Seasonal Water Quality Research Articles

Assessment of seasonal water quality and land use land cover change in Subarnarekha watershed of Ranchi stretch in Jharkhand.

In this paper, the assessment of seasonal water quality and land use land cover change in the Subarnarekha watershed in Ranchi stretch was analysed. Agricultural runoff along with climate change adds to the pollution risk to the Subarnarekha River of Ranchi stretch in Jharkhand. Water quality indicators, like acidity alkalinity (ALK), total dissolved solids (TDS), hardness (H), dissolved oxygen (DO), biochemical oxygen demand (BOD), chlorides (CL-), electrical conductivity (EC), salinity (SAL), resistivity (RES) and pH, were assessed as per the standard method. During monsoon season, acidity, alkalinity, hardness, chlorides, salinity, pH and DO decreased, whereas EC, TDS, BOD and resistivity increased in comparison to pre-monsoon season. In post-monsoon, chloride problem was observed very high. Hardness was least in monsoon and maximum in post-monsoon season. EC and BOD increased in monsoon season in comparison to other seasons. Statistical analysis like HCA (hierarchical cluster analysis) and PCA (principal component analysis) also confirmed the problem of TDS, EC, chloride and hardness in the area. WQI (water quality index) analysis showed that the water quality was poor to unsuitable on all the sampling points throughout the study area in all seasons. LULC (land use land cover) and NDWI (normalized difference water index) analysis had also concluded that due to high rate of urbanization, the area has undergone a massive change in terms of forest cover and water bodies. The need for afforestation, forest protection and wetland protection can be clearly seen from the result of this study.

Effects of land-use patterns on seasonal water quality at multiple spatial scales in the Jialing River, Chongqing, China

Land-use patterns usually impact river water quality as non-point pollution sources at both spatial and seasonal scales, which can provide important guidance for water pollution control and land-use planning after quantitative analysis. Here, surface water samples were collected from ten sampling points in the Chongqing Section of the Jialing River to monitor the physicochemical parameters of river water from March 2021 to February 2022. Our results showed significant seasonal changes in river water quality based on higher concentrations of dissolved oxygen (DO), ammonium nitrogen (NH 4+-N), and nitrate nitrogen (NO3–-N) in the wet season than those in the dry season, while the concentrations of chemical oxygen demand (CODcr) and total nitrogen (TN) in the dry season are higher than those in the wet season. Redundancy analysis (RDA) for elucidating the relationship between land-use patterns and river water quality parameters at multiple spatial (reach and riparian scale) and seasonal scales (dry and wet season) shows that water quality indices exhibited a strong positive correlation with construction land and farmland, inducing a negative impact on river water quality; while forest and grassland were inversely correlated to water quality indices, causing a positive effect on river water quality. On a seasonal scale, the land-use pattern showed more association with river water quality parameters in the wet season, compared to that in the dry season. The impact of the different buffer zones associated with land-use types on the river water quality was in the following order: 2000 > 500 ≈ 1000 m buffer. These findings contribute to sustainable land-use development and multi-scale landscape optimization to improve water quality in the Chongqing Section of the Jialing River.

Seasonal differences in water quality of highland lakes and its influencing factors: A case study of Dianchi Lake

Abstract Water quality is of great significance to human survival and development. It is of good theoretical value to study seasonal water quality variation and driving factors in plateau lakes. This paper studied the variation of water quality parameters in different seasons and their relationship with land use and climate factors in Dianchi Lake, Yunnan Province from 2001 to 2019. The results show that the factors restricting water environment in different seasons are different, but basically close. The water quality parameters of multiple water quality monitoring stations in different seasons showed a decreasing or insignificant trend. In the rainy season, the temperature and precipitation were positively correlated with most water quality parameters, while the driving effect of climate factors was not significant in the dry season. Different land types have different effects on water quality indexes in different seasons. The water quality score was higher in the rainy season than in the dry season. The scores of water quality stations in the south and middle of Dianchi Lake are higher than those in the north. In this study, the relationship between climate, land, and water quality is established, which provides a way for the research of water quality parameters.

Identifying the influence of natural and human factors on seasonal water quality in China: current situation of China's water environment and policy impact.

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

Comprehensive analysis of Vibrio alginolyticus: Environmental risk factors in the cultured Gilthead seabream (Sparus aurata) under seasonal fluctuations and water parameter alterations.

