scholarly journals Abiotic control of underwater light in a drinking water reservoir: Photon budget analysis and implications for water quality monitoring

2015 ◽  
Vol 51 (8) ◽  
pp. 6290-6310 ◽  
Author(s):  
Shohei Watanabe ◽  
Isabelle Laurion ◽  
Stiig Markager ◽  
Warwick F. Vincent
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Reservoir ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Underwater Light ◽  
Drinking Water Reservoir ◽  
Budget Analysis ◽  
Abiotic Control

Methodological Approaches to Organization of Drinking Water Quality Monitoring Programs

2020 ◽  
pp. 4-8
Author(s):  
Yu.A. Novikova ◽  
I.O. Myasnikov ◽  
A.A. Kovshov ◽  
N.A. Tikhonova ◽  
N.S. Bashketova
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Supply ◽  
Cold Water ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring ◽  
Water Supply Systems ◽  
Monitoring Programs ◽  
Supply Systems

Summary. Introduction: Drinking water is one of the most important environmental factors sustaining life and determining human health. The goal of the Russian Federal Clean Water Project is to improve drinking water quality through upgrading of water treatment and supply systems using advanced technologies, including those developed by the military-industrial complex. The most informative and reliable sources of information for assessing drinking water quality are the results of systematic laboratory testing obtained within the framework of socio-hygienic monitoring (SGM) and production control carried out by water supply organizations. The objective of our study was to formulate approaches to organizing quality monitoring programs for centralized cold water supply systems. Materials and methods: We reviewed programs and results of drinking water quality laboratory tests performed by Rospotrebnadzor bodies and institutions within the framework of SGM in 2017–2018. Results: We established that drinking water quality monitoring in the constituent entities of the Russian Federation differs significantly in the number of monitoring points (566 in the Krasnoyarsk Krai vs 10 in Sevastopol) and measured indicators, especially sanitary and chemical ones (53 inorganic and organic substances in the Kemerovo Region vs one indicator in the Amur Region). Discussion: For a more complete and objective assessment of drinking water quality in centralized cold water supply systems, monitoring points should be organized at all stages of water supply with account for the coverage of the maximum number of people supplied with water from a particular network. Thus, the number of points in the distribution network should depend, inter alia, on the size of population served. In urban settlements with up to 10,000 inhabitants, for example, at least 4 points should be organized while in the cities with more than 3,000,000 inhabitants at least 80 points are necessary. We developed minimum mandatory lists of indicators and approaches to selecting priority indices to be monitored at all stages of drinking water supply.

Turning Routine Data into Systems Insight: Multivariate Analysis of Water Quality Dynamics in a Major Drinking Water Reservoir

2020 ◽  
Vol 25 (4) ◽  
pp. 565-579
Author(s):  
Azadeh Golshan ◽  
Craig Evans ◽  
Phillip Geary ◽  
Abigail Morrow ◽  
Zoe Rogers ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Multivariate Analysis ◽  
Water Reservoir ◽  
Routine Data ◽  
Drinking Water Reservoir

Sediment record of mining legacy and water quality from a drinking-water reservoir, Aztec, New Mexico, USA

2020 ◽  
Vol 79 (17) ◽  
Author(s):  
Johanna M. Blake ◽  
Jeb E. Brown ◽  
Christina L. Ferguson ◽  
Rebecca J. Bixby ◽  
Naomi T. Delay
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
New Mexico ◽  
Water Reservoir ◽  
Sediment Record ◽  
Drinking Water Reservoir

Monitoring and modelling mining impacts on water quality in Chindwin River Basin, Myanmar

2020 ◽  
Author(s):  
Thanapon Piman ◽  
Chayanis Krittasudthacheew ◽  
Shakthi K. Gunawardanaa ◽  
Sangam Shresthaa
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Drinking Water ◽  
Water Treatment ◽  
River Basin ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Mining Activities

