Open Access
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Rapid detection of anomalous operating conditions within a water distribution network is desirable for the protection of the network against both accidental and malevolent contamination events. In the absence of a suite of in-situ, real-time sensors that can accurately identify a wide range of contaminants, we focus on detecting changes in water quality through analysis of existing data streams from in-situ water quality sensors. Three different change detection algorithms are tested: time series increments, linear filter and multivariate distance. Each of these three algorithms uses previous observations of the water quality to predict future water quality values. Large deviations between the predicted or previously measured values and observed values at future times indicate a change in the expected water quality. The definition of what constitutes a large deviation is quantified by a threshold value applied to the observed differences. Both simulated time series of water quality as well as measured chlorine residual values from two different locations within a distribution network are used as the background water quality values. The simulated time series are created specifically to challenge the change detection algorithms with bimodally distributed water quality values having a square wave and sin wave time series, with and without correlated noise. Additionally, a simulated time series resembling observed water quality time series is created with different levels of variability. The algorithms are tested in two different ways. First, background water quality without any anomalous events are used to test the ability of each algorithm to identify the water quality value at the next time step. Summary statistics on the prediction errors as well as the number of false positive detections quantify the ability of each algorithm to predict the background water quality. The performance of the algorithms with respect to limiting false positives is also compared against a simpler “set point” approach to detecting water quality changes. The second mode of testing employs events in the form of square waves superimposed on top of modeled/measured background water quality data. Three different event strengths are examined and the event detection capabilities of each algorithm are evaluated through the use of receiver operating characteristic (ROC) curves. The area under the ROC curve provides a quantitative basis of comparison across the three algorithms. Results show that the multivariate algorithm produces the lowest prediction errors for all cases of background water quality. A comparison of the number of false positives reported from the change detection algorithms and a set point approach highlights the efficiency of the change detection algorithms. Across all three algorithms, most prediction errors are within one standard deviation of the mean water quality. The event detection results show that the best performing algorithm varies across different background water quality models and simulated event strength.
37Daily changes in household water access and quality in urban slums undermine global safe water monitoring programmes.
Daily changes in household water access and quality in urban slums undermine global safe water monitoring programmes.
40An Integrated Deep Neural Network Approach for Large-Scale Water Quality Time Series Prediction
The prediction of water quality has great significance for the management of water environment and the protection of water resources. Traditional water quality prediction methods are relatively simple, and linear models are often used to predict water quality. However, such models limit the accuracy of prediction and lack the analysis of nonlinear characteristics of water quality. In addition, due to the complex water environment, the water quality time series has large noise, which makes it difficult for traditional models to effectively predict water quality indicators under complex environmental conditions. To solve this problem, this work proposes an integrated prediction method that combines Savitzky-Golay filter with Long Short-Term Memory (LSTM)-based Encoder-Decoder neural network to predict water quality at the next time interval. In this approach, the water quality time series is first smoothed by Savitzky-Golay filter, and LSTM can extract valid information from complex time series. Based on them, an integrated model is for the first time established and can well characterize statistical characteristics. Experimental results demonstrate that it achieves better prediction results than some typical prediction methods.
Read moreControls of space-time variance of water chemistry in river networks
River water quality is essential for ecosystem function and human well-being, yet anthropogenic impacts, such as pollutant input from agricultural activities or waste water, threaten water resources. An effective design of water quality monitoring networks is crucial to understanding and mitigating these impacts. However, optimizing monitoring is challenging because of the spatial and temporal variability of water quality, i.e. solute concentrations, driven by landscape and hydroclimatic heterogeneity.This study uses a stochastic modeling approach applied to artificial river networks to explore how landscape and hydroclimatic heterogeneity at different spatial scales shape the space-time variance of water chemistry. Building on a previously developed headwater-scale stochastic water quality model, we simulated daily discharge and solute concentration time series for equal area subcatchments within these networks. We systematically varied the spatial configuration of subcatchment solute source concentration across the network, the source zone distribution within subcatchments, and imposed different hydroclimatic regimes. Simulated discharge and solute loads were routed through the network, incorporating in-stream processing, to generate water quantity and quality time series for each network node. A global sensitivity analysis using the Morris method was performed to assess the influence of key parameters on the space-time variance of solute concentration.The results of the sensitivity analysis revealed that the macro-scale landscape configuration of source concentrations controls the spatial variability of solute concentrations in rivers and spatial stability, i.e. the persistence of spatial patterns through time. The relative influence of structured and random landscape heterogeneity on spatial variability was scale dependent, with distinct patterns observed across different stream orders. In contrast, subcatchment-scale processes, such as the source zone distribution, and the hydroclimatic forcing regulate temporal variability of water quality and synchrony between subcatchments. We conclude that optimal water quality monitoring network design should thus quantify spatial and temporal variability across scales, leveraging concepts like spatial stability and synchrony to maximize information gained and explicitly accounting for multiscale landscape heterogeneity.
