Impact of storm events on disinfection byproduct precursors in a drinking water source in the Northeastern United States

Storm events play a crucial role in organic matter transport within watersheds and can increase the concentration and alter the composition of NOMs and DBP formation potential. To assess the impact that storm events can have on drinking water quality, samples were collected and analyzed across four storm events in the Neversink River, Catskill region, New York in 2019 and 2022. Source water natural organic matter (NOM) was characterized, and the change of NOM quality was evaluated due to storm impacts. During storm events, a high level of NOM mobilization is initiated by heavy precipitation causing overland flow and a rise in the water table. In this way, storms result in increased access to stored NOM pools that are generated during inter-storm periods. A significant correlation was observed between several organic water quality parameters such as UV absorbance (UV254), dissolved organic carbon (DOC) and chlorine demand. Precursors for the total trihalomethanes (TTHM), dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA) exhibited comparable patterns with UV254, DOC, and chlorine demand for four storms. Despite the potential for increased dilution resulting from higher discharges, all organic water quality parameters, including yields of disinfection byproducts (i.e., DBP precursors), exhibited elevated concentrations during periods of higher flows. Three of the four storms showed hysteresis patterns with higher observed concentrations of organic constituents in the falling limb of the hydrographs. Precursors for the nitrogenous DBPs (N-DBPs) were proportional to the DOC for all four storms. The coefficient of determination (R2) for TTHM, DCAA, TCAA with UV254 is higher (R2 0.92-0.98) than corresponding correlations with DOC (R2 0.89-0.92). The R2 for UV254 showed the following hierarchy: DCAA≈TCAA>TTHM. Additionally, the R2 for DOC and specific ultraviolet absorbance (SUVA) had the following hierarchy: DCAA>TCAA>TTHM and TCAA>DCAA>TTHM respectively. A significant correlation between UV254 and DOC (R = 0.99) for all storms was observed. Chlorine demand also yielded a strong correlation (R = 0.91∼0.98) with UV254 and DOC. This research indicates that a significant and disproportionate export of NOM to source waters occurs during storm events compared to baseflow conditions. Consequently, it is recommended for drinking water treatment facilities to reassess chlorine dosages during these events. Treatment plants can employ UV254 as a tool to determine appropriate chlorine dosages, aiming to mitigate DBP formation in treated waters.

Assessing groundwater quality dynamics in Madhya Pradesh: Chemical contaminants and their temporal patterns

Groundwater is essential for maintaining ecosystem health and overall well-being as a pivotal resource for plants and animals. The increasing public consciousness of the deterioration of groundwater quality has emphasized the significance of undertaking extended evaluations of groundwater water quality, particularly in regions undergoing substantial hydrological alterations. This study primarily aims to investigate the spatio-temporal variations in groundwater quality and evaluate its suitability for potable purposes in the region of Madhya Pradesh. The study combines the Mann-Kendall (MK) test and Sen's Slope (SS) to analyze the changes in groundwater quality of all 51 districts of Madhya Pradesh, India, utilizing 12 water quality indices using MATLAB. Data was sourced from the Central Ground Water Board (CGWB) in India from the year 2001-2021. The data was then tested for homogeneity at all 1154 sampling stations using the software XLSTAT. Piper plot clustering characterized the state's groundwater as bicarbonate-calcium-magnesium (HCO3--Ca2+-Mg2+) type. The study found that the groundwater in the area is heavily impacted by high levels of nitrate and hardness, which is caused by an increase in multivalent cations. The water was classified as ranging from hard to extremely hard, and approximately 25.49% of the state's groundwater has nitrate levels that exceed the acceptable limits. The MK test showed a significant increasing correlation in trends for parameters such as nitrate, sulfate, fluoride, chloride, bicarbonate, total hardness, and electrical conductivity. It also showed a significant decreasing correlation for calcium, magnesium, potassium, and sodium. These results were observed at a confidence level of 95%. The analysis of trends has shown that human-related factors have a considerable effect on the characteristics of groundwater quality. It is therefore recommended that such human-related factors be taken into consideration when developing policies for managing groundwater resources. Consequently, these policies should emphasize the strict enforcement of rules and standards that limit the overuse of fertilizers, ensure the appropriate disposal of municipal solid and liquid wastes, and regulate industrial pollutants.

A comprehensive review of the effects of salinity, dissolved organic carbon, pH, and temperature on copper biotoxicity: Implications for setting the copper marine water quality criteria

In recent years, there has been a growing concern about the ecological hazards associated with copper, which has sparked increased interest in copper water quality criteria (WQC). The crucial factors affecting the bioavailability of copper in seawater are now acknowledged to be salinity, dissolved organic carbon (DOC), pH, and temperature. Research on the influence of these four water quality parameters on copper toxicity is rapidly expanding. However, a comprehensive and clear understanding of the relevant mechanisms is currently lacking, hindering the development of a consistent international method to establish the seawater WQC value for copper. As a response to this knowledge gap, this study presents a comprehensive summary with two key focuses: (1) It meticulously analyzes the effects of salinity, DOC, pH, and temperature on copper toxicity to marine organisms. It takes into account the adaptability of different species to salinity, pH and temperature. (2) Additionally, the study delves into the impact of these four water parameters on the acute toxicity values of copper on marine organisms while also reviewing the methods used in establishing the marine WQC value of copper. The study proposed a two-step process: initially zoning based on the difference of salinity and DOC, followed by the establishment of Cu WQC values for different zones during various seasons, considering the impacts of water quality parameters on copper toxicity. By providing fundamental scientific insights, this research not only enhances our understanding and predictive capabilities concerning water quality parameter-dependent Cu toxicity in marine organisms but also contributes to the development of copper seawater WQC values. Ultimately, this valuable information facilitates more informed decision-making in marine water quality management efforts.

Monitoring of rainwater quality in Kandy and Peradeniya, Sri Lanka

The composition of atmospheric deposition is a measure of air quality, an important aspect of the health of the ecosystem. Consequently, continuous monitoring of atmospheric deposition is crucial to obtain remedial measures to avoid undesirable aspects that would affect living things. In this context, the objective of this study was to determine the rainwater quality at selected locations in Kandy and Peradeniya area of Sri Lanka, namely, Kandy, Polgolla, and University of Peradeniya (UOP), and to identify possible correlations between quality parameters through statistical means. Forty (40) rainwater samples from the UOP site and seven (07) samples each from the Kandy and Polgolla sites were collected from 18 May 2020 to 28 April 2021. The volume-weighted average (VWA) pH values of UOP, Kandy, and Polgolla sites were determined to be 7.44, 7.19, and 7.19, respectively, and moreover, acid rain (pH < 5.6) occurrences were not detected during the sampling period. The VWA values of rainfall, conductivity, salinity, TDS, and hardness at the UOP site were 40.12 mm, 51.93 μS cm-1, 0.0300 ppt, 26.59 mg L-1, and 13.55 mg L-1, respectively. The corresponding values of the Kandy site were 16.52 mm, 64.04 μS cm-1, 0.0361 ppt, 30.80 mg L-1, and 19.49 mg L-1, respectively; and those of the Polgolla site were 33.10 mm, 53.90 μS cm-1, 0.0310 ppt, 25.76 mg L-1, and 19.31 mg L-1, respectively. The VWA values of conductivity, salinity, and TDS were the highest at the Kandy site. Further, the VWA values of hardness at Kandy and Polgolla sites were approximately equal, probably due to the spring of Ca2+ and Mg2+ particulates from the dolomite quarry located in Digana area. The most prominent anion was identified as Cl- in bulk deposition at all three sites, while NO3- showed the lowest concentration of all sites. Moreover, very strong significant positive correlations were identified between conductivity-TDS, conductivity-salinity, conductivity-hardness, TDS-hardness, TDS-salinity, salinity-hardness, SO42--Cl-, and NO3--Cl- according to the relevant Pearson correlation coefficients. It is thus concluded that the pollutants come from the same sources, either natural or anthropogenic.

