Assessment of seasonal variations in surface water quality

Hydrogeochemical investigation and water quality assessment of the Indus River in the semiarid region of Ladakh, India

The decline in water quality, particularly in river water, is a significant concern, especially in semi-arid areas and tourist destinations such as Ladakh. Periodic assessment of water quality could be a crucial step for ensuring its potability and serve as a foundation for formulating effective policies for sustainable water resource management. Consequently, this research aimed to analyze the periodic variations in the water quality of Indus River for domestic and agricultural use, focusing on the impact of geochemical processes within the basin. Various physicochemical parameters namely temperature, pH, electrical conductivity, turbidity, total dissolved solids, total hardness, total alkalinity, K+, Na+, Mg2+, Ca2+, Cl-, SO42-, SiO2, HCO3-, CO32-, and NO3- for sixty-five water samples from seven key locations were assessed during three distinct periods: April-May (early melting period, EMP), July-August (peak melting period, PMP), and October-November (late melting period, LMP), 2023. The ion contents found were in the following order: Ca2+ > Na+ > Mg2+ > K+ and HCO3- > SO42- > Cl- > SiO2 > NO3- > F-, reflecting Ca-HCO3 water types. However, temporal and spatial variation in ion content and hydrochemical facies were observed when the water moved downstream and confluences with the Zanskar River to give calcium‑magnesium-sulphate facies. Water quality indices- Canadian Council of the Ministers of the Environment Water Quality Index (CCMEWQI) and Weighted Arithmetic Water Quality Index (WAWQI) were employed to assess the water quality over these periods, identify long-term trends, evaluate the water quality status, and provide insights into immediate conditions. WAWQI values recorded for EMP (56.89-509.53), PMP (289.82-3419.23), and LMP (55.16-159.14) found were poor to unsuitable for drinking while using CCMEWQI, it was in the marginal range. Additionally, the Wilcox diagram and other important irrigational indices like Percent sodium, Sodium Absorption Ratio, Residual Sodium Carbonate, Magnesium hazard, Permeability Index, Kelly's ratio, Ryznar stability index indicated the suitability of the water for agricultural use in all the periods. Apart from arsenic (54 μg/L), all other heavy metal ions measured were within the permissible limits according to the Heavy Metal Pollution index. Principal Component Analysis identified different principal components contributing to the hydrochemistry of the river water whereas correlational analysis was conducted to understand the correlation among different parameters and their potential source in water. Rapid carbonate mineral reactions and sulphate reduction were found to affect the alkalinity and hardness of the water. This study will serve as a scientific reference and methodological guide for researchers- to understand water chemistry; committee awareness on health and agricultural impacts; and policymakers for decision-making on water use, policy, and conservation.

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.

Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993-2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p < 0.01), except for algal biomass peaks (>60 μg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.

Assessing the impact of water-sediment factors on water quality to guide river-connected lake water environment improvement

The substantial impacts of exogenous pollutants on lake water quality have been extensively reported. Water-sediment factors, which are essential for regulating water quality in river-connected lakes, have not been studied in depth under different hydrological conditions. This study has combined a 31-year water environmental dataset (1991-2021) regarding Dongting Lake and a vector autoregression model (VAR) in order to investigate the impulse response characteristics and contributions of water quality caused by water-sediment factors across different periods. Our analysis suggests that total nitrogen (TN) exhibited a significant increasing trend, whereas total phosphorus (TP) increased to 0.17 mg/L, and then decreased to 0.07 mg/L from 1991 to 2021. The inflow of suspended sediment discharge (SSD) decreased significantly during the study period, mainly because of the decrease in SSD in the three channels (TC). In the pre-Three Gorges Dam (TGD) period, water discharge (WD) and SSD were the Granger causes of TN and TP. In the post-TGD periods this relationship disappeared because of the construction of the TGD, which reduced the inflow of SSD and WD into the lake. Water quality indicators showed an instant response to the shock from themselves with high values, whereas the impulse response of the water quality to water-sediment factors exhibited lagged variations. This meant that the water quality indicators displayed a high impact by themselves across the different periods, with values varying from 67 % to 95 %. Water level (WL) and SSD were the predominant water-sediment factors for TP in the pre-TGD period, with the impact on TP changes accounting for 11 % and 9 %, respectively, whereas the contribution of SSD decreased to 2 % in the post-TGD period. WL was the most crucial water-sediment factor for CODMn during the different periods, with contributions varying from 17 % to 20 %. To improve the water quality of Dongting Lake, in addition to the implementation of strict controls on excessive external nutrient loading, regulating water-sediment factors according to the hydrological features of Dongting Lake during different periods is vital.

Transfer of chromium from environment to fish in East Kolkata wetlands - evaluation by structural equation modeling

Chromium (Cr) is a significant pollutant in the effluents from leather industries and domestic city sewage. Cr was determined in water, sediment, and different tissues (gill, muscle, intestine, liver, and kidney) of Nile tilapia, Oreochromis niloticus harvested from wastewater-fed aquaculture (WFA) situated at Bamonghata, Bantala, Chowbaga and Chingrighata of East Kolkata Wetlands (EKW), a Ramsar site in West Bengal, India. The results showed that Cr concentration in surface water ranged between 0.05 to 0.15 mg/L, while Cr was detected at high concentration (100-300 mg/kg) in the sediment soil of the first three WFAs and in moderate concentration (50-110 mg/kg) in Chingrighata WFA. Average Cr concentrations in the tissues were ranked in the following sequence: kidney>liver>intestine>gill>muscle. However, the extent of accumulation of Cr in different tissues varied between the WFAs. We used Structural Equation Modeling (SEM) to determine the route of Cr transfer. The fitness of the model was evaluated by the performance measures. Cr accumulation pathways varied between the sites depending upon the level of Cr in water or sediment. Except for Bamonghata WFA, sediment was found as the principal source of accumulation of Cr in different tissues of O. niloticus. Cr refluxed from sediment into overlying water and accumulated in fish either through the food chain or through direct accumulation from water. In Bamonghata WFA, the role of sediment in the transfer of Cr could not be established due to the high water depth or biological non-availability of Cr in the sediment. It is concluded from this study that fish reared in the WFAs of EKW are still not hazardous in respect to Cr but require proper management to avoid the influx of Cr-containing effluents into the WFAs.

Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country

Water quality monitoring networks (WQMNs) that capture both the temporal and spatial dimensions are essential to provide reliable data for assessing water quality trends in surface waters, as well as for supporting initiatives to control anthropogenic activities. Meeting these monitoring goals as efficiently as possible is crucial, especially in developing countries where the financial resources are limited and the water quality degradation is accelerating. Here, we asked if sampling frequency could be reduced while maintaining the same degree of information as with bimonthly sampling in the São Paulo State (Brazil) WQMN. For this purpose, we considered data from 2004 to 2018 for 56 monitoring sites distributed into four out of 22 of the state's water resources management units (UGRHIs, "Unidades de Gerenciamento de Recursos Hídricos"). We ran statistical tests for identifying data redundancy among two-month periods in the dry and wet seasons, followed by objective criteria to develop a sampling frequency recommendation. Our results showed that the reduction would be feasible in three UGRHIs, with the number of annual samplings ranging from two to four (instead of the original six). In both seasons, dissolved oxygen and Escherichia coli required more frequent sampling than the other analyzed parameters to adequately capture variability. The recommendation was compatible with flexible monitoring strategies observed in well-structured WQMNs worldwide, since the suggested sampling frequencies were not the same for all UGRHIs. Our approach can contribute to establishing a methodology to reevaluate WQMNs, potentially resulting in less costly and more adaptive strategies in São Paulo State and other developing areas with similar challenges.

