Spatio-Temporal Evaluation of Physicochemical Parameters, Heavy Metals, and Pesticides in the Little Zab River

Eutrophication and water pollution have become serious aquatic environmental problems worldwide. In this study, four different water quality indexing methods, the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI), the revised Comprehensive Pollution Index (CPI), the Eutrophication Index (EI), and the Trophic State Index (TSI), were used to investigate water quality and trophic status in the Tri An Reservoir (TAR), Vietnam. Nineteen water variables were monitored bimonthly between February 2018 and December 2019 to calculate these four indices. Although different input data sets were used, all the indices showed similar results in terms of water quality and eutrophic state. The CCME WQI ranged from 32 to 68 (median 36–48), which indicated that the water quality was classified between poor and marginal and was insufficient for domestic purposes. Similarly, the water quality was ranked between slightly polluted and medium polluted based on the revised CPI index, with the median values ranging from 0.72 to 0.94. In the CCME WQI and the CPI, the major parameters that contributed to water pollution in the TAR were turbidity, dissolved oxygen (DO), total suspended solids (TSS), chemical and biological oxygen demand (BOD5), ammonium (NH4+), nitrite (NO2−), iron (Fe), lead (Pb), and coliforms. Regarding the trophic status of water, the EI ranged from 10 to 464, indicating that the reservoir was eutrophic, and the TSI values ranged from 53 to 89, indicating that the water quality was classified between light-eutrophic and hypereutrophic conditions. A temporal variation in water quality was found, with the highest levels of pollution recorded in June during the study period. Our results show that the combination of different water quality indices provides a comprehensive assessment of water quality in the TAR.

Water quality monitoring is essential for understanding ecosystem health and guiding effective water management strategies. In particular, water quality indices (WQI) are crucial tools for assessing the status of surface water bodies, providing a simplified measure of water quality across various parameters along sequential monitoring stations along a river system. This study aims to assess the spatial and temporal variations in water quality along the Little Zab River in northwestern Iran, using the Iranian Water Quality Index (IRWQI). This study examines water quality at four sequential monitoring stations along the river using the IRWQI, incorporating critical water quality parameters. Data for the analysis were collected from 2015 to 2024 and analyzed using non-parametric tests, including Kruskal–Wallis, to detect significant variations in water quality across the monitoring stations. According to the results, water quality varies across the stations. Water quality varies across stations. The upstream Mirabad-Upland station has a low IRWQI (56.51) due to wastewater from Chaku village. It improves at Grzhal-Bridge (60.04) via self-purification but declines at Nalas (57.35) due to pollution from Vavan village and agriculture. Sardasht-Dam records the highest IRWQI (64.46), likely benefiting from self-purification and cleaner inflows. Mirabad-Upland has “Fairly Good” to “Moderate” water quality. Grzhal-Bridge improves slightly, with some “Good” and “Very Good” cases, but occasional “Bad” levels. Nalas declines to mostly “Bad” and “Fairly Bad,” likely due to pollution. Sardasht-Dam shows partial recovery, though some “Bad” cases persist. Overall, water quality worsens downstream due to pollution and hydrological changes. The Little Zab River’s water quality followed a seasonal pattern, improving in wet months and declining in dry months due to flow changes and pollutant levels, indicating the need for year-round monitoring. The results suggest that localized pollution sources, such as wastewater discharge impact water quality, particularly in upstream sections. These results indicate the need for improved pollution control.

Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.

The Mekong Delta produces about 70 % of Vietnam's national aquaculture output, and water quality management is a key factor in maintaining high levels of production and profitability. Effective water management strategies depend on understanding the spatial and temporal variations in water quality along major waterways. Therefore, this study aimed to assess the spatial and temporal fluctuation of water quality in the waterways of five provinces with intensive aquaculture operations in the Mekong Delta. The study sites included two inland provinces (An Giang and Can Tho) where striped catfish, Pangasianodon hypophthalmus (Sauvage, 1878), is farmed, and three coastal provinces (Soc Trang, Bac Lieu, and Ca Mau) where white leg shrimp, Litopenaeus vannamei (Boone, 1931), and black tiger shrimp Penaeus monodon Fabricius, 1798, are cultured. At each sampling site, physicochemical parameters were monitored for 12 months on the main rivers which supply water to aquaculture systems. Water quality data were analysed and compared based on the coefficient of variation (CV), Pearson correlation coefficients to identify relationships among variables and principal component analysis (PCA) to identify 2–3 key parameters having the most significant influence on spatial and temporal variation in water quality in each province. BOD, COD, PO4 3-, S2-, NO2 - and NO3 - were highly variable (CV 40–120 %). Significant interrelationships ranged from -0.5 to 0.5 between many water quality parameters. Overall, water in most provinces was considered polluted. However, the water quality parameters, except for S2- and NO2 - , were within acceptable levels specified by National Technical Regulation on Surface Water Quality for Protection of Aquatic Life.