This study aimed to investigate the relationship between seasonal variations, water parameters and the prevalence of Vibriosis in Gilthead seabream. A total of 160 Gilthead seabream fish were sampled over the course of 1 year from private earthen pond farms in the Suez Canal area and examined for abnormalities and internal lesions. Vibrio alginolyticus, the causative agent of Vibriosis, was isolated and characterized from the sampled Gilthead seabream fish. The study revealed a significant correlation between different seasons and the prevalence of V. alginolyticus, with lower occurrence during autumn. Analysis of water parameters showed that toxic ammonia concentration was not effective in distinguishing between positive and negative cases of V. alginolyticus. Dissolved oxygen showed weak predictive ability for the occurrence of V. alginolyticus, while temperature demonstrated moderate potential as a predictor of its prevalence. pH values, organic matter concentrations and salinity showed no significant association with the occurrence of V. alginolyticus. Experimental challenges highlighted the vulnerability of Gilthead seabream to V. alginolyticus and emphasized the impact of environmental factors, such as pH and toxic ammonia, on their mortality and survival. The study emphasizes the importance of considering seasonal changes and water quality parameters in managing V. alginolyticus in mariculture. It underscores the need for careful monitoring and control of environmental factors to ensure the health and well-being of cultured fish populations. The findings contribute to our understanding of Vibriosis management and provide valuable insights for developing effective strategies in the aquaculture industry.

Fire Characteristics and Hydrologic Connectivity Influence Short‐Term Responses of North Temperate Lakes to Wildfire

AbstractDespite increasing wildfires, few studies have investigated seasonal water quality responses to wildfire characteristics (e.g., burn severity) across a large number of lakes. We monitored 30 total lakes (15 burned, 15 control) monthly following the Greenwood Fire in Minnesota, USA, a lake‐rich region with historically prevalent wildfire. We found increases in median concentrations of total nitrogen (68%), total phosphorus (70%), dissolved organic carbon (127%), total suspended solids (71%), and reduced water clarity (48%) and pH (0.45) in burned lakes. Post‐wildfire responses in drainage lakes were often persistent or cumulative throughout the open‐water season, compared to isolated lakes. Total phosphorus (TP) increased linearly with watershed high‐severity burns, and shoreline high‐severity burns explained more variation in TP than lake morphometry and watershed variables. Post‐wildfire chlorophyll‐a responses were nonsignificant and inconsistent, possibly due to light limitation. Our results suggest that increasing wildfires have significant potential to affect water quality of inland lakes.

Wet Season Water Quality and Zooplanktons Community of Jibia Lake, Katsina State, Nigeria

Wet Season Water Quality and Zooplanktons Community of Jibia Lake, Katsina State, Nigeria

Effects of comprehensive landscape patterns on water quality and identification of key metrics thresholds causing its abrupt changes

Comprehensive landscape patterns influence water quality with multiple factors, complex processes, and scale dependence. However, studies identifying landscape thresholds causing abrupt water quality changes and characterizing the contribution of topography to water quality are still limited. Exploring the impact mechanisms of natural geographical and landscape characteristics on spatial and seasonal water quality variations is conducive to watershed water resource protection and ecosystem restoration. Based on water quality monitoring data of Minjiahe River in the typical headwater area of the upstream Dan River in China from 2019 to 2021, we employed redundancy analysis, partial redundancy analysis, and nonparametric change-point analysis to analyze the relationship between stream water quality and multi-spatial scale comprehensive landscape patterns, to obtain the interactive and independent contributions of different landscape categories at multi-spatial scales on water quality, and to find the key landscape threshold leading to abrupt changes in water quality. Results showed that landscape configuration, landscape composition, and topographic factors collectively explain over 89.1% of water quality variation. Most seasonal variations in water quality were primarily caused by landscape configuration. The landscape composition was mainly responsible for the differences in water quality variations among spatial scales. The topographic factors made the least independent contribution and had a potential impact on overall water quality variation. In order to protect the water quality of streams, it is more reasonable to regulate the landscape at different scales. At the sub-catchment scale, interspersion and juxtaposition index (IJI) and landscape shape index (LSI) should be controlled below 82% and 22. At the 100 m riparian scale, farmland, urban land, IJI, and LSI should be controlled below 29%, 6.5%, 92%, and 26, respectively. Our results provide important guidance for optimizing landscape patterns and water conservation in the watershed.

Influence of barrages on hydrological parameters and functional groups of phytoplankton of a subtropical river Ganga, India.