Exploring the trend effects of diffuse anthropogenic pollution in a large river passing through a densely populated area

The detection of non-point pollution in large rivers requires high-frequency sampling over a longer period of time, which, however presumably provides data with large spatial and temporal variance. Variability may mean that data sets recorded upstream and downstream from a densely populated area overlap, suggesting at first glance that the urban area did not affect water quality. This study presents a simple way to explore trend-like effects of non-point pollution in the Danube based on data that varied strongly in space and time. For one year, biweekly sampling was carried out upstream and downstream from a large city with negligible emission of untreated wastewater and the surrounding settlements, industrial and agricultural areas. Although most of the values of the 34 examined physicochemical characteristics fell within the range of data previously published for the Danube, and the mean values of all parameters indicated unpolluted surface water, different water quality was revealed upstream and downstream from the metropolitan area at each sampling time. Since the physicochemical characteristics causing the separation also differed from time to time, univariate tests and consensus ordination were used to determine which variables changed similarly during most of the examined period. With this evaluation method, several diffuse pollutants of anthropogenic origin contaminating the Danube in the long term were identified, such as nitrogen, phosphorus, sulphate, chloride, potassium and vanadium. The results demonstrated that trend-like effects of non-point pollution can be detected even in a large river, where physicochemical measurements can vary strongly in space and time.

Perspective: The unexplored dimensions behind the foam formation in River Yamuna, India

For nearly two years, a persistent foam cover has been observed during the post-monsoon season in the Yamuna River beneath the barrage near Okhla in Delhi, India. This affair has been a matter of public concern now, after the gigantic appearance of foam in November 2021, as the visibility of foam has awakened people's environmental 'conscience' over the 'concealed' chemical pollution. The mechanisms of agents responsible for foaming in rivers, particularly surfactants and phosphates, have received wide attention in the dynamic community of river pollution. Many studies in the past, around the globe, have evidently provided different rationales behind the dense foam formation in rivers, yet the Concerned Govt. Authorities have highlighted the cause of foam formation in the river Yamuna is associated with the presence of detergents and phosphates as foaming agents. Despite this, an aperture with copious unaccounted factors or underlying agents still exists to rationalize the foam formation and persistence. In this article, we outline these unaccounted factors which might be responsible for the foam formation and stabilization and give indications for future research directives towards the emergence of studies regarding the dense foam formation in river Yamuna.

Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review

Drinking water may undergo spatiotemporal changes in quality as it leaves the treatment plant and enters the distribution system. This variability means that not all consumers receive water of the same quality. Monitoring water quality in distribution networks makes it possible to verify the compliance of current regulations and reduce consumption risks associated with water quality degradation. An inaccurate interpretation of the spatiotemporal variability of water quality affects the selection of monitoring locations and the sampling frequency, which may conceal problems with the water quality and increase consumers' risk. This paper presents a chronological and critical review of the literature on the evolution, benefits and limitations of methodologies for the optimization of water quality degradation monitoring in water distribution systems supplied by surface sources. This review compares the different methodologies and examines the types of approaches, optimization objectives, variables, and types of spatial and temporal analysis, as well as the main advantages and limitations. A cost-benefit analysis was conducted to assess applicability in different-sized municipalities (small, medium and large). Future research recommendations for optimal water quality monitoring in distribution networks are also provided.

The impact of two insecticides on the pollutant cycle and quality of surface and groundwater resources in the irrigated lands of Yasuj, Iran

The increase in the need for food and agricultural development has led to an increase in the use of insecticides. The use of insecticides leads to air, soil and water pollution. This study investigated the pollutant concentration cycle in the environment by analyzing diazinon and deltamethrin in a river and groundwater sources affected by an agricultural area. The samples were analyzed based on the standard method for insecticides in water using a GC-MS. The results showed that the quality of the surface water affected by the agricultural effluents decreased so that the changes of dissolved oxygen, nitrate, turbidity, TOC, BOD, and COD were 15.2%, 189.6%, 00%, 53%, 176%, and 57.5%, respectively. The concentration of diazinon and deltamethrin in agricultural wastewater was 86 μg/L and 11.62 μg/L. The self-treatment capacity of the river reduced the concentration of diazinon in the distance of 2 km and 15 km by 80.8% and 90.3%, respectively. These conditions were observed for deltamethrin in 74.8% and 96.2%, respectively. Also, the concentration of the two insecticides in water resources has temporal and spatial variation. The difference between the maximum and minimum concentration of diazinon and deltamethrin at different times was 183.5 and 1.73, respectively. The concentration of diazinon and deltamethrin in the downstream groundwater of the studied irrigated area was 0.3-0.7 μg/L, respectively. Although the soil structure and the self-purification capacity of the river caused a significant reduction of insecticides, the remained concentration of these pollutants in underground and surface water resources can still be a health and environmental concern.

PFAS surveillance in abiotic matrices within vital aquatic habitats throughout Florida

Per- and polyfluoroalkyl substances (PFAS) are a group of manufactured chemicals that are resistant to degradation and thus persistent in the environment. The presence, uptake, and accumulation of PFAS is dependent upon the physiochemical properties of the PFAS and matrix, as well as the environmental conditions since the time of release. The objective of this study was to measure the extent of PFAS contamination in surface water and sediment from nine vulnerable aquatic systems throughout Florida. PFAS were detected at all sampling locations with sediment exhibiting greater PFAS concentrations when compared to surface water. At most locations, elevated concentrations of PFAS were identified around areas of increased human activity, such as airports, military bases, and wastewater effluents. The results from the present study highlight the ubiquitous presence of PFAS in vital Florida waterways and filled an important gap in understanding the distribution of PFAS in dynamic, yet vulnerable, aquatic environments.

Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

Vertical distribution, contamination status and ecological risk assessment of heavy metals in core sediments from a mangrove-dominated tropical river

Understanding the vertical distribution of heavy metals aids in tracing the contamination history, however, it is limited for mangrove-dominated river. Thus, this study aimed to assess the vertical distribution of nine heavy metals and their possible ecological risk in several layers of core sediments from a mangrove-dominated river inside the Sundarban mangrove forest (World heritage and Ramsar site), Bangladesh. 45 core samples from five stations were analyzed using Flame Atomic Absorption Spectrophotometer (FAAS). The findings showed that, with the exception of Fe, Ni, and Cu, which suggested increased metal release in recent times, higher metal levels were recorded in the surface layer (0-10 cm), followed by the middle layer (10-20 cm), and inner layer (20-30 cm) of sediment cores. When compared to non-industrial forest sediment, core sediment from industrial sites contained noticeably more metals (p < 0.05). Overall, the mean metal concentration (mg/kg) followed the increasing order of Cd < As < Pb < Cu < Cr < Zn < Ni < Mn < Fe. Among the studied metals, the levels of Cd and Ni exceeded the average shale value. Contamination indices such as enrichment factor (EF), contamination factor (CF), and geo-accumulation index (I_geo) showed that the studied sediments were only contaminated by Cd. Ecological risk assessed by ecological risk factor (E_rⁱ) and risk index (RI) suggested that the analyzed heavy metals, with the exception of Cd, posed no significant ecological threats. All of the heavy metals analyzed might have originated from similar anthropogenic sources, according to the correlation matrix, cluster analysis (CA), and principal component analysis (PCA).

