scholarly journals Real-Time Water Quality Monitoring System- Analysis Of Pashan Lake, Maharashtra, India

2019 ◽  
Vol 8 (6) ◽  
pp. 2331-2335 ◽  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
System Analysis ◽  
Water Quality Monitoring ◽  
Remote Access ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Notification System ◽  
Quality Status

Pashan Lake in Pune, Maharashtra, India is one of the ancient man-made lakes constructed during British era majorly as a source of water supply for the neighboring colony. Over a while, the lake has switnessed severe degradation of water quality owing to heavy deforestation on neighboring hills, hyacinth formation, industrial effluents, and various anthropogenics activities. A consistent rise in pollution is reported, making the lake water non-potable. Recently, the monitoring and analysis of the lake's water quality status are under consideration to check the suitability of water for drinking. Further, this can aid in planning suitable measures to reduce pollution levels. To address such need of real-time water quality data aforementioned, this paper proposes an online portable water quality monitoring and notification system. An Internet of things(IoT) based platform has been developed with the ability to sense, record, process and wirelessly transmit water quality data. Such platforms enable remote access to data about quality status of any water resource. Further, the developed system has been deployed in Pashan Lake and the results so obtained have been discussed.

scholarly journals Monitoring surface water quality using social media in the context of citizen science

2017 ◽  
Vol 21 (2) ◽  
pp. 949-961 ◽  
Author(s):  
Hang Zheng ◽  
Yang Hong ◽  
Di Long ◽  
Hua Jing
Keyword(s):  
Water Quality ◽  
Social Media ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Future Research ◽  
Water Quality Data ◽  
Essential Information

Abstract. Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.

scholarly journals An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 715
Author(s):  
Xiaolei Wang ◽  
Haitao Wei ◽  
Nengcheng Chen ◽  
Xiaohui He ◽  
Zhihui Tian
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Process Models ◽  
Quality Monitoring ◽  
Quality Data ◽  
Observation Data ◽  
Process Ontology ◽  
Water Quality Data ◽  
Data Process ◽  
Monitoring Process

The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

scholarly journals A Low-Cost Water Quality Monitoring System for the Ayeyarwady River in Myanmar Using a Participatory Approach

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1984 ◽  
Author(s):  
Thanda Thatoe Nwe Win ◽  
Thom Bogaard ◽  
Nick van de Giesen
Keyword(s):  
Water Quality ◽  
Mobile Phone ◽  
Low Cost ◽  
School Teacher ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Lower Accuracy

Newly developed mobile phone applications in combination with citizen science are used in different fields of research, such as public health monitoring, environmental monitoring, precipitation monitoring, noise pollution measurement and mapping, earth observation. In this paper, we present a low-cost water quality mobile phone measurement technique combined with sensor and test strips, and reported the weekly-collected data of three years of the Ayeyarwady River system by volunteers at seven locations and compared these results with the measurements collected by the lab technicians. We assessed the quality of the collected data and their reliability based on several indicators, such as data accuracy, consistency, and completeness. In this study, six local governmental staffs and one middle school teacher collected baseline water quality data with high temporal and spatial resolution. The quality of the data collected by volunteers was comparable to the data of the experienced lab technicians for sensor-based measurement of electrical conductivity and transparency. However, the lower accuracy (higher uncertainty range) of the indicator strips made them less useful in the Ayeyarwady with its relatively small water quality variations. We showed that participatory water quality monitoring in Myanmar can be a serious alternative for a more classical water sampling and lab analysis-based monitoring network, particularly as it results in much higher spatial and temporal resolution of water quality information against the very modest investment and running costs. This approach can help solving the invisible water crisis of unknown water quality (changes) in river and lake systems all over the world.

scholarly journals Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2411
Author(s):  
Seulbi Lee ◽  
Jaehoon Kim ◽  
Jongyeon Hwang ◽  
EunJi Lee ◽  
Kyoung-Jin Lee ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Euclidean Algorithm ◽  
Quality Monitoring ◽  
Quality Data ◽  
Time Warping ◽  
Water Quality Data ◽  
Dynamic Time