The combination of multiple linear regression and adaptive neuro-fuzzy inference system can accurately predict trihalomethane levels in tap water with fewer water quality parameters

Artificial Neural Network (ANN) models are accurate in predicting the levels of disinfection by-products (DBPs) in drinking water. However, these models are not yet practical due to the large number of parameters involved, which should take a significant amount of time and cost to detect. Developing accurate and reliable prediction models of DBPs with fewest parameters is essential in the management of drinking water safety. This study used the adaptive neuro-fuzzy inference system (ANFIS) and radial basis function artificial neural network (RBF-ANN) to predict the levels of trihalomethanes (THMs), the most abundant DBPs in drinking water. Two water quality parameters identified by multiple linear regression (MLR) models were used as model inputs, and the quality of the models was assessed based on criteria such as correlation coefficient (r), mean absolute relative error (MARE), and the percentage of predictions with absolute relative error less than 25% (NE<25%) and over than 40% (NE>40%), etc. The results showed that the ANFIS models had higher correlation coefficients (r = 0.853-0.898) and prediction accuracy (NE<25% = 91%-94%) compared to RBF-ANN models (r = 0.553-0.819; NE<25% = 77%-86%) and traditional MLR models (r = 0.389-0.619; NE<25% = 67%-77%). Conversely, the prediction error, as indicated by MARE and NE>40%, showed the opposite trend: ANFIS models (MARE = 8%-11%; NE>40% = 0-5%) < RBF-ANN models (MARE = 15%-18%; NE>40% = 5%-11%) < MLR models (MARE = 19%-21%; NE>40% = 11%-17%). The present study provided a novel approach for constructing high-quality prediction models of THMs in water supply systems using only two parameters. This method holds promise as a viable alternative for monitoring THMs concentrations in tap water, thereby contributing to the improvement of water quality management strategies.

A comprehensive review of water quality indices for lotic and lentic ecosystems

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms

Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.

Machine learning algorithm inversion experiment and pollution analysis of water quality parameters in urban small and medium-sized rivers based on UAV multispectral data

To examine and analyze the applicability of UAV multispectral images to urban river monitoring, this paper, taking the Fuyang River in the urban area of Handan Municipality as the object, the orthogonal image data of the river in different seasons were acquired by unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and at the same time, the water samples were collected for physical and chemical indexes detection. Based on the image data, a total of 51 modeling spectral indexes were obtained by constructing three forms of band combinations ranging from the difference index (DI), ratio index (RI), and normalization index (NDI) and combining six single-band spectral values. Through the partial least squares (PLS), random forest (RF), and lasso prediction models, six fitting models of water quality parameters were constructed: turbidity (Turb), suspended, substance (SS), chemical oxygen demand (COD), ammonia nitrogen (NH₄-N), total nitrogen (TN), and total phosphorus (TP). After verifying the results and evaluating the accuracy, the following conclusions were drawn: (1) The inversion accuracy of the three types of models is generally the same-summer is better than spring, and winter is the worst. (2) Water quality parameter inversion model based on two kinds of machine learning algorithms has more prominent advantages than PLS. RF model has good performance in the inversion accuracy and generalization ability of water quality parameters in different seasons. (3) The prediction accuracy and stability of the model are positively correlated to a certain extent with the size of the standard deviation of sample values. To sum up, by using the multispectral image data acquired by UAV and adopting the prediction models built upon machine learning algorithms, water quality parameters in different seasons can be predicted in different degrees.

Biosorption potential of viable and dead Aspergillus flavus biomass on polluted pond water

The State Industries Promotion Corporation of Tamil Nadu Ltd (SIPCOT) Lake is never dry; it is always full of water and was recently used as a waste reservoir by the native peoples and industrialists. Thus, this investigation was performed to assess the quality of the lake water and evaluate the possible biosorption potential of Aspergillus flavus on this lake water sample through batch model biosorption study. The water quality parameters analyses revealed that the lake water has been polluted with number of contaminates which including organic and inorganic. The most of the parameters such as pH (9.5 ± 0.7), turbidity (38 ± 1.1 NT unit), TDS (2350.12 ± 31.24 mg L^-1), BOD (40.21 ± 3.27 mg L^-1), and COD (278.61 ± 11.84 mg L^-1), Ca (212.85 ± 9.64 mg L^-1), Fe (3.1 ± 0.8 mg L^-1), NH₃ (15.62 ± 0.5 mg L^-1), NO_3-(5.84 ± 0.14 mg L^-1), Cl^- (1257.85 ± 4.6 mg L^-1),Cd (15.64 ± 0.29 mg L^-1), Cr (6.86 ± 0.34 mg L^-1), Pb (25.61 ± 3.41 mg L^-1), and Hg (1.8 ± 0.024 mg L^-1) content of water sample were beyond the acceptable limits. Fortunately, the A. flavus dead biomass showed considerable biosorption potential (Cd: 27.5 ± 1.1%, Cr: 13.48 ± 1.2%, Pb: 21.27 ± 1.5%, and Hg: 6.49 ± 0.86% in 180 min of contact time) than viable form on polluted lake water. Since, reduced the quantities of most of the parameters which beyond the permissible limit and also increased remarkable percentage of DO in the water sample in a short period of contact time. These findings suggest that A. flavus dead biomass can be used for bioremediation of polluted water in a sustainable manner.

Development of an electronic nose to characterize water quality parameters and odor concentration of wastewater emitted from different phases in a wastewater treatment plant

For public health consideration, it is important to ensure the wastewater discharged from wastewater treatment plant is within the regulatory limits. This problem can be effectively solved by improving the accuracy and rapid characterization of water quality parameters and odor concentration of wastewater. In this paper, we proposed a novel solution to realize the precisive analysis of water quality parameters and odor concentration of wastewater by the electronic nose device. The main work of this paper was divided into three steps: 1) recognizing wastewater samples qualitatively from different sampling points, 2) analyzing the correlation between electronic nose response signals and water quality parameters and odor concentration, and 3) predicting the odor concentration and water quality parameters quantitatively. Combined with different feature extraction methods, support vector machine and linear discriminant analysis were applied as classifiers to recognize samples at different sampling points, which reported the best recognition rate of 98.83%. Partial least squares regression was applied to complete the second step, and R² was reaching 0.992. As for the third step, ridge regression was used to predict water quality parameters and odor concentration with the RMSE less than 0.9476. Thus, electronic noses can be applied to determine water quality parameters and odor concentrations in the effluent discharged from wastewater plants.

A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Predicting Legionella contamination in cooling towers and evaporative condensers from microbiological and physicochemical parameters

BACKGROUND

Inhalation of Legionella-containing aerosols generated by cooling towers (CT) and evaporative condensers (EC) where water risk management is not performed correctly has been linked to a high percentage of community outbreaks of Legionnaires' disease (LD). Likewise, microbiological and physicochemical characteristics of the water in these facilities have been associated with this bacterium. The main aim of this study was to assess the risk of Legionella colonization in CT and EC based on the data for microbiological and physicochemical water quality provided by the Environmental Health Department and Laboratory of the City Council of L'Hospitalet de Llobregat (Barcelona, Spain).