Assessment of Gualaxo do Norte River water quality (Minas Gerais, Brazil) affected by the dam breach of Fundão utilizing exploratory multivariate techniques

At the end of 2015, the Fundão dam belonging to the Samarco S.A. mining company was ruptured, releasing a flood of mud into the Gualaxo do Norte River, which advanced into the Doce River. The aim of the present study was to apply exploratory multivariate approaches to water quality data obtained during sampling campaigns at the Gualaxo do Norte River during the dry and rainy seasons, between July 2016 and June 2017. A total of 27 locations along the river were sampled, covering unaffected areas and regions influenced by the tailings waste from the dam. Determinations of chemical, physical, and microbiological water quality parameters were performed. Application of principal component analysis (PCA) resulted in the first two components together explaining 39.49% and 37.91% of the total variance for the dry and rainy season data, respectively. In both cases, the PCA groups were related to variables such as turbidity and total solids, which both presented higher values in regions affected by the mud flow. These results are in agreement with those obtained by the Kohonen neural network method, where two-dimensional maps confirmed the samples according to the affected and unaffected area by the disaster.

Biomonitoring of Alterations in Fish That Inhabit Anthropic Aquatic Environments in a Basin from Semi-Arid Regions

Industrial, agricultural, and urban areas can be sources of pollution and a cause of habitat fragmentation. The Conlara River located in the northeast of San Luis Province suffers different environmental pressures along its course from urban to agro-industrial areas. The present study aims to assess the water quality of the Conlara basin by evaluating how metals and pesticide contamination as well as physicochemical parameters relate to physiological stress in Jenynsia multidentata. Samplings were carried out in four sites characterized by a growing gradient of anthropic impact from the springs to the final sections of the river, starting with tourism passing through urban areas and ending with large agricultural areas (from S1 to S4) during both the dry and wet seasons. A total of 27 parameters were determined (11 physicochemical, 9 heavy metals, and 7 pesticides) in surface waters. Biomarkers (CAT, TBARS, ChE, and MN) showed significant physiological and cytological alterations in J. multidentata depending on the hydrology season. The combination of physicochemical parameters, metals, and pesticide levels allowed typification and differentiation of the sites. Some metal (Cr, Mn, Pb, and Zn) and pesticide (α-BHC, chlorpyrifos, permethrin and cypermethrin, and endosulfan α) levels recorded exceeded the recommended Argentinian legislation values. A principal component analysis (PCA) allowed detection of differences between both seasons and across sites. Furthermore, the differences in distances showed by PCA between the sites were due to differences in the presence of physicochemical parameters, metals, and pesticides correlated with several biomarkers' responses depending on type of environmental stressor. Water quality evaluation along the Conlara River shows deterioration and different types of environmental stressors, identifying zones, and specific sources of pollution. Furthermore, the biomarkers suggest that the native species could be sensitive to anthropogenic environmental pressures.

Statistical assessment of seasonal variations in water quality for different regions in Lake Van (Türkiye)

On earth, surface water bodies interact and change with the natural ecosystems. These surface waters and water quality may be adversely affected due to different factors. To analyze the effects, parameters indicating water pollution and quality and the possible causes of these parameters should be examined. In addition, environmental pollution issues should be controlled by taking measures. The most important surface water body in the province of Van, located in the east of Türkiye, is the biggest soda Lake Van. The population density around the lake, human polluting factors, unconscious beach use, inadequate wastewater treatment, agriculture and livestock activities, small-scale industrial areas, and chemicals used create a pollution effect. In the study, data were obtained during year of 2018 from six important sampling points around Lake Van and from the middle of the lake. Twenty-seven water quality parameters were analyzed separately and together. These variables' yearly values were evaluated with Turkish Surface Water Quality Regulation (TSWQR, 2015). As a result, these points were determined to have class I in terms of water parameters according to the seasonal data. The basic descriptive statistics were compared with the regulation, and max, mean, and min values were examined. Data analyzed were done with probability-normality, trend analysis, correlation, and regression methods. The results of this study are that general parameters were normal and the quality of the six points continued to be similar. Na⁺, Cl^-, salinity, and TDS were highly correlated, while DO and F were high matrix value parameters. EC, TDS, and SS regression equations provided high correlation parameters.

Photochemical and Photophysical Dynamics of the Aqueous Ferrate(VI) Ion

Ferrate(VI) has the potential to play a key role in future water supplies. Its salts have been suggested as "green" alternatives to current advanced oxidation and disinfection methods in water treatment, especially when combined with ultraviolet light to stimulate generation of highly oxidizing Fe(V) and Fe(IV) species. However, the nature of these intermediates, the mechanisms by which they form, and their roles in downstream oxidation reactions remain unclear. Here, we use a combination of optical and X-ray transient absorption spectroscopies to study the formation, interconversion, and relaxation of several excited-state and metastable high-valent iron species following excitation of aqueous potassium ferrate(VI) by ultraviolet and visible light. Branching from the initially populated ligand-to-metal charge transfer state into independent photophysical and photochemical pathways occurs within tens of picoseconds, with the quantum yield for the generation of reactive Fe(V) species determined by relative rates of the competing intersystem crossing and reverse electron transfer processes. Relaxation of the metal-centered states then occurs within 4 ns, while the formation of metastable Fe(V) species occurs in several steps with time constants of 250 ps and 300 ns. Results here improve the mechanistic understanding of the formation and fate of Fe(V) and Fe(IV), which will accelerate the development of novel advanced oxidation processes for water treatment applications.

Spatial Distribution, Source Analysis and Health Risk Study of Heavy Metals in the Liujiang River Basin in Different Seasons

Three high-frequency sampling and monitoring experiments were performed at the Lutang and Luowei transects of the Liujiang River entrance and at the southeast exit of the Liuzhou during 2019 for the purpose of assessing physico-chemical variables and human health hazards of water heavy metals in different rainfall processes. There were significant seasonal variations in concentrations of 11 heavy metals and most variables showed higher levels during the dry season. The distribution of heavy metals in the Liuzhou area varied significantly by region. Pollution source analysis indicated distinct seasons of wetness and dryness. The dry season is dominated by anthropogenic activities, while the wet season is dominated by natural processes. The results of hazard quotient (HQ) and carcinogenic risk (CR) analysis showed that the health risk of non-carcinogenic heavy metals in the wet season is slightly higher than that in the dry season. Seasonal changes in carcinogenic risk are the opposite; this is due to the combined influence of natural and human activities on the concentration of heavy metals in the river. Among them, Al was the most important pollutant causing non-carcinogenic, with As being a significant contributor to carcinogenic health risk. Spatially, the downstream Luowei transect has a high health risk in both the dry and rainy seasons, probably due to the fact that the Luowei transect is located within a major industrial area in the study area. There are some input points for industrial effluent discharge in the area. Therefore, high-frequency monitoring is essential to analyze and reduce the heavy metal concentrations in the Liujiang River during dry and wet seasons in order to protect the health of the residents in the area.