It is essential to monitor water quality for river water management because river water is used for various purposes and is directly related to the health and safety of a population. Proper network installation and removal is an important part of water quality monitoring and network operation efficiency. To do this, cluster analysis based on calculated similarity between measuring stations can be used. In this study, we measured the similarities between 12 water quality monitoring stations of the Bukhan River. River water quality data always have a station-dependent time lag because water flows from upstream to downstream; therefore, we proposed a Dynamic Time Warping (DTW) algorithm that searches for the minimum distance by changing and comparing time-points, rather than using the Euclidean algorithm, which compares the same time-point. Both Euclidean and DTW algorithms were applied to nine water quality variables to identify similarities between stations, and K-medoids cluster analysis were performed based on the similarity. The Clustering Validation Index (CVI) was used to select the optimal number of clusters. Our results show that the Euclidean algorithm formed clusters by mixing mainstream and tributary stations; the mainstream stations were largely divided into three different clusters. In contrast, the DTW algorithm formed clear clusters by reflecting the characteristics of water quality and watershed. Furthermore, because the Euclidean algorithm requires the lengths of the time series to be the same, data loss was inevitable. As a result, even where clusters were the same as those obtained by DTW, the characteristics of the water quality variables in the cluster differed. The DTW analysis in this study provides useful information for understanding the similarity or difference in water parameter values between different locations. Thus, the number and location of required monitoring stations can be adjusted to improve the efficiency of field monitoring network management.

Main knowledge gaps in critical zone processes and behaviour: Extracting information from water quality time-series data and models outputs

2020 ◽  
Author(s):  
Zahra Thomas ◽  
Ophélie Fovet ◽  
Qian Zhang ◽  
Channa Rajanayaka ◽  
Christian Zammit ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Critical Zone ◽  
Quality Monitoring ◽  
Quality Data ◽  
Knowledge Gaps ◽  
Water Quality Data ◽  
Insight Into

<p>In the last few decades, the degradation of water quality and resulting regulations, such as the European Water Framework Directive, the United States Clean Water Act, and the New Zealand Resource Management Act 1991 have promoted water quality monitoring in terms of parameter richness, spatial density and high temporal resolution. Long-term catchment observatories have been strengthened to gain insight into hydrological and biogeochemical processes. New technologies have been developed and deployed to collect more in situ water quality data at higher frequencies. Thus, water quality monitoring around the world has produced a large amount of data from research catchments but also from national monitoring networks. Despite these efforts, water quality data are highly heterogeneous in terms of targeted parameters, measurement methods, sampling frequencies. Also, accessibility to water samples differ from each hydrological compartment (stream, groundwater, soil water and precipitation). Among water quality time-series, higher sampling frequencies are available for stream water where monitoring is relatively easy to carry out generating a high amount of data. However, groundwater data are rare since monitoring and access is relatively difficult. Also, the aim of monitoring network evolved with time. In fact, networks are usually established for a specific purpose which is changing with time and the questions the network is trying to answer? This raise the issue of spatial and temporal flexibility- multi purpose network and the use of network to support model development which could be seen as a “theoretical” monitoring network.</p><p>The objective of this talk is to present a review of methods used for analysing temporal water quality signals and models outputs, based on a panel of examples from few but densely monitored environmental research observatories. Such infrastructures also give an insight into critical zone (CZ) research that help to build a transdisciplinary community to identify the main knowledge gaps in CZ processes and behaviour.</p>

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

scholarly journals A Reliable Wireless System for Water Quality Monitoring and Level Control

2016 ◽  
Vol 8 (3) ◽  
pp. 1 ◽  
Author(s):  
Hyder Khaleeq ◽  
Ali Abou-ElNour ◽  
Mohammed Tarique
Keyword(s):  
Water Quality ◽  
Distribution System ◽  
Drinking Water Treatment ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Level Control ◽  
Time Data ◽  
Wireless System ◽  
Water Quality Data

