River Water Monitoring Research Articles

Analysis of the role of local government in the prevention of environmental damage in Kuala Tanjung Industrial Park, North Sumatra, Indonesia

The development of active industrial areas in the production process of course produces waste that can damage the surrounding environment. Kuala Tanjung Village is a Priority Industrial Area based on Presidential Regulation Number 56 of 2018. A review of environmental impacts needs to be carried out. Active local government participation is needed, of course through regulation. This research aims to examine the efforts made by the Government of Batu Bara Regency to prevent environmental damage to the industrial area in Kuala Tanjung village. The research method used is a qualitative method with a descriptive type of research. While the data used are primary data and secondary data. The result showed that the Regional Government of Batubara Regency has Regional Regulation Number 2 of 2022 concerning the implementation of environmental protection and management. However, it is a pity that the regulation has just been issued, even though several companies have been operating for a long time. Furthermore, there are also programs and activities carried out, namely monitoring river water, routine monitoring of companies, and planting mangrove forests. The entire program is still limited to liquid waste, while air quality is far from being a concern. Coupled with the lack of budget allocations, the programs that are run are still very limited.

Multi-element analysis of unfiltered samples in river water monitoring—digestion and single-run analyses of 67 elements

Routine analysis of inorganic analytes in whole water samples from rivers (unfiltered river water) is rarely reported in scientific publications. However, this sample type is valuable and often used in long-term monitoring, regulation, and catchment element budgets, as it includes the dissolved, colloidal, and particulate fraction in one sample type. Preservation measures are not needed and solid–liquid partitioning can be disregarded, which simplifies automated sampling and storage procedures. In this study, we provide several digestion protocols for whole water samples from rivers and the subsequent multi-element analysis of 67 major, minor, and trace elements: Li, Be, B, Na, Mg, Al, Si, P, S, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Ir, Pt, Au, Hg, Tl, Pb, Bi, Th, U. In the absence of whole water reference materials for inorganic analytes, we introduce simulated whole water samples by suspending sediment reference materials as quality control measures. The applicability for improved routine water quality monitoring was successfully tested on samples from different rivers revealing variations of the element fingerprints over time.Graphical abstract

Detection and quantitative microbial risk assessment of pathogenic Vibrio cholerae in a river used for drinking, domestic, fresh produce irrigation and recreational purposes

Cholera infection results from the ingestion of water or food contaminated with toxigenic Vibrio cholerae. This study evaluated the occurrence of toxigenic V. cholerae in Asata River, Enugu Metropolis, Nigeria, and estimated V. cholerae infection risks from use of the River water for drinking, domestic and recreational purposes. Vibrio was detected and quantified using membrane filtration technique and thiosulfate–citrate–bile salts–sucrose agar. Isolates were screened by PCR, using specific primers targeting the internal transcribed spacer (its) region between 16 and 23S rRNA and the cholera toxin (ctx) gene. Sequenced 16SrRNA gene amplicons of two selected isolates were used for phylogenetic analysis. Quantitative microbial risk assessment (QMRA) was conducted using the β-Poisson dose–response model. About 81% (58/72) of Asata River samples recorded Vibrio counts above 1.0 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 103 cfu/100 mL. Of the fifty Vibrio isolates screened, its was detected in 54% (27/50), out of which 74% (20/27) had the ctx gene of toxigenic V. cholerae. Evolutionary relatedness of the sequenced isolates to V. cholerae was revealed. The estimated risks of cholera infection in persons exposed to untreated Asata River water were above 0.5 for all the exposure scenarios, for both the rainy and dry seasons. The risks were highest (~ 0.9) for exposure via drinking water and annual risk of infection was deduced to have a probability of 1.0. Therefore, dependence on the untreated Asata River water for drinking, recreational, domestic and irrigation purposes may present a potential public health risk of cholera outbreak. We recommend increased monitoring and surveillance of River water for Vibrio abundance and that Asata River be protected from further degradation.