Temporal and Spatial Characteristics of River Water Quality and Its Influence Factors in the TAIHU Basin Plains, Lower Yangtze River, China

Water quality pollution has been a serious problem in the Taihu Basin plains, which is a highly urbanized area in China. This study aims to detect the interannual and seasonal changes and spatial patterns of water quality in this region. Based on cluster analysis, Moran’s I, and standard deviational ellipses, the site clusters, spatial heterogeneity of water quality characteristics and identified polluted regions were clarified. Results showed that (1) water quality improved since 2002, and nutrient concentrations were lower in summer and autumn than in winter and spring. (2) The monitoring sites were divided into six clusters according to the water quality during the period from 2010 to 2014. Water quality worsened from Cluster 1 to Cluster 4. Cluster 1 sites were mostly distributed beside the Yangtze River and Taihu Lake. Cluster 4 sites were mainly located along the southeast border near Shanghai, while the remaining sites were separately distributed in the main cities. (3) A polluted region of both total nitrogen (TN) and total phosphorus (TP) was present in the southeastern part of the study area near the border from 2010 to 2014. In addition, polluted regions were most likely to form near the junctions of main cities. (4) Anthropogenic factors had greater impacts on water quality than natural factors. More attention should be given to water quality protection around impervious surface areas due to the greatest considerable effect.

Evaluation of spatio-temporal variation of water quality and source identification of conducive parameters in Damodar River, India

The lower course of the Damodar River in West Bengal is one of the most polluted stretches in the Ganga River basin. There is a lack of research along the whole course of the Damodar, and parameter level analysis receives little attention. Eleven monitoring sites were chosen based on the potential sources of pollution for 6 years (2014-2019). Multivariate statistical techniques (factor analysis (FA), cluster analysis (CA), and discriminate analysis (DA)) evaluate the spatial and temporal variation of Damodar River water quality by considering 24 parameters. Factor analysis extracts the most influential seasonal parameters, and stepwise DA extracts ammonia, DO, potassium, temperature, total coliform, TFS, and turbidity, which are the most responsible parameters for seasonal variation of the water quality. CA classify sampling stations into three groups helping to identify the spatial variation of water quality. Ammonia, BOD, calcium, chloride, conductivity, DO, sodium, sulfate, temperature, Alkalinity, TDS, hardness, TSS, and turbidity are the most influential variables for spatial variation extracted through stepwise DA. Monsoon season shows a higher pollution level due to the contribution from both point and non-point sources. Due to high-density urban areas and large-scale industries, the middle course is more polluted. The Canadian Council of Ministers of the Environment (CCME) water quality index (WQI) accesses the water quality in temporal and spatial scales. The resultant water quality pattern is matched with the derived result from multivariate analysis. Poor water quality is regular at all sample sites in all seasons.

Impacts of Multiple Hurricanes and Tropical Storms on Watershed Hydrological Processes in the Florida Panhandle

Hurricanes and tropical storms (TS) are infrequent but disastrous events to human lives, social activities, and terrestrial ecosystems in coastal regions. Using the Environmental Protection Agency (US-EPA)’s Hydrologic and Water Quality System (HAWQS) model, principal component analysis (PCA), and principal factor analysis (PFA), we estimated impacts of multiple hurricanes and TS on hydrological processes in agricultural and forested watersheds. Five hurricanes and four TS that passed near or through the Apalachicola–Chattahoochee–Flint River basin (ACFRB) of the Florida panhandle from 1966 to 2018 were selected to estimate their impacts on rainfall, potential evapotranspiration (PET), evapotranspiration (ET), soil water percolation, surface runoff, stream discharge, groundwater recharge, and water yield (WYLD). Simulations showed that the category of hurricanes was not highly related to the amounts of rainfall, runoff, discharge, and WYLD. Based on PCA and PFA, PET and ET were highly and negatively, rainfall and discharge were highly and positively, and percolation, runoff, groundwater recharge and WYLD were moderately and positively affected by the hurricanes and TS at the ACFRB in the recent 50 years. This study provides water resource managers with critical insights into how multiple hurricanes and TS affected hydrological processes in agricultural and forested watersheds of the coastal region.

The Key Impact on Water Quality of Coral Reefs in Kenting National Park

Southern Taiwan’s Kenting National Park is a popular retreating place for many domestic and international tourists, with increasing tourist numbers potentially over-burdening the coastal ecosystems. To better understand human impacts, a long-term ecological research program was initiated in 2001 to track water quality at 14 coral reef-abutting sites throughout the park since then. Extracting the data from this 20-year survey, we found that increasing in the nutrient levels during the summer rainy season, together with the drops in salinity led by freshwater inputs (land- & rainfall-derived), was the main impact to coral reef ecosystem of Kenting. Cluster analysis further confirmed the nutrient influx was mainly attributed to the local discharge outlets with dense of villages and hotels at upstream. Therefore, more efforts are needed to input to control tourist number, treat waste water discharge and strengthen land protection facilities.

Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

This study aimed to transform the input of a large dataset into the output of interpretable information. Hence, multivariate statistical methods were carried out to analyze physicochemical parameters in 34 rivers during a 17-year period (1997-2014). Cluster analysis divided the study area into spatially different riverine water quality sub-regions described in ascending order of water quality as severely polluted (SP), highly polluted (HP), polluted (P), moderately polluted (MP), lightly polluted (LP), and not polluted (NP). By diagnosing threats and identifying fragile zones, water contamination sources responsible for impaired water quality in the study area recognized as natural pollutants in LP, municipal wastes in P, discharge of industrial effluents in MP, natural geochemical formations in SP and HP, and superficial flows of agricultural lands in SP, HP, and MP. The dominant water type in each zone was classified into Na-Cl, Na-Cl, Na-Mg-Ca-Cl-SO₄, Na-Ca-Mg-Cl-SO₄, Na-Ca-Cl, and Ca-Mg-HCO₃-SO₄ groups for SP, HP, P, MP, LP, and NP, respectively. To explore aesthetic aspects of drinking water application, hazard quotient (HQ) was applied for children and adults in terms of ingestion and dermal exposure. Overall health risk assessment revealed the order of impacts of the secondary water quality parameters as Cl^-  > Na⁺  > total dissolved solids (TDS) > Ca²⁺  > SO₄^2-  > Mg²⁺. Furthermore, hazard index (HI) ranged from 0.011 to 31.439 and 0.010 to 30.122 for children and adults, respectively, indicating a potential health risk regarding chloride throughout the whole region excluding NP. To identify significant agents in water quality, principal component analysis extracted 3 varifactors (VFs), with the eigenvalues of 4.74, 1.19, and 0.85, respectively, explained about 83% of the variance. The most important parameters in the first factor were TDS, electrical conductivity, SAR, TH, Na⁺, Cl^-, and SO₄^2- accounting for 58% of the total variance. The most influenced parameters in the second and third factors were pH and HCO₃^-, respectively, with variance coverage of 26%. These factors indicated that the hydrochemical characteristics of the water originated by natural interactions (existing salt domes, evaporation, weathering, and soil erosion) and anthropogenic activities (fertilizer-rich flows of agro-fields and domestic/industrial disposals), which must be minimized in rivers to supply the population with hygienic water.

