scholarly journals A High-Throughput Microfluidic Magnetic Separation (µFMS) Platform for Water Quality Monitoring

Micromachines ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 16 ◽  
Author(s):  
Keisha Y. Castillo-Torres ◽  
Eric S. McLamore ◽  
David P. Arnold
Keyword(s):  
Water Quality ◽  
Magnetic Separation ◽  
High Throughput ◽  
Magnetic Particles ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Recreational Water ◽  
Microfluidic Analysis ◽  
Biosensing System

The long-term aim of this work is to develop a biosensing system that rapidly detects bacterial targets of interest, such as Escherichia coli, in drinking and recreational water quality monitoring. For these applications, a standard sample size is 100 mL, which is quite large for magnetic separation microfluidic analysis platforms that typically function with <20 µL/s throughput. Here, we report the use of 1.5-µm-diameter magnetic microdisc to selectively tag target bacteria, and a high-throughput microfluidic device that can potentially isolate the magnetically tagged bacteria from 100 mL water samples in less than 15 min. Simulations and experiments show ~90% capture efficiencies of magnetic particles at flow rates up to 120 µL/s. Also, the platform enables the magnetic microdiscs/bacteria conjugates to be directly imaged, providing a path for quantitative assay.

Choices in Recreational Water Quality Monitoring: New Opportunities and Health Risk Trade-Offs

2013 ◽  
Vol 47 (7) ◽  
pp. 3073-3081 ◽  
Author(s):  
Meredith B. Nevers ◽  
Muruleedhara N. Byappanahalli ◽  
Richard L. Whitman
Keyword(s):  
Water Quality ◽  
Health Risk ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Trade Offs

Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches

2016 ◽  
Vol 166 ◽  
pp. 285-293 ◽  
Author(s):  
Dawn A. Shively ◽  
Meredith B. Nevers ◽  
Cathy Breitenbach ◽  
Mantha S. Phanikumar ◽  
Kasia Przybyla-Kelly ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Recreational Water ◽  
Recreational Water Quality

scholarly journals The use of bed sediments in water quality studies and monitoring programs

2017 ◽  
Vol 375 ◽  
pp. 11-17 ◽  
Author(s):  
Arthur J. Horowitz ◽  
Kent A. Elrick
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Water Quality Monitoring ◽  
Flood Plain ◽  
Quality Monitoring ◽  
Sediment Chemistry ◽  
Regulatory Requirements ◽  
Bed Sediments ◽  
Monitoring Programs

Abstract. In most water quality monitoring programs, either filtered water (dissolved) or suspended sediment (either whole water or separated suspended sediment) are the traditional sample media of choice. This results both from regulatory requirements and a desire to maintain consistency with long-standing data collection procedures. Despite the fact that both bed sediments and/or flood plain deposits have been used to identify substantial water quality issues, they rarely are used in traditional water quality monitoring programs. The usual rationale is that bed sediment chemistry does not provide the temporal immediacy that can be obtained using more traditional sample media (e.g., suspended sediment, water). However, despite the issue of temporal immediacy, bed sediments can be used to address/identify certain types of water quality problems and could be employed more frequently for that purpose. Examples where bed sediments could be used include: (1) identifying potential long-term monitoring sites/water quality hot spots, (2) establishing a water quality/geochemical history for a particular site/area, and (3) as a surrogate for establishing mean/median chemical values for suspended sediment.

Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning

2021 ◽  
Author(s):  
Bo Wang ◽  
Jinhui Huang ◽  
Hongwei Guo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Total Phosphorus ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Empirical Method ◽  
Quality Monitoring ◽  
Lake Simcoe

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R&amp;#178; for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13&amp;#956;g/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37&amp;#956;g/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words: &lt;/strong&gt;water quality monitoring; MODIS; empirical method; machine learning&lt;/p&gt;

A Nonparametric Long-Term Trend Analysis Using Water Quality Monitoring Data in Nam-River

2018 ◽  
Vol 27 (11) ◽  
pp. 1029-1048
Author(s):  
Kang-Young Jung ◽  
Myojeong Kim ◽  
Kwang Duck Song ◽  
Kwon Ok Seo ◽  
Seong Jo Hong ◽  
...  
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Term Trend ◽  
Long Term Trend
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