Seasonal Variation Of Water Quality Research Articles

Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach.

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution.Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China

Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed.

Integrated environmental and social assessment of alum-contaminated water in An Giang province

ABSTRACT This study investigates alum-contaminated water in An Giang province within the Mekong Delta, emphasizing methodological approaches to assess its distribution and impacts. The research conducted from March to May 2021 involved surveys of residents, public sector employees, and provincial managers, complemented by expert interviews and extensive fieldwork. Surface water samples were collected during both rainy and dry seasons across three districts, with parameters such as turbidity, pH, and temperature measured onsite, and aluminum (Al), iron (Fe), and copper (Cu) concentrations analyzed in the laboratory using atomic absorption spectrometry. The data were further processed using QGIS software to create comprehensive maps illustrating seasonal variations in water quality. The findings revealed significant spatial and temporal variations in water quality, with pH levels fluctuating markedly between the dry and rainy seasons. Household surveys indicated a lack of awareness regarding effective water treatment methods, with most respondents relying on local authorities for solutions. The study highlights the need for community education and participation in addressing alum contamination, offering insights into the environmental and public health implications of water quality in the region. This research contributes valuable knowledge to the ongoing efforts in environmental conservation and water quality management in the Mekong Delta.

Multivariate analysis of water quality in the Dhaleshwari River, Bangladesh: identifying pollution sources and environmental implications

ABSTRACT This study aims to evaluate the current water quality status of the Dhaleshwari River, considering seasonal variations, and identify potential pollution sources using physicochemical parameters, multivariate statistical analysis, and the water quality index (WQI) method. Sampling was conducted at six locations along the Dhaleshwari River from January 2021 to April 2022, encompassing both dry and wet seasons. Eleven physicochemical parameters were tested following standard procedures. The calculated WQI values demonstrated that the overall water quality of the Dhaleshwari River is very poor (264.7 ± 64.5). Seasonal variation in water quality was significant, with the worst quality (305.7 ± 54.3) observed during the dry season; however, water quality during the wet season was also unsatisfactory (223.6 ± 46.3). Geospatial analysis shows the distribution of water quality in the study locations. Principal components analysis was performed for source appointment, which indicated that direct wastewater discharge from surrounding industries, particularly tanneries, as well as municipal wastewater, were the major pollution sources. In conclusion, this study sheds light on the deteriorating water quality of the Dhaleshwari River due to industrialization and human activities. The findings underscore the urgent need for interventions to mitigate pollution sources and improve the overall health of the river ecosystem.

Impact of parametric seasonal variations on water quality in the Crocodile River and Inyaka Dam in the Mpumalanga Province, South Africa

This study assessed seasonal variations in water quality and their impact on the Inyaka Dam and Crocodile River in Mpumalanga Province, South Africa. A total of 206 water samples were collected across four seasons to analyse parameters including pH, turbidity, electrical conductivity (EC), temperature, Manganese (Mn), Iron (Fe), phosphate (PO43−), nitrate-nitrogen (NO3-N), E. coli, total coliforms, faecal streptococci, and Bifidobacteria. Principal component analysis (PCA) and Pearson correlation identified major pollutants and their seasonal variations, while cluster analysis grouped water quality by parameter fluctuations.Findings indicated that water quality in both the Inyaka Dam and the Crocodile River exceeded permissible limits. In the Inyaka Dam, summer rainfall (202.68 mm) spurred microbial growth, including E. coli and Bifidobacteria, while winter saw elevated levels of PO43−, EC, Fe, and NO3-N. The Crocodile River exhibited its poorest water quality in summer, with high levels of conductivity, turbidity, Fe, Mn, and NO3-N, driven largely by rainfall. Winter pollution in the river was marked by E. coli, PO43−, total coliforms, NO3-N, and Bifidobacteria.The study highlights significant pollution in the Crocodile River, particularly in summer, linked to rainfall and effluent discharges. Microbial pollution persisted across seasons, influenced by both weather and point-source contamination. The winter season exacerbated water quality deterioration in the Crocodile River due to reduced flow, while Inyaka Dam's winter pollution was attributed to lake stratification.