Comprehensive assessment of water quality through different approaches: Physicochemical and ecotoxicological parameters

The study aims to assess spatial and temporal water quality variations in the upper reaches of the Vietnamese Mekong Delta. Thirty-one water monitoring samples of the two main rivers (Tien and Hau Rivers) and six canals flowing through An Giang Province were collected in the dry season (March) and the rainy season (September) from 2009 to 2019. Seven physicochemical parameters were analyzed including temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total suspended solids (TSS), orthophosphate (P-PO43-), and coliforms. Water quality index (WQI), cluster analysis (CA), and discriminant analysis (DA) were applied to evaluate water quality, spatial and temporal variations, and seasonal discriminant water variables. WQI values (15–71) indicated surface water quality was very bad to medium in which the water quality in larger and in smaller rivers in the dry season was less polluted than that in the rainy season due to erosion and runoff water containing waste materials in the wet season. CA grouped the water quality in the dry and rainy seasons into four clusters mainly due to BOD and coliforms in the dry season; TSS and coliforms in the rainy season. Discriminant analysis revealed that DO, TSS, coliforms, temperature and BOD significantly contributed to seasonal variations in water quality. Therefore, water quality monitoring in the surveyed area could only focus on DO, TSS, coliforms, temperature and BOD to reduce monitoring cost.

Severe drought in south-eastern Australia during the 2008–2009 spring, summer and autumn period resulted in low flows in the Murray and Edward Rivers, a major river system of the region. We investigated whether such conditions produced marked spatial and temporal variations in water quality and in the abundance, community composition and cell size of six commonly occurring cyanobacterial species in the river. Water quality data and cyanobacterial samples were collected at 22 sites spanning almost 1900 km of river. Significant spatial and temporal variation in water quality was found in the rivers. Four cyanobacterial species had significantly higher abundances in the upper and middle sections of the Murray and Edward Rivers, and significant temporal variations occurred in three species. Community composition also varied significantly both spatially and temporally. There were significant spatial differences in the cell size of three species, and significant temporal variation occurred in five species. However, water quality was found to provide a poor explanation of the variance observed in the cyanobacteria, possibly due to their continual replacement by river flow at the sampling sites. Increased cyanobacterial occurrence in rivers is likely to be more common under future climate change scenarios.

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.

Variations in water quality of River Ogun around the cattle market, Isheri along Lagos-Ibadan express road were evaluated using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) to analyze the similarities or dissimilarities among the sampling points so as to identify spatial and temporal variations in water quality and sources of contamination over time. Water quality data were generated from 8 sampling points during 6 year sampling periods (i.e., 2000, 2005, 2006, 2009, 2010, and 2011). The samples were analyzed for 14 physico-chemical parameters and heavy metals such as temperature, pH, total solids (TS), total dissolved solids (TDS), suspended solids (SS), oil and grease, dissolved oxygen (DO), chemical oxygen demand (COD), Cl-1, alkalinity, total hardness (TH), SO42-, NO3 -, PO43- and heavy metals (Cd, Cu, Fe, Ni, Pb, Mn, and Zn). Three zones were differentiated based on the cluster analysis results, and implied similar water quality features. Thus, the water quality around the site may be categorized as relatively less polluted, moderately polluted and highly polluted. The PCA assisted to extract and recognize the factors responsible for water quality variations over the years. The results showed that the index which changes the quality of the water differs. The natural, inorganic and organic parameters e.g., temperature, TS, and etc., were the most significant parameters contributing to the variations in the water quality over the years. This shows that a parameter that can be significant in contributing to water quality in one season may less or not be significant in another. This result may be used to reduce the number of samples analyzed both in space and time, without much loss of information. This will assist the decision makers in identifying priorities to improve water quality that has deteriorated due to pollution from various anthropogenic activities.

A study of spatial and temporal variations on water quality and trophic status was conducted to determine the temporal (average reading by month) and spatial variations of water quality in Sembrong reservoir and to evaluate the trophic status of the reservoir. Water samples were collected once a month from November 2016 to June 2017 in seventeen (17) sampling stations at Sembrong Reservoir. Results obtained on the concentration of dissolved oxygen (DO), water temperature, pH and secchi depth had no significant differences compared to Total Phosphorus (TP) and chlorophyll-a. The water level has significantly decreased the value of the water temperature, pH and TP. The water quality of Sembrong reservoir is classified in Class II which is suitable for recreational uses and required conventional treatment while TSI indicates that sembrong reservoir was in lower boundary of classical eutrophic (TSI > 50).

Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey

The Kali River is a significant source of surface water as well as the main tributary of River Hindon that flows through major cities of western Uttar Pradesh, India. It flows throughout the urban and industrial regions; hence, it carries various amounts of pollutant. Therefore, a study was conducted to examine spatial-temporal variations in river water quality by determining physicochemical variables and heavy metal concentrations at seventeen sampling stations (S1-S17) throughout the river stretch. Various physicochemical variables, namely pH, EC, TDS, turbidity, BOD, COD, TH, TA, Ca, Mg, Na, K, HCO3-, Cl-, SO42-, NO3-, and PO43- were higher in summer than in winter. The order of mean metal concentrations was Fe > Pb > Mn > Ni > Zn > Cu > Cr > Cd. The relationships among measured physicochemical variables and pollution index were examined. Furthermore, multivariate statistical methods were used to assess spatial-temporal variation in water quality to identify current pollution sources and validate results. Water quality index and comprehensive pollution index indicated that the Kali River was less polluted from S1 to S8. However, downstream sampling sites were polluted. Pollution starts from S9 and drastically increases at and beyond S13 because of effluents from industries and sugar mills in Muzaffarnagar. The study suggests cleaning the downstream region of river to restore human health and flora and fauna in the river ecosystem.

