERisk), statistical clustering, and water quality data. Laboratory analysis showed that the levels of PTEs (Fe2+, Ni2+, Cr3+, and Pb2+) in 53.6%, 17.8%, 3.5%, and 46% of the samples were found to be above the recommended limits. Aggregated human health risk scores for ingestion and dermal routes revealed that children aged 1 to 2 years are the most vulnerable to health hazards. According to cumulative carcinogenic health risk values (0.00E+00 to 2.41E-04), samples proximal to regions with significant human activities expose the locals to a high risk of developing cancer via ingestion. Q-mode hierarchical cluster analysis successfully validated the classification schemes used for the interpretation of the HERisk code values, captured the patterns in the dataset, and brought forth new perspectives. The comprehensive approach detailed in this study can be adopted as a framework for ongoing monitoring and assessment of water quality.

Water Quality Assessment in an Area Degraded by Urban Solid Waste in Southern Brazil

ABSTRACTIn most Brazilian municipalities, urban solid waste (USW) is disposed of in open dumps, with numerous sites now being closed, monitored, and rehabilitated. One location, the “old dump” area in the Portão municipality (southern Brazil), previously served as an irregular repository for USW and industrial solid waste (ISW). Its use continued until 1993 when it was closed for a natural remediation process. For this reason, this study aimed to assess groundwater quality in the Portão “old dump,” employing physicochemical, microbiological, and toxicity quality indicators. The study's scope was extended to encompass the Portão Stream as well. Notably, the second collection campaign revealed elevated dissolved aluminum (Al3+), iron (Fe2+), and nitrate levels in all groundwater samples (from PZi wells, i = 1–6), with the PZ3 monitoring well experiencing the most pronounced impact. Further quality indicators, such as total phosphorus, thermotolerant coliforms, BOD5, as well as Al3+ and copper (Cu2+) levels in the second water collection campaign from Portão Stream, were classified as Class 4, suggesting that the water is suitable only for navigation and aesthetic purposes. Conductivity analysis identified Portão Stream as another impacted area. Moreover, during the second collection campaign, the study uncovered an escalation in micronuclei (MN) frequency and chromosomal alterations in PZ5. Downstream water samples from Portão Stream exhibited similarly heightened MN levels. This study's investigation into water quality is a concrete example of the implications of improper USW management. These findings underscore the pressing need for effective waste management strategies to mitigate environmental degradation and protect public health.

Indigenous people doing citizen science to assess water quality using the BMWP in rivers of an arid semi-arid biosphere reserve in Mexico

Arid and semi-arid areas are among the most threatened ecosystems on the planet. The Tehuacán-Cuicatlán Biosphere Reserve (TCBR), in southeastern Mexico, is an arid and semi-arid area with high biological diversity and human settlements of eight ethnic groups. Two rivers drain the reserve, Río Grande (RG) and Río Salado (RS), which are not subject to water quality monitoring by government agencies; however, measures of water quality of these rivers are needed to focus conservation actions on this resource. This work aimed to test the effectiveness of participatory water quality monitoring with the participation of three actors: Reserve management leaders, local communities, and academics, to monitoring water quality in the rivers of the TCBR. Ninety-two residents were trained to carry out water quality biomonitoring using the Biological Monitoring Working Party (BMWP) index calibrated for the reserve. The BMWP uses macroinvertebrate families to display numerical and categorical water quality scores. Additionally, the Water Quality Index (WQI) was assessed and the Normalized Difference Vegetation Index (NDVI) of the riparian zones was estimated in each study site. The mean WQI scores were 69.24 for RS (no treatment necessary for most crops and necessary treatment for public water supply) and 75.16 for RG (minor purification for crops requiring high-quality water and necessary treatment for public water supply). The BMWP showed five water quality categories (Excellent, Very Good, Good, Fair, and Poor), showing higher water quality scores in the upper portion of the basins and capable of discriminating study sites with lower scores close to human settlements. At one study site, data from participatory monitoring impelled actions taken to address a pollution source and influenced policy focus, reaching the maximum level of participatory-based monitoring. This led to avoid the discharge of wastewater into the river to conserve and protect the water resource. WQI is closely related to BMWP; however, the latter was far more sensitive to detecting areas affected by domestic water discharges. The NDVI presented low values for the TCBR, being lower in RS (the driest area). Although the NDVI showed a weak relationship with BMWP values, areas with higher NDVI values generally achieved higher BMWP values. The results of this study highlight the high sensitivity of the BMWP to detect several water quality conditions in the rivers running through the TCBR when compared to WQI. In addition, the usefulness of biomonitoring using the BMWP index was evident, as well as the importance of the participation of local inhabitants contributing to the knowledge of water quality in biosphere reserves and carrying out timely measures that allow the rivers in these reserves to be maintained in good condition.

