Assessment Of Surface Water Quality Research Articles

Assessment of surface water quality using the Water Quality Index (WQI) and multivariate statistical analysis (MSA), around tannery industry effluent discharge areas

Abstract The study attempted to assess the water quality around the tannery effluent discharge areas for suitability for irrigation purposes using different indices (Water Quality Index (WQI)) and statistical analysis in Savar Upazilas, Dhaka, Bangladesh. The samples were collected three times, from monsoon 2021 to pre-monsoon 2023. The analysis results showed that the concentrations of various parameters at most of the sampling points exceeded the surface water standard. Pollution levels were found in the following order: pre-monsoon, post-monsoon, and monsoon. At SW1 point, the CWQI, WWQI, and MWQI of the surface water quality showed a ‘poor’ category. The IWQI values showed that the surface water at all the sampling points was suitable for irrigation purposes, except for the SW1 sampling point. The NPI indicates that EC, BOD, COD, TSS, Cl−, Na+, and NO3--N parameters were potentially responsible for polluting most sampling sites. Multivariate statistical analyses like principal component analysis, cluster analysis, and Pearson correlation matrix showed significant anthropogenic intrusions of these variables in surface water in the area. A strong correlation between these parameters indicated their common origin, i.e., poorly treated tannery industry effluent entered the surface water, suggesting an improvement in the efficiency of the Central Effluent Treatment Plant (CETP).

Assessment of surface water quality in a highly anthropised semi-arid catchment (Case of Wadi El Hatab Basin, Tunisia, North Africa)

Anthropogenic contamination of surface waters is a critical issue in urbanised catchments, especially in Mediterranean environments with scarce water resources and intermittent rivers. A combined hydro-geochemical and multivariate statistical methodology has been applied to evaluate the surface water quality of Wadi El Hatab catchment (Central Tunisia). More than half of the 28 samples collected in March 2021 exceeded WHO standards for sodium, chloride and sulphate contents. Most polluted sites are directly influenced by domestic and industrial effluents that contaminate the river for kilometers. Principal Component Analysis and Hierarchical Ascendant Classification distinguished four contamination sources: water-rock interaction, domestic wastewater, untreated industrial effluents mainly from paper mills and irrigation return. A Water Quality Index model indicated that 75% of the surface water was unsuitable for drinking purposes; while according to chemical indices such as saturation index, sodium absorption coefficient (SAR), magnesium ratio (MR) and percent sodium (%Na), 25 to 49% of samples were unsuitable for irrigation. Considering that water of Wadi El Hatab is used for vegetable irrigation, serious public health issues may arise. The investigation demonstrated that a single representative sampling campaign could be a useful way to identify contamination sources when interpreted with both statistical and geochemical tools.

Assessment of surface water quality in the lower Sebou by physicochemical and microbiological approach and the impact of tributaries (Oued Beht and Oued Swaret) during the summer season

The study consists in assessing the quality of the surface waters of the Lower Sebou (from dam to Sebou mouth), by studying the impact of anthropic activities and the tributaries flowing into the Sebou (Oued Beht and Oued sawaret), and by analyzing the various physico-chemical and bacteriological parameters such as : temperature, pH, conductivity, TSS, turbidity, DCO, DBO5, total phosphorus, NO -, NO -, NH4+, SO 2-, Cl-, Mg2+, Ca2+, alkalinity, total and faecal coliforms bacteria, total and faecal streptococci. Nine stations were sampled in mid-September 2021. The water analyzed showed high values: for temperature, from 24.7 to 31.1°C; for alkalinity, from 170.80 to 207.40 mg/L; for total phosphorus, from 0.01 to 1.75 mg/L; for ammonium, from 0.03 to 16.56 mg/L; for nitrite from 0.206 to 0.676 mg/L; for turbidity from 23 to 596 mg/L; for chemical oxygen demand from 50.88 to 168 mg/L; for biochemical demand for oxygen in 5 days from 16 to 58 mg O2/L; for faecal coliforms from 30 to 4500000 B/ml. The statistical analysis is encouraging, showing a temporal evolution in the overall quality of the lower Sebou.

