Spatial Distribution Of Water Quality Research Articles

Assessment of the Spatial Distribution of Water Quality for Surface and Groundwater at Bonny Island, Nigeria

The aim of the study was to assess the spatial distribution of water quality for surface and ground water at Bonny Island in Rivers State, Nigeria. Surface and Groundwater samples were collected from 9 sampling locations and analyzed for physicochemical parameters, heavy metals, and petroleum hydrocarbons. The contaminants in the surface and groundwater were measured using a variety of methods. Heavy metals in the water were measured using gas chromatography, PAHs were measured using Atomic Absorption Spectrophotometry (ASS) while THC were measured using HACH DR/890 colorimeter. The water quality index (WQI) was calculated using the Weighted Arithmetic Index Method while Principal Component Analysis was used to understand the distribution of the physicochemical parameters, heavy metals, and petroleum hydrocarbons. The results showed that the WQI for both surface and groundwater samples ranged from 55.22 – 125.39, indicating poor to unfit water quality for consumption. In contrast, a control location away from the industrial activities had good water quality with WQI between 35.19 and 45.77. Principal component analysis revealed that the surface water in the industrial area were contaminated with lead, Poly Aromatic Hydrocarbon (PAHs), Total Petroleum Hydrocarbon which indicate oil and gas activities. High chloride, nitrate and sulphate levels were also observed in surface water close to the NLNG plant. Limiting further contamination of the surface and groundwater can be achieved by constant effluent and plume stack monitoring by regulatory agencies. Industrial stakeholder should set up subsidized clinics to provide treatment for any gastrointestinal illnesses or renal damage suffered by residents.

Hybrid analytic network process (ANP)-Entropy model, time series analysis for predicting nitrate and fluoride in groundwater and cumulative health risk assessment

The study focused on groundwater evaluation of the trans-Himalayan region using a novel concept of Hybrid Analytic Network Process (ANP) - entropy and Holt's exponential smoothing model for short-term prediction of groundwater quality. Hierarchy cluster analysis of groundwater was performed to identify the sources and spatial distribution of water quality. The PCA coupled with multivariate analysis dictated a high correlation of various ions i.e., NO3−, K+, F−, Cl−, SO42−, PO42−, and Na+ with the lithology of the area and helped in identification of various polluting sources i.e., excessive use of fertilizers, wastewater discharges and agrochemicals. The hybrid ANP-entropy technique offered a holistic approach to provide a more accurate and complete understanding of groundwater quality and revealed that groundwater quality in 21.43% (in winter) and 42.86% (in summer) of the locations fall into the poor category. The non-carcinogenic health risk analysis revealed that fluoride poses a major potential health risk followed by nitrate, primarily via the oral pathway while it was negligible through the dermal pathway. The total hazard index was observed highest in children (1.99) followed by infants (1.64). 95th percentile of the probable risk obtained through Monte Carlo simulation also dictated highest risk to infants than child. The predicted values of the Holt's exponential smoothing model for the year 2021 and 2022 was varied within ±5 % of the observed values. The proposed model effectively treats uncertainties in groundwater quality assessment and considers average and incremental increase both hence, can be used for short-term prediction and trend analysis of groundwater quality.

Spatial Distribution of Water Quality in Welang, Gembong and Rejoso Rivers, Pasuruan, East Java, Indonesia

A river is a naturally formed freshwater stream that traverses land and eventually flows into a lake, sea, or another body of water. River provides fresh water for human activities such as irrigation for their paddy fields, aquaculture, industrial purposes, and many other purposes. At the same time, there exists an inherent disparity in the demand, availability, and quality of river water, often giving rise to significant challenges and issues. Environmental experts, commonly use a multivariate statistical method such as Principal Component Analysis (PCA), Storage and Retrieval (STORET), and cluster analysis for water quality analysis. However, those methods are numerical and limited in spatial visualization. Inverse Distance Weighting (IDW) interpolation, Voronoi, and Kriging were applied to obtain the spatial representation of water quality distribution Welang, Gembong, and Rejoso rivers in Pasuruan as study. The objectives are to locate on a map any river segments that experienced poor water quality throughout the observation period. We successively combined STORET with those spatial interpolation. The result shows that IDW interpolation, Voronoi, and Kriging can visualize and map river segments that had poor water quality during the observation time. However, due to the limited input data, the interpolation results exhibit variability. For instance, at a measured location with a STORET value of -28, IDW yielded -28, Voronoi -28, and Kriging -27. Beyond the measurement points, each interpolation method began to produce less accurate values. This study involves interpolating dynamic objects with limited measurements data in narrow channels, which differs from interpolating elevation in broader area, in terms of the accuracy of representation or visualization obtained from this spatial analysis still remain unresolved in this study.