Assessment of water quality, heavy metal pollution and human health risks in the Canal system of Ho Chi Minh City, Vietnam

This study was conducted to determine the surface water quality and health risks in Ho Chi Minh City (HCMC) canals. 180 water samples and 180 sediment samples were collected from 15 canal locations in HCMC in 2021 and 2022. The Water Quality Index (WQI) assessment results indicated that the water quality in 2021 ranged from unsuitable to good, with a trend towards improvement in 2022, where good quality water was predominant. TMs PCA/FA identified domestic and agricultural wastewater from HCMC residents as influencing the water quality. Most TMs detected in surface water were within the limits the Vietnamese Ministry of Natural Resources and Environment allowed, except for Pb in 2022 (> 0.02 mg l−1). For sediments, TMs concentrations were higher than in water samples but showed a decreasing trend over the survey period in the order of Hg < Cd < As < Pb < Cu. The findings show that sediments are more strongly affected by TMs than surface water, with the main sources being industrial and agricultural human activities. The non-cancer risk assessment showed that children are more exposed to TMs than adults, mainly through ingestion. Additionally, the cancer risk assessment (CR) identified As in sediments as posing an unacceptable cancer risk (TCR > 1 × 10−4). Therefore, it is necessary to establish high-frequency monitoring policies to analyze and reduce TMs concentrations in water and sediments of the canals to protect human health.

Integration of hydrodynamic and water quality modeling to mitigate the effects of spill pollution into the Nile River, Egypt.

Mitigating spill pollution in the Nile River is crucial to protecting aquatic life, water quality, and public health. Extensive studies focused on the assessment of water quality and hydrodynamics of the Nile River, but there have been relatively few studies that have applied integrated hydrodynamic and water quality modeling approaches to simulate actual accidents in the Nile Fourth Reach. The goal of this study is to develop advanced computational models to simulate accidental spills in the Nile River and track the resulting impacts on water quality. Hydrodynamic and water quality simulations were performed using Delft3D software for 144km of the Nile River, Egypt, from El-Menia to Assuit. Once the hydrodynamic and water quality models were calibrated, two phosphate spill scenarios were modeled under maximum and minimum flow conditions. The spatial distribution of the spill plume along the studied river section was visualized every 12h following the spill occurrence for both scenarios. The results of the research were calibrated and validated against measured field data. In addition, various error and performance indicators were calculated to thoroughly assess the rigor and reliability of the results. The results demonstrated that flow velocity was the main factor influencing the spill plume characteristics and behavior. Initially, advection force plays a significant role after a spill occurs. After that, phosphate becomes mixed and diluted through dispersion. The spill plume took less time to reach downstream areas during the period of maximum flow compared to minimum flow. Additionally, the concentration of phosphate decreased as the water flowed downstream. The spatial distribution of the spill over time can assist water treatment facilities in developing mitigation strategies to address the spill impacts. However, complex Nile River dynamics demand extensive computational power. Therefore, the model was simplified for spill events, using the modeling capabilities to analyze hypothetical spills and contaminant spread in the absence of real data.

