River Water Quality Monitoring Research Articles

Deep learning for river water quality monitoring: a CNN-BiLSTM approach along the Kaveri River

Deep learning for river water quality monitoring: a CNN-BiLSTM approach along the Kaveri River

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis.

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

Multi-domain, Autonomous Measurement Buoy as an Element of the Water Quality Monitoring and Early Warning System in Rivers and Water Reservoirs

This article presents a novel, innovative, open multi-domain platform for early warning against adverse events in reservoirs and watercourses, which can measure temperature, pH, redox, conductivity, turbidity, chlorophyll and phycocyanin. These parameters are key indicators of cyanobacteria bloom. This platform allows remote and distributed monitoring of important locations on lakes and rivers. The station's design enables the use of both wired sensors directly connected to the station and wireless data collection from locally dispersed measurement points that communicate with the station-buoy. The data aggregation system is open, and the technological solution of the station is universal, which means it can use different sensors for chemical and biological parameters that are required by, for example, the Water Framework Directive, from the market and industry standards. The platform also has built-in machine learning and data analysis mechanisms that can optimise the number of stations needed to achieve the desired level of data acquisition. The sensor dispersion and station autonomy ensure the flexibility and scalability of the measurements.

G‐optimal grid designs for kriging models

AbstractThis work is focused on finding G ‐optimal designs theoretically for kriging models with two ‐dimensional inputs and separable exponential covariance structures. For design comparison, the notion of evenness of two‐dimensional grid designs is developed. The mathematical relationship between the design and the supremum of the mean squared prediction error (SMSPE) function is studied and then optimal designs are explored for both prospective and retrospective design scenarios. In the case of prospective designs, the new design is developed before the experiment is conducted and the regularly spaced grid is shown to be the G ‐optimal design. Retrospective designs are constructed by adding or deleting points from an already existing design. Deterministic algorithms are developed to find the best possible retrospective designs (which minimizes the SMSPE). It is found that a more evenly spread design under the G ‐optimality criterion leads to the best possible retrospective design. For all the cases of finding the optimal prospective designs and the best possible retrospective designs, both frequentist and Bayesian frameworks have been considered. The proposed methodology for finding retrospective designs is illustrated with a spatio‐temporal river water quality monitoring experiment.This article is protected by copyright. All rights reserved.

DESIGN AND DEVELOPMENT OF RIVER WATER QUALITY MONITORING AND SALINITY DEVELOPMENT PROPERLY REALTIME BASED IoT

The decline in raw water quality recorded by PDAM Pontianak on Jalan Imam Bonjol is caused by sea water intrusion across land during the dry season. The research objective is to design an IoT-based system as a data transmission medium for monitoring river water quality, the water pH sensor for reading pH levels of water and the Turbidity sensor for reading the level of turbidity in river water and can designing a river water quality monitoring tool using several parameters, namely salinity, pH levels and turbidity. From the overall test results of the tool it can be concluded that the tool has worked. The tool's measurement results can be seen in the Blynk application sent by the tool at an average interval. The results of measurements at 2 locations for 3 days showed that TDS ranged from 2 ppm to 6.16 ppm with an average of 3.8 ppm, turbidity ranged from 211 NTU to 574 NTU with an average of 424.4 NTU and pH ranged from 6.87 to 8.62 with an average -average of 8.02. The tool designed is capable of sending measurement data via the Blynk application to smartphone users.

Performance of equations for the longitudinal dispersion coefficient: a case study in the Orashi River

Abstract Investigation of the water quality of rivers is a key point in Water Resources Engineering. The longitudinal dispersion coefficient is one of the foremost significant parameters in river water quality monitoring. Several parameters such as hydraulic, morphology, total dissolved solids, and total suspended solids are effective parameters in the determination of the longitudinal dispersion coefficient as revealed by this study. The assessment of the river shows mean hydraulic and geometric properties such as flow, depth, velocity, longitudinal slope, and width to be 354.17 m3/s, 9.61 m, 0.69 m/s, 0.0079,101.63 m and the range of the longitudinal dispersion coefficient as (72–104.4) m2/s. Results obtained by employing the established equations revealed standard error indices and RMSE of the developed equation, and Kashefipour and Falconer equation gives correlation coefficient of about 0.819 and 4.182 and 0.421 and 12.186, respectively, as coefficient of determination and RMSE, and they are more accurate among the empirical equations. However, the newly derived equation for the longitudinal dispersion coefficient performed better when compared with others, indicating the fitness of the developed equation to estimate longitudinal dispersion coefficient.