Remote sensing inversion of water quality parameters in the Yellow River Delta

In recent years, with the rapid socio-economic development of the Yellow River Delta (YRD), the pressure on the supply of water resources has continued to rise. The development of oil-based industries has also led to a series of ecological and environmental problems, such as wetland degradation and water quality deterioration. As an increasing number of rivers are getting polluted, resulting in the deterioration of their water quality, monitoring, managing, and protecting water resources in the YRD is particularly important. In this study, water quality monitoring data and simultaneous Sentinel-2 image data from April 30, 2020, to October 26, 2021, were used to construct an experimental sample in the YRD. Water quality parameters (WQPs) concentrations were correlated with Sentinel-2 image element spectral reflectance and sensitive bands were selected. An empirical method based on the characteristic bands was used to invert a total of six water quality indicators, namely dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-H), total phosphorus (TP), total nitrogen (TN) and turbidity. The results show: (1) A total of five water quality inversion models for DO, TN, CODMn, TP and TN were effective in the areas of the Guangli River, the Tiaohe and the Branch River. The inversion accuracies of the five inversion models (R2of 0.6099, 0.9271, 0.9581, 0.8784 and 0.7387; RMSE of 1.2723, 0.3413, 0.9923, 0.0118 and 1.8476; RPD of 1.53, 2.08, 3.56, 2.76 and 1.53) indicated the feasibility of the water quality inversion method based on Sentinel-2 data using statistical theory for monitoring water quality concentration in the YRD. (2) The spatial distribution of water quality in the YRD was generally characterized by high water quality in the upper reaches and low water quality in the middle and lower reaches (except for some seasonal variations). Among them, the water quality of the upper reaches of the Guangli River was poor, with opposite trends in DO and TN concentrations. In the Tiaohe, CODMn and TP concentrations were not strongly correlated. However, CODMn and TP concentrations were high in the middle reaches where water quality was the worst. The TN concentrations in the Branch River decreased between 2020 and 2021, but the water quality is still in Category V. Therefore, continued attention and appropriate water quality management measures in the YRD are required. Further, by measuring water quality indicators at monitoring stations, regression-fitting equations for WQPs were established to obtain complementary multi-platform observations. Thus, the water quality conditions in the YRD region can be evaluated more accurately and quickly. The research results not only provide an important reference basis for the identification and monitoring of pollution sources, prevention and treatment of water environment pollution in the YRD, but also provide water security for socio-economic and ecological environment security.

Monitoring the spatial distribution of water quality of an urban stormwater pond using satellite images

Stormwater ponds play a critical role in managing urban runoff and mitigating the impact of pollutants on receiving water bodies. This study investigates the potential of remote sensing (RS) techniques in retrieving water level and key water quality parameters in urban stormwater wet ponds. Specifically, the focus is on water level, chlorophyll-a (Chl-a), total suspended solids (TSS), turbidity, and Secchi depth (SD). The results indicate that RS techniques can provide reliable estimates of water level, Chl-a, turbidity, SD, and TSS in urban stormwater wet ponds. Results revealed that Chl-a concentrations are higher near the inlets and within the forebay, with a significant decrease downstream of the forebay. TSS and total dissolved solids (TDS) concentrations follow a similar pattern to Chl-a, being higher near the inlet and within the forebay, but significantly lower downstream. Notably, the highest concentrations of TSS, TDS, and Chl-a are observed in July, likely due to heavy rainfall events during that period. Through principal component analysis (PCA) and correlation analysis, the study effectively captured the interrelationships among the water quality parameters in the stormwater pond. The spatial distribution of TSS and Chl-a shows a correlation. Furthermore, the study uncovers a relationship between water temperature and Chl-a concentration, with Chl-a decreasing as the temperature exceeds 20 °C and increasing when it drops below 20 °C. This relationship is influenced by sunlight availability, which affects Chl-a growth, as evidenced by variations in Chl-a concentration between the water column's bottom layers and surface. Overall, this research highlights the potential of utilizing RS techniques to retrieve water quality parameters, providing valuable information for water managers and decision-makers. The ability to generate maps of these parameters using reflected light measurements above stormwater wet ponds also contributes to effective water quality management strategies.

