Water Quality Classification Research Articles

Использование водных ресурсов в бассейне реки Барада

The purpose of the research is to study the current problems of water use for the Barada River basin (Syria) based on the analysis of the hydrological characteristics of natural water resources and making forecasts of the use of water resources for the future. To achieve this goal, methods are used to construct a theoretical supply curve based on average long-term data on the flow of the Barada River in Syria, as well as a predictive method based on retrospective data and a balance method for assessing the water management situation and the limiting period for the water content of the river during the year. The assessment of water use conditions and water availability periods was carried out for years of 90% availability of river flow for agriculture based on irrigation and for years of 75% availability of river flow for other industrial water users. The assessment of water resources and the analysis of the peculiarities of the use of the waters of the Barada River, taking into account local conditions, were carried out. Diffuse flows from territories are taken into account in water management balances. The pollution of river runoff by water quality class was also assessed. Recommendations are given for restoring the water quality in the river for different years in terms of water content. The recommendations take into account the use of sorbent meliorants for cleaning diffuse and drainage runoff with an efficiency of up to 90%.

EVALUATION OF CHEMICAL INDICATORS OF ANTHROPOGENIC INFLUENCE IN THE LOWER DANUBE BASIN

The article explores the nature and trends of hydrochemical parameter connection and changes in the Lower Danube basin. The research examines nitrogen and phosphorus compounds, dissolved oxygen, and biochemical oxygen demand as indicators of anthropogenic influence and surface water quality alterations. The study was conducted between 2015 and 2023 at three sampling points: the Reni and Vilkovo water intakes on the Danube River and the Yalpuh Reservoir, which is connected to the Danube. The study employed mathematical statistics approaches. The research established changes in annual median values, investigated correlation links between chemical components by Spearman correlation, and implemented factor analysis by minimizing residuals. A spatiotemporal analysis of chemical components indicative of anthropogenic impact on the Lower Danube surface water revealed notable changes in the basin during 2019-2021. These changes were manifested as a sharp increase in ammonium concentrations and phosphate growth since 2020-2021, an abnormal decrease in dissolved oxygen concentration in 2019, a decrease in BOD within Yalpuh to the Danube BOD level, and a shift in trends from decreasing to increasing for nitrites. The water quality class also changed. Currently, the Danube River water is of the third quality class for nitrates, phosphates, and ammonium. In the Yalpuh Reservoir, it is of the 2nd class for nitrogen compounds and the 3rd class for phosphates. Two principal groups of chemical components are identified based on their underlying causes and sources, as revealed by correlation and factor analysis. A consistent relationship is observed between ammonium and phosphates across all monitoring sites, suggesting their role as pollutants entering surface waters via wastewater discharge and agricultural runoff and representing the main anthropogenic impact factor. The Danube River displays a specific relationship between nitrates and dissolved oxygen, reflecting the seasonal fluctuations in their concentrations and their underlying causes of changes, which stem from both anthropogenic and natural processes.

Explainable AI and Sand Cat Optimisation Algorithm for Water Quality Classification

Explainable AI and Sand Cat Optimisation Algorithm for Water Quality Classification

Explainable AI and sand cat optimisation algorithm for water quality classification

Explainable AI and sand cat optimisation algorithm for water quality classification

Evaluating the water quality status using selected indexes and the aquatic arthropod species composition of the Idim Eye-Asana River, south Nigeria

