Surface Water Quality Monitoring Research Articles

Electrochemical Detection of Cd2+, Pb2+, Cu2+ and Hg2+ with Sensors Based on Carbonaceous Nanomaterials and Fe3O4 Nanoparticles.

Two electrochemical sensors were developed in this study, with their preparations using two nanomaterials with remarkable properties, namely, carbon nanofibers (CNF) modified with Fe3O4 nanoparticles and multilayer carbon nanotubes (MWCNT) modified with Fe3O4 nanoparticles. The modified screen-printed electrodes (SPE) were thus named SPE/Fe3O4-CNF and SPE/Fe3O4-MWCNT and were used for the simultaneous detection of heavy metals (Cd2+, Pb2+, Cu2+ and Hg2+). The sensors have been spectrometrically and electrochemically characterized. The limits of detection of the SPE/Fe3O4-CNF sensor were 0.0615 μM, 0.0154 μM, 0.0320 μM and 0.0148 μM for Cd2+, Pb2+, Cu2+ and Hg2+, respectively, and 0.2719 μM, 0.3187 μM, 1.0436 μM and 0.9076 μM in the case of the SPE/ Fe3O4-MWCNT sensor (following optimization of the working parameters). Due to the modifying material, the results showed superior performance for the SPE/Fe3O4-CNF sensor, with extended linearity ranges and detection limits in the nanomolar range, compared to those of the SPE/Fe3O4-MWCNT sensor. For the quantification of heavy metal ions Cd2+, Pb2+, Cu2+ and Hg2+ with the SPE/Fe3O4-CNF sensor from real samples, the standard addition method was used because the values obtained for the recovery tests were good. The analysis of surface water samples from the Danube River has shown that the obtained values are significantly lower than the maximum limits allowed according to the quality standards specified by the United States Environmental Protection Agency (USEPA) and those of the World Health Organization (WHO). This research provides a complementary method based on electrochemical sensors for in situ monitoring of surface water quality, representing a useful tool in environmental studies.

Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Simultaneous determination of six nitroaromatic compounds and three anions in environmental matrices using a liquid chromatography-ion chromatography coupled system

Nitroaromatic compounds are used extensively in various fields such as dyes, pesticides, spices, pharmaceuticals, and explosives. However, the residual raw materials of these compounds accumulate in the environment and pose serious risks to human health. Chronic exposure to low concentrations of nitroaromatic compounds can cause anemia, cancer, and organ damage. Currently, Fenton oxidation and natural bioremediation are the processes most often used to eliminate nitroaromatic compounds from environmental water and soil. According to previous research, the presence of inorganic anions such as chloride, nitrite, and nitrate ions in the environmental matrix exerts an inhibitory effect on the biodegradation of nitroaromatic compounds. Furthermore, high nitrate levels in drinking water can lead to the production of nitrosamine carcinogens, which affect ecological safety and human health, in water bodies. Thus, the simultaneous determination of nitroaromatic compounds and chloride, nitrite, and nitrate ions in environmental soil and water matrices is critical for selecting appropriate nitroaromatic compound degradation methods and monitoring surface water quality. Traditional detection methods require two sample pretreatment steps and two instrumental analytical techniques to determine nitroaromatic compounds and inorganic anions in environmental matrices; moreover, these methods are time consuming, labor intensive, and error prone. Therefore, in this study, a method that combines high performance liquid chromatography (HPLC) and ion chromatography (IC) was developed to simultaneously detect nitroaromatic compounds and anions in environmental matrices. In this method, sample enrichment was achieved through bulk injection and enrichment column collection, which greatly simplified the pretreatment process. The HPLC instrument was connected to the IC instrument using two six-way valves and an enrichment column. The system operation can be divided into four stages: (A) sample loading to the quantitative ring, (B) separation of nitroaromatic compounds and anions, (C) enrichment of anions in an AG20 column, and (D) simultaneous determination of nitroaromatic compounds and anions by HPLC and IC, respectively. The time of the anions flowing out of the C18 column was determined by directly connecting the C18 column to a conductivity detector. Based on the retention times of the anions, the switching time of the six-way valve was optimized to ensure that the anions completely entered the IC column, thereby ensuring the accuracy of the method. During the chromatographic analysis stage, nitroaromatic compounds were separated and analyzed by HPLC system with a mobile phase composed of potassium phosphate buffer (pH 7.0) and acetonitrile (60∶40, v/v) at a flow rate of 1.0 mL/min; in the IC system, the anions were separated and analyzed using a 20 mmol/L sodium hydroxide aqueous solution as the mobile phase under a suppression current of 50 mA. Both anions and nitroaromatic compounds exhibited strong linear correlations within certain concentration ranges, with correlation coefficients greater than 0.993. The recoveries of the nitroaromatic compounds and anions ranged from 88.20% to 105.38% at three spiked levels, with relative standard deviations ranging from 2.0% to 11.5%. The contents of six nitroaromatic compounds and three anions in five surface water and five soil samples were determined using the developed method. Although no nitroaromatic compounds were detected in these samples, the three anions were detected at contents ranging from 0.41 to 55.3 mg/L in surface water samples, and 0.56 to 30.2 mg/kg in soil samples. Methodological validation and actual sample detection demonstrated that the proposed method has a high degree of automation, simple operation, good repeatability, high accuracy, wide applicability, and high sensitivity. Thus, this method is suitable for the rapid determination of chloride, nitrite, nitrate ions and nitroaromatic compounds in soil and water and can be extended to the simultaneous determination of inorganic ions and organic matters in other samples.