The current study used phytoplankton functional groups (FGs) to assess the impact of barrages on the ecological function of subtropical river Ganga. A total of 101 phytoplankton species were identified, including 20 phytoplankton FGs with MP and D having the highest prevalence rates of more than 50%. Other FGs with higher occurrence rates, such as N, A, and J, displayed seasonal and spatial advantages on occasion, indicating the randomness of exogenous influx from catchment areas. Based on phytoplankton FGs similarities, a non-metric multidimensional scaling analysis (NMDS) revealed geographical and seasonal discrepancies. The higher concentration of chemical components in Kanpur was linked to decreased water discharged from preceding reservoirs, resulting in a highly polluted stretch in terms of spatial variation, according to our findings. During the monsoon season, however, the diluting process caused by precipitation and surface runoff significantly improved water quality. As a result, we hypothesized that a sequence of barrages alters the hydrological characteristics of cascading reservoirs, which was related to spatial and seasonal fluctuation in FG diversity and phytoplankton FGs could be useful bioindicators of water quality. In conclusion, the phytoplankton FGs approach is useful for identifying the geographical and seasonal aspects of aquatic ecosystems that are heavily influenced by human activities, but its sensitivity to changes in spatial and seasonal water quality warrants more exploration.

Assessment and interpretation of surface water quality in Jhelum River and its tributaries using multivariate statistical methods.

Water is an essential part of human life, and its pollution is a hotly debated topic on both national and international scales. Surface waterbodies in the beautiful Kashmir Himalayas are already deteriorating. In this study, fourteen physio-chemical parameters were tested in water samples taken during the spring, summer, autumn, and winter seasons from twenty-six different sampling points. The findings showed a consistent decline in the water quality of river Jhelum and its adjoining tributaries. The upstream section of the river Jhelum had the least pollution, whereas the Nallah Sindh had the poorest water quality. The water quality of Jhelum and Wular Lake was strongly impacted by the water quality of all the adjoining tributaries. To examine the link between the selected water quality indicators, descriptive statistics and a correlation matrix were used. Analysis of variance (ANOVA) and principal component analysis/factor analysis (PCA/FA) were used to identify the key variables that influenced seasonal and sectional water quality fluctuations. The ANOVA analysis revealed that there were significant differences in water quality characteristics among the twenty-six sampling locations throughout all four seasons. The PCA findings identified four principal components that accounted for 75.18% of the total variance and could be utilized to evaluate all data. The study revealed that chemical, conventional, organic, and organic pollutants were significant latent factors influencing the water quality of rivers in the region. The findings of this study could contribute to the vital management of surface water resources in Kashmir's ecology and environment.

Analysis of Influencing Factors of Chlorophyll-a in Lake Taihu Based on Bayesian Network

Global warming has aggravated the problem of lake eutrophication. As a typical large, eutrophic, shallow lake in China, the issue of cyanobacterial harmful algal blooms (cyanoHABs) was particularly prominent in Lake Taihu. We took Lake Taihu as the study area, using the meteorological (temperature, wind speed, rainfall, and sunshine hours), water quality (total nitrogen, total phosphorus, conductivity, pH, and chemical oxygen demand), and biological (chlorophyll-a in phytoplankton) monitoring data from 1992 to 2020. We built a simulation model of chlorophyll-a based on the Bayesian network model with continuous variables to study the chlorophyll-a level of Lake Taihu under different meteorological and water quality conditions. The 75th percentile of chlorophyll-a concentration was used as the threshold to judge the risk of cyanobacterial bloom. When the probability of chlorophyll-a concentration below this threshold was greater than 75%, it was regarded as "low risk" of cyanobacterial bloom outbreak. The results showed that the average level of "temperature wind ratio" (ratio of air temperature to wind speed) in spring was 6.67℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.130 mg·L-1. The average "temperature wind ratio" level in summer was 10.52℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.257 mg·L-1. The average level of total phosphorus concentration in autumn was 0.154 mg·L-1, and the probability of high chlorophyll-a was less than 75% when the "temperature wind ratio" was less than 6.30℃·s·m-1. Based on the above research, the chlorophyll-a model constructed by the Bayesian network model with continuous variables was further used to simulate the nutrient control objectives under different climate change backgrounds. In order to control chlorophyll-a in Lake Taihu at the:"low risk" level of cyanoHABs, the target concentration thresholds of total phosphorus needed to be controlled under the climate level background from 1992 to 2000, 2001 to 2010, and 2011 to 2020 were given. From 1992 to 2000, the threshold value of total phosphorus concentration was 0.135 mg·L-1 in spring, 0.174 mg·L-1 in summer, and 0.171 mg·L-1 in autumn. From 2001 to 2010, the threshold value of total phosphorus concentration was 0.115 mg·L-1 in spring, 0.164 mg·L-1 in summer, and 0.162 mg·L-1 in autumn. From 2011 to 2020, the threshold value of total phosphorus concentration was 0.059 mg·L-1 in spring, 0.145 mg·L-1 in summer, and 0.145 mg·L-1 in autumn. The results showed that the control of cyanoHABs in eutrophic lakes required more stringent nutrient control strategies with global warming. It provided a reference for preventing and controlling cyanoHABs and eutrophication in Lake Taihu. Previous studies have used multiple regression models, hydrodynamic numerical models, and other methods to predict chlorophyll-a concentrations or cyanobacterial blooms in lakes. However, there has been no study on the prediction of cyanoHABs in lakes based on the Bayesian network model with continuous variables and the "dynamic" evaluation of nutrient thresholds. Therefore, based on the seasonal meteorological, water quality, and biological monitoring data of Lake Taihu from 1992 to 2020, the chlorophyll-a model of Lake Taihu was constructed for the first time based on the Bayesian network model with continuous variables to simulate the chlorophyll-a concentration of Lake Taihu under different climate indicators and total phosphorus concentrations. The weight of its influencing factors was also analyzed, and the nutrient control objectives under different climate scenarios were "dynamically" evaluated.