RANCANG BANGUN SISTEM PEMANTAU KETINGGIAN AIR OTOMATIS MENGGUNAKAN SENSOR ULTRASONIK (HC-SR04) BERBASIS ARDUINO UNO DENGAN ANTARMUKA KOMPUTER BERBASIS MICROSOFT VISUAL BASIC 6.0

Efforts can be made to overcome this natural disaster, including providing a drainage system, maintaining forests and monitoring river water levels to determine the possibility of flooding if the level exceeds normal. In this research the author designed a tool to provide warnings before a flood disaster occurs. The design of an automatic water level monitoring system using an ultrasonic sensor (HC-SR04) based on Arduino UNO with a computer interface based on Microsoft Visual Basic 6.0 was created using HC-SR04 to measure distance, Arduino UNO as the main controller for the entire tool, LED and buzzer. When the HC-SR04 measures the distance by emitting waves from the trigger pin as the trigger and the echo pin as the receiver, the distance will then be processed by the Arduino UNO to calculate how many "cm" distances have been measured. If the distance calculated by the Arduino UNO is < 30 cm then the green LED will light up, if the distance is < 20 cm then the yellow LED will light up and the buzzer will sound slowly. However, if the distance is <10 cm, the red LED and yellow LED will light up alternately and the buzzer will sound loudly. Next, the data will be displayed on the PC interface with Microsoft Visual Basic 6.0 according to the distance received.

Water Quality of Ranowangko River That Crosses Tomohon City and Minahasa Regency, North Sulawesi Province

The Ranowangko River passes through Tomohon City and Minahasa Regency, ending in the Sulawesi Sea. Tombariri District, is the Bunaken National Park area. Ranowangko River Water Monitoring in 2022 by the Regional Environmental Service of North Sulawesi Province shows that Fecal Coli and Total Coli parameters in the Ranowangko River at Station 1 do not meet the requirements according to Government Regulation Number 22 of 2021 concerning Class 2 National Water Quality Standards (BMA). Research to assess water quality from biological, physical and chemical aspects as well as determine water quality status. Monitoring was carried out 4 times in 2 consecutive years at 3 observation stations. Quantitative data and qualitative data include water quality analysis through determination of 14 parameters compared according to BMA Class 2. The water quality data was analyzed using the Storet Method to determine water quality status in accordance with Minister of Environment Decree Number 115 of 2003. The results showed Fecal Coli and Total Coli (Biological Aspects), TSS (Physical Aspects), Total Phosphate (Chemical Aspects) exceeded the BMA. Score calculation results using the Storet method at Station 1: -68, Station 2: -72 and Station 3: -76. Based on the value system According to Minister of Environment Decree 115 of 2003, the score above shows that the Water Quality Status of the Ranowangko River is heavily polluted. Recommendations to the regional governments of Tomohon City and Minahasa Regency to carry out further monitoring of the water quality of the Ranowangko River.

Research on the analysis of Yellow River water and sand monitoring data based on ARIMA model

The article uses hydrological data from a hydropothe paperr station in Xiaolangdi Reservoir spanning six years. the paper use mutability analysis, seasonal decomposition method, and ARIMA model to reveal hydrological change trends and cyclic characteristics. the paper also use the CUSUM algorithm to identify the water-sand flux mutation points. The study's findings demonstrate that ARIMA(1,2,1) can be used to predict trends in total water flux and total sand content. It also reveals that there is significant seasonality and periodicity in the water-sand fluxes. These results offer a solid scientific foundation for decision-making and have substantial implications for the management and control of the region's water resources.

