Water Quality Classification Research Articles

Assessment of the drinking water quality from natural sources in the Kharkiv city

Improving the reliability and quality of the population's drinking water supply is one of the primary social problems, since the health of the population largely depends on the level of safety of drinking water. Purpose. To determine the composition of spring drinking water consumed by residents of different districts of the city of Kharkiv and to compare the assessment of the quality of drinking water from different natural sources. Methods. Field, laboratory-analytical, statistical. Results. Water samples from 5 natural springs in the city of Kharkiv, from which the population collects water for drinking purposes, were studied. It was determined that according to the organoleptic indicators, no excesses of the normative values are observed, the pH almost does not change seasonally, with the exception of the sample from the "Nemyshlianske" source. The analysis of total alkalinity in the autumn period showed that the standard was exceeded only in samples of the source of the Yunist park and along the street. Klochkivska, and in the spring period - only in the sample of the source on the street. Klochkivska. The total hardness in the samples ranges from 4.9 to 8.04 mmol/dm3 in the autumn period, and from 6.6 to 10.4 mmol/dm3 in the spring period. Excesses in chloride content are not observed in any of the investigated water samples. The iron content exceeds the MPC level only in water samples from the Nemyshlianske spring. The content of aluminum, zinc, cadmium, copper, lead, ammonia and nitrites in the samples is significantly below the MPC level, and almost does not change by season. Conclusions. The formation of the general quality class of water samples from natural sources is significantly influenced by the high level of total alkalinity, total hardness, and iron content in some sources of non-centralized water supply. According to the integral ecological index of water samples in the autumn period from natural sources "Nemyshlyanske", Park "Yunist" and along the street. Klochkivska belongs to the II quality class, i.e. pure water. The integrated ecological index for all other water samples in the autumn period assigns these sources to the I class of water quality - very clean. The calculation of the integral ecological index for water samples from all natural sources in the spring period refers them to the II quality class.

Characteristics analysis of water pollutants in Cihu Lake, China, based on a multivariate statistical analysis method

In order to understand the sources of pollutants and the temporal and spatial distribution characteristics of the water quality in Cihu Lake, China, the monitoring data of seven water quality indicators from 12 sampling sites from 2015 to 2019 were selected, and the temporal and spatial variation laws of the water quality and pollution sources were analyzed by the use of the multivariate statistical analysis method. The results show that nitrogen and phosphorus pollution in the lake is dominant. The average concentrations of total nitrogen (TN) and total phosphorus (TP) exceed the surface water quality Class III standards by 1.6 and 2.2 times, respectively. Spatially, the results of the cluster analysis showed that the water quality in Cihu Lake can be categorized into three regions: the northern half of the lake, the southern half of the lake, and the canal entering the lake. Temporally, the water quality in these three regions can be classified into three categories: March to May (the northern half of Cihu Lake), September to November (the southern half of Cihu Lake), and September (the canal entering Cihu Lake). The discriminant analysis results showed that NH3-N, TN, CODCr, and BOD5 are the main factors that affect the uneven spatial distribution of the water quality of Cihu Lake, while TN, DO, and CODMn are the main factors that affect the temporal difference in the northern half of Cihu Lake, and NH3-N, TP, CODCr, DO, CODMn, TN, and TP are the main factors affecting the temporal difference in the southern half of Cihu Lake and the canal entering Cihu Lake. It was found that the water pollution in the study area can be mainly attributed to the incoming water and urban domestic pollution. The main pollution sources for the canal entering Cihu Lake and the southern half of Cihu Lake are the water from the sewage treatment plant and the domestic sewage that has not been intercepted, while the northern half of Cihu Lake is mainly affected by surface runoff, mixed rainwater and sewage, and internal pollution.

Water Quality Classification Using Ensembled Learning Methodology with Matlab Software for Kshipra River

Large-scale pollution of water resources is one of the principal consequences of industrialization and urban expansion. Like pollution can be classified depending on factors such as pH, specific conductivity, dissolved oxygen (DO), temperature, and so on. The data acquired from the above attributes can aid in estimating the water quality of any body of water, although the prediction accuracy of traditional models is not particularly good due to unpredictability and imprecision. The ensemble modeling technique was used in this study to estimate the performance of the ANN study to predict the water quality of river Kshipra. The Artificial Neural Network (ANN) is a sophisticated data-driven model capable of capturing and representing both linear and non-linear correlations between input and output data. A total of 17 features is considered for determining the water quality. Some of these are: Temperature, pH, DO, COD, and Specific conductivity. After assessing the obtained results, the assembled ANN model was found to be a better classification model with an accuracy of 92.60%.

Risk Assessment of Nonpoint Source Pollution in the Huaihe River Basin

After years of treatment, the water pollution situation in the Huaihe River Basin (HRB) is still grim, and agricultural nonpoint source pollution has become the leading cause of the problem. However, agricultural nonpoint source pollution in the HRB is complicated due to the compounding effects of multiple factors. In this study, we first applied the export coefficient model to estimate the total nitrogen (TN) and total phosphorus (TP) loads used as two pollution source indicators in HRB. Then we constructed an index evaluation system of nonpoint source pollution risk by coupling the two source indicators with five additional indicators: rainfall erosion, river network distribution, soil erodibility, slope length, and land use. The primary source of TN and TP loads is fertilizer application (81.96%), followed by livestock and poultry breeding (16.3%) and rural domestic wastes (1.74%). The risk assessment results indicate that 66.43% of the HRB is at medium to high risk of nonpoint source pollution, 12.37% is at high risk, and 11.20% is at low risk. Moreover, the medium-to-high-risk areas are mainly concentrated in the Henan and Anhui provinces. In contrast, the medium-risk regions are mainly distributed along the mainstream of the Huaihe River. Finally, the observed water quality categories were used to verify our findings. The controlling areas of nonpoint source pollution in HRB are identified. This study could provide a scientific basis for effectively preventing and treating water pollution in the HRB.

Anthropic impact assessment on surface water quality in the Bălţi urban ecosystem

Massive pollution of surface waters in the Balti urban ecosystem has been established with CODCr, BOD5 and suspended matter as a result of leaks from the city due to atmospheric precipitation prior to sampling corresponding to surface water quality class IV and V, and self-cleaning capacity(SCC); SCC= BOD5 / CODCr has very low values. According to the nutrient content, the surface waters of the researched ecosystem were marked by moderate signs of disturbed ecosystem functioning. The left tributary of the river Răut, Flămînda, was classified in quality class I for chlorides, nutrients, BOD5 and mineralization, II for CODCr and suspended solids. The functioning of the aquatic ecosystem is not affected. The highest values for most of the pollutants analyzed were found in the waters of the river, downstream of the confluence with the waters discharged by the Biological Treatment Plant.

English

The shortages in the distribution of drinking water from the public network in the municipality of Jacqueville lead the population of the peri-urban villages of the said municipality to turn to well water, the quality of which remains a concern. The objective of this study is to evaluate the hygienic quality of well water used by the population for their domestic needs. The measurement and sampling were carried out on eleven wells in three peripheral villages of the commune. The analysis covered ten physico-chemical parameters (T, pH, EC, Turbidity, salinity, NO3, NO2, NH4, PO4 and SO4) and three microbiological parameters (total coliforms, Escherichia coli and faecal streptococci). Principal component analysis (PCA) was applied to the data to highlight the phenomena causing the degradation of the water. The calculation of the fecal contamination index (FCI) revealed three classes of water quality: Uncontaminated wells, moderately contaminated wells, and heavily contaminated wells. The study of the general quality shows that most of the wells studied had warm, turbid water with a high population of pathogenic microorganisms. This study highlighted the health risk incurred by the population when consuming the water from these wells without prior treatment. Key words: Assessment, physicochemistry, microbiology, well, Jacqueville, Côte d’Ivoire.

A decision fusion method based on classification models for water quality monitoring.

Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009-2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

Year-long monitoring of phytoplankton community, toxigenic cyanobacteria, and total microcystins in a eutrophic tropical dam supplying the Mexico megacity

The Valle de Bravo dam is an important source of drinking water supply for the Mexico City Conurbation (>26 million inhabitants) and is also a hotspot for nautical sports, recreational fishing, and tourism. However, anthropogenic pressures in the upper reaches of the basin have led to increased eutrophication and numerous harmful cyanobacterial blooms (HCBs). To determine the effect of abiotic variables on the characteristics of the phytoplanktonic community, as well as their influence on the diversity of toxigenic cyanobacteria and related cyanotoxins, we evaluated monthly variations in environmental factors, phytoplankton biovolume and composition and total microcystins in Valle de Bravo dam during the year of 2019. Overall, 75 phytoplanktonic species were identified, mostly belonging to the divisions Cyanobacteria, Chlorophyta, and Bacillariophyta. Cyanobacteria presented the highest biomass throughout the year, with water temperature and rainfall being the best correlated factors. The highest concentrations of total microcystins were recorded from July to September (maximum of 71 μg L−1), associated with the dominance of species such as M. smithii, M. aeruginosa, M. viridis M. flos-aquae, Aphanocapsa planctonica, and Dolichospermum crassum, and environmental factors such as rainfall, total dissolved solids, water temperature, and specific trace metals. In all months except December, the total cyanobacterial biovolume was above the World Health Organization alert level two, indicating potentially high risks to human health. The predominance of cyanobacteria and high biovolumes classified water quality as poor to very poor during most of the year. To avoid the development of HCBs in this dam, the contribution of nutrients by the tributary rivers and sewage must be controlled, since the poor-quality conditions of the water threaten the aquatic biota, the sports and recreation activities and affect the potable water supply.

The Decision Tree Aided Neuro-Fuzzy Inference Characterization of the Stochastic Hydrology of the Tana Alluvial Aquifer

The Tana Alluvial Aquifer is the name given to the little-understood aquifer which is active in the areas bordering the River Tana Flow course as the river weaves its way through the sedimentary plains of Balambala, Garissa, Fafi and Ijara and, finally, into the Tana Delta areas, with the common denominator being the proximity to the Lower Tana catchment, especially the riparian corridor of the River itself, and beyond. The aquifer may extend to between five to fifteen kilometers away from the river channels course way, and at times, it may be felt even 20 kilometers away. The geology of the locality is heterogeneous and comprise sediments whose soil mechanics may not be easily deciphered, since some areas close to the river have very fresh water while others are saline (Bura East in Fafi Sub County easily comes to mind here). There are areas far from the river but bearing fresh water (Mulanjo comes to mind). In some areas, sites close to the river discharge low yield figures, whereas those located farther afield discharge favorably. The water quality and discharge are therefore stochastic variables, subject to chance occurrence. In view of this inconsistency, and on the account of data scarcity, the neuro-fuzzy inference algorithm was developed to map the Universe of Discourse of the Tana Alluvial Aquifer, aka the T.A.A., as it relates to the longitudes, latitudes, depths, and discharges of the aquifers in the study area. The mapping was with respect to aquifer discharge, the variable used to characterize an aquifer, in terms of Transmissivity and Hydraulic Conductivity, thereby defining aquifer recharge propensity. Membership functions were developed using the trapezoidal membership family, and fuzzy rules were appropriately evolved from the fuzzified aquifer data, before finally employing the Sugeno inference engines (in Python) to make predictions of discharge, at each of the T.A.A. aquifer subsets mapped for fresh, saline, hard and brackish water species. The accuracy in the outputs achieved in the areas mapped vindicated the power of the neuro-fuzzy inference systems, as the accuracy oscillated between 92 and 99 percent, when the discharge values predicted were compared with the actual known discharge values of the wells mapped. The water quality class characterization was then undertaken using the decision tree (DT) algorithm in python which gave rise to a 100 percent prediction accuracy. The same DT algorithm could not successfully predict the discrete values of aquifer discharge or EC values, with as much accuracy (but performed excellently with salinity class data), and that was why fuzzy logic was employed. The study vindicated the use of the DT and Fuzzy Logic Algorithms as simple, yet powerful analytical tools, in characterizing the Stochastic Hydrology of the Tana Alluvial Aquifer.

Spatial Variability of Physicochemical Parameters and Phytoplankton at the Tagus Estuary (Portugal)

The estuarine phytoplankton communities are known to respond rapidly to environmental changes, being considered an important water quality indicator; thus, it is crucial to fully understand its natural variability. The objective of the present study was to assess the spatial variability of both physicochemical variables and the phytoplankton community, to understand how such variability is influenced by seasonality and to evaluate how the anthropogenic sources affect such patterns. The Tagus estuary was used as a case study, since it is one of Europe’s largest estuaries, with high spatial and seasonal variations and a high level of human pressure associated with large urban and industrial areas. To achieve this goal, environmental parameters, nutrients concentration, bivalve biomass (filter feeders) and phytoplankton pigments were quantified in a single summer sampling campaign with high spatial resolution and in monthly campaigns in eight sampling stations through the Tagus estuary, in one year. In general, suspended particulate matter and nutrients decreased from the upper part of the estuary to the estuary mouth; however, relevant local inputs were also observed in more downstream locations, near outfalls of wastewater treatment plants (WWTP). The chlorophyll-a concentrations were higher over the southern intertidal mudflats, probably due to resuspension of microphytobenthos, associated with higher nutrient concentrations. Through a grazing indicator, it was observed that grazers are important drivers of variability of the phytoplankton community composition. All water bodies achieved “good” and “high” water quality classifications for both physicochemical and biological indicators, with the worst results reported for the water bodies located at the upper estuary. Therefore, this estuary presents a decreasing trend of nutrients and chlorophyll-a in the upstream–downstream direction, except for the estuary channels and the outfall in the northern margin, which lead to an increase in nutrient concentrations. However, these increases did not affect the water quality of the three analyzed water bodies, presenting at least good ecological status, considering the nutrient and chlorophyll-a indicators.

Assessment of a Metallurgical Plant Impact on the Sea of Azov

Ferrous metallurgy enterprises have a negative impact on the air, soil, and water environment. The activities of metallurgy enterprises include a wide range of production processes (from the extraction of metals from ores to obtaining finished products) and is associated with the formation of a large amount of waste. Industrial wastewater discharge is the main source of aquatic area pollution. An assessment of the impact of wastewater discharged from the Azovstal Iron & Steel Works metallurgical plant on the state of the coastal waters of the Sea of Azov near Mariupol (Ukraine) is presented in the article. The assessment was carried out in accordance with the current Ukrainian legislation and the adopted methodology for water state assessment. The assessment was based on the available monitoring data of sea water in the area of wastewater discharges in the period 2016–2020. The assessment was carried out using the aggregated numerical indices, as well as taking into account the “limiting criterion principle”. Such a methodological approach allowed for a comprehensive assessment of the sea water quality class as well as its sanitary and ecological condition. The results of the assessment allowed us to ascertain the negative impact of industrial wastewater from the metallurgical plant on the coastal zone of the Azov Sea, which made it unsuitable for communal and recreational purposes.

Spatial evaluation of groundwater quality based on toxicological indexes and their effects on ecology and human health

Groundwater quality is affected by numerous uncertain factors, and the evaluation process is liable to randomness and fuzziness. In order to evaluate the groundwater quality effectively and know its potential risks to ecology and human health. Taking 87 wells and 174 groundwater samples during wet and dry seasons in Mihe-Weihe River Basin as the research object, samples were collected randomly in the field and sent to the laboratory for instrument testing. Four toxicological indexes (NO2−, NO3−, F−, As) were selected, and a multi-attribute decision-making model was proposed with three dimensions. First, a subjective method based on T-spherical fuzzy theory coupling with TOPSIS and an objective method based on rough set theory were adopted to calculate weights, both of which are recombined by game theory. On this basis, the single-index unascertained measurement function was adopted to classify water quality in two dimensions. Moreover, for the vertical direction, a model based on the VIKOR method was built. Consequently, a three-dimensional water quality index (TWQI) was used to assess the water quality level in three dimensions. The results show that the groundwater quality is classified as medium (classification Ⅲ) in the Mihe-Weihe River Basin, and samples with poor water quality (classification Ⅳ) are mainly distributed downstream of the Mihe River, with groundwater depth ranging from 0 to 15 m, near Laizhou Bay. Ecological and health risk assessment (EHRA) shows that the potential risks to ecology and human health are classified as high, with nitrates and nitrites being the major contributors. Moreover, the risk in the wet season is greater than that in the dry season. This highlights an urgent need to improve groundwater quality in the Mihe-Weihe River Basin, to reduce the potential risks to ecology and human health caused by groundwater pollution.

Development of entropy and deviation-based water quality index: Case of river Ganga, India

• Development of an objective and rational framework ( M E D -WQI) for water quality assessment that uses entropy and deviation-based approach. • Evaluation of M ED -WQI using synthetic dataset by comparing with existing Composite WQI. • Assessment of river Ganga's water quality status using M ED -WQI. In this study, a comprehensive multiplicative water quality index, M ED -WQI, for surface water quality assessment has been developed that uses sub-index functions based on deviation from maximum contaminant level of respective parameters. The parametric weights in M ED -WQI were computed using entropy based approach to eliminate subjectivity. Using a synthetic dataset the performance of M ED -WQI has been compared with Composite WQI (CWQI) that is based on Saaty’s analytical hierarchical process. The results indicated that in comparison to CWQI, M ED -WQI approach provides an objective and rational framework that eliminates clustering effect in providing the water quality status. Further, M ED -WQI has been applied to an exhaustive water quality dataset of river Ganga, one of the major perennial rivers of India. Out of 224 sampling locations, 167 sites have been associated with excellent or good water quality class whereas 57 sites are identified with either fair, poor, or heavily-polluted water quality class. Furthermore, the water quality classes correlated well with the type of anthropogenic activities carried out at the site.

Assessment of the supplied water quality for Sulaimani, Iraq as a case study using CCME method

Water quality index (WQI) is an important technique used for the evaluating and classifying water quality of supplied water, by summarizing large number of parameters from the result of variety tests of water into less number and terms Water quality index (WQI) value can be determined. Management of water quality is not so easy and simple, it needs to work with a lot of data. In this study evaluating of water quality of (Sulaimani city, KR, Iraq) is presented by using Canadian Council of Ministers of the Environment (CCME). The study conducted by monitoring long term water quality data over past eleven years, collected from 17 sampling points, tanks from which potable water supplied to all of the city residents, located in the (Sulaimani city, KR, Iraq). To obtain the CCME WQI value twelve parameters used : PH, Total dissolved solid (TDS), Chloride (Cl-1), Calcium (Ca), Sodium (Na), Potassium (K), Magnesium (Mg), Total hardness (TH), Total Alkalinity, Chloride (Cl2), Electric conductivity (EC), Sulphate (SO4-2).The obtained patterns present different water quality situations, WQI values ranged from good to excellent, based on the result obtained and analyzed using the from of WQI, it is revealed that the value of WQI ranged from (82) to (94) over the past 11 years, it means the main source of water supplied to the city which is Dokan lake is relatively high and clean as it is over a mountain watershed area.

Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model

The majority of what is needed to maintain life is found in the approximately 70 percent of the earth's surface that is composed of water. Water quality has been deteriorating at an alarming rate as a direct result of rapid industrialization and urbanisation, which has led to a rise in the prevalence of serious diseases. In the past, determining the quality of water was typically accomplished by employing labor-intensive, time-consuming, and statistically pricey laboratory investigations, which renders the prevalent concept of real-time monitoring meaningless. The worrisome effect of poor water quality mandates the necessity of an alternative model that is both rapid and economical to implement. There has been a lot of talk about using artificial intelligence to forecast and model water quality as a means of preventing and reducing water pollution. An artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification (AEODL-WQPC) model is presented in this paper. The primary objectives of the AEODL-WQPC model that is being given are the prediction and categorization of different levels of water quality. As a first processing step, the data normalization technique is used to the provided AEODL-WQPC model so that this goal can be achieved. In addition to this, an optimal stacked bidirectional gated recurrent unit (OSBiGRU) model is used to forecast the water quality index (WQI), and the Adam optimizer is utilised in order to fine-tune the model's parameters. AEO with enhanced Elman Neural Network (AEO-IENN) model is utilised for the categorization of water quality. This model is characterized by the fact that the AEO algorithm effectively tunes the parameters associated to the ENN model. For the purposes of the experimental validation of the AEODL-WQPC model, a benchmark water quality dataset obtained from the Kaggle repository is utilised. The research that compared several models found that the AEODL-WQPC model had superior results to more recent state of the art methods.

Water Quality Index Classification Based on Machine Learning: A Case from the Langat River Basin Model

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula.

A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI.

During the last few decades, the quality of water has deteriorated significantly due to pollution and many other issues. As a consequence of this, there is a need for a model that can make accurate projections about water quality. This work shows the comparative analysis of different machine learning approaches like Support Vector Machine (SVM), Decision Tree (DT), Random Forest, Gradient Boost, and Ada Boost, used for the water quality classification. The model is trained on the Water Quality Index dataset available on Kaggle. Z-score is used to normalize the dataset before beginning the training process for the model. Because the given dataset is unbalanced, Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Experiments results depict that Random Forest and Gradient Boost give the highest accuracy of 81%. One of the major issues with the machine learning model is lack of transparency which makes it impossible to evaluate the results of the model. To address this issue, explainable AI (XAI) is used which assists us in determining which features are the most important. Within the context of this investigation, Local Interpretable Model-agnostic Explanations (LIME) is utilized to ascertain the significance of the features.

Lake Sentani Water Quality Index based on NSF-WQI as Raw Water for Drinking Water for Lake Sentani Coastal communities, Jayapura Regency

Lake Sentani has an area of ​​about 9,630 ha and is located at an altitude of 72 m above sea level. Residents who live on the shores and shores of the lake, use the lake water for bathing, washing and latrine purposes. They also use it as raw water for drinking water and also as a means of transportation and even throw household waste into the lake water. There are 5 (five) rivers as the inlet, namely the Hawaii, Yamolo, Klandeli, Dofroko and Hobay rivers and there is one estuary, the Djaifuri River which is located in the east (Puay area). This study aims to determine the index of each water quality parameter and the total index of water quality. The water quality index parameters studied were; DO, Fecal coliform, pH, BOD, Temperature, Total Sulfate, Nitrate-N, Turbidity and Total Dissolved Solid (TDS). Sampling was carried out at 3 (three) sample points (stations) namely the mouth of the Haway River (Ifale Sentani), the middle of the lake (Ayapo Sentani) and the Upper Jaifuri River (Puay Yoka). Determination of the Total Water Quality Index is calculated using the National Sanitation Foundation's Water Quality Index (NSF-WQI) method for both Lake Sentani water and the total standard water quality index. The test results of each parameter are compared with the Class 1 Water Quality Standards Government Regulation of the Republic of Indonesia Number 22 of 2021 concerning the Implementation of Environmental Protection and Management. The results obtained from the research at 3 (three) research stations turned out that station 1 obtained an index of 44.24 and station 3 obtained an index of 46.60 both classified as Bad (bad), station 2 obtained an index of 51.42 classified as Medium (medium), while the standard water quality class 1 obtained a quality index of 47.24 classified as Bad (poor) too. Thus, when compared with the standard water quality index of class 1, what meets the requirements as raw water for drinking water is lake water at station 2 (Ayapu Sentani) where the index value is greater than the quality standard.

Biomathematical Model for Water Quality Assessment: Macroinvertebrate Population Dynamics and Dissolved Oxygen

Sustainable water management is important to ensure its availability for future generations. The study of water quality is fundamental for this purpose. Assessing the health of aquatic ecosystems through bioindicators has been shown to be reliable and inexpensive. The objective of this work was to evaluate water quality through a biomathematical model that involves environmental stress indicator organisms and their close relationship with dissolved oxygen. In this direction, a system of differential equations describing the population dynamics of aquatic macroinvertebrates under the influence of dissolved oxygen is proposed. The model is validated by its application in the Coyuca Lagoon, Mexico. Likewise, population changes over time were represented, which allowed us to deduce that the increase or decrease in the aeration/oxygenation rate significantly affects the population dynamics of the bioindicator organisms. In addition, to classify water quality, a one-to-one correspondence was established between water quality and the equilibrium points of the system of differential equations. The results obtained allow inferring that the proposed techniques are useful for the study of water quality since they can predict significant changes in the ecosystem and provide researchers and water managers with tools for decision making.

P15-01 Bioassays for determining five classes of water quality in the ‘Knowledge Impulse Water Quality – Toxicology’ project

P15-01 Bioassays for determining five classes of water quality in the ‘Knowledge Impulse Water Quality – Toxicology’ project