Algal diversity and its relationship with seasonal variation of water quality in Gajedi Lake, Rupandehi District (Nepal)

The current study focuses on the algal diversity of Gajedi Lake in different seasons in connection to the lake's water quality indicators. Algae and water samples were collected from eight peripheral sites of the lake in summer, rainy, and winter seasons in 2021. The water temperature, conductivity, total dissolved solids, hardness, alkalinity, phosphorus, free CO2, and dissolved oxygen were recorded as high during the summer season while water pH was high during the rainy season. Altogether 93 species of algae were reported under 52 genera, 30 families, 7 classes and 6 phyla in this study. The largest phylum was Bacillariophyta (44.08%) followed by Chlorophyta (17.20%), Euglenozoa (10.97%), Cyanobacteria (12.90%), Charophyta (10.75%) and Miozoa (1.07%). The Shannon-Weiner diversity index indicated high abundance during the summer season, with more evenly distributed algal species. More algal species were recorded in the winter season. Trachelomonas, Navicula, and Nitzschia were dominant genera during summer; Anagostidinema, Desmodesmus, and Pinnularia were dominant during rainy; and Trachelomonas, Tetradesmus, and Gomphonema were dominant during winter. The redundancy analysis ordination showed that the algal composition varied along with the seasonal changes in physico-chemical parameters. Temperature, pH, and conductivity were thus identified as significant factors influencing species richness.

Spatiotemporal variation of water quality in the context of suspended particulate matter in Laguna Mar Chiquita Lake, Argentina using ACOLITE

This study investigates seasonal variations in water quality at Laguna Mar Chiquita Lake, Argentina, by employing remote sensing techniques to analyze Suspended Particulate Matter (SPM) which offers valuable insights into the lake's environmental health. Landsat 8–9 OLI/TIRS data from 2014–2023 was utilized for spectral analysis of water quality, processed using ACOLITE software for accurate quantitative data. Results show SPM concentrations ranging from 0.823 to 44.172 mg/l, with significant seasonal variations. Higher SPM concentrations post-monsoon indicate increased anthropogenic influence, particularly in the northern area. Pollution from tourism and industrial activities is lower pre-monsoon but spikes post-monsoon and during monsoon, suggesting unfavorable conditions. Effective management and continuous monitoring are crucial for maintaining lake quality and ecosystem health.

Seasonal Variations and Assessment of Surface Water Quality Using Water Quality Index (WQI) and Principal Component Analysis (PCA): A Case Study

In recent decades, water pollution has become a major concern, threatening both humans and natural ecosystems. This study aims to analyze seasonal variations in the quality of surface water of the Nador Canal in Morocco, using the water quality index (WQI) and principal component analysis (PCA). Surface water samples from 22 sites along the canal were analyzed for physical, chemical, and heavy metal parameters. The results of the study revealed significant seasonal variations, with water quality decreasing in the summer months, while its quality generally improved in winter. The predominant water type was Na+-Cl− in summer, while it was the mixed Ca2+-Na+-HCO3− water type in winter. WQI values also varied seasonally, with an average of 113.04 in summer and 160.6 in winter, classifying the water as unsuitable for drinking but suitable for irrigation throughout the year. The results of the water quality index are consistent with the results of the principal component analysis of surface water in the Nador Canal, where the results of the principal component analysis showed that there are significant seasonal variations in water quality. In both summer and winter, major ions like magnesium, sodium, and calcium predominantly indicate influences from natural and anthropogenic sources. In winter, heavy metals and nutrients, signaling pollution from industrial and agricultural runoff, become especially prominent. These variations are influenced by rainfall patterns and agricultural runoff, emphasizing the need for adaptive water management practices to maintain crop and soil health. This study provides new insights into the dynamic interplay between seasonal factors and water quality, offering valuable guidance for local water resource management.

Exploring seasonal variability in water quality of Nyabarongo River in Rwanda via water quality indices and DPSIR modelling.

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being and . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

EVALUATING THE GROUNDWATER QUALITY IN THE SATNA DISTRICT'S RAM NAGAR, RAGHURAJ NAGAR, AND RAMPUR BAGHELAN TAHSILS FOR BOTH DRINKING AND IRRIGATION PURPOSES IN MADHYA PRADESH

This study also shows seasonal variations in the quality of the water and a little increase in a few chosen metrics when pre- and post-monsoon data were compared. It suggests that in the ensuing years, a few parameters might surpass the maximum amount of contamination. In present study underground water quality of some selected region at Satna district on the basis of selected parameters like PH, Temperature, Electrical Conductivity, Total hardness, TDS, BOD, NO3-, SO4-2, PO4-3, Cl-, F-, HCO3-, Mg++, K+, Alkalinity, E. coli and Alge. It was observed that underground water quality in selected area is not bad for drinking, domestic and agriculture purposes. Subsurface water quality in a few chosen regions of Satna district is currently being studied. Selected metrics, including temperature, electrical conductivity, total hardness, TDS, BOD, NO3-, SO4-2, PO4-3, Cl-, F-, HCO3-, Mg++, K+, alkalinity, E. coli, and algae, are taken into account. It was found that the subsurface water quality in a chosen location is suitable for residential use, drinking, and agricultural uses. This study also express seasonal variation of water quality and slight increment in some selected parameters were observed when data of pre mansoon and post mansoon compared. It indicates that some parameter may cross maximum contamination level in next upcoming years. This study also shows seasonal variations in the quality of the water and a little increase in a few chosen metrics when pre- and post-monsoon data were compared. It suggests that in the ensuing years, a few parameters might surpass the maximum amount of contamination.