Peringatan Dini Banjir Berbasis Internet Of Things (IOT) dan Telegram

Flooding is a natural event that can have serious impacts on ecosystems, the economy and human life. In efforts to mitigate and control flood risks, early warning plays an important role in providing quick information to the public and the authorities. In this context, the application of the Internet of Things (IoT) provides an innovative solution by utilizing the latest technology to create an efficient and accurate flood early warning system. This research is intended to create a prototype river water level monitoring system using NodeMCU and the Telegram application programming interface as a first step in anticipating flooding. The main objective is to provide assistance to supervisory officers in monitoring river water levels in real time. This research method includes the development and integration of sensors connected to an IoT network to accurately detect changes in water level. The system also implements efficient data analysis algorithms to determine whether conditions require early warning. Telegram was chosen as communication platform to convey warning messages to the public. The IoT device is connected to a buzzer which will provide a sound warning when the water level reaches a threshold. The research results obtained show that water level conditions in the range of 2cm-50cm are categorized as Alert 1 status, so the buzzer (Alarm) is On. In the 51cm-99cm range, the status is Alert 2, and in the 100cm-400cm range the status is Alert 3 with the buzzer Off. Apart from that, via the Telegram bot the status of Alert 3, Alert 2, and Alert 1 will be informed

Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches

Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 % to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 % of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality.

Biomonitoring as an Effort to Monitor River Water Quality with Parameters of BOD, DO, pH, TDS and the Presence of Macrozoobenthos in the Rolak River Area, Surabaya City

Many of the person who built settlements in riverbank areas, this allows residents in riverbank areasto become accustomed to disposing of waste directly into the river. In the end, this waste threatens the success of river flow, polluted river water has a serious impact on the health and quality of the environment around the riverbanks, such as the negative impact caused by this pollution, for example, such as health which has an impact on the surrounding environment or others. Because it is important to understand the natural health relationship, this study chose a river area that is located around the Rolak area of Surabaya, this place was chosen because the area is around a densely populated area and an industrial area. So that it can be used as research because the area certainly has a high level of pollution. As a result of the impact of the many residential and industrial areas around the river, the biomonitoring method is carried out to identify river pollution from its physical habitat by monitoring environmental health using living organisms in the river. Therefore the biomonitoring method can be applied because the advantages of this method can also be used including helping identify by using test parameters such as measuring pH, temperature and oxygen content in river water. that has been done before. Then the biotic examination can be used as a guide that can represent the quality of a body of water, from observations at several points the river is known to have a color that tends to be brownish due to turbidity levels, the pH parameters at each sample point are classified as normal and for Total Dissolved Solid (TDS) there is content of 339 mg/l, this is thought to be due to the source of domestic waste pollution from residents' settlements around the river banks. For DO test results there are only five points that meet quality standards. As for the diversity of bioindicators at each point, such as the presence of weeds at the first point, while the presence of water hyacinth can only be seen at the third and fourth points, the macrozoobenthos that are commonly found in the mangrove area are from the Mollusca, Annelida, and Arthropoda.

Real Time Water Quality Monitoring of River Pamba (India) using Internet of Things

— All of Earth’s living things depend on food and clean water to survive. River water monitoring is essential for gathering the data required to manage water quality in the present as well as the future. In this research, the water quality of the Pamba River is monitored and analyzed using the Internet of Things (IoT) during the monsoon and summer seasons. The Pamba River is the third-longest river in the Indian state of Kerala and one of the primary sources of drinking water in Kerala’s Pathanamthitta and Alappuzha districts. In conventional systems, the monitoring procedure entails manually collecting water samples from various places, followed by testing and analysis in a lab. This is costly and time-consuming. With developments in sensors, communication, and the Internet of Things (IoT), monitoring water contamination has become more and more crucial in recent years and provides accurate data. The system collects information from IoT sensors to evaluate the quality of the water, including pH, dissolved oxygen, temperature changes, and other parameters. The relevant officials are alerted in the case of any variation, and all of this data is stored in the cloud. The system also ensures real-time water quality monitoring.

Analysis of the Yellow River water and sediment monitoring data

Abstract Graphics are drawn about the sediment content, water flow, and water level of the Yellow River with time. A cluster scatter plot of sand content is made with changes over time. Using variance analysis of a factor, it is concluded that sediment content is affected by two variables month and year. By studying the changing pattern of water-sand flux, the timing diagram of the water and sediment flux of the Yellow River with time is drawn. The flux of water and sand is predicted for the next two years. According to the prediction results, the optimal sampling and monitoring scheme is developed.