Recommending Surface Water Quality Monitoring for the Nature Reserve Using Multivariate Statistical Methods

Lung Ngoc Hoang Nature Reserve has a crucial role in conserving and protecting the natural ecosystem and biodiversity in the Mekong Delta, Vietnam, and the local communities also receive great benefits from aquatic resources in this nature reserve. This study was conducted to assess water quality in the Lung Ngoc Hoang Nature Reserve and to provide important information for the monitoring program using multivariate statistical methods. Water samples were collected bimonthly from fifteen locations belonging to five functional zones of the nature reserve (i.e., buffer zone, main canal, administrative and service zone, ecological restoration zone, and strictly protected zone). The physiochemical properties of water samples were measured, including temperature, pH, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), sulfate (SO42-), iron (Fe2+), and aluminum (Al3+). The results showed that the levels of TSS, COD, and Fe2+ exceeded the Vietnamese standard on surface water quality, and the DO level was also far below the standard. Besides, the concentrations of TN, TP, and Al3+ in the nature reserve area showed the risk of eutrophication and negative effects on aquatic organisms. Problems of water quality were observed in the main canal and the administrative and service zones more than in the other zones. Cluster analysis (CA) suggested a reduction in the number of monitoring frequencies and locations to four months (i.e., January, April, July, and September) and twelve locations, respectively. This reduction allows for a decrease in the effort and cost of the monitoring program with adequate information to evaluate water quality. Moreover, principal component analysis (PCA) identified five principal components, which could explain 80.98% of the total variance of the initial dataset. Potential pollution sources were also recognized based on PCA, including the nature properties of sulfate-acid soils, livestock, fertilizer, and domestic activities. The findings of this study can enhance our understanding of water quality in the nature reserve area and the effectiveness of future monitoring programs. Doi: 10.28991/CEJ-SP2023-09-015 Full Text: PDF

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna. Six ML models were adopted to predict contamination alarms from Daphnia behavioral parameters. The light gradient boosting machine model demonstrated the most significant improvement in predicting alarms from Daphnia behaviors. Compared with the traditional BEWS alarm index, the ML model enhanced the precision and recall by 29.50% and 43.41%, respectively. The speed distribution index and swimming speed were significant parameters for predicting water quality warnings. The nonlinear relationships between the monitored Daphnia behaviors and water physicochemical water quality parameters (i.e., flow rate, Chlorophyll-a concentration, water temperature, and conductivity) were identified by ML models for simulating Daphnia behavior based on the water contaminants. These findings suggest that ML models have the potential to establish a robust framework for advancing the predictive capabilities of BEWS, providing a promising avenue for real-time and accurate assessment of water quality. Thereby, it can contribute to more proactive and effective water quality management strategies.

