Literature DB >> 32446456

Effectiveness of a fixed-depth sensor deployed from a buoy to estimate water-column cyanobacterial biomass depends on wind speed.

Justin D Chaffin1, Douglas D Kane2, Alex Johnson3.   

Abstract

Water quality sondes have the advantage of containing multiple sensors, extended deployment times, high temporal resolution, and telecommunication with stakeholder accessible data portals. However, sondes that are part of buoy deployments often suffer from typically being fixed at one depth. Because water treatment plants are interested in water quality at a depth of the water intake and other stakeholders (ex. boaters and swimmers) are interested in the surface, we examined whether a fixed depth of approximately 1 m could cause over- or under-estimation of cyanobacterial biomass. We sampled the vertical distribution of cyanobacteria adjacent to a water quality sonde buoy in the western basin of Lake Erie during the summers of 2015-2017. A comparison of buoy cyanobacteria RFU (Relative Fluorescence Unit) at 1 m to cyanobacteria chlorophyll a (chla) measured throughout the water column showed occurrences when the buoy both under and overestimated the cyanobacteria chla at specific depths. Largest differences between buoy measurements and at-depth grab samples occurred during low wind speeds (< 4.5 m/sec) because low winds allowed cyanobacteria to accumulate at the surface above the buoy's sonde. Higher wind speeds (> 4.5 m/sec) resulted in better agreement between the buoy and at-depth measurements. Averaging wind speeds 12 hr before sample collection decreased the difference between the buoy and at-depth samples for high wind speeds but not low speeds. We suggest that sondes should be placed at a depth of interest for the appropriate stakeholder group or deploy sondes with the ability to sample at various depths.
Copyright © 2020. Published by Elsevier B.V.

Entities:  

Keywords:  Buoyancy; Harmful algal bloom; Lake Erie; Microcystis; Water quality

Mesh:

Substances:

Year:  2020        PMID: 32446456     DOI: 10.1016/j.jes.2020.03.003

Source DB:  PubMed          Journal:  J Environ Sci (China)        ISSN: 1001-0742            Impact factor:   5.565


  2 in total

1.  Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River.

Authors:  Christopher T Nietch; Leslie Gains-Germain; James Lazorchak; Scott P Keely; Gregory Youngstrom; Emilee M Urichich; Brian Astifan; Abram DaSilva; Heather Mayfield
Journal:  Water (Basel)       Date:  2022-02-18       Impact factor: 3.530

2.  Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.

Authors:  Alberto Mozo; Jesús Morón-López; Stanislav Vakaruk; Ángel G Pompa-Pernía; Ángel González-Prieto; Juan Antonio Pascual Aguilar; Sandra Gómez-Canaval; Juan Manuel Ortiz
Journal:  Sci Rep       Date:  2022-08-08       Impact factor: 4.996

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.