METHODS

Legionella was analysed in 789 samples collected from 127 CT and EC in 46 companies in Catalonia from 2002 to 2019. A two-step logistic regression analysis was carried out to assess the risk of colonization by Legionella in the studied facilities according to the microbiological (aerobic heterotrophic bacteria) and physicochemical (pH, alkalinity, hardness, turbidity, conductivity, total iron and Langelier Index) water parameters. The optimal cut-off points for the water parameters predictive of Legionella contamination were defined as the values on the receiver operating characteristic (ROC) curve where sensitivity and specificity were jointly maximized.

RESULTS

Legionella was isolated in 8.49% of the 789 analysed samples, 22.39% of which were heavily contaminated (with counts higher than 1.0 × 10⁴ CFU/l). L. pneumophila was isolated in 82.09% of the samples, with 41.82% belonging to serogroup 1. Logistic regression analysis revealed that aerobic heterotrophic bacteria concentrations ≥6.90 × 10² CFU/ml [Odds ratios (OR) (95% CI) = 3.56 (1.39-9.43), p = 0.01], a pH ≥ 8.70 [OR (95% CI) = 3.60 (1.34-10.09), p = 0.01], and water hardness ≥5.72 × 10² mg/l [OR (95% CI) = 6.30 (2.34-18.56), p < 0.001] were each independently associated with a higher risk of CT and EC colonization by Legionella.

CONCLUSIONS

The present study shows the importance of risk assessment for improving the control measures aimed at preventing or reducing Legionella populations in CT and EC, thus minimizing potential dangers for public health.

Short-term variations and correlations in water quality after dam removal in the Chishui river basin

Dam damming has an adverse effect on river connectivity, leading to downstream nutrient transport and ecosystem fragmentation. Dam demolition has already been used as an effective measurement to promote the ecological restoration of rivers. Few studies have analyzed the short-term variations of water quality following dam removal. This study investigated the response of multi-element and multi-form water quality parameters, such as water temperature (TEM), dissolved oxygen (DO), pH, biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total nitrogen (TN) and total phosphorus (TP), to the demolition of 4 dams in Chishui River Basin in short term. The study employed Spearman correlation analysis and Generalized Additive Models to identify the critical variables and examine the inter-relationship between these water quality parameters. Our results show that COD, BOD_5, and TP increased after two weeks of dam removal, while NH₃-N and TN decreased. Dams with larger volumes and higher heights led to more obvious deterioration for DO, COD, and BOD₅. We also found that denitrification and resuspension dominantly affect the water quality indicators following dam removal. Denitrification is responsible for downstream TN increase, and resuspension and related sediment transport contribute to downstream TP increase. Our study provides an opportunity to explore the transformation and migration of N and P in reservoirs following dam removal in the short term and presents a scientific basis and new thought for the short-term protection and management following the clean-up and rectification of multiple small hydropower plants.

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.

A spectral learning path for simultaneous multi-parameter detection of water quality

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO₃^--N, NH₃-N) at 1.3%, coefficient of determination (R²) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

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.

Bioretention soil capacity for removing nutrients, metals, and polycyclic aromatic hydrocarbons; roles of co-contaminants, pH, salinity and dissolved organic carbon

Conventional bioretention soil media (BSM: e.g., loamy sand) is employed in infiltration-based stormwater management practices, but concerns exist on its limited sorption capacity. However, limited quantitative data is available, particularly considering the wide range of contaminants and water quality conditions that occur in stormwater. This study utilized batch tests to investigate the capability of conventional BSM for simultaneous removal of three nutrients (ammonium, nitrate, and phosphate), six metals (Cd, Cr, Cu, Ni, Pb and Zn), and four polycyclic aromatic hydrocarbons (PAHs: naphthalene, acenaphthylene, phenanthrene, and pyrene) from synthetic stormwater. Moreover, the effects of co-contaminants and different stormwater chemistry parameters (pH, salinity, and dissolved organic carbon (DOC)) on BSM sorption capacity were investigated. BSM was not effective for nutrients removal; however, it had good removal efficiency for metals such as Cu, Pb, and Cr which are less soluble at neutral pH values compared to metals such as Ni, Cd and Zn. Moreover, BSM was effective for removing PAHs with higher hydrophobicity such as pyrene and phenanthrene. Metals sorption capacity of BSM was greater at higher pH, lower salinity and DOC; however, the sorption capacity of BSM for PAHs was not sensitive to stormwater chemistry parameters. However, competitive sorption had a notable effect on low molecular weight PAHs, Cd, and Ni. This study provides a quantitative evaluation of the BSM performance and compares the sorption capacity to potential sorptive amendments used in stormwater management. While select sorbent amendments out-performed the BSM, this was not universal and was contaminant specific; careful consideration of water quality enhancement goals and solution chemistry are required in selecting a sorbent. Overall, this study identifies the possible limitations in BSM compositions and factors that may adversely affect BSM sorption capacity, and finally describes options to enhance BSM performance and recommendations for future research.

Baseline water quality of the Gold Coast Broadwater, southern Moreton Bay (Australia)

This study establishes baseline water quality characteristics for the Gold Coast Broadwater, southern Moreton Bay (Australia) utilising routinely monitored parameters between 2016 and 2021, across 18 sites. Combined site mean concentrations of NO_x-N, NH₃-N and total nitrogen were 11.4 ± 33.4 μg/L, 12.7 ± 27.2 μg/L, and 169 ± 109 μg/L, respectively, whilst PO₄-P and total phosphorous were 7.30 ± 5.10 μg/L and 21.7 ± 14.1 μg/L. Additionally, total suspended solids and turbidity combined site means were 6.6 ± 6.0 mg/L and 3.4 ± 2.9 NTU, respectively. During high rainfall periods nutrient concentrations increased by up to >200-, >150-, 15-, 12- and >12-fold for NO_x-N, NH₃-N, TN, PO₄-P and TP, respectively, compared to quiescent conditions. Furthermore, TSS and NTU values increased by up to 15- and 40-fold during periods of measured rainfall compared to quiescent conditions.

Spatiotemporal variations of water quality and their driving forces in the Yangtze River Basin, China, from 2008 to 2020 based on multi-statistical analyses

Water quality deterioration is a prominent issue threatening water security worldwide. As the largest river in China, the Yangtze River Basin is facing severe water pollution due to intense human activities. Analyzing water quality trends and identifying the corresponding driver factors are important components of sustainable water quality management. Thus, spatiotemporal characteristics of the water quality from 2008 to 2020 were analyzed by using a Mann-Kendall test and rescaled range analysis (R/S). In addition, multi-statistical analyses were used to determine the main driving factors of variation in the permanganate index (COD_Mn), ammonia nitrogen (NH₃-N) concentration, and total phosphorus (TP) concentration. The results showed that the mean concentrations of NH₃-N and TP decreased from 0.31 to 0.16 mg/L and 0.16 to 0.07 mg/L, respectively, from 2008 to 2020, indicating that the water quality improved during this period. However, the concentration of COD_Mn did not reduce remarkably. Based on R/S analysis, the NH₃-N concentration was predicted to continue to decrease from 2020 to 2033, whereas the COD_Mn concentration was forecast to increase, highlighting an issue of great concern. In terms of spatial distribution, water quality in the upstream was better than that of the mid-downstream. Multi-statistical analyses revealed that the temporal variation in water quality was predominantly influenced by tertiary industry (TI), the nitrogen fertilizer application rate (N-FAR), the phosphate fertilizer application rate (P-FAR), and the irrigation area of arable land (IAAL), with contribution rates of 15.92%, 14.65%, 3.46%, and 2.84%, respectively. The spatial distribution of COD_Mn was mainly influenced by TI, whereas that of TP was primarily determined by anthropogenic activity factors (e.g., N-FAR, P-FAR). This study provides deep insight into water quality evolution in the Yangtze River Basin that can guide water quality management in this region.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH₄-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN₄-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH₄-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH₄-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Optimization of water quality monitoring programs by data mining

