Water Quality Monitoring Methods Research Articles

Analysis of River Channel Change Using Remote Sensing Images at Kelantan River Basin

This study presents the utilities of remote sensing technique for analysis of river channel change using remote sensing images at Kelantan River Basin. Understanding river channel change is a key component in developing ecologically friendly and long-term fluvial projects, as well as a way to reduce maintenance costs. The project's study and conclusions will aid in determining how to recognise probable morphological change in river channels. Remote sensing is one of the effective methods for water quality monitoring through image analysis of study area. These tools provide valid means of studying river channel changes. In this study, river channel changes are monitored and quantified by using remote sensing and GIS in Kelantan River. This study focus on four interval years between year 1988 until year 2021 by using Landsat satellite images of 30 m spatial resolution. Landsat Thematic Mapper (TM) 5 images of three different years which are 1988, 1999 and 2010, and Landsat 8 of OLI/TIRS year 2021 were compared. The quantity of accretion and erosion was assessed using parcel-based geoprocessing in a GIS system. The locations are labelled with the year they were created. The area of each year is changing by study period of time for all four interval years 1988, 1999, 2010, and 2021 for research area 1 and 2. The results prove that the river channel in Kelantan is altering, where between 1988 and 2021, the Kelantan River's course has changed significantly. The years 2010 to 2021 are the most severely affected by erosion as a result of the flood in 2014 on the Kelantan River.

Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms.

Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH3-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH3-N for the period of 2018-2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.

IoT-based drinking water quality measurement: systematic literature review

Sustainable development throughout the world depends on several factors such as the economy, quality education, agriculture, industry, among others, but the environment is one of the most important. Industrialization and new land use plans have caused the proliferation of pollutants in water resources, which poses a serious public challenge. As outlined in the sustainable development goals (SDGs), innovative water quality monitoring methods are needed to ensure access to water, sustainable management and sanitation. In this sense, technologies are sought that contribute to the development and implementation of groundwater and surface water quality monitoring systems in real time, so that their parameters can be evaluated through descriptive analysis, in rural populations and areas of difficult access. Nowadays, the internet of things (IoT) and the development of modern sensors are more used, so this research reviews the latest technologies to monitor and evaluate water quality using the potential and possibilities of the IoT. The main contribution of this article is to present an overview of the state of the art of IoT applications and instrumentation for water quality monitoring, focusing on the latest innovations, in order to identify interesting and challenging areas that can be explored in future research.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology.

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH4-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN4-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH4-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH4-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

A Review on Water Quality Monitoring Methods Based on Electronics and Optical Sensing

Water pollution is a detrimental issue that can affect health, economy, society, flora, and fauna. Monitoring water quality is important to mitigate water pollution issues. The purpose of this research is to investigate several water quality monitoring methods based on electronics and optical sensing. Electronics and optical sensing are among common and popular methods used to monitor water quality. The smart platform is used to work together with electronics and optical sensors to assist users in controlling the system. Both methods have their own benefit such as electronics sensing being portable and easy to handle whereas optical sensing does not affect the water sample, leading to higher accuracy results. Thus, the selection of the suitable method depends on the consumer’s requirement, cost, budget, and time.

Pollutant Flux Estimation of the Lijiang River Based on an Improved Prediction-Correction Method

Pollutant flux estimation and the analysis of flux variations are the basis for water quality assessment and water pollution control. At present, pollution flux estimation has certain shortcomings, such as a low frequency of water quality monitoring and inadequate calculation methods. To improve the rationality and reliability of river pollution flux estimation results, an improved prediction-correction pollution flux estimation method was developed by combining the LOADEST model and the Kalman filtering algorithm. By establishing the regression equation between pollutant flux and daily discharge, the predicted pollution flux procedure can be calculated using the LOADEST model. In a subsequent step, the pollutant flux is corrected based on the Kalman filtering algorithm. The improved method was applied to estimate the fluxes of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) at the Guilin Section of the Lijiang River from 2010 to 2019. The estimated fluxes were in good agreement with the measured ones, with relative deviation values for COD, NH3-N, and TP of 2.27, 3.20, and 1.39%, respectively. The improved method can reasonably estimate fluctuations in river pollution fluxes without requiring more data. The results in the present study provide powerful scientific basis for pollutant flux estimation under low-frequency water quality monitoring.

Research on the Water Quality Intelligent Monitoring System of River Gushing Based on Federal Learning

Intelligent video surveillance technology is increasingly widely used in social production and life and plays an important role in economic development and environmental protection. This paper focuses on the field of water quality monitoring, and aims at the problems of traditional river gushing including difficulty of water quality monitoring, poor timeliness and insufficient automation, and proposes the intelligent water quality monitoring method from the perspective of intelligent image treatment. A federated learning system combined with a NVIDIA Jetson TX2 edge nodes is constructed by HD cameras deployed at various monitoring points of the gushing. On this basis, based on the convolutional neural network technology, the identification and monitoring of the river gushing water quality can effectively reduce the operation amount and water quality monitoring automation degree under the case of ensuring the accuracy.

Assessment of Spatial and Vertical Variability of Water Quality: Case Study of a Polymictic Polish Lake

UE regulations focus on methods of water quality monitoring and their use in rational management practices. This study investigated horizontal and vertical variations of electrical conductivity (EC), pH, dissolved oxygen (DO), and chlorophyll a (Chl-a) in a shallow polymictic lake. Monitoring of short-term variability of physical and chemical lake water parameters is a critical component in lake management, as it influences aquatic life. Based on the field research, maps of spatial distribution of the parameters were drawn. Using two methods: (1) a classical approach to water column measurements, from the top to the bottom (TB), in which the reference point is always a surface layer (SL), and (2) a newly introduced method of lake water quality monitoring based on a nearest neighbor (NN) approach; a comparison of higher and lower layers of the water column. By subtracting partial maps of spatial variability for different depths, final raster images were obtained. The NN method is rather absent in the limnology literature worldwide. Vertical and horizontal variability of the tested parameters in the polymictic, shallow Lake Bikcze (Poland) was presented in the results. In the presented paper, the commonly used TB method emphasized the role of the surface layer in shaping the variability of physicochemical parameters of lake waters. It shows a general trend of parameters’ changes from the top, to the bottom. The newly presented NN method, which has a major advantage in its simplicity and objectivity, emphasized structural differentiation within the range of variability. The nearest neighbor method was more accurate in showing the actual structure of fluctuation of parameters with higher fluctuation in the water column. Its advantage is a detailed recognition of the vertical variability of selected parameters in the water column. The method may be used regardless of the lake depth, its location in climatic zone, and/or region.

Research on Wireless Water Quality Monitoring Method Based on STM32

Since the 21st century, people’s production and life have caused water pollution to varying degrees, so real-time monitoring of water quality has become crucial. Based on STM32, this paper proposes a water quality monitoring method that can continuously monitor three water quality parameters: water temperature, PH value, and turbidity. The water quality monitoring method is mainly based on the extended circuit of the STM32 series single-chip microcomputer, including PH sensor control circuit module, temperature sensor circuit module, wireless network communication circuit module, turbidity sensor circuit module, etc. Then based on the C language to write the MCU data acquisition program and the PC program developed on the virtual instrument design language platform, the PC program is mainly composed of the main PC and the MCU communication, data analysis and processing, process monitoring and other subroutines. The water quality monitoring method proposed in this paper can detect different water quality in real time through wireless transmission. This new method is not only suitable for the future development of the current water quality monitoring market, but also has a very promising application prospect.

Research on the design of lake water intelligent monitor

Recently, my country’s industrial production has caused great damage to wat er resources, especially the pollution degree of inland lakes is getting worse every year. Therefore, the monitoring of the water quality of inland lakes in China has become i mportant as one of the basic measures to protect the lake’s water resources and enviro nment. At present, most water quality monitoring methods in China are still in a relati vely backward stage. The water quality monitoring methods have the disadvantages of l ow efficiency and insufficient timeliness of results, which cannot meet the needs of cur rent water quality monitoring. The purpose of this study is to design a lake water intel ligent monitor with autonomous mobility and sustainable uninterrupted monitoring capab ilities. To optimize the design of water quality monitor, this paper analyze the existing water quality monitors, summarizes the deficiencies that exist in the existing water qual ity monitor products and draws the use requirements of the product, and Propose the d esign direction of the new intelligent water quality monitor; At the same time, accordin g to the technical principles required for the design of water quality monitoring instrum ents, the feasibility of product design is analyzed; finally, the overall design of the lake water intelligent monitor with independent mobility and sustainable uninterrupted monit oring capability is realized.

Assessment of Water Quality Parameters in Real-Time Environment

Assessment of drinking water quality has been an important issue nowadays as the water available is severely polluted and can be the cause of diseases like cholera, diarrhea, dysentery, etc. The traditional methods for water quality monitoring require a high-labor-cost and tine consumption as these methods include a sample collection followed by lab-based chemical testing. In addition, the chemicals used in the testing are toxic and of high-cost. So, there is a need for real-time monitoring and chemical-free testing of water quality parameters. This paper presents a real-time water quality monitoring system based on the Raspberry Pi 3 development board and a Python framework. The water quality parameters utilized for water quality monitoring are temperature, pH, oxidation reduction potential, electrical conductivity, and dissolved oxygen and E. coli. The water quality sensors were interfaced with the designed embedded platform. Finally, the acquired parameters were compared with the benchmark equipment for validation.

Research on Fishery Water Quality Monitoring System Based on 6LoWPAN

A fishery water quality monitoring system, based on 6LoWPAN, is proposed which can automatically implement various kinds of supervising tasks according to the changing environment. This system is applied to water quality monitoring of fisheries, realizing remote automatic online monitoring of water quality parameters such as temperature, salt content, pH, and dissolved oxygen, which have a significant impact on the aquatic product growth environment. The monitoring centre software performs the received data analysis, processing, storage, graphic display and alarm, providing the best growth environment for aquatic products. Compared with the existing fishery water quality monitoring methods, the system uses the IPv6 over IEEE 802.15.4 standard to construct a wireless sensor network. 6LoWPAN technology is easier to integrate into larger networks and easier to integrate with Internet-based services. This system can effectively improve the level of water quality management, reduce the risk of aquaculture, and achieve sustainable development of the aquaculture industry.

MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING

Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.

Water Quality Monitoring and Standardization in China: A Review

Water quality monitoring is very important for environmental protection. At the same time, it is also an effective detection method for pollution control. In this paper, we review the standardization of water quality monitoring and in China systematically. Then, the differences in water quality standards between China and foreign countries are analyzed. Moreover, the water quality monitoring methods are investigated. Finally, the conclusion and perspective are given.

A novel approach for surface water quality modelling based on Landsat-8 tasselled cap transformation

ABSTRACT Updating the physicochemical properties of surface water is important to water quality researchers and decision-makers. Routine surface water quality monitoring methods are inadequate; whereas, remote sensing (RS) is a source of processing data in both spatial and temporal domains. However, RS is a good tool of monitoring freshwater ecosystems, accurate modelling of surface water quality parameters from satellite imagery is still a challenging task. High correlation between the adjacent satellite signals, which creates redundancy, is one of the main challenges that negatively affect the stability of the developed models. Therefore, this paper focuses on the development of a novel approach for modelling concentrations of both turbidity (T) and total suspended solids (TSS) using Landsat-8 tasselled cap transformation (TCT). As a result, robust models were developed between measurements from satellite-derived T and TSS and in situ measurements with coefficient of determination values of 0.854 and 0.812, respectively. These findings confirmed the possibility of using TCT to establish strong models for assessing surface water quality due to its capability of transforming a set of correlated data onto a convenient set of uncorrelated data, which maintain the major information associated with a physical phenomenon.

Silver-organic coordination networks for magnetic solid-phase extraction of trihalomethanes from environmental water samples: experimental and theoretical calculation study

Trihalomethanes (THMs) are the primary toxic and carcinogenic byproducts during disinfection of drinking water. THMs have been frequently detected in water body and posed a huge threat to human health. Thus, analyzing the trace levels of THMs in an accurate and rapid method for water quality monitoring is important. In this paper, silver-based organic coordination networks (Ag-OCN) were fabricated with different diameters under mild pH condition. After modification with magnet, Fe3O4 @ Ag-OCN as extractant was applied to the magnetic solid-phase extraction of THMs from water samples. Gas chromatography-tandem mass spectrometry was used for sample quantification and detection. The magnetic extractant displayed good linearity in the range of 0.03–10 ug/L, low limits of detection (1.41–10.13 ng/L), and good reproducibility (relative standard deviations < 6.31%). Moreover, density-functional theory (DFT) calculation was also applied to investigate the possible interaction mechanism. Combining the experimental data with theoretical calculation, results showed that Fe3O4 @ Ag-OCN was a potential magnetic material for the enrichment and extraction of formed THMs at trace levels from water samples.

Benchmarking inference methods for water quality monitoring and status classification

River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing

ABSTRACT Monitoring of inland water quality is of significant importance due to the increase in water quality related issues, especially within the Midwestern United States. Traditional monitoring techniques, although highly accurate, are vastly insufficient in terms of spatial and temporal coverage. Using a virtual constellation by harmonizing Landsat-8 and Sentinel-2 data a high temporal frequency dataset can be created at a relatively fine spatial scale. In this study, we apply a novel deep learning method for the estimation of blue-green algae (BGA), chlorophyll-α (Chl), fluorescent dissolved organic matter (fDOM), dissolved oxygen (DO), specific conductance (SC), and turbidity. The developed model is evaluated against previously studied machine learning methods and found to outperform multiple linear regression (MLR), support vector machine regression (SVR), and extreme learning machine regression (ELR) generating R2 of 0.91 for BGA, 0.88, 0.89, 0.93, 0.87, and 0.84 for Chl, DO, SC, and turbidity respectfully. This model is then applied to all available data ranging from 2013–2018 and time series for each variable were generated for four selected waterbodies. We then use the Empirical Data Analytics (EDA) anomaly detection method on the time series to identify abnormal data points. Upon further analysis, the EDA method successfully identifies abnormal events in water quality. Our results also demonstrate strong correlation between non-optically active variables such as SC with Chl and fDOM. The framework developed in this study represents an efficient and accurate empirical method for inland water quality monitoring at the regional scale.

Discussion on Quality Monitoring of Water by Wireless Sensor Networks

Water quality is one of the main aspect to control health and the state of diseases in people. Disorder akin to unstable water conditions have over 300 collection cases announced annually. Water quality is a measure of the plight of water aunt to any human need. Basin and estuary are the cable authority of drinking water, angling, flooding, and efficiency production, which appreciably depend on water aspect. Therefore, water aspect of basin and estuary should be kept at a certain level. This paper surveys the utilization of WSN in environmental monitoring and area monitoring, with particular attention on water quality. Various WSN based water quality monitoring methods advised by other composer are considered and evaluate, taking into account their coverage, energy and security involvement. In this paper we surveys the issues of quality monitoring of water and surveys the various sensors used for monitoring quality of water