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

Traditionally, water quality was assessed by physicochemical parameters. However, a more comprehensive analysis is needed to study the effects of polluted water bodies on key species over time. So, the aim of this study was to monitor through physicochemical and ecotoxicological indicators the surface water quality of four study sites with different land uses from the lower Paraná river basin (Argentina) during spring and summer of two years: Morejón stream (S1), De la Cruz stream upstream (S2), downstream (S3) and Arrecifes river (S4). Physicochemical parameters were measured in situ and in laboratory, and a Water Quality Index (WQI) was calculated. Chronic toxicity bioassays were performed with surface water samples using Rhinella arenarum embryos and larvae. Also, oxidative stress (catalase, superoxide dismutase, glutathione S-transferase, reduced glutathione and lipid peroxidation), neurotoxicity (butyrylcholinesterase) and genotoxicity (micronuclei frequency) biomarkers were measured at acute exposure, and an Integrated Biomarkers Response (IBR) index was calculated. The water quality varied between excellent and bad in S1, good and bad in S2 and S3, and bad and marginal in S4. S1 presented the greatest variability of pesticides and S4 the highest number of metals exceeding the limits for the local protection of aquatic life. Mainly, S4 caused lethality in R. arenarum larvae, reaching a maximum mortality of 83.3% at 504 h of exposure. The lethal toxicity of S1 and S2 varied between periods. Water samples from all sites altered the oxidative stress, neurotoxicity and genotoxicity biomarkers, and the IBR was negatively correlated with the WQI. The IBR reflected the effects of the degraded water quality on the exposed organisms. So, the importance of evaluating both physicochemical and ecotoxicological parameters to analyze integrally the water quality of polluted areas is highlighted. A degradation of the studied water bodies and its negative impact to the native amphibian R. arenarum were observed.

A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NO_X, NH₄ and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Temporal trends and source apportionment of water pollution in Honghu Lake, China

Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004-2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004-2011, with better water quality in the wet period than in the dry periods, while the results over 2012-2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting CODMn concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH3-N, TP, and TN decreased by 0.2 mg L-1, 0.039 mg L-1, and 0.37 mg L-1, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to CODMn decreased by 1.17 mg L-1. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L-1 and 887 cfu L-1, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.

Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea

To investigate the effects of rapid urbanization on water pollution, the water quality, daily unit area pollutant load, water quality score, and real-time water quality index for the Jinwi River watershed were assessed. The contribution of known pollution sources was identified using multivariate statistical analysis and absolute principal component score-multiple linear regression. The water quality data were collected during the dry and wet seasons to compare the pollution characteristics with varying precipitation levels and flow rates. The highest level of urbanization is present in the upstream areas of the Hwangguji and Osan Streams. Most of the water quality parameter values were the highest in the downstream areas after the polluted rivers merged. The results showed a dilution effect with a lower pollution level in the wet season. Conversely, the daily unit area pollutant load was higher in the rainy season, indicating that the pollutants increased as the flow rate increased. A cluster analysis identified that the downstream water quality parameters are quite different from the upstream values. Upstream is an urban area with relatively high organic matter and nutrient loads. The upstream sewage treatment facilities were the main pollution sources. This study provides basic data for policymakers in urban water quality management.

Hydrochemical Characteristics and Evolution of Groundwater in the Alluvial Plain (Anqing Section) of the Lower Yangtze River Basin: Multivariate Statistical and Inversion Model Analyses

The alluvial plain (Anqing section) of the lower reaches of the Yangtze River basin is facing increasing groundwater pollution, not only threatening the safety of drinking water for local residents and the sustainable development and utilization of groundwater resources but also the ecological security of the Yangtze River Basin. Therefore, it is necessary to conduct a preliminary analysis on the hydrochemical characteristics and evolution law of groundwater in this area. This study aimed to evaluate potential hydrogeochemical processes affecting the groundwater quality of this area by analyzing major ions in groundwater samples collected in 2019. Compositional relationships were determined to assess the origin of solutes and confirm the predominant hydrogeochemical processes controlling various ions in groundwater. Moreover, factors influencing groundwater quality were evaluated through the factor analysis method, and the control range of each influencing factor was analyzed using the distribution characteristics of factor scores. Finally, reverse hydrogeochemical simulation was carried out on typical profiles to quantitatively analyze the hydrochemical evolution process along flow paths. The Piper trilinear diagram revealed two prevalent hydrochemical facies, Ca-HCO3 type (phreatic water) and Ca-Na-HCO3 type (confined water) water. Based on the compositional relationships, the ions could be attributed to leaching (dissolution of rock salt, carbonate, and sulfate), evaporation and condensation, and cation exchange. Four influencing factors of phreatic water and confined water were extracted. The results of this study are expected to help understand the hydrochemical characteristics and evolution law of groundwater in the alluvial plain (Anqing section) of the lower Yangtze River basin for effective management and utilization of groundwater resources, and provide basic support for the ecological restoration of the Yangtze River Basin.

A flow-weighted approach to generate daily total phosphorus loads in streams based on seasonal loads

Phosphorus (P) is an essential nutrient for all forms of life but its over-enrichment can result in eutrophication of surface waters. For many watersheds around the world, some seasonal total P (TP) load datasets may exist but the continuous and multi-year daily TP concentrations and/or load datasets are not available due to the lacks of in situ P sensor measurement, time-consuming, and budget constraint. Traditionally, the seasonal TP loads are normally obtained with measuring daily TP concentrations for a couple of times within a season in a watershed, and then these daily TP concentrations along with their respective daily discharges are used to calculate the seasonal TP loads for the watershed. However, without the continuous and multi-year daily TP load dataset, development of total maximum daily load (TMDL) and calibration of watershed models for TP cannot be achieved. A flow-weighted method was developed (with detailed procedures) here to generate the daily TP loads based on the seasonal loads. The method was rigorously validated using the measured daily TP datasets from three different US Geological Survey gage stations. With very good statistical comparisons between the method predicted and field measured TP loads, we demonstrated that the flow-weighted method herein is a useful tool to disaggregate the seasonal TP loads into the daily TP loads when the measured daily TP data are not available while the TMDL development and model calibrations/validations are inevitable.

Response of water quality to land use in hydrologic response unit and riparian buffer along the Dan River, China

Land uses determine water quality within riparian environments to a certain extent and directly affect human health via drinking water. The main objective of this paper is to investigate the influences of land use, both in hydrologic response units (HRUs) and 200-m-wide buffer areas, on surface water quality. The variations and interrelationships between water physicochemical properties and land uses were assessed for better management of water environment. Nitrogen was the dominant nutrient and was significantly correlated with other water quality parameters. In the HRUs and buffer areas, the dominant landscape was grassland and farmland, respectively. Total organic carbon (TOC) and dissolved oxygen (DO) had negative correlation with land use factors; nitrate nitrogen, total nitrogen, total phosphorus, electrical conductivity, and temperature, in contrast, were positively correlated with them. Industrial and residential land was the critical land use for the aquatic environment in the Dan River, indicating that point pollution should receive more attention. Vegetation area had strong regression relationships with TOC and DO. Furthermore, more specific types of land use (subcategory classification) had a greater role in water quality. The land use in buffers can act on the water body more directly and effectively.

The assessment of the amount of water pollution and its suitability for drinking of the Tyśmienica River Basin, Poland

The quality and potability of waters of the Tyśmienica River Basin were determined in 2017 at eight measuring stations. The paper presents analyses of the physical and chemical parameters of surface waters of the basin. With regard to suspended solids, pH value, electric conductivity, sulphates, ammonia, chlorides and phosphate, the water was classified as having a very good ecological status. In the case of Kjeldahl nitrogen, the waters were classified as having a good ecological status. In the case of the remaining parameters, i.e. BOD, DO, TOC and COD, the status varied among stations. The values of all the physical and chemical parameters complied with the values for undisturbed conditions. Only ammonia and COD showed significant differences among stations. The WPI values for each measuring point ranged from 0.90 to 1.56, what corresponds to the descriptive indicators of moderately polluted water. The high concentrations of COD and TOC indicate that high-performance treatment processes (category A3) must be used to ensure that the water is suitable for drinking.

