U.S. beach water quality monitoring

Shore & Beach ◽  
2021 ◽  
pp. 26-35
Author(s):  
Angelos Hannnides ◽  
Nicole Elko ◽  
Tiffany Roberts Briggs ◽  
Sung-Chan Kim ◽  
Annie Mercer ◽  
...  
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Local Governments ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Coastal Water Quality ◽  
Beach Water Quality ◽  
Beach Water

Coastal water quality is an important factor influencing public health and the quality of our nation’s beaches. In recent years, poor water quality has resulted in increased numbers of beach closures and corresponding negative impacts on tourism. This paper addresses some of the issues surrounding the management challenge of coastal water quality, in particular, beach water quality monitoring. For this effort, data on beach water quality monitoring activities conducted by states were assessed and synthesized. In total, 29 states were surveyed: 16 reported information for seawater; six reported for freshwater only; eight reported for both seawater and freshwater. Thresholds for advisories and closure vary nationally; however, all 29 states have established an online presence for their monitoring programs and display advisories and closures in real time, most often on spatial information (GIS) portals. Challenges in monitoring, prediction, and communication are assessed and discussed. Based on this assessment, the committee offers the following recommendations, as detailed in the text: • Standardization of water quality data and the distribution medium; • Enhanced public access to water quality monitoring data; • Consistent thresholds for swim advisories; • Water quality regulation reviews with stakeholder participation; • Enhanced predictive models incorporating rapid testing results; • Holistic water quality monitoring that includes indicators beyond fecal indicator bacteria; • Managing contaminants of emerging concern through identification, monitoring and control; and • Funding for water quality monitoring and reporting -- from federal, state, and local governments.

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.

A New Assessment Model for Coastal Water Quality Based on Nonlinear Mapping Theory

2013 ◽  
Vol 726-731 ◽  
pp. 1073-1077
Author(s):  
Ren Qiang Lu
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Water Quality Management ◽  
Mapping Method ◽  
Assessment Model ◽  
Quality Data ◽  
Nonlinear Mapping ◽  
Water Quality Data ◽  
Coastal Water Quality ◽  
Mapping Theory

A new assessment model for coastal water quality was proposed based on the nonlinear mapping theory. Taking the water quality monitoring data of Tianjins coastal marine as an example, firstly, the high-dimension water quality data were mapped to the two-dimension plane by nonlinear mapping method. Secondly, the water quality assessment model was established according to the position relationship of mapping points. Then, the water quality was assessed based on the model. Through application we could found the method proposed in this paper was simple and practicable. It is science and effectiveness of applying the nonlinear mapping method to assessment the water quality. It could be used to supply the decision support for the coastal water quality 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.

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