The objective of the study was to evaluate the water quality status and the aquatic arthropod species composition of the Idim Eye-Asana River, Edebom 1, between May and July 2023. The sampling area was divided into four (4) sampling sites with unique coordinates. The physico-chemical parameters such as dissolved oxygen (DO), pH, temperature, total dissolved solute (TDS), inorganic contents; nitrate, sulphate, nitrite, and phosphate were measured using standardised methods. The aquatic arthropod specimens were collected using standardised circular-framed net with a 0.05µm mesh size and 35cm in diameter with an iron handle of 2m in length. Single factor pollution index (Pi), Biological Monitoring Working Party (BMWP) index, and diversity indices were calculated to evaluate the water quality status of the river. The physico-chemical parameter results revealed that they were differently significant between the sampling sites. The inorganic contents (contaminants) results showed that nitrate, phosphate, nitrite, and sulphate were differently significant at a 95% t-test between the sampling sites. The Pi values for the inorganic contents revealed that the water quality at site 2 was moderately polluted (3.25) with nitrite, whereas sites 3 (2.85) and 4 (2.50) were lightly polluted. Three (3) arthropod classes, sorted into seven (7) orders, and thirty-four (34) families were collected. Through the BMWP index results, the water quality classification for the water status at site 1 was regular (moderately contaminated), site 2 (poor), and sites 3 and 4 (Good water quality). Sites 3 and 4 sections of the river had good water quality, and as indicated by the results, it was concluded that the sites (i.e., 3 and 4) have better ecological conditions that can sustain the development and survival of the aquatic arthropod species.

Research on Watershed Water Quality Classification Prediction Based on WOA-CNN-GRU Model

River water quality monitoring plays a crucial role in water environment protection and management. This paper aims to explore and innovatively propose an efficient and accurate water quality classification prediction model WOA-CNN-GRU composite model. By ingeniously integrating WOA, GRU, and CNN, this model conducts comprehensive predictive research on the complex and dynamic water quality environments of the Yellow River basins in China. Addressing limitations of traditional water quality prediction methods such as single-model approaches, inadequate data processing capabilities, and limited prediction accuracy, the WOA-CNN-GRU model constructed in this study integrates core technologies from different algorithms to achieve comprehensive analysis and efficient handling of water quality data.

Water quality evaluation of the Danube River basin in Bačka (northern Serbia) using multivariate statistical techniques

The study aimed to analyze changes in water quality based on ten physicochemical parameters for the Danube River in Bačka. Data from three profiles over a twenty-year interval were used and processed using multivariate statistics. An ANOVA indicated that the quality of surface water is mostly at a satisfactory level. Sufficient amounts of dissolved oxygen indicate a favorable aeration regime. The surpassing of the annual and monthly values of suspended solids was recorded for all profiles (especially in summer). The summary results show that most parameters, except for suspended solids, are within the allowed values for water quality Class II. A Pearson’s correlation test indicates a moderate to weak correlation of the analyzed parameters. A PCA analysis determined the influence of (a) eutrophication processes; (b) ecological factors; and (c) concentrated sources of pollution. Changes in the Danube’s water quality, primarily increased concentrations of suspended solids, can exert a limiting influence on some economic activities and their planning.

Development of a model for assessing water quality and its impact on agro-industry using the random forest method

This article considers the application of the random forest algorithm to build a model designed to assess water quality and analyze its impact on agro-industrial complex. The main objective of the study is to identify the key factors affecting water quality and their interaction with indicators important for agricultural production. The random forest algorithm was chosen for its ability to process large amounts of data and identify complex non-linear dependencies. The random forest model was trained on historical data and tested on new samples to assess its accuracy and reliability. The study analyzed various physical and chemical parameters of water such as pH, organic and inorganic content, mineralization and their impact on agro-industrial indicators including crop yield, soil health and crop health. The results showed that the random forest algorithm is able to effectively classify water quality and identify its impact on agro-industrial complex. Analyzing the importance of attributes allowed us to identify the key parameters that most strongly affect water quality and agricultural land health.

Spatio-temporal variability of public water supply characteristics and associated health hazards for children and adults in selected locations of Ambala, India.