Water quality monitoring programs are essential planning and management tools, but they face many challenges in the developing world. The scarcity of financial and human resources and the unavailability of infrastructure often make it impossible to meet the legal requirements of water monitoring. Many approaches to optimizing water quality monitoring programs have already been proposed. However, few investigations have developed and tested data mining for this purpose. This article has developed data-based models to reduce the number of water quality parameters of monitoring programs using data mining. The objective was to extract patterns from the database, expressed by association rules, which together with field parameters, measured with automatic probes, can estimate laboratory variables. This approach was applied in 35 monitoring stations along 27 river basins throughout Brazil. The data are from fifty years of monitoring (1971-2021), constituting 6328 observations of 60 water quality parameters investigated in different environmental contexts, water quality, and the structuring of monitoring programs. With the applied approach it was possible to estimate 56% of the laboratory parameters in the monitoring stations investigated. The influence of environmental characteristics on the optimization capacity of monitoring programs was evident. The methodology used was not influenced by different water quality levels and anthropogenic impacts. However, the number of parameters was the most influential element in optimization. Monitoring programs with 20 or more water quality variables have the highest potential (≥44%) of optimization by this methodology. Results demonstrate that this approach is a promising alternative that can reduce the frequency of analyses measured in the laboratory and increase the spatial and temporal coverage of water quality monitoring networks.

Event mean concentration and first flush from residential catchments in different climate zones

Most studies on EMC (Event mean concentration) and first flush are reported as local studies; however variations of EMC and first flush across catchments in different climate zones has not been studied. This research collected continuous flow and discrete water quality data and rainfall measurements from 17 catchments, EMC and rainfall data from 14 catchments, and an additional dataset where only average EMC values are reported (19 catchments). The data are from residential sites across temperate, tropical, dry, and continental climate zones and include water quality parameters in particulate (total suspended solids), mixed (total nitrogen and total phosphorus) and dissolved (orthophosphate and ammonium nitrogen) forms. Our study shows that EMC differs significantly between climate zones. The average EMC is highest in dry followed by continental and temperate, with lowest in the tropical zone. Pearson's correlation analysis revealed that the rainfall depth is negatively correlated with EMC for particulate and mixed form parameters for the tropical, temperate and dry zones, but positively correlated for the continental zone. The discrete time-series data from the 17 catchments were used to evaluate first flush and it was found that catchments in the tropics exhibit stronger first flush than temperate zone catchments, for all the water quality parameters with particulate showing a stronger first flush compared to dissolved forms. Based on the distribution of the data, new limits for very strong, strong, moderate, and very weak to no first flush are suggested for TSS for different climate zones. The new limits were quantified by fitting the function L^=V^^bwhere L^and V^are the normalized cumulative runoff load and volume, respectively. For catchments in the tropics, this corresponds to b < 0.47, 0.6 > b > 0.47, 0.76 > b > 0.6 and b > 0.76, respectively. For the temperate zone, b < 0.5, 0.67 > b > 0.5, 0.85 > b > 0.67 and b > 0.85, are appropriate. From a design perspective, the FF₂₀ concept defined as the load corresponding to 20% runoff volume, is often used. The ranges FF₂₀ < 0.27, 0.36 > FF₂₀ > 0.27, 0.45 > FF₂₀ > 0.36 and FF₂₀ > 0.45 and FF₂₀ < 0.24, 0.31 > FF₂₀ > 0.24, 0.31 > FF₂₀ > 0.39 and FF₂₀ > 0.39 are proposed for tropical and temperate catchments, respectively. Other limits for TP, TN, OP and NH₄-N are also suggested. This is the first study of its kind and an expended dataset especially for continental and dry regions is needed to further validate the findings.

Purification effect evaluation of the designed new volcanic soil adsorption material containing bioreactor for eutrophic water treatment

The purpose of this study was to investigate the purification effect of a new adsorption material containing bioreactor and the critical role of viable but non-culturable (VBNC) bacteria in a eutrophication ecosystem. Major water quality parameters of the prepared eutrophic water were determined, and the microbial community was analyzed during 2 years. The results showed that removal rates of total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), and chemical oxygen demand (COD) were 90.7-95.9%, 84.5-92.4%, 87.9-95.8%, and 68.3-82.7%, respectively, indicating the high efficiency of the bioreactor in the eutrophic water treatment. Although the bioreactor had been operated for 2 years, water from the treatment group was much clearer and odorless than from the control group, exhibiting the long service life of the bioreactor. Stopping operation in August caused significant decrease of the removal rates of major water quality parameters (p < 0.05). This operational stop event and high temperature in summer exerted a dual effect on the bioreactor, whereas the impact could be minimized when the bioreactor was running. Moreover, the total bacteria under +Rpf (active resuscitation-promoting factor) treatment were higher than under -Rpf (inactive resuscitation-promoting factor) treatment, implying that Rpf could resuscitate VBNC bacteria in the eutrophication ecosystem. Nine strains of VBNC bacteria were isolated based on the BLAST results of the 16S rRNA gene. Also, these bacteria might contribute to the eutrophic water treatment based on their functions of phosphorus collecting and denitrification. These results provided new insights for engineering technology innovations, and consequently these findings had benefits in eutrophic water treatment.

Antibiotic resistance of culturable heterotrophic bacteria isolated from shrimp (Penaeus vannamei) aquaculture ponds

Shrimp aquaculture is one of the fastest growing food-producing avenues, where antibiotics usage has become an issue of great concern due to the development of antimicrobial resistance in bacteria. A total of 2304 bacterial isolates from 192 samples (sediment, water, shrimp, and source water) from Andhra Pradesh, India were screened. Antibiotic resistance of bacterial isolates was highest for oxytetracycline (23.4%) followed by erythromycin (12.7%), co-trimoxazole (10%) ciprofloxacin (9.6%), and chloramphenicol (6%), of which 11.9% isolates were multi-drug resistant. Bacterial isolates from shrimp (26.7%), water (23.9%), and sediment (19.6%) samples exhibited more resistance (p ≤ 0.05) towards oxytetracycline. Higher antibacterial resistance was observed from samples of southern Andhra Pradesh (locations L6 and L7). Gram negative bacteria were more prevalent (64%) and showed significantly (p ≤ 0.01) higher resistance. This study indicated the wider distribution of antibiotic-resistant bacteria in shrimp aquaculture ponds with potential risk to humans and the environment.