Yellow River Water and Sand Monitoring Data Model based on SARIMAX Rediction

With the evolution of history, the problems faced by the Yellow River Basin have been an important research topic in hydrology. In this paper, for the problems in the monitoring of water and sand in the Yellow River, a seasonal time series model of the monitoring data is established based on the time series prediction model, and the CUSUM algorithm is used to test the model. First, the data are processed to calculate the spearman correlation for each pair of categories. Then, for analyzing the description of single dimensional data, data descriptive analysis was used. Next, the data were forecasted using a seasonal time series forecasting model. Finally, the seasonal time series prediction model was used to predict water fluxes and solved using double interpolation to obtain the predicted effect plots for the next ten years. The proposed model is close to the reality, can reasonably solve the proposed problem, and is characterized by high practicality and high efficiency of the algorithm.

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.

ASSESSMENT OF THE CURRENT SPATIOTEMPORAL VARIATIONS OF TOTAL SUSPENDED SOLID ON THE SURFACE WATERS OF KUALA PERLIS, PERLIS

Rivers are vital water sources for human existence and environmental health. Due to freshwater scarcity, monitoring river water quality is crucial, especially concerningtotal suspended solids (TSS), which pose potential risks to health and the environment. Despite this importance, there is a lack of assessment of spatiotemporal TSS variation in Malaysia, particularly in Sungai Kuala Perlis. This study aims to evaluate spatial-temporal TSS variations in the surface water of Kuala Perlis, Perlis. Sampling points were GPS recorded in December 2021. Five sites were established for morning, afternoon, and evening sampling. Water samples were collected and subjected to gravimetric analysis using the American Public Health Assessment (APHA) standard. ANOVA (p=0.05) in SPSS version 26 found TSS ranges of 110.67 mg/L -177.67 mg/L, 27.67 mg/L – 132 mg/L, and 78 mg/L – 304.67 mg/L for morning, afternoon, and evening, respectively. Surprisingly, no significant mean TSS differences were found for temporal (p>0.05) or spatial (p>0.05) variations. Factors influencing TSS variation such as water flow, salinity, and anthropogenic activities, were discussed. These findings inform researchers, governments, and NGOs for future planning in Kuala Perlis, promoting river health and eco-friendly management

Biotoxicity monitoring of Bisphenol A using microbial fuel cell sensors: Optimizing the response of sensor’s voltage changes to toxicity and assessing the impact on the application to river water monitoring

The voltage variation (ΔV) of a microbial fuel cell (MFC) sensor that is used to monitor the concentration of bisphenol A (BPA) in wastewater fluctuates with the chemical oxygen demand (COD) concentration of wastewater, destabilizing a sensor that is used for this purpose. The results obtained by the response surface method reveal that the ΔV of the MFC sensor was significantly influenced by the BPA and COD concentrations, with an R2 of 0.981. Biotoxicity analysis revealed that the response of Vibrio fischeri to toxicity was consistent with the sensor’s output, indicating that the sensor was highly sensitive to toxicity. The developed MFC sensor provides a high ΔV even in long-term toxicity detection. The voltage output of the MFC sensor did not significantly vary in the detection of river water containing BPA at concentrations higher than 60 mg L−1. This indicates that the toxic effect of high BPA concentration is outweighed by the interference of chemical substances in the river water, demonstrating the potential of the MFC sensor for detecting river water with a BPA concentration above 60 mg L−1. With its simplicity, rapid detection, and long-term operability, the MFC sensor has the potential for practical use.