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Estimating Recreational User Day Value and Impacts of Congestion and Water Quality: Application to Salt Ponds, Rhode Island
The objective of this study is to estimate values associated with recreation in three Rhode Island salt ponds: Ninigret, Quanochontaug and Point Judith pond. The study estimates values per recreation day, as well as changes in values associated with changes in water quality and congestion. First the study applies the Travel Cost Method to estimate the user-day value for recreation in Rhode Island salt ponds, based on an on-site survey. The study then applies the Conjoint stated preference approach to estimate changes in recreational values associated with changes in water quality and congestion at the recreation sites. Next the study provides a more robust estimation of the opportunity cost of time spent to traveling to visit the salt ponds by calibrating the opportunity cost of time used in Travel Cost Method using stated preference regarding travel distance obtained from responses to the Conjoint Analysis. The data were collected by administering a survey of visitors at coastal salt ponds, in Rhode Island during July and August of 2015. The survey included questions about the travel related information, followed by a series of choice questions that asked the respondents to select the most preferred site from 3 hypothetical sites described in terms of different levels of water quality, congestion, parking lot size, travel distance and entrance fee. A total of 309 visitors responded to the survey, of which 287 completed the entire survey. The study estimates the value per recreational user day to be approximately $17. It also estimates a total of approximately 161,576 visits at the three salt ponds during July and August. Applying these results, the annual recreational value of the three salt ponds is estimated to be approximately $2.8 million for the month of July and August. Water quality is currently rated as fair in the three Salt Ponds. Conjoint Analysis Stated Preference Method estimates a Willingness To Pay (WTP) of $17 to avoid poor water quality, WTP of $29 to improve water quality to good from fair, and an additional WTP of $12 from good to excellent. Stated Preference Conjoint results estimate that recreational users are willing to pay $23 per user-day to avoid sites becoming over-congested. The incremental willingness to pay to reduce congestion below the status quo level is not statistically significant. The opportunity cost of time is generally specified to be a fraction of the wage rate. However, there is no consensus in the literature
Read more7Trends in Water Quality in Laborec River, Slovakia
Trends in Water Quality in Laborec River, Slovakia
28Chamoli disaster: pronounced changes in water quality and flood plains using Sentinel data
The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.
Read more14Water quality monitoring with online change-point detection methods
We develop an approach for water quality time series monitoring and contamination event detection. The approach combines affine projection algorithms and an autoregressive (AR) model to predict water quality time series. Then, we apply online change-point detection methods to the estimated residuals to determine the presence, or not, of contamination events. Particularly, we compare the performance of four change-point detection methods, namely, sequential probability ratio test (SPRT), cumulative sum (CUSUM), binomial event discriminator (BED), and online Bayesian change-point detection (OBCPD), by using residuals obtained from four water quality time series, chlorine, conductivity, total organic carbon, and turbidity. Our fundamental criterion for the performance evaluation of the four change-point detection methods is given by the receiver operating characteristic (ROC) curve which is characterized by the true positive rate as a function of the false positive rate. We highlight with detailed experiments that OBCPD provides the best performance for large contamination events, and we also provide insight on the choice of change-point detection algorithms to consider for designing efficient contamination detection schemes.
Read more1Study of the Water Temperature and Quality Change with Hot and Cold Springs
Taiwan hot spring is rich in resources, in addition to the sodium bicarbonate spring, but also includes cold spring. Cold spring Including low temperature and rich in free carbon dioxide. Because the hot springs and cold spring characteristics are different and need for heating or cooling before soaking. Heating and cooling action of hot spring water quality of the situation is also different. Therefore, this study explores the relationship between water quality and temperature. In this study, four kind of cold springs were selected to temperature test. After heated the spring water, then let the springs cool down naturally. the test process at the same time measuring water quality changes. Draw relationship curves to observe the correlation between water temperature and water quality changes. The results show that the total dissolved solids (TDS) show a continuous increase during the test, which is the main factor affecting the cooling rate of hot spring. Conductivity is positively correlated with temperature, pH increased, free carbon dioxide decreases, oxidation reduction potential is higher with the reduction of the test process. Suao cold spring heating temperature exceed 50 °C, the free carbon dioxide value will drop below 500 ppm. Nantong cold spring temperature exceed 46 °C, the pH will be acidic to alkaline phenomenon.