Qualitative and quantitative water investigation of Erin-Ijesha (Olumirin) Waterfall, Erin-Ijesha, Nigeria

This study focused on the assessment of water quality in Erin-Ijesha (Olumirin) Waterfall, a prominent natural attraction in southwestern Nigeria. The physiochemical parameters, heavy metal concentrations, and bacteriological characteristics were examined in the upstream, midstream, and downstream sections, to ensure the resource quality and safety from harm. The results revealed notable variations in water quality. The pH, Total Dissolved Solids, Electrical Conductivity, and Temperature were highest in the midstream while Total Hardness, Alkalinity, salinity, Chloride, sulphate, Phosphate, Nitrate, Calcium, and Magnesium were highest in the downstream section. The physicochemical parameters were within the acceptable limits of World Health Organization (WHO), United States Environmental Protection Agency (USEPA), and the Standard Organization of Nigeria (SON) standards, except the pH, temperature, and Total Hardness were higher than the acceptable limits of 6.5–8.5, <25 °C or >50 °C and 50 mg/L in all the sections. Iron was above the WHO, USEPA, and SON permissible limits of 3.0 mg/L in all the sections of the river while there was no indication of copper, lead, and cadmium. Bacterial contamination, particularly the presence of E. coli, exceeded recommended safety thresholds. The Total Bacterial Count (TBC) exceeded safety limits by 0.1 million cfu/mL in the downstream. The mean of the parameters was higher in some instances, and sometimes lower than the values in the various sections of the river. A significant relationship existed between most physical, chemical, and bacteriological parameters at p < 0.01. The appraisal of water quality in Olumirin Waterfalls emphasizes the need for proactive measures to ensure water safety, preserve ecosystems, and promote responsible water resource management.

AO-SVM: a machine learning model for predicting water quality in the cauvery river

Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.

Assessment of Ground Water Quality for Drinking Purpose through WQI in Surman (Zakry Locality), Libya.

Assessment of Ground Water Quality for Drinking Purpose through WQI in Surman (Zakry Locality), Libya.

Seasonal Variations and Assessment of Surface Water Quality Using Water Quality Index (WQI) and Principal Component Analysis (PCA): A Case Study

In recent decades, water pollution has become a major concern, threatening both humans and natural ecosystems. This study aims to analyze seasonal variations in the quality of surface water of the Nador Canal in Morocco, using the water quality index (WQI) and principal component analysis (PCA). Surface water samples from 22 sites along the canal were analyzed for physical, chemical, and heavy metal parameters. The results of the study revealed significant seasonal variations, with water quality decreasing in the summer months, while its quality generally improved in winter. The predominant water type was Na+-Cl− in summer, while it was the mixed Ca2+-Na+-HCO3− water type in winter. WQI values also varied seasonally, with an average of 113.04 in summer and 160.6 in winter, classifying the water as unsuitable for drinking but suitable for irrigation throughout the year. The results of the water quality index are consistent with the results of the principal component analysis of surface water in the Nador Canal, where the results of the principal component analysis showed that there are significant seasonal variations in water quality. In both summer and winter, major ions like magnesium, sodium, and calcium predominantly indicate influences from natural and anthropogenic sources. In winter, heavy metals and nutrients, signaling pollution from industrial and agricultural runoff, become especially prominent. These variations are influenced by rainfall patterns and agricultural runoff, emphasizing the need for adaptive water management practices to maintain crop and soil health. This study provides new insights into the dynamic interplay between seasonal factors and water quality, offering valuable guidance for local water resource management.