Development of River Flow and Water Quality Using IOT-based Smart Buoys Environment Monitoring System

Efficient and accurate river water quality monitoring is needed to support laboratory testing based on on-site sampling. Therefore, we have developed a monitoring system for river flow and water quality using sensor-equipped buoys and the Internet of Things (IoT) concept. An ESP32 NodeMCU microcontroller integrated with WiFi and compatible with Arduino IDE is used in the system. The buoy is equipped with GPS to determine the position and flow speed and sensors to measure water quality parameters of pH and temperature. Data on position, flow velocity, and water quality parameters are transmitted over a WiFi network using the MQTT protocol. The data is recorded by the buoy and uploaded and displayed on the adafruit.io platform. Positioning was done by comparing the values displayed on the Neo-6M GPS with the Maps application on the smartphone. The results show that the GPS coordinate values are accurate. The water quality parameter values obtained have an error rate of 3.3% for the pH sensor and 1.02% for the temperature sensor. Thus, the system we have developed has the potential to be used as a substitute for field sampling-based river water quality monitoring systems.

Evaluation of Spatiotemporal Patterns and Water Quality Conditions Using Multivariate Statistical Analysis in the Yangtze River, China

As a crucial surface water resource, the Yangtze River has raised concerns about its water quality due to its importance in economic and social development, environmental conservation, and agricultural development. The principal component analysis (PCA), hierarchical clustering analysis (HCA), and the water quality index (WQI) were utilized to assess the overall condition and detect spatiotemporal patterns and the key parameters of water quality in the Yangtze River. All usage data were determined monthly from samples taken in 2021 at the 33 Yangtze River water quality monitoring stations. The results demonstrated that 85% of the monitoring stations in the whole Yangtze River were maintained at a “good” condition, with average WQI values ranging from 71.16 to 81.25. The water quality was slightly poorer in the summer, with 56.6% of monitoring stations being in “medium” condition. Spatially, there was a downward trend in the water quality from upstream to downstream. Two significant principal component scores (PCs) were produced as a result of PCA and HCA, explaining 60.3% of the total variance in the upstream, 67.4% in the transition zone, and 50.4% in the downstream, respectively. In addition, the middle–upper reaches of water quality were found to correlated with CODMn, whereas the water quality in the downstream were mainly influenced by TUR, TP, T, and DO. The results primarily motivated our understanding of the Yangtze River’s water quality status and suggested the main targets for water quality improvement in different monitoring areas.

Development of statistical regression and artificial neural network models for estimating nitrogen, phosphorus, COD, and suspended solid concentrations in eutrophic rivers using UV–Vis spectroscopy

River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet–visible (UV–Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this, a framework integrating desktop and in situ UV–Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO3-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH4-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R2) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

Intelligent monitoring of water quality based on image analytics

Owing to the limitations of the spatial arrangement of monitoring stations and time acquisition of satellite remote sensing images, the water quality monitoring of rivers, especially small- and medium-sized rivers, cannot be satisfied in terms of time and space continuity. In this study, we propose a standardization method for the camera images derived at different locations on different time considering the influences of light, shadows, reflections, ripples, leaves and so on. After this algorithm is established, an estimation algorithm based on the comprehensive appearance of water body is proposed, which has the potential of realising real-time, mobile, and continuous monitoring of water quality with low costs. The test results showed that the accuracy of the model was quite high compared to the results of the hydrological monitoring stations. Compared with the single-point detection of water quality monitoring stations, this method has advantages in terms of dynamic detection and small- andmedium-sized water body detection, which can serve as a supplement to traditional detection.

Analisis Kualitas Air di Sungai Marbau

This study aims to analyze the quality of river water and the water quality status of the Marbau watershed in North Labuhanbatu Regency based on water quality criteria and to formulate a pollution control strategy that needs to be implemented. The method used is a quantitative descriptive method by analyzing the status of river water quality based on the pollution index. The results showed that the P1 value of Marbau river water ranged from 0.94 to 1.82. The recommended pollution control strategies are: 1) maintaining river protection zones involving environmental cadres and green communities in monitoring; 2) monitoring and controlling water pollution along the river; 3) improve river water quality monitoring; 5) supervision of the discharge of waste water into rivers; 4) granting permits for disposal of wastewater (IPLC) into rivers must pay attention to the condition of the capacity to accommodate river pollution loads and enforce environmental law against business actors who violate established environmental quality standards. In conclusion, the quality of the Marbau river has decreased with the water quality status being slightly polluted. The recommendations for water pollution control strategies that need to be implemented are progressive strategies.
 Keywords: Water Quality, North Labuhanbatu, Marbau