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

It’s vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ‘’turning-back” pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (β = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (β = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

This study was carried out to understand how land use patterns influence surface water quality in Tien Giang Province using remote sensing and statistical approaches. Surface water quality data were collected at 34 locations with the frequency of four times (March, June, September, and November) in 2019. Water quality parameters were used in the analysis, including pH, temperature, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2−), nitrate (N-NO3−), sulfate (SO42−), orthophosphate (P-PO43−), chloride (Cl−), total nitrogen (TN), total phosphorus (TP), and coliform. The relationship between land use patterns and water quality was analyzed using geographic information techniques (GIS), remote sensing (RS), statistical approaches (cluster analysis (CA), principal component analysis (PCA), and Krustal–Wallis), and weighted entropy. The results showed water quality was impaired by total suspended solids, nutrients (N-NH4+, N-NO2−, P-PO43−), organic matters (BOD, COD), and ions (Cl− and SO42−). Kruskal–Wallis analysis results showed that all water quality parameters in the water bodies in Tien Giang Province were seasonally fluctuated, except for BOD and TN. The highest levels of water pollutants were found mostly in the dry season (March and June). The majority of the land in the study area was used for rice cultivation (40.64%) and residential (27.51%). Water quality in the study area was classified into nine groups corresponding to five combined land use patterns comprising residential–aquaculture, residential–rice cultivation, residential–perennials, residential–rice–perennial, and residential–rice–perennial crops–aquacultural. The concentrations of the water pollutants (TSS, DO, BOD, COD, N-NH4+, N-NO2−, Cl−, and coliform) in the locations with aquaculture land use patterns (Clusters 1 and 2) were significantly larger than those of the remaining land use patterns. PCA analysis presented that most of the current water quality monitoring parameters had a great impact on water quality in the water bodies. The entropy weight showed that TSS, N-NO2−, and coliform are the most important water quality parameters due to residential–aquaculture and residential–rice cultivation; EC, DO, N-NH4+, N-NO2−, Cl−, and coliform were the significant variables for the land use type of residential–perennial crops; N-NO2−, P-PO43−, and coliform for the land use pattern of residential–rice cultivation–perennial crops) and N-NH4+, N-NO2−, Cl−, and coliform for the land use pattern of residential–rice cultivation–perennial crops–aquaculture. The current findings showed that that surface water quality has been influenced by the complex land use patterns in which residential and rice cultivation may have major roles in causing water impairment. The results of the water quality assessment and the variation in water properties of the land use patterns found in this study provide scientific evidence for future water quality management.

Temporal and spatial variations of surface water quality in the Nile River of Damietta Region, Egypt

Temporal/spatial variations of surface water quality were examined for the Nile River in the Damietta region where it serves as the major source of water for the inhabitants of Damietta Governorate. A total of 32 water quality parameters were monitored at six sampling sites for 12 months from February 2016 to January 2017. Higher values of chemical oxygen demand (COD), biological oxygen demand (BOD), heavy metals, and nutrients were observed upstream. About ~ 70% of the total variance in observations was explained by five main influences using factor analysis. The first factor (24.6% of the variance) was indicative of the mixed sources of natural and anthropogenic inputs. The second (nutritional) and the third (organic) factors were mainly controlled by the discharges from agricultural and domestic sources, respectively. Human activities and natural processes controlled the fourth and fifth factors. Only 11 parameters (K, temperature, COD, HPC, total hardness, DO, NO₂, Na, TDS, Cl, and EC) were necessary for distinguishing temporal variations according to Discriminant analysis (DA). Seven parameters (BOD, PO₄, SiO₃, Al, Turbidity, Fe, and Chlorophyll-a) were the most important variables responsible for spatial variations. Using the results we developed a water quality index (WQI) using only those parameters identified as important. All water quality parameters were below the permissible limits except for turbidity according to the World Health Organization standards, BOD and COD according to the Egyptian regulations. The calculated WQI values ranged between 12.73 and 33.73. According to these values, the Nile River Damietta branch represents a good to an excellent source of drinking water for entering secondary treatment.

Diatom community structure in relation to environmental factors in human influenced rivers and streams in tropical Africa

The contemporaneous effect of natural and anthropogenic factors involved in a watershed contribution to the seasonal and spatial variation of diatom community composition is widely discussed in the scientific literature. Yet, there is a paucity of scientific evidence indicating the effect of these factors on diatoms in tropical African regions characterized by distinct dry and wet seasons and season associated human activities like rainfed agriculture are commonly practiced. We applied multivariate techniques to determine the spatio-temporal drivers of diatom assemblage and diatom species richness in human influenced rivers and streams in Ethiopia. We simultaneously collected water and diatom samples from 24 sampling points during the wet (July) and dry (February) seasons. Both water and diatom samples were processed following standard procedures. We identified 169 species belonging to 45 genera in the studied lotic systems. We found that both season and land use factors were important in defining diatom composition (PERMANOVA, p<0.05) and species richness (ANOVA, p<0.05) patterns. Diatom community composition was driven by conductivity, dissolved oxygen, pH, and turbidity parameters (Monte Carlo permutation test, p<0.05). Besides, diatom species richness was driven by dissolved oxygen, soluble reactive phosphorus, and turbidity (GLMM, p<0.05). The study highlighted physicochemical parameters influenced by seasonal variation and human activity determined the composition of diatoms. This implies that the unique feature of heavy rain during the rainy season in the region followed by extensive flooding aggravated by the steep slope from the highlands to the lowlands plays a major role in shaping the diatom autecology in the region. Therefore, in applying biomonitoring in such regions considering the effect of runoff and dilution is imperative.

A Review of Water Stress and Water Footprint Accounting

Production and consumption activities deplete freshwater, generate water pollution and may further lead to water stress. The accurate measurement of water stress is a precondition for sustainable water management. This paper reviews the literature on physical water stress induced by blue and green water use and by water pollution. Specifically, we clarify several key concepts (i.e., water stress, scarcity, availability, withdrawal, consumption and the water footprint) for water stress evaluation, and review physical water stress indicators in terms of quantity and quality. Furthermore, we identify research gaps in physical water stress assessment, related to environmental flow requirements, return flows, outsourcing of water pollution and standardization of terminology and approaches. These research gaps can serve as venues for further research dealing with the evaluation and reduction of water stress.

A workflow for spatio-seasonal hydro-chemical analysis using multivariate statistical techniques

Multivariate statistical techniques are powerful in data interpretation and pattern recognition, which play a vital role in pollutant source identification for water environment management. Despite of their wide application in hydro-chemical analysis, absence of a comprehensive workflow hinders the practices and further studies. The present study constructed a workflow on the application of multivariate statistical techniques in spatio-seasonal hydro-chemical analysis, which provided a basic guidance for practices and a systematic support to future exploration. Selection of the methods and work paths for spatio-seasonal analysis largely depends on the structure of data set and the requirements of specific tasks. Trial and adjustment could be repeatedly performed to optimize the analysis strategy and identify more underlying patterns. Given a multiscale dataset concerning complex spatio-seasonal variations, temporal or spatial grouping using appropriate methods to reasonably divide the complicated data set contributes to data interpretation and pattern recognition. The upper Yangtze River basin (UYRB, China) was employed for case analysis to demonstrate how the workflow guides an efficient and effective data exploration. Efforts could be made in future works to continually improve the workflow to involve more complicated analysis and techniques and the integrated application in various fields.