Water Quality Assessment of Remote Sensing Techniques: A Comparative Insight Between Yangtze of China and Porong of Indonesia

The river water quality, exceptionally the total suspended solid (TSS) in China and Indonesia, has deteriorated due to human activities. Remote sensing makes it easier for observers to monitor river water quality, especially TSS. However, measuring the river water quality by remote sensing is still in the model and algorithm development stage in China and Indonesia. This study aims to identify the river water quality on remote sensing in China's Yangtze River and Porong River, East Java, Indonesia, and to analyze comparisons of river water quality on remote sensing in Yangtze River, China, and Porong River, East Java, Indonesia. This method uses a literature review based on journals, articles, and primary sources to review related literature on TSS concentrations in rivers and remote sensing in China and Indonesia. River water monitoring methods can measure the TSS in China and Indonesia using remote sensing. Many water quality models for waterways are based on different satellite images. In the Yangtze Downstream River, the algorithm of TSS uses the latest random forest on Landsat-8. The algorithm of TSS in the Porong River estuary used linear regression on sentinel-2 imagery. These TSS algorithms can more precisely assess TSS in water quality for scientific studies. The results show that the latest random forest is a more precise remote sensing algorithm in China than Linear regression in Indonesia. The suspended solid models and remote sensing images such as China's MODIS, Landsat-8, and MERIS are accurate in China. Therefore, developing more precise remote sensing techniques, total suspended solid models composed of Wiggin's Algorithm and Markert Algorithm, NDWI Algorithm, and remote sensing imagery such as Sentinel-2 and Landsat-8 in Indonesia is crucial to determine total suspended solids. The researchers additionally contribute to advanced research toward advancing suitable remote sensing techniques in various areas in Indonesia.

Development of a Wireless Monitoring System to Monitor River Water Levels in Real Time

This wireless monitoring system is developed to alert and warn the resident regarding the upcoming flood. Due to the floods that occur every year, especially during the monsoon season, the loss and damage done by this disaster are uncontrollable. When the water level rise-up above the safe ground, it may hit the communication substation, as well as the power tower. In most cases, many residents are not alert with their surroundings because there is no alert system that can remind them regarding to the issues that will arise. By not preparing themselves for the upcoming nature disaster, it could endanger themselves and destroy their property. Thus, this project focus on monitoring a real-time river water level system for flood prediction purposes which utilize three devices connected together in a centralized system. By having this system, people will be notified about the current situation and the water level of the river near to them via Internet of Things (IoT) such as Blynk application. ESP32 and ESP32-Cam are used as the main microcontroller to control the system. The sensor used to detect the water level at the river is TF Mini Lidar and ultrasonic sensor for the water level detection of the rain gauge. The ESP32-cam are used to monitor the surrounding area at the river. The combination of these three systems is used to monitor and predict the flood occurrence.

Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery

ABSTRACT Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale.

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.

Quantification of 68 elements in river water monitoring samples in single-run measurements

Triple-quadrupole inductively coupled plasma mass spectrometry (ICP-QQQ-MS) is a unique analytical technique which is, next to speciation analyses, applied for the determination of total element concentrations in several matrices. Due to its wide linear range, short analysis times, and the collision-reaction gas technology, it is capable of addressing a high number of analytes in a single run with sufficient low limits of quantification for river water monitoring. Over the last decades, the focus of the environmental monitoring changed from “traditional” and regulated analytes to elements of possibly rising concern from new applications such as the so-called technology-critical elements (TCE). By widening the analytical window of this method for applications in networks of future river water monitoring, a better understanding of natural transport processes and global biogeochemical element cycles will be established and the total number of methods can be reduced. During method development and validation, certified reference materials, calibration check solutions, and spiked river water samples from 12 major German rivers covering different catchment areas were measured and evaluated with the three cell gases He, H2 and O2. The method delivers a best as possible undisturbed simultaneous determination for 68 out of 71 target analytes with recoveries in an accepted range of 80–120% for river water samples (dissolved fraction; <0.45 μm). After comprehensive evaluation, we offer a novel best-practice multi-element method for river water monitoring with the goal of fostering the exchange and discussion between practitioners in long-term river monitoring. It enables the readers to create their own methods based on the scientific needs to monitor elemental “fingerprints” of rivers and their catchments.