[Comparative Analysis of Direct Drinking Water and the Relevant Sanitation Standards in China]

Along with the economic and technological development and growing demand for high-quality drinking water, direct drinking water has gained general popularity in China. However, no authoritative policy has been issued, giving a clear definition of direct drinking water and existing standards and regulations concerning direct drinking water are not definitive in nature. Existing water quality parameters are not well supported and sometimes even contradict each other. We elaborated, in this paper, the history of direct drinking water in China and systematically reviewed the existing regulations and standards related to direct drinking water. We also compared and analyzed the important microbiology, toxicology, sensory perception and general chemistry parameters in the standards. This paper is the first ever attempt at an in-depth analysis of the chaotic state of the direct drinking water industry. We have also highlighted the problems in the current standards and regulations for direct drinking water. Our study provides a basis for market regulation and the supervision and management of direct drinking water. In addition, the paper provides helpful information for laying down a definition of direct drinking water, calling for and approving of project proposals concerning the establishment of national standards for direct drinking water, and actually formulating the standards. We have made a number of suggestions: A. defining direct drinking water clearly and formulating the national standards for direct drinking water as soon as possible; B. conducting research on water quality benchmarks to provide scientific support for the formulation of the national standards for direct drinking water; C. giving more attention to the formulation of standards concerning microbiology parameters and their limits and giving consideration to the inclusion of parameters concerning viruses.

Water quality modeling in sewer networks: Review and future research directions

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

Sea water quality monitoring using remote sensing techniques: a case study in Tangier-Ksar Sghir coastline

Water quality is an extremely important factor as it affects the ecological balance of ecosystems and the development of the social and economic wellbeing of the countries bordering it. Remote sensing multiconcept helps to understand the natural environment, managing water resources and assessing water pollution on local and regional levels. Landsat 8 data were used to monitor coastal water quality in the region of Tangier-Ksar Sghir. The main purpose of the current study is to establish a mathematical relationship between the amount of light emitted from the water bodies and the measured water parameters. The results permit to create a spatial distribution maps for the water quality parameters. The present work study three water parameters: total suspended solids (TSS), dissolved oxygen (DO), and total dissolved sediments (TDS). Thirty-four sampling points were used to represent water parameters measurements along the coastline. The 75% of the in situ measurements were used to build the statistical models by using the spectral characteristics obtained from the sensors, while the remaining 25% were used for testing the accuracy of the developed equations. For the correlation analysis and the regression development, the Statistical Package of the Social Sciences (SPSS) software was used. The final results of the statistical analysis showed a high correlation between the calculated data and the observed ones with R² ˃ 0.713 and p value ˂ 0.001. The obtained values showed a high accuracy as well (RMSE ranging between 0.23 and 0.69 and SEE ranging between 0.01 and 0.47). SNAP software and Qgis were used to do the image processing and to create the spatial distribution maps for the water parameters in the coastline of Tangier-Ksar Sghir region.

Interrelation of quality parameters of surface waters in five tidewater glacier coves of King George Island, Antarctica

For further understanding of glacial meltwater's (GMW) impacts on marine environments, five coves adjacent to diverse glaciers of King George Island, Antarctica were investigated through surface measurements of water quality parameters. Measurements were conducted 49 times during January, February and March of 2019, with sampling performed in unprecedently close proximity to glacial fronts (<50 m distance from glacier termini in each cove) to create a unique dataset. Four out of five of the coves were inspected through vertical profiling to show water-column stratification. The findings showed synchronized GMW influence causing decreases of salinity, temperature, and dissolved organic matter contents, combined with increased pH, turbidity, and dissolved oxygen values. GMW presence was most correlated with dissolved organic matter content (93% of the cases >0.5 correlation noted with either turbidity or salinity) and least correlated with water temperature (from 22% to 77% of the cases with >0.5 correlation, dependent on the cove). In contrast to previous studies, the pH values of seawater infused with GMW were higher than those of the surrounding water. GMW was shown to stay in the boundary surface layer of the water column. Phytoplankton pigment quantities depending on the localization, time and distance from the glacial termini presented varied response to GMW (positive, negative or ambivalent with hotspots of simultaneous high GMW and phytoplankton quantities). The positive response to glacial water input was more often noted in measurements of phycoerythrin (from 0 to 80% of the cases depending on the cove) rather than chlorophyll A (from 0 to 25%) and maximum quantities of both biological pigments were found at a depth of approximately 5-10 m.

An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during multiple water pollutant modeling

An automatic calibration framework of water quality parameters for surface runoff during modeling with InfoWorks ICM was constructed. The framework is based on a genetic algorithm (GA) and fully considers the calibration sequence for multiple water pollutants, namely, total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP). Meanwhile, four different objective functions including the Nash-Sutcliff efficiency coefficient (NSE), coefficient of determination (R²), percentage error in the peak (PEP), and percentage bias (PBIAS) were selected as fitness evaluators for the GA. The framework was applied successfully to a specific area of Fuzhou in China, and the multi-objective results were compared with the single-objective results. The comprehensive indexes of TSS, COD, TN, and TP by multi-objective calibration were lower than that of the single-objective calibration in both scenarios. Compared with single-objective calibration, the iterations to reach the optimal value were shortened 9, 5, 13, and 15 iterations by multi-objective calibration. Therefore, the findings showed that the multi-objective function GA was more balanced and more efficient than the single-objective function GA. Then, the uncertainty of the model was evaluated by using the samples generated by automatic calibration, which provided a reliable basis for the subsequent application of the model. This framework can be applied to other programs through adjustments of the number and weight of objective functions according to the specific situation, which will make the modeling more efficient and accurate.

Monitoring urban black-odorous water by using hyperspectral data and machine learning

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.

Impact of water quality on Chronic Kidney Disease of unknown etiology (CKDu) in Thunukkai Division in Mullaitivu District, Sri Lanka

Background

Increase in the number of cases in Chronic Kidney Disease of Unknown etiology (CKDu) in Sri Lanka has become a health issue of national concern. Even though, Northern Province is not identified as a high-risk province, there is an increasing trend of CKDu after the end of civil war in the Northern Province.

Methods

The present study was conducted in Thunukkai Division in Mullaitivu District to investigate the socio demographic and clinical pattern of CKDu patients and to evaluate the quality of their water sources. The samples were selected by using stratified purposive random sampling method which represented 29% of total CKDu patients in Thunukkai Division. Pretested structured questionnaire was administered to collect the data from the CKDu patients. The association between serum creatinine excreted by CKDu patients and the water quality parameters were determined by using linear regression model.

Results

Among the patients, 80% were male with over 68% falling in the age range of 50–70. Majority (90%) were involved in agriculture related occupation. Smoking and alcohol consumption were detected as common habits among 40% of the patients. Secondarily developed, hypertension (60%) and diabetes (34%) were reported as common diseases in the area. Dug wells served as the commonest source of drinking water in the area (90% households) together with few tube wells. Physicochemistry of more than 50% of the water samples revealed higher electric conductivity, salinity, total dissolved solids, total hardness and Na levels compared to drinking water standards in Sri Lanka.

Conclusions

Serum creatinine levels of the CKDu patients were significantly and negatively correlated with phosphate while positively correlated with total dissolved solids (TDS) and arsenic content of the drinking water. Geospatial mapping of TDS and arsenic in drinking water with the occurrence of higher serum creatinine levels confirmed the same trend. Thus, the total dissolved solids and arsenic in drinking water may have positive correlation with the occurrence of CKDu in Thunukkai region in the Mullaitivu District of Sri Lanka.