Assessment of seasonal variations in water quality in the vicinity of Hathaikheda Reservoir, Bhopal, Madhya Pradesh

Assessment of seasonal variations in water quality in the vicinity of Hathaikheda Reservoir, Bhopal, Madhya Pradesh

A Comparative Physical Parameters of Sri Kurmanadha Temple Pond and Sri Maninageswara Temple Pond, Srikakulam District, Andhra Pradesh, India

Data collected on certain physical parameters of Sri Kurmanadha, Srikurmam and Sri Maninageswar, Kallepalli, Temple Ponds, Srikakulam District, Andhra Pradesh from May 2014 to April 2016. The seasonal variations in water quality were recorded on monthly basis for a period of two years and correlation studies were also conducted among different physical parameters of both temple ponds. Water samples were analyzed for Physical Parameters like air temperature (28.250 C, 27.40 C), water temperature (240C, 24.350 C), transparency (32.1cm., 20.35cm.), EC (621μs/cm., 513.5μs/cm.), TSS (27mg/l., 35.5mg/l.) TDS (367mg/l., 298mg/l), pH (7.35, 7.5), DO (6.75mg/l, 6.5mg/l), COD (10.63mg/l, 10.36mg/l), BOD (3.31mg/l., 2.86mg/l.) total alkalinity (132.5mg/l., 128.5mg/l.), total hardness (80mg/l., 87.5mg/l.). Inter relationship between physical parameters (Pearson’s Correlation matrix) was also recorded. In this observation temperature showed strong positive correlation with COD (r=0.91, r=0.88), BOD (r=0.96, r=0.89), and negative correlation with DO (r=-0.84, r=-0.97). EC showed significant positive correlation with TDS (r=0.85, 0.89), and negative correlation with DO (r= -0.82, r=-0..84), TDS showed positive correlation with pH (r=0.77, r=0.72), and total hardness (r=0.81, 0.78), pH showed positive correlation with COD (r=0.77, r=0.84), total alkalinity (r=0.94, r=0.86), total hardness (r=0.81, r=0.84), and negative correlation with DO (r= -0.87, r=-0.77), Dissolved Oxygen showed negative correlation with COD (r= -0.88, r=-0.93), BOD (r= -0.95, r=-0.93). Chemical Oxygen Demand showed positive correlation with BOD (r= 0.92, r=0.91) and total alkalinity (r= 0.75, r=0.79). Biological Oxygen Demand showed positive correlation with total alkalinity (r= 0.74, r=0.63). Total Alkalinity showed positive correlation with total hardness (r= 0.90, r=0.87). These observations could useful in the water quality monitoring and regulation in order to enhance the quality of water with better sustainable management of Sri Kurmanadha, Srikurmam and Srimaninageswar, Kallepalli, Temple Ponds.

Seasonal Variation of Water Quality of Taiping Lake in Eastern China

Lakes and reservoirs are an important sources of drinking water for human life. However, numerous water quality indexes show seasonal fluctuation under different climatic conditions. We conducted a one-year investigation of Taiping Lake in year 2022 to construct relationship between water quality parameters and water quality. High water temperature promotes the growth of algae, and the existence of N species could provide the essential nutrient substance for the growing of algae. Under the irradiation of sunlight, algae will consume carbon dioxide through photosynthesis and release oxygen which can raise oxygen levels in the water. However, the rapid growth of algae could influence the acid-base balance in water through the consumption of carbon dioxide; thus, resulting in the increasing of pH values. These pH changes may pose a threat to drinking water.

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

Variaciones fisicoquímicas del agua de una gran presa Hidroeléctrica Mexicana, Región Centro semi-árida

The Zimapan hydropower plant built in 1995 was the first project to take advantage of Mexico's City wastewater for electric power generation. This tropical reservoir receives pollutants that affect water quality from municipal, agricultural, and industrial wastewater through the Tula and San Juan rivers. The study considered the changes in the physicochemical parameters (surface to 20 m depth) for May (wet warm), November (wet cold-after rains), and February (dry cold) in seven dam sites: two lotic, two transitional, and three lentic sites. Mean values ranges were temperature 20.71-24.38 °C, 1.59-7.56 mg/L dissolved oxygen, Secchi disk transparency 2.26-3.98 m, pH 7.12-8.40, electrical conductivity 48-1334 µS/cm, total nitrogen 14.20-30.74 mg/L, total phosphorus 10.45-15.83 mg/L, and dissolved organic carbon 9.11-20.63 mg/L. In November, all the reservoir surface water was hypoxic due to increased dissolved organic carbon by river input. Even though dissolved oxygen has a seasonal variation, this reservoir has a sustainable fishery of tilapia and black bass (~400 tons/y). The dam is meromictic and hypereutrophic, with seasonal water quality variations with no significant spatial variations. Water quality variation observed in this study can be useful to Mexican decision-makers on water and fisheries management, prevent massive fish kills, and be a support guide for commercial and sport fishers.