Spatiotemporal Variation Characteristics of Water Pollution and the Cause of Pollution Formation in a Heavily Polluted River in the Upper Hai River

Blackening and odorization of heavily polluted rivers has become a serious concern and threat to ecological and human health. This paper aims to gain a deeper understanding of changes in water pollution and the cause of pollution formation in a heavily polluted river in the upper Hai River. In this study, comprehensive water quality index (CWQI) and multivariate statistical techniques (MSTs) were applied to assess the spatiotemporal variation characteristics of water pollution and to identify potential pollution sources. The seasonal Mann–Kendall (SMK) test and the SMK test of flow-adjusted concentrations were effectively used to explore the temporal variation trends of major pollutants and the causes of their formation. Data of 15 water quality parameters were analyzed during 1980–2018 at 19 monitoring sites in the mainstream and major tributaries of the Xinxiang Section of the Wei River (XSWR). The results showed that the rivers were seriously polluted from 1991 to 2009, but the water quality improved after 2010. Nineteen sampling sites were divided into a low pollution region and a high pollution region. In the flood season, the pollution sources were mainly domestic sewage, industrial wastewater, agricultural runoff, biochemical pollution, and natural sources. In the nonflood season, the pollution sources were mainly domestic sewage and industrial wastewater. In recent years, the water quality of seriously polluted river has generally improved, mainly due to reductions in pollutant discharge from point sources and nonpoint sources.

Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey

Irrigation water quality has important implications on salinity, ion toxicity, production cost, and crop failures. There is a need for a comprehensive analysis of spatial and temporal dynamics in parameters at a watershed scale. This information is critical for irrigation management in agricultural production. The Lake Uluabat watershed is a significant agricultural area of Turkey, which is studied using monitored water data. Multivariate assessment is performed using cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) to evaluate temporal and spatial variations in water quality in the watershed. The data is processed by clustering, reducing data dimensionality, delineating indicator parameters, assessing source identification, and evaluating temporal changes and spatial patterns. The results show that the most representative discriminant parameters had more than 90.98% validity in both temporal and spatial analyses. Runoff rate (Q) and water temperature (WT) were identified in the temporal study, while spatial analysis showed bicarbonate (HCO₃^-), sulfate (SO₄^2-), and boron (B³⁺) as indicators. Salinity, sodicity, boron hazard, and alkalinity affect both spatial and temporal water quality patterns in the watershed. It is observed that continued use of poor-quality irrigation water can adversely affect agriculture and soil health in a watershed. Spatio-temporal relationships in parameters will be useful in sustainable irrigation management and farm planning for improving crop productivity and soil health.

Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China

Anthropogenic activities pose challenges on security of water quality. Identifying potential sources of pollution and quantifying their corresponding contributions are essential for water management and pollution control. In our study, 2-year (2017-2018) water quality dataset of 15 parameters from eight sampling sites in tributaries and mainstream of the Min River was analyzed with multivariate statistical analysis methods and absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique to reveal potential sources of pollution and apportion their contributions. Temporal and spatial cluster analysis (CA) classified 12 months into three periods exactly consistent with dry, wet, and normal seasons, and eight monitoring sites into two regions, lightly polluted (LP) and highly polluted (HP) regions, based on different levels of pollution caused by physicochemical properties and anthropogenic activities. The principal component analysis (PCA) identified five latent factors accounting for 75.84% and 73.46% of the total variance in the LP and HP regions, respectively. The main pollution sources in the two regions included agricultural activities, domestic sewage, and industrial wastewater discharge. APCS-MLR results showed that in the LP region, contribution of five potential pollution sources was ranked as agricultural non-point source pollution (22.13%) > seasonal effect and phytoplankton growth (19.86%) > leakage of septic tanks (15.73%) > physicochemical effect (12.86%) > industrial effluents and domestic sewage (11.59%), while in the HP region ranked as point source pollution from domestic and industrial discharges (20.81%) > municipal sewage (16.66%) > agricultural non-point source pollution (15.23%) > phytoplankton growth (14.82%) > natural and seasonal effects (12.67%). Based on the quantitative assessment of main pollution sources, the study can help policymakers to formulate strategies to improve water quality in different regions.

Seasonality of drinking water sources and the impact of drinking water source on enteric infections among children in Limpopo, South Africa

Enteric infections and water-related illnesses are more frequent during times of relative water abundance, especially in regions that experience bimodal rainfall patterns. However, it is unclear how seasonal changes in water availability and drinking water source types affect enteric infections in young children. This study investigated seasonal shifts in primary drinking water source type and the effect of water source type on enteric pathogen prevalence in stool samples from 404 children below age 5 in rural communities in Limpopo Province, South Africa. From wet to dry season, 4.6% (n = 16) of households switched from a source with a higher risk of contamination to a source with lower risk, with the majority switching to municipal water during the dry season. In contrast, 2.6% (n = 9) of households switched from a source with a lower risk of contamination to a source with higher risk. 74.5% (n = 301) of the total households experienced interruptions in their water supply, regardless of source type. There were no significant differences in enteric pathogen prevalence between drinking water sources. Intermittent municipal water distribution and household water use and storage practices may have a larger impact on enteric infections than water source type. The limited differences in enteric pathogen prevalence in children by water source could also be due to other exposure pathways in addition to drinking water, for example through direct contact and food-borne transmission.

A spatiotemporal analysis of water quality characteristics in the Klip river catchment, South Africa

Understanding the spatial and temporal patterns of water quality is central to its management as it provides information essential to the restoration as well as protection of water resources. The main objectives of this study were (i) to analyze the spatial and temporal trends of water quality and (ii) to identify the critical sources of pollution in the Klip River catchment (KRC). Water samples were collected at 12 sampling points along the Klip River, monthly from February 2016 to January 2017 and analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and spectrophotometry for heavy metals and nutrients, respectively. Multivariate statistical techniques (cluster analysis and discriminant analysis) were used to delineate homogeneous water quality zones and seasons, and principal component analysis was used to identify pollution sources. Comprehensive pollution index (CPI) was also computed to classify the overall pollution of the river. The spatial grouping yielded two homogenous water quality zones namely upstream and downstream. Temporal grouping yielded two clusters, which were attributed to the effects of the El Nino (2015/16 season) and La Nina phenomena (2016/17 season). The CPI revealed that the KRC was critically polluted in the upstream for domestic (162.16-323.28) and aquatic uses (617.70-837.09) in both the 2015/2016 and 2016/2017 seasons. It can be concluded that pollutants, which influence water quality in the KRC in one season and/or location, may not necessarily be the same in the other season or location. Therefore, there is need to develop a water quality management plan in the KRC that targets the most impaired uses, pollutants of priority, and the critically polluted areas.