Hydrochemical and Microbiological Assessment of Water Quality in Koudiat Medouar Dam (Northeastern Algeria)

Hydrochemical and Microbiological Assessment of Water Quality in Koudiat Medouar Dam (Northeastern Algeria)

Microbial and Physicochemical Assessment of Water Quality Reserving in Silver Utensils

Microbial and Physicochemical Assessment of Water Quality Reserving in Silver Utensils

Assessment of Water Quality of the Marsh of Dalmaj Based on some Physico-Chemical Parameters

Assessment of Water Quality of the Marsh of Dalmaj Based on some Physico-Chemical Parameters

Artificial Intelligence for Application in Water Engineering: The Use of ANN to Determine Water Quality Index in Rivers

To improve water quality, total daily loads must be established, and this requires determining the quality of the water in rivers, storage tanks, ponds, and coastal areas. Current methods to evaluate water quality involve the collection of water samples for subsequent laboratory analysis. Although these technologies offer precise measurements for a specific location and time, they are expensive, time-consuming, and do not provide the continuous, temporal, or spatial conditions of water quality that are required for managing, assessing, and monitoring water quality. In order to calculate the water quality, the water quality index is modeled using artificial neural network models that incorporate feedforward neural network backpropagation neural networks and radial neural networks. The water quality index of Malaysia’s Klang River was determined by training the artificial network using six major sub-quality parameters. Compared to the current method, the artificial neural network simplifies and expedites the computation of the water quality index. The artificial neural network method could provide a significant saving in terms of money and time while offering a robust assessment of water quality. The proposed method could also be used as an early warning system for pollution of water bodies. The best artificial neural network was the feedforward neural network with one hidden layer containing 5 neurons. Furthermore, conventional approaches for calculating the water quality index rely on empirical equations, often introducing a high degree of approximation and uncertainty into the results. Moreover, these equations cannot be applied when some parameters are not measured. In contrast, the artificial neural network methods and technique offer an efficient and straightforward process for estimating and creating prediction models for water quality index. Doi: 10.28991/CEJ-2024-010-07-012 Full Text: PDF

Selectively Isolation and Molecular Identification by dnaJ Gene of Legionella pneumophila Found in Various Water Sources in Dhaka, Bangladesh

Abstract BACKGROUND: Legionella spp. are the etiological agents of Legionnaires’ disease, often severe pneumonia. After the first severe outbreak of Legionella spp., it was found widely dispersed in natural water sources and humid environments and has become a concern for these sources’ water quality and safety. The fecal indicators used for water quality assessment do not predict the presence of these pathogens; thus, in the absence of targeted analysis, the risk is unobserved until the emergence of infections. There is no study on the presence of Legionella in unattended water bodies in Bangladesh, such as swimming pools, air-conditioned cooling water, and stagnant circulatory water systems. METHODS: A total of 42 water samples were collected from 12 different sites in Dhaka, Bangladesh. Acid, heat, and chlorine treatment were applied to reduce the load of non-Legionellae in water samples. Chlorine treatment was used to kill unwanted organisms in water samples. After successful pretreatment, samples were cultured on buffered charcoal yeast extract agar (BCYE) media. Optimum pH, concentrations of chlorine, and temperature were set up by extensive trial and error culturing of Legionella on BCYE media to minimize other bacterial growth and maximize Legionella growth. Isolated colonies were confirmed primarily by morphological characteristics on BCYE media. Isolates were then confirmed by polymerase chain reaction and gel electrophoresis techniques. We detected the presence of 16S rDNA for Legionella spp. and the dnaJ gene for Legionella pneumophila species identification. Antibiotic sensitivity tests (minimum inhibitory concentration) were also conducted to see if it was alarming for our community’s health. RESULTS: Most of the Legionella spp. could grow at various temperatures, such as 45°C, 50°C, and 55°C, and they could also survive at the normal levels of chlorination, such as 0.25 mg/L and 0.5 mg/L. We have found that, out of 12 different sites, 4 out of 5 (80%) isolates from cooling tower water, 1 out of 4 (25%) from the swimming pool, and 1 out of 3 (33%) household stagnant water are contaminated with Legionella. However, 3 out of 5 isolates found are L. pneumophila from cooling water sites. Furthermore, strains of Legionella spp. were resistant to many antibiotics, such as ciprofloxacin, azithromycin, and tetracycline. CONCLUSIONS: Serious and fatal Legionella spp. (L. pneumophila) infections may be transmitted because of the widespread presence of these water sources and the large number of users in the community. This threat is worsened by the survival of Legionella under various commonly used antibiotic conditions and further exacerbated by their antibiotic resistance. Health-care professionals may face great challenges due to the lack of studies about its prevalence and treatment procedures.