Water quality of wetland ecosystems is a critical concern due to its implications on human health and aquatic life. This study was conducted to assess the spatial and temporal variation of water quality in Kotagala wetland to understand its impact on water safety for domestic uses and ecosystem health. Water samples were collected (n = 70) from six inlets and the wetland outlet from November 2021 to August 2022, and analyzed for pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), total suspended solids (TSS), nitrate, and phosphate. The pH, EC, and nitrate showed significant correlations. Spatial clustering divided the monitoring sites into two clusters, as areas of higher and lower pollution levels. Discriminant analysis highlighted the significance of EC, pH, and nitrate concentrations in differentiating these clusters. Principal According to principal component analysis (PCA) 88.8% of the total variance of spatial variation in water quality was explained by the first two components. Temporal clustering following seasonal variations revealed the influence of rainfall pattern on water quality. EC, TDS, TSS, and nitrate concentrations emerged as the most important factors in this temporal categorization. In PCA, 76.8% of the total variance of temporal variation in water quality was explained by the first two components. Findings highlight the urgent need for sustainable strategies and policies to mitigate the impacts of human activities on wetland water quality, since water quality variations in the wetland was significantly impacted by direct human activities, and variations in rainfall trends in the area.

Increasing pressures caused by human activities pose a major threat to water availability and quality worldwide. Water resources have been declining in many catchments during recent decades. This study investigated patterns of river water quality status in a peri-urban/rural catchment in Bolivia in relation to land use during a 26 year period. Satellite images were used to determine changes in land use. To assess water quality, data in the dry season from former studies (1991–2014), complemented with newly collected data (2017), were analysed using the National Sanitation Foundation-Water Quality Index method and the Implicit Pollution Index method. The highest rates of relative increase in land use area were observed for forest, urban, and peri-urban areas, whereas relative decreases were observed for water infiltration zones, bare soil, shrubland, and grassland areas. The water quality indices revealed clear water quality deterioration over time, and from catchment headwaters to outlet. Statistical analyses revealed a significant relationship between decreasing water quality and urban expansion. These results demonstrate the need for an effective control programme, preferably based on water quality index approaches as in the present study and including continuous monitoring of runoff water, mitigation of pollution, and water quality restoration, in order to achieve proper water management and quality.

This study presents a comprehensive assessment of water quality in Estie Densa Spring and Wanka River in Ethiopia through the application of multivariate statistical methods. Water quality is a critical environmental parameter, and understanding its variations is essential for sustainable resource management. The research involves the collection of water samples from Estie Densa Spring and Wanka River, followed by the analysis of various physicochemical, nutrients and heavy metals parameters. Multivariate statistical methods, including principal component analysis (PCA) and cluster analyses, are employed to discern patterns and relationships within the dataset. Physicochemical parameters were measured using a multimeter and nutrients were measured using a portable photometer 7100 whereas heavy metals were determined by FAAS, after wet acid digestion. From the result, the range of physicochemical parameters and nutrients found in water samples were pH (6.4-8.1), EC (9.9-90 μS/cm), TDS (4.8-44.8 mg/L), Turbidity (4-315 NTU), Total alkalinity (1600 - 6800 mg/L), Temperature (21.3-28.6 ℃), Chlorine (35.6-213 mg/L), Phosphate (0.14-0.7 mg/L), Sulphate (1-4.25 mg/L), Ammonia (0.23-0.595 mg/L), Nitrate (1.2-11.8 mg/L) and Nitrite (0.015-0.139 mg/L). Among these parameters, only the level of turbidity was above the permissible limit. The levels of heavy metals (mg/L) in water samples were Cr (0.005-0.010), Mn (0.007-0.020), Ni (0.030-0.073), Fe (0.5- 0.71), Pb (0.005-0.006) and Cd (0.004-0.011). The levels of Fe and Cd, were higher than the permissible limit of WHO for drinking water which might have a risk for the consumers. The cumulative carcinogenicity risks of trace elements in the spring and river drinking water for adults and children were in the range of (9.2 ×10-3 - 2.52×10-2). which were above the acceptable monitored and controlled levels (1 × 10-4 – 1 × 10-6). Findings from this research contribute to the existing knowledge of water quality in the region, offering valuable information for decision-makers and environmental stakeholders. The application of multivariate statistical methods enhances the understanding of the interplay between various water quality parameters, facilitating more informed and targeted management strategies. Ultimately, this study serves as a basis for promoting sustainable water resource management practices in the context of Estie Densa Spring and Wanka River in Ethiopia.