The contamination of public water supply and groundwater resources is a major concern in many parts of developing nations. Polluted water poses serious health risks to humans and the environment. This research was conducted to investigate the seasonal variations of the water quality parameters in the public water supply. To assess the supply water quality in different blocks of Ambala District, hydro-chemical analysis was conducted after a series of systematic sampling in various locations. The statistical tools for water quality indexing including water quality indexing (WQI), heavy metal pollution indexing (HMPI), pollution indexing (PI), overall pollution indexing (OPI), metal indexing (MI), and hazard indexing (HI) were used for data as well as the health hazard analysis through water pathway. Overall, 40 water samples were taken from the public water supply systems covering winter and summer seasons, and the levels of pH, total dissolved solids (TDS), EC, F- , Cl- , NO3 - , SO4 2- , HCO3 - , As, B, Cd, Co, Pb, Zn, Cr, Fe, and Mn were investigated. The weight arithmetic index method was used for WQI, and water pollution indices such as HMPI, PI, OPI, and MI were calculated using different models to check the severity of contamination. The mean hazard quotient and hazard index values calculated using the concentration levels of As, B, Cd, Co, Pb, Cr, Fe, Mn, Zn, F- , and NO3 - reveal that supply water may pose a significant health risk to both adults and children that further varies with temporal and spatial changes. During both seasons, a high carcinogenic risk for both adults and children was observed in the studied area because of high levels of As, Pb, Cd, and NO3 - . PRACTITIONER POINTS: The quality of public supply water was assessed at the selected sites of Ambala, India. High levels of NO3 - , As, Cd, and Pb were observed posing a health risk to adults and children via water pathway. 95% of the samples qualified for the excellent water quality category with respect to the levels of F- , Cl- , NO3 - , SO4 2- , HCO3 - , pH, EC, and TDS. Statistical analysis (HMPI, PI, MI, OPI, HI) using different models revealed water contamination with reference to the levels of NO3 - , As, Pb, Cr, Ni, and Cd. Immediate measures are needed to uphold the safety and health of the natives.

Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured parzen estimators

The research introduces an innovative approach to enhance deep learning models for air quality classification by integrating tree-structured Parzen estimators (TPE) into the hyperparameter tuning process. It applies this approach to CNN, LSTM, DNN, and DBN models and conducts extensive experiments using an air quality dataset, comparing it with grid search, random search, and genetic algorithm methods. The TPE algorithm consistently outperforms these methods, demonstrating improved classification accuracy and generalization. This approach’s potential extends to enriching water quality classification models, contributing to environmental sustainability and resource management. Bridging deep learning with TPE offers a promising solution for optimized air quality classification, supporting informed environmental preservation efforts.

Analisis Potensi Riam Jolan sebagai Sumber Air Baku IPA Jagoi Babang Kabupaten Bengkayang

One of the problems with the need for clean water in each region is the inadequate availability of existing raw water. The Jagoi Babang WTP is one of the Clean Water Treatment Plants that once operated with a discharge of 30 liters/second, but it stopped temporarily because the previous source of raw water, namely the Gombul River, did not have sufficient discharge, causing raw water needs to not be met. There is an alternative source of raw water in Jagoi Village, namely Riam Jolan, physically the water quality is very clear, and the flow is very adequate. The aim of this research is to determine the availability and quality of raw water in Riam Jolan. The research approach applied is descriptive quantitative by analyzing the availability of raw water using the mock method with a probability of 99%, analyzing instantaneous discharge, and testing the quality of raw water. The results of this research show that the calculation of reliable discharge using the Mock model with a probability of 99% Riam Jolan obtained the maximum reliable discharge in December, namely 0.0227 m3/second or 22.7 liters/second, and the minimum reliable discharge in September was 0.0018 m3/second or 1.8 liters, then the instantaneous discharge calculation shows a figure of 0.0855 m3/s or the equivalent of 85.5 liters/second, the instantaneous discharge obtained meets the raw water requirements for the Jagoi Babang WTP. The results of the Riam Jolan water quality laboratory test show that the water is suitable for use as a clean water source and meets water quality class I.