Development of a drinking water quality index based on a participatory procedure using mixed multicriteria methods

The objective of this study is to propose and apply a novel approach to develop a drinking water quality index that enables water managers to routinely identify the vulnerabilities in a distribution system (DS) while taking into account the priorities of water managers and operators. Here, we propose an innovative approach based on the participation of eight researchers and water managers. The input from these participants was included through a combination of the Delphi and MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) methods, which were applied to an index developed using fuzzy logic. The index was then validated by conducting sensitivity and comparative analyses on a database from field sampling campaigns conducted in various small municipal DSs in two Canadian provinces: Quebec and Newfoundland and Labrador. Finally, the drinking water quality index was applied to a database of the DS in Quebec City, Canada. Sensitivity and comparative analyses revealed that the developed index could allow for the characterization of water sample quality and could prove useful for prioritizing interventions in the DS. The index was also useful for representing the spatial variability of water quality, and provided nuanced information about water quality, especially when the water quality parameter values complied with guidelines and regulations. These results provide a transferable and integrated approach for developing customized drinking water quality indices. This study will help pave the way for water quality managers to better prevent episodes of possible water quality deterioration.

Assessing nutrient salts and trace metals distributions in the coastal water of Jeddah, Red Sea

In this study, eighteen sites at Jeddah coastal area, Red Sea, have been assessed for water quality status, depending on nutrients, metals, Chlorophyll-a (Chl-a) and physical variables during 2018 and 2019. The investigated parameters of the Water Quality Index (WQI) are temperature, pH, salinity, dissolved oxygen (DO), DO saturation, oxidizable organic matter (OOM), suspended particulate matter (SPM), Chl-a, ammonium, nitrite, nitrate, total nitrogen, reactive phosphate, total phosphorus, silicate, Zn, Fe, Mn Cu, Cd, Pb, and Ni. The results revealed that the pH values were slightly alkaline with a range of 7.85–8.20. The results of other parameters were as follow: salinity (36.95–42.61PSU), DO (5.22–6.67 mg/L), OOM (0.40–1.23 mg/L), SPM (12.39–21.5 mg/L), Chl-a (0.10–0.83 µg/L). The range of nutrients (μM) were 0.07–0.22, 0.45–1.47, 9.62–18.64, 23.31–57.65, 0.05–0.15, 0.55–2.78 and 2.54–5.51 for NH₄/N, NO₂/N, NO₃/N, TN, PO₄/P, TP and SiO₄/Si, respectively. Cluster analysis was used to classify the stations studied. From the current study, five clusters were found, indicating the need to perform cluster analysis in the water quality assessment process to confirm the durability and consistency of the data discovered in the current application.

Exploration of the critical factors influencing the water quality in two contrasting climatic regions

Over the past few decades, rivers have become severely polluted as a result of receiving vast quantities of domestic and industrial wastewater. The identification of the major factors that influence water quality is crucial to understand the interactions of anthropogenic and natural factors and develop river restoration projects. In this study, the QUAL2Kw water quality model was used to quantitatively evaluate the most critical factors for water quality at two sites with different meteorological conditions and urban scales. The genetic algorithm (GA) was used to optimize the parameters in the model. The Monte Carlo simulation (MCS) method was used to assess the model uncertainty and sensitivity in all reaches for five water quality outputs (temperature, CBOD, DO, TP, and TN) in two seasons. The K-means clustering method associated with the sensitivity results was used to identify the major factors influencing the water quality in all reaches from the input data and the model parameters. The results showed that CBOD, TN, and TP were most sensitive to headwater and tributary quality. DO tended to be affected by more natural reactions than the other water quality indicators. In the cold and dry seasons and the more urbanized areas, river pollution was more severe, and the impact of natural reactions was reduced. The simulation results revealed the reliability of QUAL2Kw in modeling the quantity and quality of all river reaches. The method applied in this study is beneficial for the improvement and management of the water environment.

Eutrophication and heavy metal pollution patterns in the water suppling lakes of China's south-to-north water diversion project

This study used non-supervised machine learning self-organizing maps (SOM) in conjunction with traditional multivariate statistical techniques (e.g., hierarchical cluster analysis, principle component analysis, Pearson's correlation analysis) to investigate spatio-temporal patterns of eutrophication and heavy metal pollution in the water supplying lakes (i.e., the Gao-Bao-Shaobo Lake, GBSL) of the eastern route of China's South-to-North Water Diversion Project (SNWDP-ER). A total of 28 water quality parameters were seasonally monitored at 33 sampling sites in the GBSL during 2016 to 2017 (i.e., 132 water samples were collected in four seasons). The results indicated that: 1) spatially, the western and south-western GBSL was relatively more eutrophic and polluted with heavy metals; and 2) temporally, the lakes suffered from high risks of heavy metal contamination in spring, but eutrophication in summer while water quality in winter was the best among the four seasons. Two main potential sources of pollution and transport routes were identified and discussed based on the pollution patterns. These findings contributed considerably to providing in-depth understanding of water pollution patterns, as well as potential pollution sources in the water-supplying region. Such understanding is crucial for developing pollution control and management strategies for this mega inter-basin water transfer project.

Impact of fecal contamination on surface water quality in the São Francisco River hydrographic basin in Minas Gerais, Brazil

Proper water quality monitoring is a valuable tool for water resource management, helping to identify polluting sources and risks related to the use of water resources. One of the main types of contamination found in Brazilian water bodies is fecal contamination, which originates mainly from point source pollution through wastewater disposal. Thus, this study analyzed water quality monitoring data from the responsible environmental body (Minas Gerais Institute of Water Management, IGAM), related to the fecal contamination indicator (FCI), for the years 2000-2018. This was done for the Minas Gerais portion of the São Francisco River basin (SFRB-MG), one of the most important basins in the country. The 10 sub-basins in the area were compared using statistical tools. The work found significant differences between the sub-basins in terms of FCI concentration, highlighting the most impacted ones (SF2, SF3, and SF5) as also being the most densely populated. It is necessary to invest in sanitation measures in order to ensure that water resources are preserved, as well as to reduce the public health risks of downstream municipalities that are supplied with previously contaminated water.

Female Reproductive Performance and Maternal Birth Month: A Comprehensive Meta-Analysis Exploring Multiple Seasonal Mechanisms

Globally, maternal birth season affects fertility later in life. The purpose of this systematic literature review is to comprehensively investigate the birth season and female fertility relationship. Using PubMed, we identified a set of 282 relevant fertility/birth season papers published between 1972 and 2018. We screened all 282 studies and removed 131 non-mammalian species studies on fertility and 122 studies that were on non-human mammals. Our meta-analysis focused on the remaining 29 human studies, including twelve human datasets from around the world (USA, Europe, Asia). The main outcome was change in female fertility as observed by maternal birth month and whether this change was correlated with either temperature or rainfall. We found that temperature was either strongly correlated or anti-correlated in studies, indicating that another factor closely tied to temperature may be the culprit exposure. We found that rainfall only increases fertility in higher altitude locations (New Zealand, Romania, and Northern Vietnam). This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility - birth season relationship. We discuss other environmental and sociological factors on the birth season - female fertility relationship. Future research should focus on the role of birth season and female fertility adjusting for additional factors that modulate female fertility as discussed in this comprehensive review.

Multivariate water quality analysis of Lake Cajititlán, Mexico

Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm³ per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO₃^-, and NO₂^-, consistent with the configuration of point and nonpoint sources that affect the lake's water quality. The temporal DA results suggested that conductivity, hardness, NO₂^-, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants.