Water Quality Monitoring Technology Research Articles

Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing

Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R2 increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R2 results for total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R2 results for TP, NH3-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.

Evaluation of Water Quality and Pollution Source Analysis of Meihu Reservoir

Under the background of increasingly serious global environmental pollution, ensuring the safety of drinking water has become one of the focuses of global attention. In this study, Meihu Reservoir, a drinking water source, was selected as the research object, and the main pollution problems and their sources were revealed through conventional water quality analysis, suitability evaluation of the drinking water source and eutrophication evaluation of the reservoir. Using modern water quality monitoring technology and methods, the paper monitors and analyzes various water quality parameters of the Meihu Reservoir. The results showed that the water quality indexes, except total nitrogen, met the class II–III standard of drinking water, and the comprehensive nutrient state index method (TLI) evaluated the reservoir, and its index met 30≤TLI(∑)≤50, indicating that the reservoir belongs to the medium nutrition category. Therefore, the water quality of the reservoir has been affected by different degrees of agricultural, domestic and livestock pollution, mainly reflected in the serious excess of the total nitrogen index (the peak has reached 2.99 mg/L). The results of the on-site investigation showed that the main sources of nitrogen in the reservoir included agricultural non-point-source pollution, domestic sewage pollution, domestic garbage pollution and livestock and poultry pollution, accounting for 50.09%, 23.99%, 14.13% and 11.80% of the total load, respectively. On this basis, this paper puts forward some countermeasures for pollution control in order to provide a scientific basis and practical path for water quality protection and improvement of the Meihu Reservoir and other similar reservoirs.

Measurement of Fe and Mn concentrations using image processing techniques based on color intensity approach

Water quality monitoring is an important activity to create a good environment quality with clean and healthy water sources. Various monitoring methods that are generally used such as spectrometry-based instruments certainly have various limitations, such as expensive, requiring a lot of reagents, sensitive instruments, and takes quite a long to get measurement results. Due to the development of population growth and the increasing of water pollutant, water quality monitoring technology that cheap, practical, quick and accurate is important to be made. The main subject in this work was to develop a water quality monitoring method based on multiparameter image processing techniques. This method utilizes the approach of color intensity, light, and number/size/shape of particles. This work will be focus on iron (Fe) and manganese (Mn) concentration measurement by color intensity approach performed using ColorSlurp and Microsoft Excel in its RGB (Red, Green, Blue) matrices. The correlation between the parameter concentration and color intensity was obtained by transforming the RGB into greyscale intensity (GI) value. A linear response was observed in the Fe concentration range 0 to 2.4 mg L−1 with the highest R2 = 0.971, and in the Mn concentration range between 0 to 1.6 mg L−1 with the highest R2 = 0.9432. This work demonstrates that image processing techniques provide a great promise as water quality monitoring method.

Surface-enhanced Raman scattering detection of thiram and ciprofloxacin using chitosan-silver coated paper substrates.

Fast detection of contaminants of emerging concern (CECs) in water resources is of great environmental interest. Ideally, sustainable materials should be used in water quality monitoring technologies implemented for such purposes. In this regard, the application of bio-based materials aimed at the fabrication of analytical platforms has become of great importance. This research merges both endeavors by exploring the application of chitosan-coated paper, decorated with silver nanoparticles (AgNPs), on surface-enhanced Raman scattering (SERS) spectroscopy studies of two distinct types of CECs dissolved in aqueous samples: an antibiotic (ciprofloxacin) and a pesticide (thiram). Our results indicate the superior SERS performance of biocoated substrates compared to their non-coated paper counterparts. The detection limits achieved for thiram and ciprofloxacin using the biocoated substrates were 0.024 ppm and 7.7 ppm, respectively. The efficient detection of both analytes is interpreted in terms of the role of the biopolymer in promoting AgNPs assemblies that result in local regions of enhanced SERS activity. Taking advantage of these observations, we use confocal Raman microscopy to obtain Raman images of the substrates using ciprofloxacin and thiram as molecular probes. We also demonstrate that these biobased substrates can be promising for on-site analysis when used in conjunction with portable Raman instruments.