Machine and deep learning-based trophic state classification of national freshwater reservoirs in Taiwan using Sentinel-2 data

Since the beginning of the industrial revolution in the nineteenth century water pollution has grown into a global transboundary problem that negatively impacts aquatic ecosystems a risk linked directly to sustainable economic development, human, aquatic, and wildlife health. However, one major impediment to monitoring lake water quality is a lack of data for the relevant monitoring parameters at the relevant temporal and spatial scales. Therefore, this study investigates sixteen major reservoirs on a national scale based on 2020 in-situ sampling and Sentinel-2 MSI satellite data. Machine and deep learning algorithms were employed to classify and predict various water quality classes using Sentinel-2 MSI pixel color values and spectral indices. Multivariate techniques such as PCA transformation and regularized MANOVA were used as input feature preprocessing for variable reconstruction to deal with collinearity. Consequently, this improved the model classification performances overall. The results highlight deep neural net (DNN) and XGBoost as the outstanding classifying algorithms yielding F1-score of 0.94 and 0.93; Recall of 0.93 and 0.92; ROC-AUC of 0.92 and 0.89 respectively. Nutrient pollution affects all of the investigated reservoirs as they are classified as either meso- or hypereutrophic, and the situation is deteriorating. This study does not only give a feasible solution for effective water quality observation, but also provides a unique data pipeline for basic creation of training data for biogeochemical parameters for studying lake water quality using an integrated Google Earth Engine (GEE) Python Application Programming Interface (API) run in Google Colaboratory notebook.

Heavy Metal(Loid)s Contamination of Groundwater in Khyber District, Pakistan: Spatial Dependence, Source Apportionment, and Human Exposure

Heavy metal(loid)s (HM) including Fe, Mn, Cu, Zn, Cd, Cr, Co, Ni, and Pb were analyzed using ICP-MS in the groundwater of the Khyber District. Two indexical approaches were applied for the evaluation of HM contamination and toxicity in groundwater. The study also examined the health risks, sources, and spatial dependence of these HM. The results revealed that Mn, Ni, Cd, and Pb, concentrations violated drinking water guidelines in 44.26%, 52.45%, 1.63%, and 9.83% of the samples, while the rest of the HM were well within the limits. Based on the results it was observed that 18.03%, 6.55%, 6.55%, and 13.1% of the samples were classified as unfit, very poor, poor and marginal, water quality classes, presenting, very severe, excessive, strong, and moderate toxicity to consumers, respectively. Among HM, the Pb, Mn, and Ni required the highest removal rates in comparison to others. Health risk assessment suggested that 8.2% and 16.39% of samples showed non-carcinogenic risks to adults and children, respectively. Hotspots for HM were identified showing enhanced HM-enriched waters in specific regions, indicating areas of concern for future management. Source apportionment suggested that Pb, Cd, Zn, and Cu originated from a mix of geogenic and anthropic sources, while the rest of the HM were attributed to natural occurrences

Characteristics of phytoplankton functioning in urban water reservoires (based on the example of Basivkut Reservoir, Ukraine)

The study states fundamental patterns of development for species and taxonomic composition, structure and seasonal population dynamics, biomass and informational diversity of phytoplankton in Basivkut Reservoir. During the research (June–October, 2022) it has been identified 121 algae species represented with 125 intraspecific taxa that belong to 84 genera, 43 families, 29 orders, 12 classes and 8 divisions. Floristic aspect of plankton algae is presented with Chlorophyta (40.1% of the total species number), Bacillariophyta (26.4%), Euglenozoa (13.2%) and Cyanobacteria (12.4%). The population of phytoplankton in Basivkut water storage was ranging from 4654 thous. cells/dm3 to 14212 thous. cells/dm3 (August), and the population of biomass was changing from 0.8732 mg/dm3 (July) to 9.4828 mg/dm3 (October). Shannon index has changed within 0.62–5.07 bit/mg according to biomass, 2.13–4.50 bit/sp. according to the population. The average values of the saprobic index have ranged from 1.74 to 2.14 that correspond to the third class of water quality (poorly polluted). The author highlights the increasing of Cyanobacteria development whose population and biomass reached 86.1% and 33.2% that caused intensive algal blooms in all summer months and is the impact of algal groups on the anthropogenic factors effect and climate changes. Aphanizomenon flos-aquae (10.5–32.2% and 10.6–12.4%) and Cuspidothrix issatschenkoi (11.6–31.2% and 10.2–14.5%) have dominated according to their population and biomass. Bacillariophyta impacted on population (39.5–74.0%) and biomass (88.7–98.4%) in autumn. Small cell type Stephanodiscus hantzschii has been characterized with significant population (16.9–65.4%) and biomass (51.1–92.9%) that focuses on increasing degree of trophic level in the Basivkut water storage.