Investigation and Comparative Assessment of Surface Water Quality for Drinking Purposes by Using Relief Algorithm, GIS, and Machine Learning: A Case Study of Mahanadi River Basin, Odisha (India)

Surface water is the best source of drinking water available. However, climate change, over-pumping, and a variety of contaminants have all led to the depletion of this valuable resource. Conducting surface water quality assessments for home usage, especially drinking water, is essential to safeguarding human health and effectively managing resources. In this study, this work has highlighted an evaluation of surface water quality of river Mahanadi, Odisha, for drinking purposes using Relief Algorithm (RA) based WQI (RA-WQI), with reliability-based MLs (Machine Learning) such as Weight of Evidence (WOE) have been employed. For this, water samples from 19 locations were taken for a period of 2018-2023, to test 20 physicochemical parameters in the selected sampling sites. The findings indicated that although pH changes, the water is alkaline and its value spanned from 7.73 to 7.9. The concentration of coliform and TKN is found to be higher at all locations. The highest levels of Cl- and SO42- are located close to the downstream area. Based on the results, anions and cations are observing a shift in the trend, i.e., Fe2+ > B+ and Cl- > SO42- > NO3- > F- respectively, throughout the occupied duration. Further, the calculated RAWQI revealed that 63.16% belong to poor water quality while 31.57% of sites come under the zone of excellent water. However, 5.26% of samples indicated an unsuitable water class. The analysis primarily revealed that at 8 samples, the main cause could be deterioration of domestic water, illegally dumped municipal solid waste, and agricultural runoff were the leading sources causing adulteration of the river’s water quality. As a result, a renowned ML models, such as WOE, were adopted and it suggests location SP-(9) was the most polluted in comparison with other locations, followed by SP-(8), (19), and (2) respectively. Following this, the analytic findings also suggests from the highest RA-WQI values that consists of 488, 243, 277 and 285 at this location. However, it was relevant that the degree of pollution at these stations was more closely linked to a wide range of expanding human activities, such as excessive water use, fertilizer effects, agricultural runoff, and industrial activity in and around the river corridor. According to the drinking water quality indices, the surface water in the area under investigation is classified as suitable for human consumption. Thus, the results illuminate the preservation and distribution of drinkable and irrigable surface water supplies, and provide decision-makers with a valuable resource for implementing successful surface water protection strategies in the area under study.

AVALIAÇÃO MULTIVARIADA DA QUALIDADE DAS ÁGUAS SUPERFICIAIS E ÁGUA FINAL DE ABASTECIMENTO PÚBLICO DE BELÉM, PARÁ, BRASIL

MULTIVARIATE ASSESSMENT OF SURFACE WATER QUALITY AND FINAL PUBLIC WATER SUPPLY IN BELÉM, PARÁ, BRAZIL. The present study investigated the sources of variability in the water quality of surface springs that supply the Metropolitan Region of Belém (Pará, Brazil), as well as the treated water produced by the main water treatment station in the region. Data provided by Companhia de Saneamento do Pará were used, referring to physical-chemical and bacteriological analyzes carried out in the period from 2010 to 2020. In addition, precipitation data were considered in this study, in units of mm of rain, obtained by consulting the database of the National Institute of Meteorology. The application of principal component analysis, as an exploratory data analysis tool, allowed the grouping of the most relevant parameters of the results and proved to be efficient in reducing the data set, explaining more than 65% of the total variance, in all objects of study. Thus, it was possible to observe a general profile of the quality of surface water investigated, and, in this way, demonstrate the impact of urban pressure on the environment, as well as the need for maintenance and conservation of surface water resources.

Assessment of Surface Water Quality Based on Landsat 9 Operational Land Imager Combined with GIS and IOT

Assessment of Surface Water Quality Based on Landsat 9 Operational Land Imager Combined with GIS and IOT

Surface water and groundwater quality assessment using the WQI method and human health risk assessment (HHR) in the lower seybouse (Annaba Plain), northeast Algeria

Surface water and groundwater quality assessment using the WQI method and human health risk assessment (HHR) in the lower seybouse (Annaba Plain), northeast Algeria

Groundwater and surface water quality assessment for irrigation purpose

Groundwater and surface water quality assessment for irrigation purpose

Assessment and modeling of surface water quality for drinking and irrigation purposes using water quality indices and GIS techniques in the Inaouene watershed, Morocco

Assessment and modeling of surface water quality for drinking and irrigation purposes using water quality indices and GIS techniques in the Inaouene watershed, Morocco

Assessment of surface water quality: Case study of Oued Fez catchment areas (Morocco)

Under sustainable development conditions, water quality and quantity play a crucial role in meeting basic human needs. To meet the growing demand for water, surface water quality management is an important aspect. Consequently, water quality must be monitored and assessed via the use of innovative techniques, such as water quality index (SEQ-Water).For that purpose, water quality of Oued Fez catchment areas was evaluated according to Moroccan water quality guidelines by using the weighted quality index (SEQ-Water), physicochemical parameters, trace elements and bacteriological characterization. Five Sites were chosen after a diagnosis based on different criteria such as activity and density of population. These sites were Amont oued Fez (S1), Oued Fez (S2), Oued Ljawahir (S3), Oued Tgat (S4) and Oued Ain Smen (S5). Physicochemical quality was determined by evaluating Chemical Oxygen Demand (COD), total Kjeldhal nitrogen (NTK), suspended solids (SS), nitrates (NO3-), nitrites (NO2-), ammonium (NH4+), sulphates (SO42-), total phosphorus (TP) and trace elements (Cr, Cd, Cu, Ni, Co, Pb and Zn). Bacteriological analyses were carried out by testing enumeration of total coliforms (TC) and fecal coliforms (FC).Except Oued Ain Smen, physico-chemical parameters exceed by far the Moroccan standards of surface water quality. High concentrations of Se, Cd, Pb and As were detected. Fecal and total coliforms were also present at all sites, and varied from 1150 CF/100 mL to 100,000 CF/100 mL. All results were statistically validated with a p value < 0.05. Overall quality of the water sites assessed basing on the concept of the weight quality index, showed high pollution of the Oued Fez catchments. System based on the concept of the weighted quality index (SEQ-Water) is a suitable method to monitor point sources of pollution in surface water, and could provide a crude guideline for selecting the priorities of possible preventative measures in the proper management of the surface water resources.To summarize, it would be dangerous to use water from these rivers for domestic and agricultural purposes without some form of physical and chemical treatment. Sensitization of the authorities on the extent of water pollution of this Oued and the harmful consequences for man and the ecosystem should be urgent and mandatory.

Effects of source-sink landscape proximity on the spatial–temporal water quality from the perspective of cost distance in an agricultural watershed

Spatial patterns of source-sink landscapes play a critical role in assessing surface water quality. However, this relation may be different due to various proximity of source-sink landscapes. The effect of the source-sink landscape proximity on surface water quality remains poorly understood. The impact of source-sink landscape proximity on the spatial–temporal water quality were quantified based on field monitoring data (2013–2019) in an agricultural watershed. A new approach considering the cost distance of landscape types, slope, and vegetation on the transformation of nutrients from one point to water bodies was used to construct different buffer zones which reflected very high, high, moderate, low, very low proximities, while the subwatershed represented the very low proximity. Then the effects of the source-sink landscape proximity were assessed based on sub-watershed and other four buffers. The resutls showed that the TN and NO3−-N were the main non-point source pollution indices with significant spatial variation (p < 0.05). The water quality index (WQI) was higher during the dry season than during the wet season, indicating that the sampling sites also experienced seasonal variations. The multi-scale analysis demonstrated the significance of the source-sink landscape proximity in improving the quality of surface water. A stronger correlation between the ratios of sink landscape and the main water quality parameters (the TN and NO3−-N) was observed in the low proximity zone, suggestiong that the low proximity zone with moderate non-point source pollution risk was a critical region in water quality management. The results provide important insights into understanding the effects of the source-sink landscape proximity on water quality and how scientific and effective measures can be made to improve water quality.

Rapid assessment of surface water quality using statistical multivariate analysis approach: Oder River system case study

Many physicochemical and biological monitored parameters must be taken into consideration to fully evaluate the surface water environmental condition. However, there are situations where a simple and rapid assessment of the poor water quality situations is critically important. This work presents a universal methodology for monitoring of many parameters simultaneously and early detection out-of-control samples in a real-time mode. The approach uses multivariate statistical quality control chart based on Principal Component Analysis (PCA) model with two well-known measures of abnormal behaviour in a process or system: Hoteling's T2 statistics and Q-statistic. The proposed TQ_PCA quality index provides on-line assessment of the water sample quality, with no specific knowledge and assumptions about control limits for monitored parameters required. A water sample is assessed through the simple control chart using the PCA model established for training/reference samples. The power of the proposed index has been tested using water quality data from the Oder River, including the time of the largest ecological disaster in recent European river history. The proposed index showed excellent analysis performance for physicochemical water quality dataset from Polish stations and physicochemical and biological water quality dataset from German/Frankfurt station, confirming earlier reports. There were consecutive number of alarms reported by the statistical index, a month prior to the disaster when there were no evident changes in the individual parameters. The method presented in this study demonstrated capability of assessment of the major water quality parameters, whose changes preempt the uncommon event. The presented TQ_PCA index could be easily extended to any research involving a large dataset of monitoring parameters from any industrial chemical process.

Assessment of surface and ground water quality around Korba Coalfield, India: an integrated approach of water quality index, multivariate statistics analysis and GIS technique

Assessment of surface and ground water quality around Korba Coalfield, India: an integrated approach of water quality index, multivariate statistics analysis and GIS technique

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

Assessment of Long-Term Surface Water Quality in Mekong River Estuaries Using A Comprehensive Water Pollution Index

Surface water quality (SWQ) has been degraded in the Mekong River Basin under increasing pressures of population growth, economic development, and global climate change. This study employed the comprehensive water pollution index (CWPI) to assess the spatio-temporal variation of SWQ in the downstream Mekong River estuaries. Eight water quality parameters were measured between 2005 and 2021 at 21 sampling sites downstream of the Mekong River. These parameters included total suspended solids (TSS), biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonia (N-NH4+), nitrate (N-NO3-), phosphate (P-PO43-), iron (Fe), and total coliform. Most of the monitoring locations in the estuaries of Ham Luong, Cua Dai, Ba Lai, and Co Chien exhibited slightly to moderately polluted conditions, as indicated by the CWPI values ranging from 0.67-2.91, 0.41-2.20, 0.27-3.02, and 0.37-2.95, respectively. TSS and Fe concentrations consistently exceeded the allowable limits, while the majority of values for N-NH4+, N-NO3-, P-PO43-, and coliform remained within acceptable thresholds. Additionally, parameters indicative of organic pollution, namely BOD5 and COD, displayed a noticeable upward trend between 2005 and 2021. SWQ exhibited significant spatial and temporal variations with TSS, organic matter, nutrients, and iron being the main areas of concern. These findings can provide guidance to policymakers involved in the assessment and enhancement of water quality in the presence of pollutant compounds that lead to a decline in water quality.