An integrated approach for an impact assessment of the tank water and groundwater quality in Coimbatore region of South India: implication from anthropogenic activities.

Groundwater in the Coimbatore region is very much essential for irrigation and human beings' day-to-day life activities. This research focuses on the impact of anthropogenic activities on the surface water quality of fourtanks in Coimbatore city and its influences on disturbing the groundwater quality as well. Water quality is degrading as a result of encroachment and other anthropogenic activities. The disposal of municipal garbage and waste from other enterprises into the nearby tank degrades the quality of the water in the tanks. This pollutant-concentrated surface water, together with the leech organism, may percolate via the pore spaces between soil particles, where it will interact with groundwater. The quality of groundwater will be impacted as a result of this interaction. As a result, water quality metrics for both surface and groundwater have been assessed in this study, and spatial interpolation was performed using ArcGIS. The map of the spatial distribution of water quality was produced using ArcGIS. This spatially interpolated water quality map helped researchers understand how the quality of surface and groundwater changed over time. Visual MODFLOW and MT3D were used to simulate the groundwater flow. The MODFLOW program is used to simulate the direction of groundwater flow based on groundwater level and rainfall data. The output of MT3D allowed for accurate calculation of the pollutant movement's amount and direction. In this study, chloride was used as the pollutant transport parameter. A semi-structured interview has been done in the study region to find out more about the availability of drinking water, how waste is disposed of, and how diseases are spread among residents who live close to the tanks.

Spatial Distribution Characteristics of Coal Mine Drainage Water Quality in China

Coal mine drainage (CMD) reuse is an important method for optimizing water resource supply in arid/semi-arid regions. This study analyzed the spatial distribution of water quality, management, and utilization strategies for CMD in China. In northwest, northeast, and north China, CMD was characterized by high levels of suspended solids, high salinity, neutral/weakly alkaline pH, and low levels of Fe/Mn. Based on existing water treatment processes, mine water reuse is safe and can support local water supply in these areas. In southern China, especially the southwestern regions, the CMD was characterized by low pH, high salinity, and high levels of Fe/Mn. These areas face high costs associated with mine water reuse and ecological and environmental risks. Therefore, the prevention and control of water pollution should be the focus of mine water management in these areas. To maximize the utilization of CMD, improving the norms and standards for mine water utilization is necessary, policy support for CMD reuse should be fortified, and a complete chain of coal mine water utilization should be developed. This study provides a reference for implementing water environmental protection strategies and mine water reuse policies.

Characterization of the groundwater quality in Udham Singh Nagar of Kumaun Himalaya, Uttarakhand