Развитие методики расчёта нормативов допустимого сброса жидких производственных отходов с учётом региональных особенностей водных объектов

The problem of regulating the discharge of liquid wastes into water bodies is the most urgent task and is carried out on the basis of establishing standards of permissible discharge (SPD). Many authors note that the key problem of setting the SPD is the use of maximum allowable concentrations (MAC) as threshold values, determined without taking into account regional peculiarities of formation of natural water composition and its ability to dilute wastewater. We have proposed the development of the methodology for calculating SPD, based on the condition that ensuring the disposal of liquid wastes at the level of permissible discharge standards will not lead to a change in the water quality class determined using the specific combinatorial index of water pollution (SCIWP). In the previous publication we have substantiated territorial threshold concentrations (TTC) based on the analysis of long-term data sets of hydrochemical observations, which allow us to take into account regional features of the geochemical background of water bodies, including the content of substances of dual genesis. They also implicitly allow taking into account hydrological and hydrochemical factors that ensure the ability of natural waters to dilute liquid waste effluents. The obtained TTCs are proposed to be used for calculation of permissible concentrations of substances in wastewater instead of MAC. SPD is established by cyclic iteration of TTC values by 10%, with parallel calculation of SCIWP, until the index is transferred to another water quality class. This paper presents the results of approbation of the proposed development of the methodology of SPD calculation for liquid waste from Оpen JSC “Kazan Synthetic Rubber Plant” discharged into the Volga River. The obtained calculation results make more stringent requirements to the value of SPD for substances mainly of anthropogenic origin. For some substances of dual genesis, the value of SPD was higher, which allows us to make more adequate requirements to water users for treatment of sewage effluents.

A diatom-based approach to refine nutrient concentrations compatible with the “good” status of Northern Italy rivers

The identification of ecologically sound thresholds represents an important step toward improving the ecological status of rivers through appropriate measures to contain nutrient loads. The aim of the present study was to estimate phosphorus and nitrogen concentrations compatible with the achievement of the “good” ecological status of rivers from data collected in the Po River District, the largest hydrographic system in Italy. For this purpose, relationships between the diatom index used in Italy for the national assessment of the stream ecological status, the ICMi (Intercalibration Common Metric index), and total phosphorus and nitrate concentrations were analyzed using monitoring data collected between 2009 and 2019. The Po River Basin encompasses five distinct river types, from Alpine to Mediterranean to Lowlands, characterized by different anthropogenic pressures and water quality. Through regression analysis between the ICMi and nutrient concentrations, we estimated ranges of the latter values corresponding to a “good” ecological status for each river type. The resulting thresholds are far more stringent than the limits set by the Italian legislation for water quality classification. This is particularly true for total phosphorus, whose threshold value should be roughly halved for all river types. For nitrates, the results are more differentiated according to river type: the estimated thresholds are much more stringent than those currently in use for siliceous Alpine and Mediterranean rivers. Moreover, the availability of such a large database allowed also to assess the influence of one nutrient over the other on the diatom community and to highlight some critical issues in the formulation of ICMi for Mediterranean rivers.