Passive sampler housing and sorbent type determine aquatic micropollutant adsorption and subsequent bioassay responses

The combination of integrative passive sampling and bioassays is a promising approach for monitoring the toxicity of polar organic contaminants in aquatic environments. However, the design of integrative passive samplers can affect the accumulation of compounds and therewith the bioassay responses. The present study aimed to determine the effects of sampler housing and sorbent type on the number of chemical features accumulated in polar passive samplers and the subsequent bioassay responses to extracts of these samplers. To this end, four integrative passive sampler configurations, resulting from the combination of polar organic chemical integrative sampler (POCIS) and Speedisk housings with hydrophilic-lipophilic balance and hydrophilic divinylbenzene sorbents, were simultaneously exposed at reference and contaminated surface water locations. The passive sampler extracts were subjected to chemical non-target screening and a battery of five bioassays. Extracts from POCIS contained a higher number of chemical features and caused higher bioassay responses in 91% of cases, while the two sorbents accumulated similar numbers of features and caused equally frequent but different bioassay responses. Hence, the passive sampler design critically affected the number of accumulated polar organic contaminants as well as their toxicity, highlighting the importance of passive sampler design for effect-based water quality assessment.

Water quality level estimation using IoT sensors and probabilistic machine learning model

ABSTRACT Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensembles the random forest model and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.

Decision support system for urban river water quality as a source of clean water using the SMART method

Abstract Rivers play an essential role as vital water sources with roles spanning sanitation, hygiene, drinking, and agriculture, particularly in rural areas. However, Kabupaten Sleman’s river water quality, including its Kabupaten Sleman region in Yogyakarta, has severely degraded due to pollution from solid and liquid sources. Chemical contaminants have rendered the water unsafe, posing health risks. The primary objective of this study is to evaluate the water quality in the rivers of Bedog and Sembung located in Kabupaten Sleman. This evaluation utilizes Water Quality Criteria from the Republic of Indonesia Government Regulations Number 82 of 2001, alongside assigned weights. The research employs a diverse range of methodologies, including in-depth literature analysis, meticulous field observation, interviews, and comprehensive water quality assessment. A Decision Support System is adeptly employed as a strategic tool to grapple with the intricate facets inherent in the issue, thereby formulating pragmatic resolutions that facilitate judicious decision-making across diverse contexts. In addressing the dimension of water quality for communities along the Bedog and Sembung rivers, the study seamlessly integrates the SMART (Simple Multi-Attribute Rating Technique) approach. SMART’s agility in steering multi-attribute decisions expedites the decision-making process. SMART’s computational outcomes yield values of 0.304 and 0.395 for the Bedog and Sembung Rivers, respectively, both falling within the 0 to 0.49 range. This collective conclusion indicates that neither river meets the criteria for clean water quality, making it unsuitable for activities like sanitation, hygiene, drinking, and agriculture. The study underscores the urgency of rectifying river water quality, particularly in urban settings, to ensure safe and sustainable water sources for communities. The seamless integration of SMART as a decision-making tool amplifies the potential for effective interventions in navigating water quality management complexities.

Assessment of Bottled Drinking Water Quality, Safety and Community Perceptions in Dodoma City, Tanzania

Assessment of Bottled Drinking Water Quality, Safety and Community Perceptions in Dodoma City, Tanzania

Assessment of water quality of the tropical stream Somavahini in the Bhadra Wildlife Sanctuary, Western Ghats, India

Assessment of water quality of the tropical stream Somavahini in the Bhadra Wildlife Sanctuary, Western Ghats, India