Impact of source water quality on total organic carbon and trihalomethane removal efficiency in a water treatment plant: A case study of Upper Awash, Ethiopia.

This study addresses the limited understanding of factors affecting the efficiency of water treatment plants in reducing trihalomethane (THM) formation through total organic carbon (TOC) removal, highlighting significant challenges in improving treatment effectiveness. The aim of this study was to examine the influence of water quality on the efficiency of water treatment plants to remove TOC and reduce THM formation. Linear regression and correlation analyses were conducted to examine the relationship between water quality parameters and THM concentrations. The results showed that there was a negative relationship between turbidity, metals, and TOC concentration with TOC removal efficiency. Positive correlations were found between parameters and the formation of THMs in water. Of these parameters, water temperature was observed to have relatively less influence on THM formation. It was observed that seasonal variations in water quality affect the efficiency of TOC removal and THM content in treated water. THM levels in chlorinated water were found to be within the permissible range of the World Health Organization's drinking water quality guidelines. However, it is still important to maintain continuous monitoring and take measures to reduce THMs. The model demonstrated a strong correlation (R2 = 0.906) between predicted and measured THM values.

Algal Diversity and its Relationship with Seasonal Variation of Water Quality in Gajedi Lake, Rupandehi District, Nepal

The current study focuses on the algal diversity of Gajedi Lake in different seasons in connection to the lake's water quality indicators. Algae and water samples were collected from eight peripheral sites of the lake in summer, rainy, and winter seasons in 2021. The water temperature, conductivity, total dissolved solids, hardness, alkalinity, phosphorus, free CO<sub>2</sub>, and dissolved oxygen were recorded as high during the summer season while water pH was high during the rainy season. Altogether 93 species of algae were reported under 52 genera, 30 families, 7 classes and 6 phyla in this study. The largest phylum was <i>Bacillariophyta </i>(44.08%) followed by <i>Chlorophyta </i>(17.20%), <i>Euglenozoa </i>(10.97%), <i>Cyanobacteria </i>(12.90%), <i>Charophyta</i> (10.75%) and <i>Miozoa </i>(1.07%). The Shannon-Weiner diversity index indicated high abundance during the summer season, with more evenly distributed algal species. More algal species were recorded in the winter season. <i>Trachelomonas, Navicula</i>, and <i>Nitzschia </i>were dominant genera during summer; <i>Anagostidinema, Desmodesmus</i>, and <i>Pinnularia</i> were dominant during rainy; and <i>Trachelomonas, Tetradesmus</i>, and <i>Gomphonema </i>were dominant during winter. The redundancy analysis ordination showed that the algal composition varied along with the seasonal changes in physico-chemical parameters. Temperature, pH, and conductivity were thus identified as significant factors influencing species richness.

Analysis of the seasonal water quality variation at the hydraulic junction of a dual-source water distribution system.

The implementation of a dual-source water supply system offers an increased level of reliability in water provision; however, intricate hydraulic dynamics introduce apprehensions regarding water safety at the hydraulic junction. In this study, we gathered data of the water quality at the hydraulic junction of a dual-source water supply system (plant A and plant B, sampling site A10 was near plant A, and sampling site A12 was near plant B) for one year in Suzhou Industrial Park. Our findings indicated that seasonal variations and water temperature exerted significant influence on the composition and formation of disinfection byproducts (DBPs). Notably, during the warmer months spanning from June to September, the concentration of trihalomethanes was the highest at the hydraulic junction, whereas the concentration of residual chloride was the lowest. The analysis on DBPs revealed that more Br-containing precursors in water in plant A resulted in the accumulation of more Br-containing DBPs at A10, whereas the highest concentration of Cl-containing DBPs accumulated at A12. The analysis of the dissolved organic matter (DOM) composition indicated an increase in concentration at A10 and A12 compared with that in plant A and plant B. The highest concentration of humic acids was observed at A10, whereas A12 accumulated the highest concentration of aromatic proteins and microbial metabolites. Owing to the fluctuations in water consumption patterns at the hydraulic junction, the water quality was susceptible to variability, thereby posing an elevated risk. Consequently, extensive efforts are warranted to ensure the maintenance of water safety and quality at this critical interface.

Assessment of seasonal variation of water quality of Agartala City, Tripura through PCA, PcoA approach & Water Quality Index

Assessment of seasonal variation of water quality of Agartala City, Tripura through PCA, PcoA approach & Water Quality Index