PHYTOPLANKTON IN THE LAKES OF THE REPUBLIC OF BASHKORTOSTAN

The phytoplankton in a number of lakes in the Bashkir Trans-Urals (Atavdy, Sultankul (2017), Bursunsy, Kuldybay (2019), Bolshiye Uchaly, Uzunkul (2021)) and Bashkir Pre-Urals (Bolshoy Tolpak, 2022)) was studied. As a result of the study, the taxonomic structure, quantitative characteristics, dominant species, distribution features of main algal groups were reveald, and their ecological and geographical characteristics werre identified. 257 species and intraspecific taxa of algae from 9 divisions were registered in the algal flora. Green algae (33%), cyanoprokaryotes and diatoms (21% each of total species number) show the highest species diversity in the lakes. The algal flora is specific for each lake and has low degree of floristic similarity (0.13–0.57 according to Sørensen coefficients). In the ecological and geographical terms, the algal floras of lake plankton is represented by typical planktonic species, widespread in waterbodies of the globe, indifferent to water salinity, living only in fresh waterbodies and preferring neutral waters. Quantitative indicators of phytoplankton varied in a wide range of values: number of cells from 4.4 to 344 mln cells/L, biomass from 0.23 to 22.97 mg/L. Dominant species in terms of abundance are represented predominantly by cyanoprokaryotes (Snowella lacustris, Aphanocapsa delicatissima, A. incerta, Aphanizomenon flos-aquae, Gloeotrichia echinulata, Merismopedia tenuissima, Pseudanabaena galeata, Planktolyngbya limnetica, Woronichinia compacta, Anathece clathrata, Rhabdoderma lineare), in terms of biomass by green (Oocystis lacustris, O. borgei, O. marssonii, Scenedesmus apiculatus), dinophyte (Peridinium cinctum, Gymnodinium sp., Ceratium furcoides), desmidium algae (Staurodesmus cuspidatus, Cosmarium contractum, C. cucumis) and cyanoprokaryotes (Pseudanabaena galeata, Aphanizomenon flos-aquae, Gloeotrichia echinulata). According to the value of the saprobity index (Pantle – Bucc by number 1.57–2.80), the lake waters correspond to β- and α-mesosaprobic types of waterbodies with III and IV water quality classes (moderately polluted or dirty water).

Surface water environmental carrying capacity and surface water quality based on economy-society-environment nexus – Evidence from China

Impacts of economy-society-environment on surface water resource in China are complex and unclear. Revealing these connections is vital to understand responses of surface water quality to anthropogenic activities. This study made an attempt to explore potential indications on surface water quality from economic, social and environmental factors in eight separate regions of China during the period of 2000–2019. Five machine learning models were employed including Greedy Thick Thinning Bayesian Belief Network, Naive Bayes, Augmented Naive Bayes (ANB), Logistic Regression and Random Forest. A total of 8 economic variables, 5 social variables and 8 environmental variables were introduced into the models. Results showed that ANB presented the best performance in estimating the surface water quality class with the highest accuracies of 81%, 75% and 87% for three surface water quality groups (Class I–III, Class IV–V and worse than Class V), respectively. The higher the surface water environmental carrying capacity in a region, the better the estimation performance of ANB on the surface water quality class. Surface water quality with Class I–III was more closely related to economic and social development, while environmental variables largely interpreted the quality of surface water with Class IV–V in most regions. The critical factors filtered by the importance analysis were indicative on surface water quality. This study provided a feasible framework in revealing the economy-society-environment nexus in the context of comprehensive management on regional surface water quality.

Analyze Predict and Classify Water Quality and Usage of Water using Machine Learning Techniques

Analyze Predict and Classify Water Quality and Usage of Water using Machine Learning Techniques

Application and comparison of four assessment methods for water quality of Sancha Lake in Central Sichuan Province, China

Abstract In order to analyze and compare the characteristics and applicability of different water quality evaluation methods applied to lake water quality evaluation, four monitoring sections were set up in Sancha Lake in 2019, 2020, and 2021, and 20 water quality parameters were selected. The single factor index method, the comprehensive pollution index method, the Nemerow pollution index method, and the improved Nemerow pollution index method were used to comprehensively evaluate water quality. The research results showed that the single factor index evaluation method is simple to operate and can quickly determine the water quality category by identifying the worst single water quality indicator. The comprehensive pollution index method and the Nemerow pollution index method determine the degree of water pollution based on the numerical values representing the overall pollution level of the representative water body. The evaluation results showed that except for the evaluation results of the single factor evaluation method with categories II and III, the results of other evaluation methods were all category I, indicating that the water quality was good.