Evaluation of a Three-Dimensional Hydrodynamic and Water Quality Model for Design of Wastewater Stabilization Ponds

A three-dimensional coupled hydrodynamic and water quality model (Deflt3D-WAQ) was applied to evaluate the ability of a computational simulation to reproduce seasonal water quality conditions over a seven-month period in a wastewater stabilization pond located in eastern Ontario, Canada. Trends in effluent nutrient concentrations, pH, and dissolved oxygen were visually reproduced, and root-mean square errors, in comparison with weekly observations (alkalinity, 43 gHCO3 m−3; dissolved oxygen, 4.0 gm−3; NO3, 2.2 gN m−3; PO4, 0.10 gP m−3; NH4, 0.14 gN m−3; pH, 0.65), were consistent with literature values. Three phytoplankton groups (green algae, diatoms, and flagellates) were also simulated. The calibrated model was extended to investigate the effects of changes in pond design on treatment efficiency, including removing the baffle, increasing the pond depth, increasing wind sheltering, and relocating the influent pipe from the pond bottom to the surface. Increasing the pond depth and wind sheltering reduced vertical mixing, sequestering nutrients near the sediments and improving effluent water quality. Removing the baffle had no impact on removal efficiency, and relocating the inlet to the surface reduced pond efficiency. Three-dimensional biogeochemical models thus provide a virtual process-based means for testing pond prototype design.

Seasonal and longitudinal water quality dynamics in three effluent-dependent rivers in Arizona.

Effluent-fed streams, which receive inputs from wastewater treatment plants, are becoming increasingly common across the globe as urbanization intensifies. In semi-arid and arid regions, where many natural streams have dried up due to over extraction of water, many streams rely completely on treated effluent to sustain baseflow during dry seasons. These systems are often thought of as 'second-class' or highly disturbed stream ecosystems, but they have the potential to serve as refuges for native aquatic biota if water quality is high, especially in areas where few natural habitats remain. In this study, we investigated seasonal and longitudinal water quality dynamics at multiple sites across six reaches of three effluent-dependent rivers in Arizona (USA) with the objective (1) to quantify changes in effluent water quality due to distance traveled and season/climate and (2) to qualify whether water quality conditions in these systems are sufficient to support native aquatic species. Study reaches ranged in length from 3 to 31 km and in geographic setting from low desert to montane conifer forest. We observed the lowest water quality conditions (e.g., elevated temperature and low dissolved oxygen) during the summer in low desert reaches, and significantly greater natural remediation of water quality in longer vs. shorter reaches for several factors, including temperature, dissolved oxygen and ammonia. Nearly all sites met or exceeded water quality conditions needed to support robust assemblages of native species across multiple seasons. However, our results also indicated that temperature (max 34.2 °C), oxygen levels (min 2.7 mg/L) and ammonia concentrations (max 5.36 mg/L N) may occasionally be stressful for sensitive taxa at sites closest to effluent outfalls. Water quality conditions may be a concern during the summer. Overall, effluent-dependent streams have the capacity to serve as refuges for native biota in Arizona, and they may become the only aquatic habitat available in many urbanizing arid and semi-arid regions.