Water Quality Data Analysis from the Onlimo Monitoring System at 16 Stations in Priority Watersheds and Lakes

Currently, the decline in the quality of river and lake water continues, for this reason, activities to control pollution and improve river and lake water quality are required. One effort to prevent this problem is to implement online water quality monitoring technology continuously and in real time. The information generated from the water quality monitoring system can be used as a basis for planning watershed management and determining river and lake water management policies at the national and regional levels. The purpose of this activity is to analyze river and lake water quality data and evaluate the performance of the monitoring systems in 16 water quality monitoring stations that have been built. The data being analyzed is from 2016 to 2020 and includes the parameters of temperature, conductivity, TDS, DO, pH, and turbidity. The 16 station locations are spread over the Ciliwung River, Cisadane River, Citarum River, Serayu River, Bengawan Solo River, and Lake Toba. This research method uses descriptive statistical analysis and the STORET method, which refers to the Decree of the Minister of Environment and Forestry Number 115 of 2003 and Government Regulation of the Republic of Indonesia Number 82 of 2001. The results of this study indicate that most of the rivers monitored fall into the moderate to heavily polluted category, especially the rivers downstream of the Ciliwung, Cisadane, and Bengawan Solo rivers. The results of the analysis of DO, TDS, conductivity, and turbidity parameters from 16 monitoring stations, mostly met the Class II quality standards.

Securing drinking water supply in smart cities: an early warning system based on online sensor network and machine learning

Abstract To enhance the quality of life and ensure sustainability in crowded cities, safe management of drinking water using cutting-edge technologies is a priority. This study developed an intelligent early warning system (EWS) for alarming and controlling risks from bacteria and disinfection byproducts in a drinking water distribution system (DWDS), named BARCS (Bacterial Risk Controlling System). BARCS adopts an artificial intelligence (AI) approach to data-driven prediction and considers total chlorine (TCl) concentration as the pivot indicator for risk identification and control. First, the machine learning-based AI model in BARCS can provide a reliable prediction of TCl concentration in a DWDS, with an average R2 of 0.64 for the validation set, while offering great flexibility for BARCS to adapt to various conditions. Second, TCl concentration was proven to be a good indicator of bacterial risk in a DWDS, as well as a cost-effective surrogate variable to assess disinfection byproduct risk. Third, the robustness analysis demonstrates that with state-of-the-art water quality monitoring technologies, online implementation of BARCS in real-world settings is feasible. Overall, BARCS represents a promising solution to the safe management of drinking water in future smart cities.

Analytical Review on Membrane Water Filter using Different Materials to Prevent Microbial Activities

Drinking water quality monitoring technologies have made significant progress in monitoring water resources and water treatment plants. This paper discusses the adverse effect of microbial contamination and also gives a brief description of the important parameters for drinking water and the technologies currently available used in this field. This paper is focused on studying the requirement for the development of low-cost filter materials that can be suitable as well as economical to be produced on a large-scale for real applications. There are several parameters such as porosity, contact angle, water flux, thickness, microbial activity needed to be focused on in the future to study the transformation of the hydrophilic property on the surface of the water.

Water Quality Monitoring and Early Warning Technology of Zebrafish Behavior Based on 3s Technology

Water Quality Monitoring and Early Warning Technology of Zebrafish Behavior Based on 3s Technology

Health Technology Assessment of a new water quality monitoring technology: Impact of automation, digitalization and remoteness in dialysis units.

BackgroundWater quality monitoring at the dialysis units (DU) is essential to ensure an appropriate dialysis fluid quality and guarantee an optimal and safe dialysis treatment to patients. This paper aims to evaluate the effectiveness, economic and organizational impact of automation, digitalization and remote water quality monitoring, through a New Water Technology (NWT) at a hospital DU to produce dialysis water, compared to a Conventional Water Technology (CWT).MethodsA before-and-after study was carried out at the Hospital Clínic Barcelona. Data on CWT was collected during 1-year (control) and 7-month for the NWT (case). Data on water quality, resource use and unit cost were retrospective and prospectively collected. A comparative effectiveness analysis on the compliance rate of quality water parameters with the international guidelines between the NWT and the CWT was conducted. This was followed by a cost-minimization analysis and an organizational impact from the hospital perspective. An extensive deterministic sensitivity analysis was also performed.ResultsThe NWT compared to the CWT showed no differences on effectiveness measured as the compliance rate on international requirements on water quality (100% vs. 100%), but the NWT yielded savings of 3,599 EUR/year compared to the CWT. The NWT offered more data accuracy (daily measures: 6 vs. 1 and missing data: 0 vs. 20 days/year), optimization of the DU employees’ workload (attendance to DU: 4 vs. 19 days/month) and workflow, through the remote and continuous monitoring, reliability of data and process regarding audits for quality control.ConclusionsWhile the compliance of international recommendations on continuous monitoring was performed with the CWT, the NWT was efficient compared to the CWT, mainly due to the travel time needed by the technical operator to attend the DU. These results were scalable to other economic contexts. Nonetheless, they should be taken with caution either when the NWT equipment/maintenance cost are largely increased, or the workforce involvement is diminished.

Milestone Studies on Ion-exclusion Chromatography of Ionic and Nonionic Substances Utilizing Multifunctional Separation Mechanism of Ion-exchange Resins.

Ion-exclusion chromatography (IEC) is categorized as a type of ion chromatography and is recognized as a simple and convenient water quality monitoring technology for a variety of ionic and nonionic substances. This review, mainly focusing on historical milestone studies by various authors, outlines the archives that concern the separation sciences and practical applications obtained from a variety of IEC modes used for water-quality monitoring as follows: (1) early-developed IEC; (2) IEC using enhanced conductivity detection for weak ionic substance; (3) IEC using nonionic substances eluents such as sugars or polyols; (4) vacancy IEC based on a novel separation concept; (5) applications to the water quality monitoring of inorganic ionic-nutrients; (6) simultaneous IEC and cation-exchange chromatography of anions and cations; and (7) the multicomponent IEC combining different separation modes and detection methods with the expansion of applicable fields, such as for food analysis or material evaluations.

Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base.

In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the Daphnia magna model and assessing acute toxicity determined as immobilisation. Cadmium (Cd2+) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd2+ adsorption capacity (ca. 4.5 times) in reconstituted water (Daphnia medium). The acute toxicity values (48 h-EC50) observed were 0.18 mg L−1 for Cd2+-only and 0.29 and 0.61 mg L−1 following co-exposure of Cd2+ with GO and BSA@GO materials, respectively, at a fixed non-toxic concentration of 1.0 mg L−1. After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd2+-only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets.

Research on Sewage Monitoring and Water Quality Prediction Based on Wireless Sensors and Support Vector Machines

Water resource protection has an important impact on ecosystem security and human survival. Therefore, water quality testing and early warning of the sewage status are getting more and more attention. In order to solve the problems of information transmission delay and insufficient water quality prediction in current water quality monitoring, this paper proposes a wireless sensor-based dynamic water quality monitoring and prediction technology. Firstly, this paper uses the wireless sensor technology and ZigBee protocol to establish a sewage monitoring model and real-time dynamic monitoring of total nitrogen, total phosphorus, ammonia nitrogen, and other indicators of the water quality of the basin. Secondly, on the basis of wireless monitoring, a support vector algorithm is used to construct a water quality prediction model to make a reasonable prediction of the water quality of the watershed. Finally, the actual test results show that the technology can automatically and real-timely monitor the water quality of the watershed to meet the requirements of water quality monitoring in practical applications.

Design Configuration of Water Quality Monitoring System in Surabaya

Water have been important needs for human life in many sectors such as in industry, agriculture, and household that its quality must be conserved so is in Surabaya city. The quality of water could influence the quality of human life directly, thus it is important to have an integrated water quality monitoring system. Information regarding water quality monitoring such as pH, dissolved oxygen, turbidity, and conductivity were collected to produce a periodic decision for controlling, analyzing, and fixing the condition of the water. This paper proposed a design configuration of water quality monitoring system for tap water in Surabaya. First, a comparison study of water quality monitoring technology in terms of area, parameter, and methodology from the previous researchers is presented. From the study, the design configuration of water quality monitoring system to be implemented in Surabaya is concluded. The data collection method is better to be done by using Internet of Things (IoT) technology where it is possible to do multiple data type and multiple point real-time data collection throughout the water distribution network remotely.