Reducing the dimension of water quality parameters in source water: An assessment through multivariate analysis on the data from 441 supply systems

In this research, multivariate statistical analysis was performed on twenty water quality parameters (WQP) collected on tri-monthly basis (four times/year) from 441 drinking water sources in Newfoundland and Labrador (NL), Canada for 18 years (1999-2016). The WQP included alkalinity (Alk), color (Col), conductivity (Cond), hardness (Hard), pH, total dissolved solids (TDS), turbidity (Turb), bromide (Br), calcium (Ca), chloride (Cl), fluoride (F), potassium (K), sodium (Na), sulfate (SO₄), dissolved organic carbon (DOC), ammonia (NH₃), nitrate (NO₃), Kjeldahl nitrogen (N), total phosphorus (P) and magnesium (Mg). The assessment was conducted on surface water (SWS) and groundwater (GWS) sources separately. In SWS and GWS, number of samples analyzed for each WQP were in the ranges of 3434-6057 and 1915-1919 respectively. Averages of DOC and pH showed increasing trends (SWS: DOC = 0.0722 mg/L/year; pH = 0.0375 units/year; GWS: DOC = 0.0491 mg/L/year; pH = 0.0441 units/year) while the other WQP showed variable characteristics, which could increase treatment cost and deteriorate tap water quality. Strong correlations were observed for Ca-Hard (r = 0.97-0.98), TDS-Cond (r = 0.91-0.99) and Na-Cl (r = 0.87-0.96). In SWS, Alk had stronger correlations with Cond, Hard, pH, TDS, Ca and Mg (r = 0.62-0.94) than GWS (r = 0.56-0.63). Principal Component Analysis revealed separate clusters for DOC-Col, Na-Cl, TDS-Cond, Ca-Alk and Mg-Hard, indicating that these WQP moved together. In SWS and GWS, six principal components were significant (eigenvalue ≥ 1.0), and explained 74.8% and 72.9% of overall variances respectively. In Factor Analysis, six varifactors explained 73.4% and 70.5% of total variances in SWS and GWS respectively. For SWS and GWS, eleven and ten WQP, respectively explained these variances, indicating 45% and 50% data reduction respectively. The findings can assist in controlling water quality through monitoring reduced number of WQP, which is likely to minimize the monitoring cost.

Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO₃, Mg, NO₂ + NO₃, SiO₂ and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO₄ are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO₂ in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.

Groundwater quality evaluation for different uses in the lower Ketar Watershed, Ethiopia

Groundwater quality samples from 33 wells were collected in the lower Ketar watershed (Ethiopia) to study its suitability for domestic and irrigation purposes. Samples were evaluated for major ions and physicochemical properties. In 58% of the samples analyzed, Ca²⁺ is the dominant cation and Na⁺ dominates the remaining 42% of the samples. Among the anions found during analyzation, HCO₃^- is the solo dominant ion in all the wells sampled. The order of the concentration of the major ions was Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ for the cations and HCO₃^- > SO₄^2- > Cl > NO₃^- for the anions. AquaChem analysis shows that Ca-HCO₃ and Na-HCO₃ are the major water types in the area. The analyses indicated that the dissolution of fluorite or fluorapatite is the possible source of the high fluoride concentration in the area. And, the interactions between water and rock and cation exchanges mainly determine the water quality. The suitability of the groundwater for use in irrigation was evaluated based on the salinity (EC), SAR, %Na, RSC, PI, KR, and the USSL Salinity diagram. The groundwater from most of the wells can be used for irrigation without any significant restriction except for a few of the wells downstream. Its suitability for domestic use was evaluated by comparing with the WHO standard limits. The parameters limiting the use of this groundwater for drinking purposes are F^- (94%), HCO₃^- (45%), and Ca²⁺ (33%). All the remaining major cations and anions complied with the WHO standard limits for drinking.

Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (CTP), chemical oxygen demand (CCOD), chlorophyll-a (CChla), total suspended matter (CTSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin'anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013-2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters' distributions exhibited a significant spatio-temporal difference in Xin'anjiang Reservoir. Spatially, relatively high CTP, CCOD, CChla, and CTSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, CChla and CTSM reached high values in summer and winter, whereas SDD and CTP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of CCOD could be observed. This may be why CCOD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on CChla, CTP, SDD, and CTSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

Assessing land use and landscape factors as determinants of water quality trends in Nyong River basin, Cameroon

Changes in LULC and landscape factors impact water quality at spatial and temporal scales. In this study, we investigated the current status in water quality for sub-watersheds of the southern portion of the Nyong River basin of Cameroon from 1994 to 2014 using the WHO guideline. The trends in the water quality parameters were explored using Mann-Kendall test, and their relationship with changes in LULC and landscape factors were analysed using multiple linear regression and Pearson correlation. The current status in water quality did not exceed the WHO guideline limits for drinking water despite a 16% decrease in forest cover and 10% increase in agricultural areas during the period of record. The concentration and changes in water quality trends varied significantly among the sub-watersheds. The concentration of Ca²⁺, Mg²⁺, Na⁺, SiO_2, K⁺, DOC, SPM and WT showed significantly increasing trends in the Nsimi small sub-watershed, while only Ca²⁺, Mg²⁺, Na⁺ and NO₃^- showed significantly increasing trends in the large sub-watersheds of Mbalmayo and Olama. A combination of one to five LULC and landscape factors, including changes in urban cover, young secondary forest, slope, elevation and population explained 10 to 70% of the changes in water quality trends at watershed scale. Although the interaction of LULC and landscape factors seems to have low impact on the water quality so far, maintaining greater than 70% forest cover and appropriate fallow farming system is invaluable to protecting water quality in the Nyong River basin in the Congo basin and in other forest-rich regions.

Assessment of watershed characteristics with limited water quantity and quality data

The evaluations of water quantity and quality and then modeling of its phenomenon are required for water quality protection in Brady Creek watershed. This study is to predict the monthly flow, reservoir storage volume, and salinity; estimate the benefit of brush and aging flood-retardation dam control; and evaluate the impact of evaporation and discharge from Waste Water Treatment Plans (WWTP). The Soil and Water Assessment Tool (SWAT) model is verified with the monthly flow developed for pre-dam construction condition and shows the difference of - 9% and 10% than measured average flow for calibration and validation periods, respectively. The SWAT monthly flows are adjusted by 1963-2010 yearly conversion factors and are used as the input in the Water Rights Analysis Package (WRAP) model. The WRAP model verified with measured reservoir storage volume and total dissolved solid shows the average differences of simulated reservoir storage volume and salinity by - 13% lower and 11% higher for calibration period and 0% and 3% higher for validation period than measured average value, respectively. The impacts from brush and dams controls increase the runoff but also increase the water quality parameters. The impact of evaporation and WWTP discharge provide the information of relative impact for watershed phenomenon.

Ecological risks of heavy metals/metalloid discharged from two sewage treatment works to Mai Po Ramsar site, South China

The concentrations of six heavy metal/metalloids (HMs) cadmium (Cd), copper (Cu), mercury (Hg), lead (Pb), zinc (Zn), and arsenic (As) were determined in the influent, effluent, and dewatered sludge of two sewage treatment works (Yuen Long Sewage Treatment Work (YLSTW) and Shek Wu Hui Sewage Treatment Work (SWHSTW)) and river waters and sediment (Shan Pui River and Ng Tung River) within the Mai Po Ramsar site in Hong Kong SAR, China. In both STWs, Pb had the highest removal efficiencies (YLSTW 86.5 ± 19.0% to 97.3 ± 3.04%; SWHSTW 87.4 ± 12.8% to 100 ± 0.0800%). In the whole effluent toxicity test, both STWs were effective in lowering the toxicity of the effluent to zebrafish and cherry shrimp. The environmental risk assessments of these HMs on three local aquatic organisms, three species of fish, two species of crustaceans, and three species of algae, were calculated based on measured HMs concentrations in river water and sewage samples, and the predicted no-effect concentrations. Results showed that Zn concentrations in all collected samples posed potential risks to all studied aquatic organisms (average risk quotient = 445). The concentrations of Cu in the rivers posed potential risks (average risk quotient = 5.42) to all fish species. It was concluded that Zn and Hg derived from the STWs might pose potential risks to the living organisms inhabiting the Ramsar site, and Cu, Cd, Pb, and As in the rivers were originated from the two rivers and possibly other tributaries.