Read more5A framework to identify critical dynamics of water quality for diagnosing river basin ecosystem resilience and management
This study proposed a novel framework to identify critical water quality dynamics as early warning signals for diagnosing changes in the resilience of river basin ecosystems. We established empirical linkages between the theoretical background of three resilience capacities (robustness, adaptability and transformability) and water quality dynamics. Then, the processes of resilience degradation and their risk transfer or accumulation have been identified based on the shifts among different states. The methods of time-domain analysis and frequency-domain analysis were integrated into this framework, aiming to identify gradual and transient responses of water quality and its periodic fluctuation characteristics at multiple temporal scales. The time-domain analysis methods obtained the trend, cumulative periodic fluctuation of water quality by extracting the key characteristic parameters from the time-series data. The wavelet transform methods were introduced into the frequency-domain analysis to reveal the water quality fluctuation patterns at specific temporal scales. We tested the proposed framework in a typical agriculture-intensive watershed in eastern China. The results showed that this framework can be effectively used to identify three resilience states of river basin ecosystems. The degraded resilience regions were mainly distributed in the downstream area, which was influenced by their specific land use/cover and different agricultural soil health conditions. Particularly, urban sewage discharge was the main cause of periodic fluctuation in water quality time series at multiple high-frequency scales. The theoretical background of resilience capacities was elaborated in non-equilibrium dynamics before resilience degradation. Thus, this novel framework could reveal the pollution processes and driving mechanisms in different river reaches, and it also can provide adaptive management suggestions according to resilience dynamics traits.
Read more2Water Quality Analysis using Pollution Index Method in Klampok Sub-watershed, Semarang Regency, Indonesia
The Klampok Sub-watershed flows in the Semarang Regency, Central Java, Indonesia, located in the upstream part of the Jragung Watershed, used as a source of irrigation. Klampok Sub-watershed experiences environmental pressures in the form of decreasing water quality because of various human activities. For this reason, this study aims to determine the changes in the water quality and pollution index of the Klampok Sub-watershed in 2016 and 2020. The water quality observations were carried out in 2020 utilizing the Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Nitrate, Zinc (Zn) and Copper (Cu) parameters. The laboratory analysis results were compared with water quality standards based on Government Regulation Number 22 of 2021 and the status of water quality based on the pollution index method under the Decree of the Minister of the Environment Number 115 of 2003. The study results showed a change in the quality and index of water pollution in 2016 and 2020. The study results revealed a decrease in the value of water quality on the parameters BOD, COD, Zn and Cu, which indicated that the water quality was improving. Moreover, the Klampok Sub-watershed pollution index decreased in 2016 and 2020 so that the Klampok Sub-watershed had quality criteria for lightly polluted water to moderately polluted to lightly polluted at each sampling point. Therefore, several efforts to control pollution and management of the Klampok Sub-watershed, such as community outreach, water quality monitoring and industrial compliance, are expected to improve so that the Klampok Sub-watershed's sustainability can provide various benefits to the community.
Read more37Does river restoration affect diurnal and seasonal changes to surface water quality? A study along the Thur River, Switzerland
Does river restoration affect diurnal and seasonal changes to surface water quality? A study along the Thur River, Switzerland
Read more8Analysis of Water Quality Conditions of Lake Hachiroko Using Fuzzy C-Means
Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake, and the effect of seasons on the quality, is important. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of the water quality parameters. To obtain maps that are in good agreement with the experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM), and considered the effect of specific disturbances and uncertainties on the remote sensing data. Furthermore, FCM using only remote sensing data creates estimation maps in which relative water surface conditions are classified. Therefore, determining the relationship between FCM results and water quality facilitates the creation of low-cost, high-frequency water quality estimation maps. Our results indicated that FCM was particularly effective in determining the presence of suspended solids (SS) during water quality analysis. However, the relationship between FCM results and water quality has not been determined in detail. In this study, we analyzed the water quality conditions of Lake Hachiroko with FCM using the data collected by the Advanced Space-borne Thermal Emission and Reflection Radiometer on Terra and, the Operational Land Imager on Landsat-8. In addition, FCM results were compared with the maps created by fuzzy regression analysis and the actual conditions of water pollution. The results indicated that (i) the maps created using FCM are effective in determining the water surface conditions, (ii) the FCM maps using data obtained during August and September have a strong relationship with biochemical oxygen demand (BOD) and SS, and (iii) the FCM maps using data obtained during May and June have a strong relationship with chemical oxygen demand (COD), SS, and total nitrogen (T-N).