With the ever increasing growth in population water quality monitoring has become a critical issue in the recent years. Water quality monitoring is very important for aquaculture, waste water management, drinking water treatment, water distribution system, and other environmental applications. Recently numerous researchers have been initiated to build wireless system for water quality monitoring (WSWQM). The two fold objectives of WSWQM are (a) monitoring of water quality from a remote location with minimum supervision, and (b) initiating immediate corrective actions to maintain the required water quality standard. In this paper we present a system model for WSWQM. In this system we integrate a number of sensors, transmitters, receiver, myRIO microcontroller, and IEEE 802.11 Wi-Fi technology. The sensors generate water quality data including pH, conductivity, and temperature. The real-time data are then sent wirelessly to a local control unit for analyzing, recording, and displaying. The system is also able to send alarm messages automatically to a remote management center when water quality fails to meet the required standard. In order to ensure high accuracy and reliability we use industry standard sensors and instruments to implement this system.

scholarly journals The European Collaborative Project SOLUTIONS developed models to provide diagnostic and prognostic capacity and fill data gaps for chemicals of emerging concern

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Jos van Gils ◽  
Leo Posthuma ◽  
Ian T. Cousins ◽  
Claudia Lindim ◽  
Dick de Zwart ◽  
...  
Keyword(s):  
Water Quality ◽  
Ecological Status ◽  
Real Life ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
System Level ◽  
Quality Data ◽  
Chemical Pollution ◽  
Concentration Data ◽  
Water Quality Data

Abstract The European Union Water Framework Directives aims at achieving good ecological status in member states’ water bodies. Insufficient ecological status could be the result of different interacting stressors, among them the presence of many thousands of chemicals. The diagnosis of the likelihood that these chemicals negatively affect the ecological status of surface waters or human health, and the subsequent development of abatement measures usually relies on water quality monitoring. This gives an incomplete picture of chemicals’ contamination, due to the limited number of monitoring stations, samples and substances. Information gaps thus limit the possibilities to protect against and effectively manage chemicals in aquatic ecosystems. The EU FP7 SOLUTIONS project has developed and validated a collection of integrated models (“Model Train”) to increase our understanding of issues related to emerging chemicals in Europe’s river basins and to complement information and knowledge derived from field data. Unlike pre-existing models, the Model Train is suitable to model mixtures of thousands of chemicals, to better approach a “real-life” mixture exposure situation. It can also be used to model new chemicals at a stage where not much is known about them. The application of these models on a European scale provides temporally and spatially variable concentration data to fill gaps in the space, time and substance domains left open by water quality monitoring, and it provides homogeneous data across Europe where water quality data from monitoring are missing. Thus, it helps to avoid overlooking candidate chemicals and possible hot spots for management intervention. The application of the SOLUTIONS Model Train on a European scale presents a relevant line of evidence for water system level prognostic and diagnostic impact assessment related to chemical pollution. The application supports the design of cost-effective programmes of measures by helping to identify the most affected sites and the responsible substances, by evaluating alternative abatement options and by exploring the consequences of future trends.

scholarly journals Predicting sediment and nutrient concentrations from high-frequency water-quality data

2019 ◽  
Author(s):  
Catherine Leigh ◽  
Sevvandi Kandanaarachchi ◽  
James M. McGree ◽  
Rob J. Hyndman ◽  
Omar Alsibai ◽  
...  
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Quality Reporting ◽  
River Level ◽  
Manual Sampling

AbstractWater-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

scholarly journals Remote sensing-based algorithms for water quality monitoring in Olushandja Dam, north-Central Namibia

2020 ◽  
Author(s):  
Taimi S. Kapalanga ◽  
Zvikomborero Hoko ◽  
Webster Gumindoga ◽  
Loyd Chikwiramakomo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Linear Relationship ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Measured Water

Abstract Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality.

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