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.

Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization

The scarcity of freshwater causes the necessity for water delineation of brackish water. Reverse osmosis (RO) is one of the popular strategies characterized with lower cost and simple processing procedure compared to the other desalination techniques. The current research is conducted to investigate the efficiency the RO process based on one-week advance prediction of total dissolved solids (TDS) and permeate flow rate for Sistan and Bluchistan provinces located in Iran region. The water parameters including pH, feed pressure temperature, and conductivity are used to construct the prediction matrix. A newly hybrid data-intelligence (DI) model called multilayer perceptron hybridized with particle swarm optimization (MLP-PSO) is developed for the investigation. The potential of the proposed MLP-PSO model is validated against two predominate DI models including support vector machine (SVM) and M5Tree (M5T) models. The results evidenced the potential of the proposed MLP-PSO model over the SVM and M5T models in predicting the TDS and permeate flow rate. In addition, the proposed model attained lower uncertainty for the simulated data. Overall, the feasibility of the hybridized MLP-PSO achieved remarkable predictability for the RO process.

Capacity challenges in water quality monitoring: understanding the role of human development

Monitoring the qualitative status of freshwaters is an important goal of the international community, as stated in the Sustainable Development Goal (SDGs) indicator 6.3.2 on good ambient water quality. Monitoring data are, however, lacking in many countries, allegedly because of capacity challenges of less-developed countries. So far, however, the relationship between human development and capacity challenges for water quality monitoring have not been analysed systematically. This hinders the implementation of fine-tuned capacity development programmes for water quality monitoring. Against this background, this study takes a global perspective in analysing the link between human development and the capacity challenges countries face in their national water quality monitoring programmes. The analysis is based on the latest data on the human development index and an international online survey amongst experts from science and practice. Results provide evidence of a negative relationship between human development and the capacity challenges to meet SDG 6.3.2 monitoring requirements. This negative relationship increases along the course of the monitoring process, from defining the enabling environment, choosing parameters for the collection of field data, to the analytics and analysis of five commonly used parameters (DO, EC, pH, TP and TN). Our assessment can be used to help practitioners improve technical capacity development activities and to identify and target investment in capacity development for monitoring.

Oxidation of tetracycline and oxytetracycline for the photo-Fenton process: Their transformation products and toxicity assessment

Advanced oxidation processes have gained significant attention for treating tetracycline (TC) and oxytetracycline (OTC), however, their oxidation using the photo-Fenton process has not been sufficiently studied. Although degradations of TC and OTC were enhanced by increasing H₂O₂ and Fe²⁺ within the ranges investigated (H₂O₂ = 20-50 mg/L and Fe = 1-10 mg/L) under UV irradiation, further experiments for the photo-Fenton process were conducted with 20 mg/L of H₂O₂ and 5 mg/L of Fe²⁺ to balance efficiency and cost. The photo-Fenton process (UV/H₂O₂/Fe²⁺) was shown to be more effective to remove TC and OTC than H₂O₂, ultraviolet (UV), and UV/H₂O₂ at the same doses of oxidants. Inorganic anions and cations were shown to inhibit the degradation of TC and OTC during the photo-Fenton process, in the following order: HPO₄^2- > HCO₃^- ≫ SO₄^2- > Cl^- and Cu²⁺ ≫ Ca²⁺ > Na⁺. The TC and OTC degradation are generally improved by increasing pH, which is opposite to the k_pCBA,obs values, caused by increasing the deprotonation degree of TC and OTC. Four and nine transformation products of TC and OTC, respectively, were detected over the treatment period. Among the transformation products, m/z 443.14 (C₂₂H₂₂N₂O₈) formed during TC degradation, and m/z 433.16 (C₂₀H₂₀N₂O₉) and m/z 415.15 (C₂₀H₁₈N₂O₈) formed during OTC degradation, were reported for the first time. Vibrio fischeri toxicity assessment indicated that the inhibition ratio was decreased with a decreasing TC concentration, while, OTC transformation lead to higher toxicity. The product (m/z 477.15b) was determined to be the compound causing toxicity during degradation of OTC by using the quantitative structure activity relationship (QSAR). This toxic transformation product caused higher inhibition ratios than its parental compound (OTC), but its further oxidization resulted in decreasing the inhibition ratios.

Towards the comprehensive water quality control in Lake Taihu: Correlating chlorphyll a and water quality parameters with generalized additive model

In this study, the generalized additive model (GAM) was used to analyze seasonal monitoring data from Lake Taihu, collected from 2010 to 2014, with the aim to explore the correlation between chlorophyll a (Chla) and other water quality parameters. The selected optimal multivariable GAM could effectively explain the concentration variation of Chla occurring during each season, and the interpretation degree followed the order: summer > autumn > spring > winter. The fitting results indicated that the concentration variation of Chla could reflect that of biochemical oxygen demand and chemical oxygen demand in all seasons. In addition, the total phosphorus showed strong ability to explain the concentration change of Chla in spring and summer, as the growth of algae would be affected when the concentration of phosphorus shifted high or low. Nitrogen showed strong ability to explain the variations in Chla concentration in autumn. The conclusions of the optimal multivariable GAM could provide decision basis for the eutrophication control. In other words, the prevention of eutrophication outbreaks could be carried out via the targeted control of key water pollutants. According to these results, the concentration of Chla was higher in northern and western lake during summer and autumn, the management should focus on nutrient input of adjacent rivers.

Dataset for a wireless sensor network based drinking-water quality monitoring and notification system

This paper presents the collected experimental data for water quality monitoring which was conducted in ten experiments by using five different common sources of water contaminants namely soil, salt, washing powder, chlorine and vinegar and their combination. The data were collected indoors at room temperature during the day for several days using sensors that measure pH, turbidity, flow rate, and conductivity in water. The water consumption risk (CR) was calculated as deviation based on the water quality parameters standards proposed by the World Health Organisation (WHO) and the South African Department of Water Affairs (DWA), with respect to the sensor measurement readings obtained. While the error measurements were calculated based on the expected parameter measurement per conducted experiment and repeated for 26 measurements. Pure tap water was the benchmark of water safe for human consumption. The first five experiments were performed by introducing each contaminant into the water and thereafter, two contaminants in the sixth experiment and their additions until all different contaminants were experimented at once in the last experiment.