Read moreStudy of Water Quality Sensors Locations Optimization and Signals Analysis Based on Dynamic Water Quality Path
It is an important task to real-time monitor the water quality in the water distribution networks. But it is difficult to select the representative locations from the whole water distribution networks because of the dynamical hydraulic status in the water distribution networks. The Lagrange method was used to analysis the water dynamic quality path of every node in every water quality time step during the water quality simulation in the networks. An objective function based on the node demand factors and the water dynamic quality path was represented to evaluate the water quality importance of each node in every water quality time step. So the optimal nodes that adapted to be fixed the sensors were selected from the networks. Fast Fourier Transformation and convolution method were used to analysis the signals collected by the water quality on-line sensors. The contamination source can be traced because the sensors impulse reaction matrix can be defined by the water dynamic path analysis. Dynamic water quality path was calculated out in an example water distribution networks. The optimal water quality sensors’ locations were defined by the water quality path calculation and the contamination sources were distinguished in a hypothetical condition through the sensors ‘ signals analysis. It shows that the water dynamic path analysis based on the dynamic node percent information is a useful tool to optimize the water quality sensors locations and the contamination source identification.
Read more7Analyzing multi-variate water quality signals for water quality monitoring station placement in water distribution systems
Placing fixed water quality monitoring stations in a water distribution system can greatly improve the security of the system via prompt detection of poor water quality. In the event that a harmful substance is injected into a water distribution system, large populations can be put at risk of exposure to the contaminant. Promptly detecting the presence of a contaminant will reduce the number of people put at risk of exposure. However, to protect against a wide variety of possible contaminants, a water quality monitoring station will need to identify contamination via recognition of anomalous changes in a suite of surrogate water quality indicators (chlorine, pH, etc.). This work attempts to place water quality monitoring stations within the water distribution at locations that best detect contamination events via surrogate water quality signals. Networks of water quality monitoring stations are designed to minimize the population affected prior to contamination event detection, and simultaneously minimize the expected number of false positive detections, under uncertain water quality conditions. Solutions generated in this study are compared to solutions designed via classical detection methods. Results show the sensor networks designed without consideration to detection via surrogate water quality parameters have higher false positive detection rates.
Read more2Counseling on Good Water Quality for the Cultivation of Batak Fish in Toba Lake Waters
This service aims to provide information about the value of good water quality and demonstrate directly the tools used to measure it which aim to cultivate Batak fish based on biological, physical and chemical parameters such as temperature, dissolved oxygen, carbon dioxide, pH, and provide information on the names of measuring devices. . This service has been carried out in Gorat Village, Toba Samosir Regency with the method of counseling and material discussion (exposure and question and answer), as well as the practice of measuring water quality externally and analyzing the results of its value. The results of the counseling show that many fish farmers do not understand the value of good water quality for batak fish cultivation and not many know the equipment that can be used to check water quality. From this counseling, it can be seen that the interest of fish farmers and the community is very high, and they become more aware of the value of good water quality for fish farming and know the equipment.
 Abstrak
 Pengabdian ini bertujuan untuk memberikan penyuluhan tentang nilai kualitas air yang baik dan mendemonstrasikan langsung alat yang digunakan mengukurnya yang bertujuan untuk membudidayakan ikan batak berdasarkan parameter biologi, fisika dan kimia seperti suhu, oksigen terlarut, karbondioksida, pH, serta memberikan informasi nama alat-alat pengukurnya. Pengabdian ini telah dilakukan di Desa Gorat, Kabupaten Toba Samosir dengan metode penyuluhan dan diskusi materi (paparan dan tanya jawab), serta praktek pengukuran kualitas air secara eksitu dan menganalisis hasil nilainya. Hasil penyuluhan menunjukan bahwa banyaknya petani ikan yang belum paham tentang nilai kualitas air yang baik untuk budidaya ikan batak serta belum banyak yang mengetahui peralatan yang dapat digunakan untuk memeriksa kualitas air. Dari penyuluhan ini terlihat animo petani ikan dan masyarakat sangat tinggi, dan mereka menjadi lebih paham tentang nilai kualitas air yang baik untuk budidaya ikan serta mengetahui peralatannya.
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