scholarly journals Portable, Disposable, Paper-Based Microbial Fuel Cell Sensor Utilizing Freeze-Dried Bacteria for In Situ Water Quality Monitoring

ACS Omega ◽  
2020 ◽  
Vol 5 (23) ◽  
pp. 13940-13947 ◽  
Author(s):  
Jong Hyun Cho ◽  
Yang Gao ◽  
Jihyun Ryu ◽  
Seokheun Choi
Keyword(s):  
Water Quality ◽  
Fuel Cell ◽  
Microbial Fuel Cell ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Freeze Dried

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Fundamentals of in Situ Digital Camera Methodology for Water Quality Monitoring of Coast and Ocean

Sensors ◽  
2009 ◽  
Vol 9 (7) ◽  
pp. 5825-5843 ◽  
Author(s):  
Lonneke Goddijn-Murphy ◽  
Damien Dailloux ◽  
Martin White ◽  
Dave Bowers
Keyword(s):  
Water Quality ◽  
Digital Camera ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Field Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC

2017 ◽  
pp. 41-55 ◽  
Author(s):  
Caitlyn C. Mayer ◽  
Khalid A. Ali
Keyword(s):  
Water Quality ◽  
South Carolina ◽  
Chlorophyll A ◽  
Algal Species ◽  
Water Quality Monitoring ◽  
Water Systems ◽  
Quality Monitoring ◽  
Least Squares Regression ◽  
Monitoring Methods

The Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from an influx of contaminants, leading to hypoxia, fish kills, and eutrophication. Conventional in situ water quality monitoring methods are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color-producing agents (CPAs), including chlorophyll-a, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. In this study, field, laboratory, and historical land use land cover (LULC) data were collected during the summers of 2002, 2011, 2015, and 2016. The results indicated higher levels of chlorophyll, ranging from 2.94 to 12.19 μg/L, and TSM values were from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. A model was developed using multivariate, partial least squares regression (PLSR) to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.49; RMSE = 1.8 μg/L) and TSM (R2 = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. The results from this study provide a strong foundation for the future of water quality monitoring and for the protection of biodiversity in the ACE basin.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

scholarly journals Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

2018 ◽  
Vol 378 ◽  
pp. 85-92 ◽  
Author(s):  
Ronald Muchini ◽  
Webster Gumindoga ◽  
Sydney Togarepi ◽  
Tarirai Pinias Masarira ◽  
Timothy Dube
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Information ◽  
Web Portal

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

Wetland Ecosystem Monitoring Through Water Quality Perspectives

2022 ◽  
pp. 51-70
Author(s):  
Shahid Ahmad Dar ◽  
Sami Ullah Bhat ◽  
Sajad Ahmad Dar
Keyword(s):  
Water Quality ◽  
Field Testing ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Physical Parameters ◽  
Chemical Parameters ◽  
Ecosystem Monitoring ◽  
Physico Chemical

Water quality monitoring is an important tool in determining the safety and suitability of water for various desired and intended uses. The procedures involved in the evaluation of water quality are numerous and multifaceted. Therefore, taking into consideration the specific objectives of water quality monitoring, sampling design is of vital importance. Most of the physical parameters of water quality are determined via in-situ measurements using modern testing equipment/field testing kits. Although there are some good field-based sensors that are being used for evaluation of water quality, the chemical parameters traditionally are mostly analyzed through laboratory-based experiments. This chapter is aimed to offer an inclusive knowledge and insights on the importance and assessment of physico-chemical parameters that are of high priority for monitoring the water quality of wetlands.

Installation Matters: Implications for In Situ Water Quality Monitoring

2021 ◽  
Vol 57 (3) ◽  
Author(s):  
Elizabeth G. Erwin ◽  
Daniel L. McLaughlin ◽  
Ryan D. Stewart
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring
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