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

Dissolved polycyclic aromatic compounds in Canada’s Athabasca River in relation to Oil Sands from 2013 through 2019

The Canada-Alberta Oil Sands Monitoring (OSM) Program began long-term surface water quality monitoring on the lower Athabasca River in 2012. Sampling of low level, bio-accumulative polycyclic aromatic compounds (PACs) targeted a suite of parent and alkylated compounds in the Athabasca River (AR) mainstem using semi-permeable membrane devices (SPMDs). Samples were collected along a gradient from upstream reference near Athabasca, Alberta, through exposure to the Athabasca oil sands deposit (AOSD), various tributary inflows, and mining activities within the OSMA, to downstream recovery near Wood Buffalo National Park (WBNP) and reference on the Slave River. The program adapted over the years, shifting in response to program review and environmental events. The AOSD chemical fingerprint was present in samples collected within the AOSD, through the oil sands mineable area (OSMA), downstream to recovery from 2013 to 2019. PACs were dominated by alkylated phenanthrenes/anthracenes (PAs) and dibenzothiophenes (Ds), with elevated levels of alkylated fluorenes (Fs), naphthalenes (Ns), fluoranthenes/pyrenes (FlPys) and benzo[a]anthracenes/chrysenes (BaACs), increasing in concentration from C1 < C2 < C3 < C4. Concentrations of these petrogenic PACs were at their highest within the OSMA and downstream of tributaries. The AOSD fingerprint was absent from sites located outside of the influence of the AOSD and downstream of the Peace-Athabasca Delta on the Slave River. PAC concentrations in the AR increased with mainstem discharge and loadings from tributaries, were moderated by the PAD, and diluted by the Peace River. This work bolsters the baseline PAC information previously reported for the Athabasca River and waters downstream, reporting 7 years of data, from all sites within the mainstem monitoring program, and exploring potential regional and hydrological drivers of these between sites and over time.

Spatio-temporal analysis of colored dissolved organic matter over Ebinur Lake in Xinjiang, China

A lack of water quality monitoring stations in arid regions makes it difficult to estimate the exact level of water pollution. Satellite technology is crucial for monitoring the water quality of salt lakes, but due to the diversity of terrestrial water bodies, it is challenging to establish relationships between lake water color parameters and remote sensing reflectance. This study aims to evaluate the reliability of the colored Quasi-Analytical Algorithm Dissolved Organic Matter (QAA-CDOM) model over Ebinur Lake. The absorption coefficient measured on May 19, 2021 was used to analyze the optical properties of Ebinur Lake. The findings show that the QAA-CDOM model performed well in estimating the absorption coefficient of CDOM at 443 nm, with the r2 and RMSE values of 0.69 and 0.12 m−1, respectively. The Landsat 8 OLI data and QAA-CDOM model were then used to derive the spatial-temporal characteristics of the CDOM concentration over Ebinur Lake from 2014 to 2021. The concentration of CDOM changes significantly across time and space, ranging from 0 to 2.5 m−1, with the lowest concentration in May and the highest in August. The central region of the lake has a lower concentration of CDOM than the other regions due to the reflectance of the shallow parts of the lake. This study clarified the inherent optical characteristics of saline lakes and proved the applicability of using the QAA-CDOM model to monitor surface water quality in arid regions of China. This research may be useful for future analyses of the carbon cycle calculations in salt lakes in arid regions.

Assessment of Surface Water and Estuarie Quality and Soil Condition Monitoring

Assessing the quality of surface waters and estuaries and monitoring soil health are critical to monitoring and maintaining the health of aquatic ecosystems and ensuring sustainable land management practices. This involves the assessment of various parameters, such as chemical composition, physical characteristics and biological indicators, to determine the overall condition and potential impact on human health and the environment

Design of High-precision Water Quality Ammonia Nitrogen Online Detection Technology System Based on Micro-control Technology