The Udham Singh Nagar district is affluent in fertile land and water resources and even favoured for industrial advancement, resulting in a rapid increase of industrialization and urban growth, which are the inevitable causes of groundwater contamination. In recent years the increased rate of urbanization and Integrated Industrial Estate (IIE) in Pantnagar, Kashipur and Sitarganj area (of the district) have increased the demand for groundwater. Proper groundwater resource planning and monitoring on a regional scale are required to cope with the pace of rapid urbanization and industrialization along with dire irrigation needs. To suffice this strategy, the spatial distribution of water quality shall determine the sustainable approach to be implemented in an area. This research aims to assess the hydrochemical quality of the groundwater in Udham Singh Nagar district (areal extent 3055 Km2), Uttarakhand. The groundwater samples collected from the aquifer have shown anomalous values of Total Hardness (TH), Total Dissolved Solids (TDS), Magnesium (Mg2+), Iron (Fe), and Lead (Pb2+) on undergoing major elemental analysis, which confirms degradation in the groundwater quality. Moreover, the Piper Trilinear Diagram (PTD) has identified groundwater's hydrochemical facies and characterization. Based on the interpretation of TDS, most groundwater samples < 500 mg/L of dissolved solids, making it desirable for drinking. Therefore, this phenomenon attributes to suitable drinking water present in the aquifer of Udham Singh Nagar district, Uttarakhand.

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Karst watersheds accommodate high landscape complexity and are influenced by both human-induced and natural activity, which affects the formation and process of runoff, sediment connectivity and contaminant transport and alters natural hydrological and nutrient cycling. However, physical monitoring stations are costly and labor-intensive, which has confined the assessment of water quality impairments on spatial scale. The geographical characteristics of catchments are potential influencing factors of water quality, often overlooked in previous studies of highly heterogeneous karst landscape. To solve this problem, we developed a machining learning method and applied Extreme Gradient Boosting (XGBoost) to predict the spatial distribution of water quality in the world's most ecologically fragile karst watershed. We used the Shapley Addition interpretation (SHAP) to explain the potential determinants. Before this process, we first used the water quality damage index (WQI-DET) to evaluate the water quality impairment status and determined that CODMn, TN and TP were causing river water quality impairments in the WRB. Second, we selected 46 watershed features based on the three key processes (sources-mobilization-transport) which affect the temporal and spatial variation of river pollutants to predict water quality in unmonitored reaches and decipher the potential determinants of river impairments. The predicting range of CODMn spanned from 1.39 mg/L to 17.40 mg/L. The predictions of TP and TN ranged from 0.02 to 1.31 mg/L and 0.25–5.72 mg/L, respectively. In general, the XGBoost model performs well in predicting the concentration of water quality in the WRB. SHAP explained that pollutant levels may be driven by three factors: anthropogenic sources (agricultural pollution inputs), fragile soils (low organic carbon content and high soil permeability to water flow), and pollutant transport mechanisms (TWI, carbonate rocks). Our study provides key data to support decision-making for water quality restoration projects in the WRB and information to help bridge the science:policy gap.

Mapping the spatial distribution of water quality as a function of land use and occupation and rainfall in the Pará River Basin, MG

RESUMO O conhecimento da distribuição espacial da qualidade da água em uma bacia hidrográfica se torna fundamental na aplicação de medidas de planejamento e manejo. A geoestatística, por predizer sem tendência as variáveis em locais não amostrados, tem sido uma técnica apropriada no estudo de variáveis ambientais. Assim, objetivou-se com este trabalho mapear a distribuição espacial da qualidade da água, a precipitação e o uso e a ocupação do solo na Bacia Hidrográfica do Rio Pará, em Minas Gerais (MG), por meio da geoestatística. Os dados de qualidade e precipitação, amostrados em 25 pontos, entre 1997 e 2018, de 15 estações climatológicas, foram submetidos ao teste de coeficiente de Pearson. Para aquelas estações que apresentaram correlações forte e muito forte entre si, foram gerados mapas de krigagem ordinária. Também foram elaborados mapas de uso e ocupação do solo. Houve diferença na distribuição da precipitação entre os períodos seco e chuvoso, porém, em ambas as análises, o maior volume de chuva ocorreu na cabeceira da bacia. Como já esperado, os teores elevados das variáveis nitrogênio, fósforo, coliformes termotolerantes, demanda bioquímica de oxigênio, oxigênio dissolvido, sólidos dissolvidos totais e turbidez correlacionaram-se com as áreas dos centros urbanos da bacia. Portanto, por meio da krigagem ordinária, foi possível demonstrar o comportamento espacial das variáveis relacionadas à qualidade da água e ao uso e à ocupação do solo.