Assessment of the effect of urban pollution on surface water-groundwater system of Adi Ganga, a historical outlet of river Ganga

Adi Ganga, an open canal in Kolkata, constitutes a dump to a large part of the urban wastes produced. The constant and uncontrolled disposal of the wastes contributes to the degradation of water quality of Adi Ganga, which in turn might have adverse effect on groundwater in the adjoining areas. Surface water (SW) and groundwater (GW) collected from six locations along Adi Ganga were analyzed to understand the extent of degradation caused due to SW pollution. Among the important water quality parameters and heavy metal analysis - dissolved oxygen, turbidity, total hardness, alkalinity, biological oxygen demand, oil and grease and zinc of both SW and GW were found to be much greater than their respective permissible limits (WHO, 2004). Both the SW and GW samples depicted phosphate and bicarbonate beyond their recommended values whereas; cations were well within the limit. Hydrochemical analysis through Piper, Stiff, Stabler, Schoeller-Berkaloff and Wilcox diagrams indicate that the water is dominated by calcic and magnesian facies with chlorinated and bicarbonate water types with higher alkalinity. Average water quality index of 33.7 and 52.4 for SW and GW indicate that these are severely and marginally threatened, respectively. Cluster analysis and Pearson's correlation studies show similar trend for both SW and GW indicating role of SW pollution in quality degradation of GW. The SW quality parameters found beyond permissible limits are mainly contributed anthropogenically therefore, immediate stoppage of further pollution of SW is imperative to stop degradation of GW quality, a regular and reliable source of drinking water.

Immobilization possibility of tannery wastewater contaminants in the tiles fixing mortars for eco-friendly land disposal

The Hazaribagh tannery area of Bangladesh is currently facing an enormous problem regarding the harmful impacts of wastewater produced from leather industries on the surrounding environment due to the presence of contaminants at a toxic level. As such, the current study aims to analyze the entrapment of tannery wastewater's pollutants inside the mortar specimens for sustainability. Two types of binding agents such as Portland Composite Cement (PCC) and Ready Mixed Dry Mortar (RMDM) were employed to prepare separate mortar pastes in which the collected tannery wastewater was used as mixing liquid. Also, five types of samples including brick walls made with only the PCC, where tiles walls and blocks constructed with both types of binding agents were built. Analytical results show that the surrogate contaminated water mixed mortar blocks possessed about 6-14% lower compressive strength than that of the blocks prepared with drinking water. Moreover, the examined heavy metals were observed below the limit of detection in the curing liquid of studied tiles walls during the whole test protocol of 360 days period. The explicit outcomes of this study might be a promising solution to minimize the effects of tannery wastewater contaminants on the environment by utilizing this wastewater as a mixing component in the tiles fixing mortar of walls and floors.

Spatial and temporal risk quotient based river assessment for water resources management

Malaysia depends heavily on rivers as a source for water supply, irrigation, and sustaining the livelihood of local communities. The evolution of land use in urban areas due to rapid development and the continuous problem of illegal discharge have had a serious adverse impact on the health of the country's waterways. Klang River requires extensive rehabilitation and remediation before its water could be utilised for a variety of purposes. A reliable and rigorous remediation work plan is needed to identify the sources and locations of streams that are constantly polluted. This study attempts to investigate the feasibility of utilising a temporal and spatial risk quotient (RQ) based analysis to make an accurate assessment of the current condition of the tributaries in the Klang River catchment area. The study relies on existing data sets on Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), and Ammonia (NH₃) to evaluate the water quality at thirty strategic locations. Analysis of ammonia pollution is not only based on the limit established for river health but was expanded to include the feasibility of using the water for water intake, recreational activities, and sustaining fish population. The temporal health of Klang River was evaluated using the Risk Matrix Approach (RMA) based on the frequency of RQ > 1 and associated colour-coded hazard impacts. By using the developed RMA, the hazard level for each parameter at each location was assessed and individually mapped using Geographic Information System (GIS). The developed risk hazard mapping has high potential as one of the essential tools in making decisions for a cost-effective river restoration and rehabilitation.

Effect of physicochemical parameters on zooplankton at a freshwater body of Euphrates Basin (Elazıg-Turkey)

In this study zooplankton was determined between June 2015-May 2016 in Keban Reservoir. Also some chemical and physical parameters as water temperature, pH, dissolved oxygen, electrical conductivity and Chlorophyll a were measured in situ, monthly. Total of 40 zooplankton species; 27 Rotifera, 11 Cladocera, 2 Copepoda species were identified in this study. The data of this study were evaluated number of individuals, species richness and species diversity. Maximum, minimum, SD and mean values of water quality parameters were calculated. As a result of Shannon Wiener index analysis of current study, species diversity was found highest in January (H'=2.03) and the least index value was found in May (H'=0.46). Margalef index value recorded in its highest value in January (D=1.07) in the 2nd station and the lowest value in February (D=0.11) in the 1st station.

Increased water temperature altered hemato-biochemical parameters and structure of peripheral erythrocytes in striped catfish Pangasianodon hypophthalmus

The increase of water temperature due to global warming is a great concern of aquaculturists and fishery biologists. In the present study, we examined the effects of high temperature on hematological parameters and blood glucose levels in striped catfish, Pangasianodon hypophthalmus exposed to three temperature conditions (28, 32, and 36 °C) for 7 days. Fish were sacrificed at days 1, 3, and 7. Erythroblasts (Ebs), erythrocytic cellular abnormalities (ECA), and erythrocytic nuclear abnormalities (ENA) were assayed using peripheral erythrocytes of the sampled fishes. Hemoglobin (Hb) and red blood cell (RBC) significantly (P < 0.05) decreased at 36 °C after 3 and 7 days of exposure, whereas white blood cell (WBC) showed opposite scenario. Blood glucose levels significantly (P < 0.05) increased at 36 °C on day 3. Frequencies of Ebs, ECA, and ENA were found to be elevated at increased temperature. Differential leucocytes count showed significant increases in neutrophil and decreases in lymphocytes in the highest temperature (36 °C). Dissolved oxygen decreased and free CO₂ increased significantly (P < 0.05) with increasing temperature, while the pH and total alkalinity of the water were almost unchanged throughout the study period. Therefore, the present study demonstrated that striped catfish feel better adaptation at 28 and 32 °C, while high temperature 36 °C is likely stressful to this fish species.

Groundwater quality in open-defecation-free villages (NIRMAL grams) of Kurukshetra district, Haryana, India

Groundwater is an indispensable natural resource for drinking water, generally in rural areas due to unavailability of treated water. The study was designed to investigate the groundwater quality in nine open-defecation-free (ODF) villages (Nirmal Grams) of Kurukshetra district, Haryana, India. For the research, 81 groundwater samples were collected from different borewells of Nirmal Grams during pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) seasons and were analyzed for 13 water quality parameters, i.e., pH, EC, TDS, Cl^-, TH, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, F^-, SO₄^2-, and PO₄^2-, using standard methods. The Water Quality Index (WQI) was calculated from the analyzed parameters and the results were compared with the World Health Organization (WHO 2011) and the Bureau of Indian Standards (BIS 2012) in order to have an overview of the groundwater quality. The concentration of major anions and cations was found to be in the order of HCO₃^- > Cl^- > SO₄^2- > F^- > PO₄^2- and Ca²⁺ > Na⁺ > K⁺ > Mg²⁺, respectively. Statistical Package of Social Sciences (SPSS) version 23 was used for statistical analysis, Aquachem 2014 software (trial version) was used for Piper diagram, Box-Whisker plot, Schoeller, and Gibbs diagram, whereas WQI was calculated manually. The values of WQI in the study area were ranged between 32.08 and 88.38 in PRM, 46.62 and 102.33 in MON, and 34.99 and 79.81 in POM seasons, respectively. Groundwater quality index reveals that the majority of the samples fall under 'good' to 'excellent' water category. It indicates that the groundwater is suitable for drinking and other domestic uses.