Risks of Surface Water Pollution in Southern Vietnam

The study was carried out to assess surface water quality and ecological risks in water bodies in the southern region of Vietnam. The study used monitoring data at 58 locations, which were collected in March, May, June, July, August, October, November, and December of 2022, with 11 water quality parameters (temperature, pH, DO, TSS, BOD, COD, NH4+-N, NO3--N, Fe, Pb, and Cd). Comprehensive pollution index (CPI), ecological risk level, and multivariate statistical analysis methods were utilized. The values of CPI showed that the surface water quality was mildly polluted, moderately polluted, and severely polluted, accounting for 37.93, 46.93, and 15.52%, respectively. In particular, heavy pollution was concentrated in the water bodies of the Sai Gon and Vam Co Rivers. TSS, BOD, COD, NH4+-N, and Fe had a moderate to high level of risk, while water samples contaminated with NO3--N, Pb, and Cd had a level of risk from low to safe. High levels of risk were concentrated in the water bodies of the Sai Gon River and Vam Co River, typically BOD and COD. Based on the impact level, the positions were classified into five groups, with the locations on the Sai Gon River and Vam Co River (Groups 4 and 5) being affected by various waste sources in the inner city of Ho Chi Minh City. The PCA results presented three sources, such as discharge from residential areas, soil erosion, and agriculture, that have caused water quality fluctuations and increased the impact on the water quality of water bodies. Measures to protect water resources according to environmental protection laws must be implemented soon to minimize ecological risks from water-polluting sources. Doi: 10.28991/CEJ-2023-09-11-06 Full Text: PDF

Assessment of Interstitial and Surface Water Quality of Ibaka Creek Rivers State, Nigeria: The Case of Artisanal Refinery Activities

The study examined how artisanal refinery activities affect interstitial and surface water quality along Ibaka Creek, Rivers State, Nigeria. A multi-meter was used to measure the temperature, pH, electrical conductivity (EC), salinity, and total dissolved solids. In addition to dissolved oxygen (DO), dissolved oxygen demand (BOD5) was measured with an Extech multimeter. All of the physicochemical parameters were within the limits of the acceptable limits (WHO, EPA, and SON). As a result, both surface and interstitial water in Ibaka Creeks showed variations in its EC, TDS, pH, temperature, salinity, BOD5, and DO values. However, surface water recorded higher EC (30.04±0.19 µS/m) and TDS (29.87±0.18 mg/L) values, while interstitial water showed a higher pH value of 7.25±0.07 and lower temperature values of 27.94±0.09°C. The surface water had a higher salinity (0.64±0.15 ppt), BOD5 of 3.36mg/L and a DO value of 4.61mg/L than interstitial water. BOD and pH values were not significantly different between surface water and interstitial water (p&gt;0.05). There were the highest values recorded in November for surface and interstitial water, except for BOD5 values, which were highest in December. The lowest temperatures and pH were recorded in February, while the lowest levels of oxygen were recorded in April. The salinity and BOD5 levels were also lowest in March, while electrical conductivity and TDS levels were lowest in February. In March, salinity and BOD5 were the lowest, while electrical conductivity and TDS were the lowest. The seasonal variation and anthropogenic influences were attributed to fluctuations in parameters across the months. To ensure the health of aquatic ecosystems and support sustainable management practices, continuous monitoring and assessment are essential.

Assessment of Surface Water and Estuarie Quality and Soil Condition Monitoring

Assessing the quality of surface waters and estuaries and monitoring soil health are critical to monitoring and maintaining the health of aquatic ecosystems and ensuring sustainable land management practices. This involves the assessment of various parameters, such as chemical composition, physical characteristics and biological indicators, to determine the overall condition and potential impact on human health and the environment

Environmental Assessment of Surface Water Quality in the Umanka River Basin

Environmental Assessment of Surface Water Quality in the Umanka River Basin

Influence of Seasonal Variations on Physicochemical Parameters of a Freshwater Ecosystem -Valiyakulam Pond Thiruvananthapuram, Kerala, India

The present study deals with the water quality assessment of surface water from Valiakulam pond for a period of one year. The physico chemical parameters of water samples were analyzed based on standard laboratory procedures.. The present study aimed to analyze the influence of seasonal variations on physicochemical parameters of pond water. The parameters of water such as temperature, PH, transparency, turbidity, conductivity, hardness, alkalinity, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and free carbon dioxide (CO2) were studied from four stations of the pond. Results of the data were subjected for statistical analysis such as correlation and two way ANOVA with post hoc test. The results of ANOVA showed significant difference in all the twelve physicochemical parameters between the seasons and no significant difference was observed between study sites (P&lt;0.05). The analysis of post hoc test in the form of Tukey also showed the same pattern. The results of the study concluded that the physicochemical parameters of pond water can be used for domestic propose. The results of the study may become useful data for productive water resource utilization in future.