Distribusi spasial kualitas perairan dan hubungannya dengan aktifitas budidaya perikanan di Teluk Lampung

Coastal water is water areas that receive much input of organic matter originating from human activities on land. Anthropogenic activities such as marine aquaculture in Teluk Lampung impact water quality on that waters. This study was conducted to determine the distribution of water quality around Teluk Lampung and its correlations to marine aquaculture activities. The analysis carried out in this study is spatial analysis to determine the spatial distribution of water quality in three focus area, namely Teluk Hurun (HB), Selatan Teluk Hurun (SHB) dan Pantai Sari Ringgung (SRB) as part of Teluk Lampung. In addition to spatial analysis, principal component analysis (PCA) was also carried out to determine water quality as a variable and its correlation to each area. Based on the results of this study, the spatial distribution of water quality varies based on the human activities type in each area. Based on the results of principal component analysis, it is known that the variability of several water quality variables also correlates with the human activities types, such as marine aquaculture and beach tourism.

Water Quality Characteristics and Source Analysis of Pollutants in the Maotiao River Basin (SW China)

Rivers are an important mediator between human activities and the natural environment. They provide multiple functions, including irrigation, transportation, food supply, recreation, and water supply. Therefore, evaluations of water quality and pollution sources are of great significance for ecological restoration and management of rivers. In this study, the improved “vušekriterijumska optimizacija i kompromisno rješenje” (VIKOR in Serbian; in English: Multicriteria Optimization and Compromise Solution), and a geodetector were used to analyze the water quality characteristics and pollution sources of the Maotiao River Basin (Gizhou province, SW China). The results showed that the water quality of the Maotiao River Basin deteriorated significantly during the summer drought period, as was evident in the reservoirs and lakes. It improved in the wet season (i.e., during the summer period) due to runoff dilution. Water quality decreased along the river’s course, from upstream to downstream sections. The results of the geographic detector analysis showed that agricultural areas were the primary factor affecting the spatial distribution of water quality in the river basin. In July, August, and November 2020, the influence of agricultural land was 0.72, 0.60, or 0.80, respectively, and the interactions among urban, industrial, agricultural, and forested areas explained 99.2%, 83.2%, or 99.9% of the spatial differentiation of water quality, respectively. Due to the influence of spatial scale, settlements have a small influence on the spatial distribution of water quality. Their impact factors were 0.38, −0.24, and −0.05, respectively. Notably, the negative relationship of water quality and forested areas reflects that topography, types of landscapes, and soil thickness have considerable influences on the Maotiao River Basin’s water quality. Based on the findings, we infer that good farmland water conservancy projects and comprehensive management of different types of landscapes, such as forests, agriculture, and urban area and water bodies, are of great significance for improving water quality.

Evaluation of eutrophication level changes in Baiyangdian Lake based on multiple biological groups

Baiyangdian Lake is the largest shallow lake in the North China Plain. Due to the increases in upstream water consumption in recent decades, the amount of natural water entering the lake has decreased, resulting in a significant drop in the water level of Baiyangdian Lake. Severe eutrophication has occurred in Baiyangdian Lake owing to the discharge of domestic sewage around the sub-lakes. With the establishment of the Xiong'an New Area in 2017, the importance of environmental governance and protection of Baiyangdian Lake has been unprecedentedly increased. The implementation of ecological water supplement from upstream reservoirs and other basins has significantly increased water level of Baiyangdian Lake. Moreover, domestic sewage from surrounding rural areas was collected. In order to understand the current state of water quality in Baiyangdian Lake, and to evaluate the effects of the previous water replenishment and pollution control projects, we investigated water physicochemical variables and aquatic organisms of Baiyangdian Lake in August 2019. We evaluated water quality status of Baiyangdian Lake based on water quality, phytoplankton, zooplankton and macrozoobenthos, by comparing with the evaluation based on the survey data in 2010. In addition, submerged macrophytes were used to evaluate the water ecological status. The results showed that the water quality state of Baiyangdian Lake in 2019 was greatly improved compared with 2010. Among them, total phosphorus concentration decreased by 88.6%, total nitrogen concentration decreased by 83.9%, chlorophyll a concentration decreased by 47.8%, and the Seechi depth increased by 43.4%. The diversity of zooplankton and macrozoobenthos were significantly increased, the density of phytoplankton was significantly reduced, the dominant species of phytoplankton changed from a general highly eutrophic type to a eutrophic type, and the distribution of the clean-type submerged macrophytes was narrowed. The evaluation methods based on submerged macrophytes species and based on zooplankton diversity were not suitable for water quality evaluation in Baiyangdian Lake. In the early stage, the pollution source of Baiyangdian Lake was concentrated in the west, which led to lower water quality. Nowadays, due to the water replenishment from the west, north and south, the hydrodynamic conditions changed significantly. In the west, pollution control projects were carried out, and the spatial distribution of water quality in Baiyangdian Lake are showing a characteristics of homogenization.