Studies on Seasonal Variation of Water Quality Parameters of River Mara Bharali in Sonitpur District of Assam

Background: Freshwater is not only a finite resource but also essential for agriculture, industry and even human existence. The present investigation on seasonal water quality parameters was conducted in the remnants of old channel of river Jia Bharali called as Mara Bharali at Tezpur in the Sonitpur district of Assam from December, 2017 to November, 2020. Methods: Five sampling stations were selected that covers a stretch of 16.5 km. Physico-chemical parameters were determined seasonally by following the standard methods of APHA (1998) and Trivedy et al., 1987. The statistical analyses of the data were done by one way ANOVA using Statistical Package for Social Scientists. Result: TDS in monsoon in all the locations remained lowest (171.33a±3.059 mg/L at MB2) and highest in winter (320.11c±2.441 mg/L at MB3). The pH value is within the permissible limit except for site MB4 during the winter and post monsoon when the values were 6.38±0.146 and 6.44±0.039 respectively. Maximum DO of 7.878d±0.074 mg/L was observed at site MB1 during the winter season and minimum DO of 5.444a±0.047 mg/L was observed at site MB4 during the monsoon season. BOD was recorded below the tolerance limit of 3 mg/L in all sampling stations.

Landscape Patterns and Topographic Features Affect Seasonal River Water Quality at Catchment and Buffer Scales

Effects of landscape patterns or topographic features on the river water environment have been broadly studied to control non-point source (NPS) pollution and to cut off potential pathways for pollutants to affect human health. However, spatio-temporal dynamics and scale effects with respect to the impact of landscape patterns and topographic features on the aquatic environment over successive years have not been elucidated. In this study, water quality parameters and land cover data for three consecutive years mainly in Tangshan City, located in the northeast of the Haihe River Basin, China, were obtained to determine the associations between landscape patterns and topographic features with the water environment. Results indicated that seasonal differences in dissolved oxygen (DO) and total nitrogen (TN) were significant (p < 0.001), and spatial variation was generally observed for each water quality parameter. Redundancy analysis revealed that landscape patterns and topographic features have different impacts on the aquatic environment as seasonal spans and spatial scales change. Overall, the best explanatory variables explained an average of 58.6% of the variation in water quality at various spatial scales over the two seasons. Topographic features made a greater contribution to river water quality changes at the buffer scale; conversely, at the catchment scale, water quality changes stemmed primarily from differences in landscape composition and configuration. The landscape shape index of cropland (LSIcrop) was an important factor influencing seasonal river water quality changes at various spatial scales. These results suggest that considering landscape connectivity at distinct spatial scales could enhance the understanding of the alteration of hydrological processes across multiple topographic features, which in turn has an impact on seasonal river water.

Analysis of Seasonal Water Characteristics and Water Quality Responses to the Land Use/Land Cover Pattern: A Case Study in Tianjin, China

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Proposal for a new customization process for a data-based water quality index using a random forest approach

As the water quality index (WQI) represents water quality, it is crucial to customize the WQI for a specific purpose. In this study, to better represent water quality data using WQI, a random forest (RF) approach was used to derive the parameter weight and calculate the WQI according to the watershed and its use. Eight parameters (water temperature, dissolved oxygen, pH, electrical conductivity, suspended solids, total nitrogen, total phosphorus, and total organic carbon) were evaluated using a total of 220,103 data points collected from 900 monitoring sites throughout South Korea between 2011 and 2020. The estimation of parameter weights, key elements in developing the WQI model, was performed through the variable importance estimation method that can be derived from the RF model. The parameter weights were derived based on various spatiotemporal datasets, and it was confirmed that the spatiotemporal differences in weights according to data characteristics represented the regional and seasonal water quality characteristics. Consequently, a customized WQI representing water quality characteristics could be calculated using data-based weights, and it is expected that a data-based customized WQI could be developed to better match the previous WQI to the purpose and target source.

