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.

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.

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.

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 multiple parameters in complex water scenarios using a low-cost open-source data acquisition platform.

Water monitoring faces challenges that are driven by the infrastructure, protection, financial resources, science and innovation policies, among others. A modular, low-cost, fully open-source and small-sized Unmanned Surface Vessel (USV) called EMAC-USV (EMAC: Estación de Monitoreo Ambiental Costero), is proposed for monitoring bathymetry and water quality parameters (i.e. temperature, suspended solids concentration and hydrocarbon concentration) in complex water scenarios. A detailed description of each part of the platform as well as all electronic connections and functioning is presented.The field works were carried out in two small waste stabilization ponds and in a portion of the main tidal channel of the Bahía Blanca port. The EMAC-USV is the result of a cautious design, regarding the balancing performance, communications, payload capacity, among others.

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.

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.

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.

Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images.

The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R2 > 0.90.

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), NO3-(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.

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.

An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai.

The optical insensitivity of non-optically active water quality parameters (NAWQPs) presents a significant challenge for remote sensing-based quantitative monitoring, which is an important tool for water quality assessment and management. Based on the analysis of the samples from Shanghai, China, it was found that the spectral morphological characteristics of the water body were obviously different under the combined effect of multiple NAWQPs. In view of this, in this paper, a machine learning method was proposed for the retrieval of urban NAWQPs by using multi-spectral scale morphological combined feature (MSMCF). The proposed method integrates both local and global spectral morphological features, and employs a multi-scale approach to enhance its applicability and stability, providing a more accurate and robust solution. To explore the applicability of the MSMCF method in retrieving urban NAWQPs, different methods were tested in terms of the retrieval accuracy and stability on the measured data and three different hyperspectral data. As can be seen from the results, the proposed method has good retrieval performance, which can be applied to hyperspectral data with different spectral resolutions with certain ability to suppress noise. Further analysis indicates that the sensitivity of each NAWQP to spectral morphological features varies. The research methods and findings in this paper can promote the development of hyperspectral and remote sensing technology in the prevention and treatment of urban water quality deterioration, and provide reference for related research.

[Characteristics of Phytoplankton Communities and Key Impact Factors in Three Types of Lakes in Wuhan].

To reveal the characteristics and key impact factors of phytoplankton communities in different types of lakes, sampling surveys for phytoplankton and water quality parameters were conducted at 174 sampling sites in a total of 24 lakes covering urban, countryside, and ecological conservation areas of Wuhan in spring, summer, autumn, and winter 2018. The results showed that a total of 365 species of phytoplankton from nine phyla and 159 genera were identified in the three types of lakes. The main species were green algae, cyanobacteria, and diatoms, accounting for 55.34%, 15.89%, and 15.07% of the total number of species, respectively. The phytoplankton cell density varied from 3.60×106-421.99×106 cell·L-1, chlorophyll-a content varied from 15.60-240.50 µg·L-1, biomass varied from 27.71-379.79 mg·L-1, and the Shannon-Wiener diversity index varied from 0.29-2.86. In the three lake types, cell density, Chla, and biomass were lower in EL and UL, whereas the opposite was true for the Shannon-Wiener diversity index. NMDS and ANOSIM analysis showed differences in phytoplankton community structure (Stress=0.13, R=0.048, P=0.2298). In addition, the phytoplankton community structure of the three lake types had significant seasonal characteristics, with chlorophyll-a content and biomass being significantly higher in summer than in winter (P<0.05). Spearman correlation analysis showed that phytoplankton biomass decreased with increasing N:P in UL and CL, whereas the opposite was true for EL. Redundancy analysis (RDA) showed that WT, pH, NO3-, EC, and N:P were the key factors that significantly affected the variability in phytoplankton community structure in the three types of lakes in Wuhan (P<0.05).

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.

Embryonic and Larval Development of Stinging Catfish, Heteropneustes fossilis, in Relation to Climatic and Water Quality Parameters.

In terms of hatchery-based seed production, one of the most important aquaculture species in Bangladesh is the stinging catfish (Heteropneustes fossilis). Scientific and evidence-based embryonic and larval development research on this fish species in the context of climate change is limited. This experimental study was conducted via induced breeding of stinging catfish using a conventional hatchery system, rearing the larvae in hapas placed in ponds. A series of microscopic observations using a trinocular digital microscope and an analysis of the relationship between larval growth and climate-driven water quality parameters such as temperature, pH, dissolved oxygen, total dissolved solids, alkalinity, and ammonia were performed. During embryonic development, the first cleavage was observed between 30 and 35 min of post-fertilization. Embryonic development (ranging from the 2-cell to the pre-hatching stage) took 21:00 h. Hatching occurred at 22:30 to 23:00 h after fertilization, with an average larvae length of 2.78 ± 0.04 mm. In the post-hatching stage, four pairs of tiny barbels appeared at 36:00 h, and the larvae started feeding exogenously after 72:00 h. These larvae fully absorbed their yolk sacs on the 6th day and attained an average length of 6.44 ± 0.06 mm. Aerial respiration of the larvae was investigated through naked-eye observation on the 10th day of hatching. The average length of the larvae was 32.00 ± 2.0 mm at the end of the 30-day post-hatching observation period. Bivariate correlation analysis showed significant correlations between key climatic variables and water quality parameters under hapa-based larval-rearing conditions. According to canonical correlation analysis, the first canonical function revealed the highest significant correlation between the two sets of variables (r1 = 0.791). The response variable weight of larvae (6.607) was linked to two explanatory variables: pH (0.321) and dissolved oxygen (0.265). For the second canonical correlation function, a positive correlation (0.431) was observed between the two sets of variables. Larval weight (-18.304) was observed to be linked to climatic variables, including air temperature (-0.316) and surface pressure (0.338). Results of this study reveal the subtle correlation between larval growth and water quality driven by climatic variables.

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.

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.

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.

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, BOD5, 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.