A Hybrid Neural Network-Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines.

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

Using a Mamdani Fuzzy Inference System Model (MFISM) for Ranking Groundwater Quality in an Agri-Environmental Context: Case of the Hammamet-Nabeul Shallow Aquifer (Tunisia)

Using an adaptive Mamdani fuzzy inference system model (MFSIM), the purpose of this paper is mainly to assess and rank the assessment and ranking of water quality for irrigation occurring in the Hammamet-Nabeul (Tunisia) shallow aquifer. This aquifer is under Mediterranean climate conditions and affected by intensive and irrational agricultural activities. In the current study, the Mamdani fuzzy logic-based decision-making approach was adapted to classify groundwater quality (GW) for irrigation. The operation of the fuzzy model is based on the input membership functions of electrical conductivity (EC) and sodium absorption ratio (SAR) and on the output membership function of the irrigation water quality index (IWQI). Validation of the applied MFISM showed a rate of about 80%. Therefore, MFISM was shown to be reliable and flexible in quality ranking for irrigation in an uncertain and complex hydrogeological system. The results demonstrated that water quality contamination in the aquifer is affected by the overlaying of three types of negative anthropogenic practices: the excess use of water for irrigation and chemical fertilizers, and the rejection of partially treated wastewater in some areas. The implemented approach led to identifying the spatial distribution of water quality for irrigation in the studied area. It is considered a helpful tool for water agri-environmental sustainability and management.

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation

A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the CODMn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the CODMn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the CODMn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.

Assessment of the water quality of groundwater in Bohai Rim and the controlling factors—a case study of northern Shandong Peninsula, north China

This study collected 184 groundwater (GW) samples from 92 wells during the dry and wet seasons, respectively to understand the hydrochemical characteristics, water quality, and risk of GW nitrate (NO3−) to human health in northern Shandong Peninsula (NSP), China. The results showed that GW in the NSP is weakly alkaline and classified as hard fresh water. The mean concentration of NO3− in GW exceeded 100 mg·L−1, total hardness exceeded 450 mg·L−1, and total dissolved solids (TDS) was less than 1000 mg·L−1. A Piper diagram showed that the water chemistry of GW in the NSP was mainly of the SO4·Cl-Ca·Mg type. A Gibbs diagram and ion ratio analysis indicated that the weathering of silicate rocks and agricultural production were the dominant factors affecting the hydrochemical characteristics of GW in the NSP, with cation exchange, dissolution of salt rock, and weathering of carbonate rock also making contributions. Na+ and Cl− in GW are significantly affected by seawater aerosols in coastal areas. During the wet season, the hydrodynamic conditions of the aquifer are improved, agricultural activities are strengthened, and GW becomes generally homogenized. The water quality index classified the GW quality of the NSP as mainly of medium quality. There was a relatively consistent spatial distribution of water quality during different periods. Water samples of poor water quality were mainly distributed in the lower reaches of the Huangshui River. In addition, total hardness and NO3− concentrations were the main variables affecting the quality of GW in the NSP. The assessment of the risk NO3− in GW in the NSP to human health through the ingestion of drinking water demonstrated a significant risk (infants > children > adults). These results indicate the need for local management measures to reduce the potential health risks of GW quality in the NSP.