Review on water quality monitoring technologies

<span>Water quality monitoring is always the prior element to ensure the drinking water is safe to be consumed. A lot of researches have been carried out over the past decades to design and develop a robust and cost-effective water monitoring system. The conventional water monitoring techniques were based on laboratory instruments which is time-consuming and laborious. Furthermore, it is not suitable when the water sampling point is far from the commercial laboratory centres. These limitations were then been solved by the developments of portable testing kits and microwave technique. The microwave techniques such as spectroscopy techniques and microwave sensor approach have improved the water quality monitoring experience which is convenient without sacrificing the measurement accuracy and sensitivity. Its portability enables the on-site measurement at rural areas and thus reduce the transportation and manpower cost. This paper intends to review the water contaminant detection techniques which include standardized drinking water parameter testing and microwave-based in terms of physical, chemical and microbiological parameters. Furthermore, this review also emphasizes the current trend of the water quality testing method in microwave technique. At the end of this paper, a significant advantages and drawbacks of the techniques are summarized, and recommendations are provided for future development in the water quality monitoring.</span>

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Research on Water Quality Monitoring Technology of Industrial Park Based on Internet of Things and Cloud Computing

Aiming at the problems of high cost, slow response and unstable data in industrial wastewater quality monitoring in industrial parks, this paper proposes a water quality monitoring system for industrial parks based on Internet of Things and cloud computing technology by studying data acquisition technology of water quality sensors, wireless networking technology of water quality acquisition equipment, connection technology between equipment and cloud computing platform, and end of data model interface. The monitoring technology is also applied in the water quality monitoring system of an industrial park in Hebei Province, China. The data response is timely and the pollution alarm is accurate, which improves the level of water pollution control.

Research on Marine Oil Spill Pollution Detection Based on Image Recognition Algorithm

With the development of offshore oil exploration and transportation activities, the threat of oil spill disasters to the marine ecological environment and even human beings is increasing. How to deal with the harm caused by oil spill is urgent. This paper introduces and analyzes the marine oil pollution situation and the current situation of marine oil pollution in China, analyzes the sea surface and sea surface marine monitoring technology, through marine monitoring technology and based on pattern recognition technology marine oil pollution detection technology, seeks to solve the oil spill pollution in time detection and image processing recognition based water quality monitoring technology, in order to assist in rapid intelligent recovery and clear oil pollution treatment.

Study on the application of water quality monitoring technology based on unmanned ship

Study on the application of water quality monitoring technology based on unmanned ship

Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications.

Water monitoring technologies are widely used for contaminants detection in wide variety of water ecology applications such as water treatment plant and water distribution system. A tremendous amount of research has been conducted over the past decades to develop robust and efficient techniques of contaminants detection with minimum operating cost and energy. Recent developments in spectroscopic techniques and biosensor approach have improved the detection sensitivities, quantitatively and qualitatively. The availability of in-situ measurements and multiple detection analyses has expanded the water monitoring applications in various advanced techniques including successful establishment in hand-held sensing devices which improves portability in real-time basis for the detection of contaminant, such as microorganisms, pesticides, heavy metal ions, inorganic and organic components. This paper intends to review the developments in water quality monitoring technologies for the detection of biological and chemical contaminants in accordance with instrumental limitations. Particularly, this review focuses on the most recently developed techniques for water contaminant detection applications. Several recommendations and prospective views on the developments in water quality assessments will also be included.

Application of Unmanned Aerial Vehicle Imagery for Algal Bloom Monitoring in River Basin

The purpose of this study is to enhance control capability of river water quality and environment by developing algal bloom monitoring technology using Unmanned Aerial Vehicle. The study acquired remote sensing data using UAV on the midstream area of Nakdong River, one of four major rivers in South Korea. The study also conducted water quality analysis and spectral reflectance measuring in the field to identify emergence aspect of phytoplankton in waters of the river and to induce algal bloom detection index equation through wavelength mixing of sensors. The result showed that the result values of algal bloom detection obtained from wavelength band mixing had strong positive correlation with algae standing crops. Besides, the results of analyzing spatial distribution of algal bloom of the overall investigated water area showed that the area of 0.22 km2 ~ 0.26 km2 had severity equivalent to the level of warning (5,000 ~ 1,000,000 cells/mL) in 2015 criteria for issuing algae warnings. It is expected that when this remote water quality and pollution monitoring technology is applied in the field, it would be able to improve capabilities to deal with the river water quality and pollution at the early stage.