Based on Lambert’s theorem, this paper adopts the integration of micro-control technology and sequential injection technology and designs a water-quality ammonia nitrogen online detection system to improve the accuracy of ammonia nitrogen detection and to solve the demand for the determination of ammonia nitrogen index in the surface I to V water. The linearity of the assay data can be improved by controlling the variables in a control experiment, reducing the volume of the digestion cell, and reducing the inner diameter of the Teflon tube. Compared with the traditional ammonia nitrogen detection system, it has higher detection accuracy, and the number of reagents used and waste liquid generated have been greatly reduced, effectively improving the accuracy of ammonia nitrogen index detection. In this experiment, the determination of the ammonia nitrogen index was accomplished by salicylic acid spectrophotometry, and the range of determination covered the standard limits of ammonia nitrogen in a surface water environment from Class I to V water. The experimental results showed that: The regression model for the detection of ammonia index by the salicylic acid method was developed using the least squares fitting algorithm, and the linear range was from 0 to 2 mg/mL. The correlation coefficient of the experimental data for the 10 mm inner diameter tube was 0.9981 and that for the 15 mm inner diameter tube was 0.923. And the concentration and absorbance were both positively correlated, and the trend of the experimental data was consistent with the theoretical analysis. The system is designed to reduce operation and maintenance costs, improve detection accuracy, and efficiently analyze the ammonia nitrogen index in surface I-V water, providing technical support for environmental protection and surface water quality monitoring.

Monitoring of Surface Water Quality in King Talal Dam Using GIS: A Case Study

King Talal Dam is the largest surface water reservoir in Jordan. Zarqa River and some springs are the major flow contributors to this dam. The other inflow comes in the form of effluents from Al-Samra, Baqa'a, and Jerash wastewater treatment plants, and industries located in Amman and Zarqa. These effluents dramatically deteriorate the water quality of the dam. This study applied Geographical Information System to monitor the temporal and spatial changes in the water quality of King Talal Dam. The parameters included toxicity and infiltration risks, clogging in irrigation systems (drip), soil degradation through soluble salt accumulation, and excessive nitrogen-associated issues. The study expanded from winter 2014 to summer 2016. The final integrated maps revealed two suitable and unsuitable classes for irrigation purposes. The study also provided essential information about the main water quality parameters and pollution sources such as EC, and E. coli. They were recorded throughout the study period in unsuitable zones whereas TSS and bicarbonate (HCO3-) were found in the spring of 2015 and spring of 2016, respectively. Autumn 2014 was found to be the worst season among all the study years. During that season, the unsuitable area expanded up to 93% of the total reservoir area. Then, the unsuitable zone reached up to 68.4% of the total area during the winter of 2016. The mapping results indicated that site 100 remained polluted during all the study seasons. This was expected as site 100 is in Zarqa River where Al-Samra and Jerash wastewater treatment plants are discharged.

Application of artificial intelligence for forecasting surface quality index of irrigation systems in the Red River Delta, Vietnam

Water sources for irrigation systems in the Red River Delta are crucial to the socioeconomic growth of the region's communities. Human activities (discharge) have polluted the water source in recent years, and the water source from upstream is limited. Currently, the surface water quality index (WQI), which is calculated from numerous surface water quality parameters (physical, chemical, microbiological, heavy metals, etc.) is frequently used to evaluate the surface water quality of irrigation systems. However, the calculation of the WQI from water quality monitoring parameters remains constrained due to the need for a large number of monitoring parameters and the relative complexity of the calculation. To better serve the assessment of surface water quality in the study area, it is crucial and essential to conduct research to identify an efficient and accurate method of calculating the WQI. This study used machine learning and deep learning algorithms to calculate the WQI with minimal input data (water quality parameters) to reduce the cost of monitoring surface water quality. The study used the Bayes method (BMA) to select important parameters (BOD5, NH4+, PO43−, turbidity, TSS, coliform, and DO). The results indicate that the machine learning model is more effective than the deep learning model, with the gradient boosting model having the most accurate prediction results because it has the highest coefficient of determination R2 (0.96). This is a solid scientific basis and an important result for the application of machine learning and deep learning algorithms to calculate WQI for the research area. The study also demonstrated the potential of artificial intelligence algorithms to improve water quality forecasting compared to traditional methods with minimal cost and time.

Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

Surface water quality in Amazonian Floodplain Lakes, data set of the Lago Grande de Curuai Floodplain Lake, Pará‐Brazil

AbstractBetween 2013 and 2017, we carried out nine field missions in the Lago Grande de Curuai floodplain, located in Pará state – North of Brazil – to collect samples for monitoring surface water quality. This site separated from the river by a narrow bank is composed of a network of channels and shallow lakes, a morphology shared by the floodplains of the lower Amazon. A multiparameter probe was used in situ to measure electrical conductivity, temperature, pH, dissolved oxygen, chlorophyll, turbidity, depth and a Secchi disk to estimate transparency. Water grab samples were analysed for suspended material, alkalinity, humic acid, phosphorus, nitrogen, carbon and chlorophyll. Sampling stations were distributed over the seven larger lakes in the floodplain and sampled at different periods of the hydrological cycle. The number of samples varied with the floodplain water level, with a minimum of 25 samples for each field visit. This data set is a collection of water quality data to assist in the limnological or biogeochemical studies of surface waters in Amazonian floodplain lakes, and the product of successive French‐Brazilian projects: (1) the Clim‐FABIAM ‘Climate changes and Floodplain lake biodiversity in the Amazon Basin: how to cope and help the ecological and economic sustainability’ funded by the French Foundation for biodiversity research (FRB), (2) the project Bloom‐ALERT –‘Environmental sensitivity and population health vulnerability to cyanobacteria in the Amazon: towards shared indicators’ funded by the French‐Brazilian research program GUYAMAZON 2014, and the project (3) ‘Ecossistemas das várzeas e biodiversidade: Impactos das mudanças ambientais e climáticas considerando cenários de desenvolvimento sustentáveis’ (Project number 490634/2013‐3) funded by the Brazilian National scientific Research Council CNPq.

Multi-Tier Validation of a Macroscale Nitrogen Model for Groundwater Management in Watersheds Using Data from Different Monitoring Networks

For the Hessian river basins, an area-differentiated modeling of the nitrogen input to the groundwater and surface waters was carried out for six diffuse input pathways and six point source input pathways on the basis of the geodata available at the state level. In this context, extensive plausibility checks of the model results were carried out using the data from several official monitoring networks at the state level. These include the comparison of modeled runoff components and input pathways for nitrogen using the data from the network of discharge monitoring stations. For the validation of the modeled nitrate concentrations in the leachate, the data from groundwater monitoring wells for controlling the chemical status of groundwater were used. The validation of the modeled nitrate inputs to the groundwater and denitrification in the groundwater was carried out using the data from a special monitoring network of groundwater monitoring wells that include N2/Ar measurements. The data from the Surface Water Quality Monitoring Network were used to verify the plausibility of the modeled total N inputs to the surface waters from diffuse sources and from point sources. All of the model results evaluated by the plausibility checks prove that the nitrate pollution situation in Hesse is adequately represented by the model. This is a prerequisite for accepting the model results at the state level as a basis for developing and implementing regionally appropriate mitigation measures. The Hessian State Agency for Nature Conservation, Environment and Geology uses the model results in the broader context of the work on implementing the EU Water Framework Directive and the EU Nitrate Directive.

Data fusion strategy based on ultraviolet–visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water

Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet – visible (UV–Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV–Vis and NIR spectral data (UV–Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV–Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample’s UV–Vis spectrum and NIR diffuse transmission spectrum. The UV–Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV–Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.

Imprints of wastewater discharge on trace element dynamics in the Grand River, Ontario.

Discharge of treated wastewater effluent can be an important source of contaminants to downstream environments, but only a handful of specific effluent parameters are regulated and monitored in Canada. Consequently, the importance of effluent discharge for the surface water budgets of trace elements remains poorly understood. Here, we report concentrations of > 50 major and trace elements in > 30 riverine and effluent samples collected in the Grand River catchment, Ontario, in an attempt to assess imprints of effluent discharge on riverine trace element loads. We find that effluent-derived loads of major and trace elements generally outweigh those of tributaries when contrasted to their hydraulic contribution at the point of confluence. In particular, effluent-derived loads of conservative elements (> 30-fold the receiving riverine load), but also those of heavy metals and rare earth elements (> tenfold and twofold their receiving riverine loads, respectively) exerted important controls on trace element dynamics in the Grand River. Yet, multiple elemental tracers suggest that detectable imprints of these trace element inputs remain spatially restricted and limited to the catchment's upper reaches, urban areas, and confluences and effluent inputs with low mixing ratios. This study presents important baseline data for trace elements in this complex river system and highlights the need for expanded surface water quality monitoring to disentangle anthropogenic from natural factors affecting trace element budgets.