<p>The Chindwin River, a major tributary of the Ayeyarwady River in Myanmar, is approximately 850 km long with a watershed area of 115,300 km<sup>2</sup>. The Chindwin River is essential for local livelihoods, drinking water, ecosystems, navigation, agriculture, and industries such as logging and mining. Over the past two decades, Myanmar’s rapid economic development has resulted in drastic changes to socio-economic and ecological conditions in the basin. Water users in the basin reported that there is a rapid extension of gold and jade mining and they observed a noticeable decline in water quality along with increased sedimentation and turbidity. So far, however, Myanmar has not undertaken a comprehensive scientific study in the Chindwin River Basin to assess water quality and sources of water pollution and to effectively address issues of river basin degradation and concerns for public health and safety. This study aims to assess the status of water quality in the Chindwin River and the potential impact of mining activities on the water quality and loading through monitoring program and modeling approach. 17 locations in the upper, middle and lower parts of the Chindwin River Basin were selected for water quality monitoring. These sites are located near Homalin, Kalewa, Kani and Monywa townships where human activities and interventions could affect water quality. Water quality sampling and testing in the Chindwin River was conducted two times per year: in the dry season (May-June) and in the wet season (September-October) during 2015-2017. We monitored 21 parameters including heavy metals such as Lead (Pb), Mercury (Hg), Copper (Cu) and Iron (Fe). The observed values of Mercury in Uru River in the upper Chindwin River Basin which located nearby gold mining sites shown higher than the WHO drinking standard. This area also has high values of turbidity and Total Suspended Solid. The SHETRAN hydrological model, PHREEQC geochemical model and LOADEST model were used to quantify the heavy metal loads in the Uru River. Results from scenario analysis indicate an increase in Arsenic and Mercury load under increment of concentration due to expansions in mining areas. In both baseline and future climate conditions, the Uru downstream area shows the highest load effluent in both Arsenic and Mercury. These heavy metal loads will intensify the declining water quality condition in Chindwin River and can impact negatively on human health who use water for drinking. Therefore, we recommend that water quality monitoring should continue to provide scientific-evidence for decision-makers to manage water quality and mining activities properly.  Water treatment systems for drinking water are required to remove turbidity, Total Suspended Solid, and Mercury from raw water sources. Raising awareness of relevant stakeholders (local people, farmers, private sectors, etc.) is necessary as many people living in the Chindwin River Basin are using water directly from the river and other waterways without proper water treatment.</p>

Monitoring of water transfer from Katse Dam into the Upper Vaal river system: water utility’s perspective

2003 ◽  
Vol 48 (10) ◽  
pp. 97-102 ◽  
Author(s):  
T. Lepono ◽  
H.H. Du Preez ◽  
M. Thokoa
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
River System ◽  
Water Quality Monitoring ◽  
Water Transfer ◽  
Monitoring Programme ◽  
Quality Monitoring ◽  
Transfer Scheme ◽  
Chemical Monitoring ◽  
Before And After

Water quality is of prime importance to Rand Water’s core business of ensuring a reliable supply of good quality drinking water to more than 10 million people. Rand Water has, therefore, implemented a water quality monitoring programme of the source water as well as the drinking water produced. The establishment of the Lesotho Highlands Water Transfer scheme necessitated the expansion of the monitoring programme. In 1996, Rand Water and Lesotho Highlands Development Authority (LHDA) signed an agreement to jointly develop an extensive water quality monitoring programme for the Lesotho Highlands Water Project (LHWP). Prior to this agreement, monitoring was mainly undertaken by consultants on behalf of LHDA in the main feeder rivers within the Katse Dam catchment (donor system). On the recipient system (Ash/Liebenbergsvlei), extensive physical and chemical monitoring was undertaken by Rand Water and Department of Water Affairs and Forestry (DWAF). Biological monitoring was however only carried out superficially prior to the release of water. Information gained from carrying out biological and chemical assessments clearly indicates that the water from LHWP has negatively impacted on the biological communities in the recipient system. The importance of detailed before and after biological and physio-chemical monitoring of both donor and recipient systems is emphasised.