Classification of water quality in low-lying area in Vietnamese Mekong delta using set pair analysis method and Vietnamese water quality index.

A rational water quality assessment program directly affects a success of a national socio-economic development strategy. This study was aimed to evaluate and classify surface water quality in Dong Thap province, Vietnam, using set pair analysis (SPA) and national water quality index (WQI_VN) methods. The water quality data was collected at 58 locations in 2019 by the Department of Natural Resources and Environment of Dong Thap province. Sixteen variables including temperature (°C), pH, turbidity (NTU), dissolved oxygen (DO, mg/L), biological oxygen demand (BOD, mg/L), chemical oxygen demand (COD, mg/L), total suspended solids (TSS, mg/L), ammonia (N-NH4+, mg/L), nitrite (N-NO2-), nitrate (N-NO3-, mg/L), total nitrogen (TN, mg/L), orthophosphate (P-PO43-, mg/L), chloride (Cl-, mg/L), sulfate (SO42-, mg/L), coliform (MPN/100mL), and Escherichia coli (MPN/100mL) were monitored four times a year (58 water samples × 16 parameters × 4 monitoring times). The findings presented that TSS, BOD, COD, N-NH4+, N-NO2-, P-PO43-, coliform, and E. coli were the main constraints on water quality. The results of the entropy weight calculation indicated that deteriorated water quality was in the order of microbiological > nutrients > organic matters. Surface water quality was evaluated at medium (level III) and poor (level IV) by SPA and WQI_VN, respectively; however, the combination of SPA and entropy weight was considered more efficient in this classification and a positive spatial autocorrelation was also found through Moran's I. The spatial distribution of water quality based on SPA classification revealed that better water quality was found in the inner parts of the study area. Due to its ease and effectiveness, set pair analysis should be considered for inclusion in the water quality assessment program of Vietnam.

Spatial distribution of water quality in Belawan River, North Sumatra

Belawan river is one of the rivers in North Sumatera Province. Belawan river which crosses Deli Serdang District and Medan City is used for the activities of plantation, agriculture, livestock, sand mining, a place for bathing and washing as well as serving as a lavatory, hospital, hotel, settlements, and industry. Utilization of the river as a wastewater disposal site can cause a decrease in the quality of river water. The purpose of this research was to analyze the Belawan river water quality spatially. Water sampling was taken at nine points which included three upstream segment points in Deli Serdang District, three middle segment points in Deli Serdang District and Medan City, and three downstream segment points in Deli Serdang District. Based on the results of the research it can be concluded that the value of Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) at several monitoring points in Belawan river has exceeded the water quality standard limits of class I Government Regulation of Republic of Indonesia Number 82 the year 2001 and North Sumatera Governor Regulation Number 21 the year 2006.

Application of Water Quality Index and Multivariate Statistical Analysis for Assessing Coastal Water Quality

Contamination of coastal water by seawater is a global phenomenon, which deteriorates the water quality. The present study aims to assess the suitability of coastal water for drinking and irrigation purposes with regard to seawater contamination. Conventional hydrochemical data have been utilized in conjunction with mathematical and statistical methods. Sixty groundwater and nine surface water samples were collected from the eastern coastal plains of Odisha, India and were analyzed for various physico-chemical parameters. A significant influence of seawater on water quality has been observed. Additionally, ion-exchange process has been identified to play a vital role in controlling the water chemistry. Application of hierarchical cluster analysis on water quality data produces two clusters of samples (C-1 and C-2) that are well-explained by the factors of principal component analysis. Higher dissolved salt contents in a majority of samples in the C-1 cluster have resulted in higher water quality indices that make them unsuitable for drinking purposes. Further, their placement in the Wilcox and USSLS diagrams indicated that these samples are even unsuitable for irrigation purposes. The C-2 cluster comprises 38% of water samples, which can be utilized for both drinking and irrigation purposes. The spatial distribution of water quality shows that the drinkable and irrigational water is mainly confined to a few patches in the area. These patches may be utilized for supplying the drinking water to the coastal population.