Remote sensing monitoring of mining tailings in the fluvial-estuarine-coastal ocean continuum of the Lower Doce River Valley (Brazil)

Water clarity is a key parameter of aquatic ecosystems impacted by mining tailings. Tracking down tailings dispersion along the river basin requires a regional monitoring approach. The longitudinal fluvial connectivity, river-estuary-coastal ocean, and the lateral connectivity, river-floodplain-alluvial lakes are interconnected by hydrological flows, particularly during high fluvial discharge. The present study aims to track the dispersal of iron ore tailing spill, from the collapse of the Fundão dam (Mariana, MG, Brazil), on November 5, 2015, in the Lower Doce River Valley. A semi-empirical model of turbidity data, as a water clarity proxy, and multispectral remote sensing data (MSI Sentinel-2), based on different hydrological conditions and well-differentiated water types, yielded an accuracy of 92%. Five floods (> 3187m3s-1) and five droughts (< 231m3s-1) events occurred from 2013 to 2020. The flood of January 2016 occurred one month after the mining slurries reached the coast, intruding tailings on some alluvial and coastal plain lakes with highly turbid waters (> 400 NTU). A fluvial plume is formed in the inner shelf adjoining the river mouth on high flow. The dispersion of river plume was categorized as plume core (turbidity > 200 NTU), plume core and inner shelf waters (100-199 NTU), other shelf water (50-99 NTU), and offshore waters (< 50 NTU). Fluvial discharge and local winds are the main drivers for river plume dispersion and transport of terrigenous material along the coast. This work provides elements for evaluating the impact of mining tailings and an approach for remote sensing regional monitoring of surface water quality.

REVIEW PAPER ON PREDICTION OF WATER QUALITY PARAMETER USING MACHINE LEARNING

Surface water pollution become a nuisance for humankind as river water fulfill requirement of a major population and traditional method of water quality assessment and evaluation is inadequate in this era. So using advance method of machine learning in prediction of surface water proves to be helpful to prevent future water accident. As we seen many recent studies of water quality prediction and river water assessment using machine learning approach for better accuracy and less labor and to optimize its overall results. It’s become essential to review the recent studies which used Machine Learning algorithms for prediction, analysis, evaluation and assessment of river water quality and different models used in these studies for different environmental conditions. Machine learning models are superior to handle such complex and non linear data such as water quality parameters with greater accuracy, reliability, cost-effectiveness and efficiency as considered as great tool for surface Water Quality monitoring, prediction, future projects and help lawmakers in policy. In this report we reviewed around 17 research papers which uses machine learning approach from different journal and concise it to covers the structure of study, datasets used, methodology analysis, models performance, environment susceptibility, comparative analysis and assessments of Machine Learning models progress in river water quality research. For better management and control of surface water quality and its treatment, this study will help in understanding and analyzing the studies reviewed in this paper and its future application. We can conclude that research on Water Quality prediction using Machine Learning model are inadequate in the context future vulnerability, observing increasing pollution in recent years we require more research in this field. Finally, this study provides breakthrough in Surface Water Engineering and Management to give a new direction to fore coming studies and fortified it scope also gives a comparative approach for its implementation in new studies.

Finite element analysis of the influence of ice temperature expansion force on surface water quality monitoring system

Ice will form in winter in high-latitude areas of China. As the external temperature rises, the ice cover will generate thermal expansion force on the restrained object. Based on the single-factor sensitivity method, the thermal solid coupling model is used to calculate the ice layer thermal stress. The intensity of the water quality monitoring system in China's high latitude icing period is modeled and calculated. The results show that with the increase of the external temperature rise rate, the external initial temperature decreases and the thickness of the ice layer increases, the expansion force of the ice layer temperature increases, and the stress field of the ice layer presents nonlinear distribution. The extreme point is located at 1/3 of the ice layer thickness. The water quality monitoring system will produce high stress at the edge of the body, which should be paid attention to when it is concentrated on engineering use.