scholarly journals Iot Based Water Quality Monitoring System using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11801-11805
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Drinking Water ◽  
Distributed Computing ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Web Of Things ◽  
Water Condition ◽  
Minimal Effort

In the present occasions, because of urbanization and contamination, it has gotten important to screen and assess the nature of water arriving at our homes. Guaranteeing safe inventory of drinking water has become a major test for the cutting edge progress. In this desk work, we present a structure and improvement of a minimal effort framework for continuous checking of the water quality (WQ) in IoT (web of things). The framework comprise of a few sensors are accustomed to guesstimatingsomatic and element limitations of the water. The parameters like temperature, PH, turbidity, conductivity, broke up oxygen of the water can be estimated. The deliberate qualities from the sensors can be prepared by the center controller. The RBPI B+ (RBPI) model can be consumed as a center controller. At last, the instrument facts can be understood on web utilizing distributed computing. Here the information's are handled utilizing AI calculation it sense the water condition if the WQis great it open the entryway divider else it shuts the door divider. This whole procedure happens naturally without human mediation therefore spare an opportunity to contract with the circumstance physically. The uniqueness of our proposed research is to get the water observing framework with high recurrence, high portability, and low controlled.

scholarly journals Prioritizing riparian corridors for ecosystem restoration in urbanizing watersheds

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8174 ◽  
Author(s):  
Samuel F. Atkinson ◽  
Matthew C. Lake
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Ecosystem Services ◽  
Watershed Management ◽  
Water Reservoir ◽  
Riparian Restoration ◽  
Surface Slope ◽  
Management Options ◽  
Riparian Corridors ◽  
Drinking Water Reservoir

Background Riparian corridors can affect nutrient, organic matter, and sediment transport, all of which shape water quality in streams and connected downstream waters. When functioning riparian corridors remain intact, they provide highly valued water quality ecosystem services. However, in rapidly urbanizing watersheds, riparian corridors are susceptible to development modifications that adversely affect those ecosystem services. Protecting high quality riparian corridors or restoring low quality corridors are widely advocated as watershed level water quality management options for protecting those ecosystem services. The two approaches, protection or restoration, should be viewed as complementary by watershed managers and provide a foundation for targeting highly functioning riparian corridors for protection or for identifying poorly functioning corridors for restoration. Ascertaining which strategy to use is often motivated by a specific ecosystem service, for example water quality, upon which watershed management is focused. We have previously reported on a spatially explicit model that focused on identifying riparian corridors that have specific characteristics that make them well suited for purposes of preservation and protection focused on water quality. Here we hypothesize that focusing on restoration, rather than protection, can be the basis for developing a watershed level strategy for improving water quality in urbanizing watersheds. Methods The model described here represents a geographic information system (GIS) based approach that utilizes riparian characteristics extracted from 40-meter wide corridors centered on streams and rivers. The model focuses on drinking water reservoir watersheds that can be analyzed at the sub-watershed level. Sub-watershed riparian data (vegetation, soil erodibility and surface slope) are scaled and weighted based on watershed management theories for water quality, and riparian restoration scores are assigned. Those scores are used to rank order riparian zones –the lower the score the higher the priority for riparian restoration. Results The model was applied to 90 sub-watersheds in the watershed of an important drinking water reservoir in north central Texas, USA. Results from this study area suggest that corridor scores were found to be most correlated to the amount of: forested vegetation, residential land use, soils in the highest erodibility class, and highest surface slope (r2 = 0.92, p < 0.0001). Scores allow watershed managers to rapidly focus on riparian corridors most in need of restoration. A beneficial feature of the model is that it also allows investigation of multiple scenarios of restoration strategies (e.g.,  revegetation, soil stabilization, flood plain leveling), giving watershed managers a tool to compare and contrast watershed level management plans.

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