Application of multivariate statistical methods to enhance the water quality monitoring system of Kashmir Valley with special emphasis on side-stream pollution

Abstract Surface waterbodies, on which the growing population of Kashmir Valley is reliant in a variety of ways, are increasingly deteriorated due to anthropogenic pollution from rapid economic development. This research aims to assess the quality of the surface waterbodies in the north-eastern region of Kashmir Valley. Standard analytical procedures were used to analyze the water samples taken from 11 distinct sampling stations for 14 physicochemical parameters. The results were compared with the standard permissible levels which showed that the water quality of rivers and lakes in the north-east Himalayan region has steadily declined. Furthermore, multivariate statistical techniques were used with the goal of identifying key variables that influence seasonal and sectional water quality variations. The analysis of variance (ANOVA) analysis revealed that there is substantial spatio-temporal variability in the water quality parameters. According to principal component analysis (PCA) results, four primary components, which together accounted for 79.23% of the total variance, could be used to evaluate all data. Chemical, organic, and conventional pollutants were found to be significant latent factors influencing the water quality of rivers in the study region. The results indicate that PCA and ANOVA may be used as vital tools to identify crucial surface water quality indices and the most contaminated river sections.

Land Use Land Cover (LULC) and Surface Water Quality Assessment in and around Selected Dams of Jharkhand using Water Quality Index (WQI) and Geographic Information System (GIS)

Abstract Surface water quality deterioration is mainly occurring due to anthropogenic activities at an alarming rate in developing countries. Jharkhand has been undergoing exponential urbanisation and mining, causing immense surface water pollution and water stress. The state is heavily dependent on artificial dams for its daily water supply demands. Therefore, an effort is made to monitor and ascertain the surface water quality and the influence of nearby land use pattern on water quality, in the selected five dams, namely, Hatia dam, Kanke dam, Getalsud dam, Galudih barrage, and Chandil dam are done. These dams are built on the Subarnarekha river basin, located in the Jharkhand state on a seasonal basis and associated land use land cover (LULC) changes, changes in vegetation cover using normalised difference vegetation index (NDVI) and water body changes using normalised difference water index (NDWI) that have occurred in a 5-year gap i.e. 2016 and 2021. The secondary data for the year 2016 was obtained from the Jharkhand pollution control board report published by the government of Jharkhand, India. For the year 2021, the samples were collected from sampling sites for pre, post and monsoon seasons. The chemical analysis of collected water samples was done in the laboratory for parameters like pH, dissolved oxygen, biological oxygen demand, total calcium and magnesium, hardness, total dissolved and suspended solids, alkalinity, chlorine etc. and compared with the standard values prescribed by world health organisation (WHO) and Indian standards (IS) 10500:2012. The seasonal water quality status was analysed using the water quality index (WQI) for the pre, post and monsoon seasons of 2016 and 2021. Then, the use of supervised classification method for land use land cover (LULC), normalised difference vegetation index (NDVI) and normalised difference water index (NDWI) was opted to understand the relation between the change in water quality and quantity concerning its land use and land cover, by comparison of results from the year 2016 to 2021. LULC were found using the supervised maximum likelihood classification method in ArcGIS and its accuracy was checked using the kappa accuracy method, which was found to be varying from 87 to 95% for all sites. The results showed that the overall water quality varied from good to poor indicating that it can be used for human activities but may need pre-treatment before drinking. NDWI showed a massive increase in severe drought areas for Hatia, Kanke, Chandil and Galudih barrage, whereas moderate drought regions increased for Hatia, Getalsud, and Kanke. NDVI showed dense and moderate vegetation both decreased massively for all the dam sites indicating an alarming situation and the need to adopt better land management practices.

Contribution of self-organizing maps in the classification of water resources in the production of drinking water by ultrafiltration

The ultrafiltration process has become essential in most drinking water production facilities. However, the variation in water quality remains difficult to manage, especially during major fouling phenomena. For this reason, the objective of this study is to develop a membrane fouling prediction tool based on seasonal water quality variation. For this, monthly monitoring of 6 water resources in Poitiers and Paris over 8 months was carried out, in which a filtration test was carried out on all the resources to assess their fouling potential and 13 physico-chemical parameters were measured. The collected data were used to build a self-organizing map (SOM) to classify water resources according to their fouling potential. The resulting SOM is able to classify the water resources according to the measured parameters into four clusters: (1) Waters with high TOC and turbidity with high fouling tendency, (2) Waters with average TOC and turbidity with average fouling tendency, (3) Waters with low TOC and turbidity that induce negligible fouling and (4) Very good quality waters that do not cause fouling.