Quality Assessment and Monitoring of River Water Using IoT Infrastructure

The quality assessment of water is a challenging task due to extensive experimental requirements. However, the process of water quality monitoring can be automated with the help of Internet of Things (IoT) devices using sensor probes. This paper presents the IoT infrastructure-based river water quality monitoring and assessment. Experiments were performed to assess the water quality during different months and seasons for the Ganga river and Sangam (confluence of Ganga and Yamuna rivers) at Prayagraj, Uttar Pradesh, India. The data samples were collected for 15 months continuously using the Libelium smart water kit. The Smart Water IoT (SWIoT) kit was equipped with sensors to assess specific parameters like pH, dissolved oxygen, temperature, conductivity, and oxidation-reduction potential. An algorithm is also presented that harnesses PCA and factor analysis for feature selection and weight assignment for river water quality assessment. Further water quality is quantified using the water quality index that helps to categorize the water quality for different usages. The results corroborate that the water quality of the Ganga River was found to be better than the Sangam site most of the time, owing to the higher level of pollution in Yamuna River. Additionally, the water quality of both rivers were found to be suitable for irrigation and fisheries but not for drinking purposes, considering the average oxygen levels.

Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River

Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R2. The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R2 ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.

Fish Tissue Contamination with Organic Pollutants and Heavy Metals: Link between Land Use and Ecological Health

Under pressure from existing and emerging contaminants, lotic ecosystems are becoming increasingly susceptible to ecological deterioration. Therefore, investigations of the impacts of persistent organic pollutants (POPs) and heavy metals on riverine fish health, water quality, and biotic integrity are critical. We examined the effects of 52 POPs and heavy metals on ecological health and land use, based on the monitoring of fish assemblages and river water quality from 2007 to 2014. Among the 52 chemical species, 35 were present in water and fish tissues, including eight heavy metals. The concentrations of these POPs and heavy metals in 12 fish species are presented. Most POPs were either undetected or present at negligible levels in fish tissues, although a few polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs; dichlorodiphenyltrichloroethanes and heptachlor epoxide) were detected above the screening values (SVs). Hg, As, and Cd were detected in most water samples and fish species at concentrations above their SVs. Among the fish species in the investigated trophic guild, heavy metal contents were in the order of Zn < Cr < Cu < Pb < Se, while trophic levels were in the order of insectivorous < omnivorous < carnivorous. Agricultural cover showed an association with endosulfan II (R2 = 0.50, r = 0.70), followed by alachlor (R2 = 0.43, r = 0.66). For PAHs, all detected substances showed significant relationships with forest cover. Ecological health assessment revealed that most river sites are in poor condition, indicating the direct impacts of pollutants. In conclusion, of the 28 POPs detected, 16 PAHs and 3 OCPs (hexachlorobenzene, lindane, and heptachlor epoxide) were of significant concern, such that action is needed to curb their inflow to the riverine environment. The studied river basin is under substantial threat from harmful POPs that endanger ecological health and fish biodiversity.

Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region.

This paper investigated whether rainfall promotes dilution or increase in nutrient concentrations and which land use indicators are the main predictors of nutrients in intermittent rivers in a large Brazilian semiarid region. The total phosphorus (TP) and total inorganic nitrogen (TIN) were monitored between 2013 and 2018 at 92 river water quality monitoring sites. The monthly rainfall (Rn) was obtained from 575 rain gauges. Pearson's correlation (R) between Rn and nutrient concentration was performed. The correlation patterns were also analysed based on land use data: urban area (%), agricultural field area (%), demographic density (inhabitants/km2), sewer system coverage (%), and reservoir density (reservoir/km2). Backward stepwise regression was performed to identify predictors of nutrient concentrations. The results revealed a marginal effect of rainfall on nutrients when the effects of urbanisation outweigh all other aspects. However, in regions with greater accumulated rainfall and lower reservoir density, the rainfall was related to a linear increase in nutrient concentrations (R > 0.8). Contrastingly, in the basins with less accumulated rainfall and greater inter-basin hydrological disconnection, there was a linear reduction in nutrient concentration (R < - 0.5). In the backward stepwise regression, sewer system coverage and Rn had the greatest influence for TP, and the urban area was the strongest predictor for TIN. Importantly, our results demonstrated that in semiarid rivers in densely populated regions, there is no single pattern of variability in nutrient concentration, on a wide scale of assessment. Therefore, adaptative and decentralised management can be more effective in improving water quality in these regions.

Assessment of Phytoplankton Diversity in Downstream Region of Meenachil River,kerala, During the Pre-monsoon and Monsoon Season

The present study aimed to assess the Phytoplankton diversity in the downstream of Meenachil River ,Kerala,during pre-monsoon and monsoon seasons. A total of 39 species belonging to five taxonomic divisions were identified, namely Chlorophyceae, Bacillariophyceae, Cyanophyceae, Euglenophyceae, and Xanthophyceae. The results revealed that Chlorophyceae was the dominant group, followed by Bacillariophyceae, Cyanophyceae, Euglenophyceae, and Xanthophyceae. The season-wise distribution of phytoplankton showed a higher abundance in the pre-monsoon season compared to the monsoon season. This study provides valuable insights into the phytoplankton diversity of Meenachil River, Kerala, and highlights the significance of monitoring seasonal changes in phytoplankton abundance for effective water resource management. The findings of this study can be useful for further research on water quality monitoring and management in rivers

Combined Effect of Climate and Anthropopressure on River Water Quality

This study was a continuation of our investigation of the spatio-temporal variability of the Bzura River's water chemistry. Our research is of particular importance in the context of the recent ecological disaster on the Oder River and concerns the international problem of surface water contamination. The study area was a 120 km section of the Bzura River. We tested more measurement points and with a higher sampling frequency than those used in the national monitoring of river water quality. During two hydrological years, 360 water samples were collected. The selected parameters: electrical conductivity, temperature, dissolved oxygen, dissolved organic carbon, nitrates, phosphates, bicarbonates, chlorides, sodium, potassium, calcium, and magnesium were determined. Numerous results exceeded the Polish threshold limits. Spatio-temporal variability and water quality were assessed using principal component analysis (PCA), cluster analysis (CA), and water quality index (WQI) approaches. Many point sources of pollution related to urbanization, agriculture, and industry were detected. Moreover, due to the changing climatic conditions, a significant difference between temporal variability in both years was observed. Our results indicated that it is necessary to increase the number of measurement stations for surface water monitoring; it will allow for a faster detection of the threat.

ASSESSMENT OF ARSENIC CONTAMINATION IN THE INDIAN RIVERS

The present study analyses in order to determine whether or not arsenic contamination exists in rivers and what are cause responsible for it. India has 764 river water quality monitoring sites across the subcontinent. The study analyses 432 monitoring stations due to data availability. The most important rivers in India flow through Himachal Pradesh, Haryana, Punjab Uttarakhand, Uttar Pradesh, Bihar, West Bengal, Assam, Chhattisgarh, Jharkhand, Maharashtra, Karnataka, and others. The current study is shaped by causal research under the applied research approach used by academics and practitioners in river water assessment. Quantitative techniques were implemented during initial secondary data collection &amp; analyses. The study also conducted a content analysis and sweeping review of how institutions in India talk about the origins and effects of arsenic toxicity. The central water commission data has discovered that almost all rivers have arsenic amounts below the permissible limit except eight rivers. Arsenic contamination can be caused by a variety of natural and human activities. However, research indicates that industrial processes are the primary contributor.

Water quality assesment of Pengambang River, Pekanbaru City, Riau Province, Indonesia using pollution index (PI)

River water quality monitoring must be carried out because rivers are a source of fresh water for the community. Tributaries in the city of Pekanbaru receive waste from domestic activities, industry, livestock, and agricultural runoff, reducing river water quality. The pollution index is one of the most effective tools for informing river water quality conditions. This research was conducted in one of the tributaries in Pekanbaru City, Indonesia, with a quantitative approach to assess the water quality status of a river using a pollution index. For this research, water samples were collected from three locations upstream, an outlet dam site, and downstream from May to July 2023. The collected samples were analyzed for eight water quality indicators: TSS, pH, DO, BOD, COD, Nitrate, Total Phosphate and Total Coliform. The Pollution Index (PI) method is used to assess the water quality of Pengambang Rivers according to the Indonesian Minister of Environment Decree number 27 of 2021. Guidelines for water quality indicators use class III water quality standards (PPRI 22/2021) for cultivating freshwater fish, livestock, Water for irrigated plants. The water quality of the Pengambang River still meets quality standards (Class III) for the parameters TSS, DO, COD, pH, nitrate, and phosphate, except BOD and total coliform.The PI results show that Pengambang Rivers are categorized as lightly polluted to moderately Polluted with a value of 1.61 to 8.17. The contamination of the Pengambang River was caused by the BOD and Total Coliform values exceeding the water quality standards (BOD = 8.78-17.5 mg/l and T.Coliform ranging from 2600-24,000 MPN/100 ml). The findings indicated that these water bodies were subjected to anthropogenic activities and untreated sewage inflow. Due to the poor quality of the water, these water bodies must be appropriately managed and conservation efforts made.