Algorithmic Characterization of Lake Stratification and Deep Chlorophyll Layers From Depth Profiling Water Quality Data

2019 ◽  
Vol 55 (5) ◽  
pp. 3815-3834 ◽  
Author(s):  
Wenzhao Xu ◽  
Paris D. Collingsworth ◽  
Barbara Minsker
Keyword(s):  
Water Quality ◽  
Depth Profiling ◽  
Quality Data ◽  
Water Quality Data ◽  
Lake Stratification

Preliminary Trial Results for the Redesigned STRIDER Platform With Sampling Capability From Different Depths

2017 ◽  
Author(s):  
Rakesh Joshi ◽  
Nathan Bane ◽  
Justin Derickson ◽  
Mark E. Williams ◽  
Abhijit Nagchaudhuri
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Depth Profiling ◽  
The United States ◽  
Environmental Research ◽  
Quality Data ◽  
United States Department ◽  
Water Sampling ◽  
Water Quality Data ◽  
Preliminary Trial

STRIDER: Semi-Autonomous Tracking Robot with Instrumentation for Data-Acquisition and Environmental Research, a semi-autonomous aquatic vessel, was envisioned for automated water sampling, data collection, and depth profiling to document water quality variables related to agricultural run-offs. Phase-I of the STRIDER project included the capability for STRIDER to collect water samples and water quality data on the surface of water bodies. This paper discusses the Phase-II efforts of the project, in which the previous design of STRIDER was adapted to extend its capabilities to include monitoring, depth profiling, and visualization of in-situ water quality data at various depths as well as collect water samples at each depth for bacterial analysis. At present, the vessel has been utilized for navigation to specified locations using remote control for collecting water quality data and water samples from the surface, as well as 2 feet and 4 feet below the surface at multiple UMES ponds. In a series of preliminary trial runs with the supervision of UMES faculty members and collaborators from the United States Department of Agriculture (USDA), STRIDER successfully collected 48 water samples for bacterial analysis at different locations and depths of ponds on the UMES campus. Design alternatives are being explored for more efficient water sampling capabilities.

Hydraulic fractionation of conventional water quality constituents in municipal dry- and wet-weather flow samples

2009 ◽  
Vol 59 (6) ◽  
pp. 1159-1167 ◽  
Author(s):  
K. Exall ◽  
J. Marsalek ◽  
B. G. Krishnappan
Keyword(s):  
Water Quality ◽  
Chemical Characterization ◽  
Quality Data ◽  
Municipal Sewage ◽  
Combined Sewer Overflows ◽  
Water Quality Data ◽  
Treatment Processes ◽  
Treatment Facilities ◽  
Wet Weather

The effective design of treatment processes for combined sewer overflows (CSOs) requires understanding of the CSO characteristics and treatability. Environment Canada partnered with four municipalities to evaluate water quality and treatability of wet- and dry-weather flows at local sewage or CSO treatment facilities. Chemical characterization of the samples indicates that the municipal sewage at all of the sites is of relatively weak strength, with several differences between the water quality data for dry-weather and wet-weather flows (assumed to represent CSOs). Hydraulic separation of constituents with an elutriation apparatus illustrated the removals that can be expected with conventional settling techniques and differences in settling of various constituents.

Identification and characterization of water quality transients using wavelet analysis. II. Application to electronic water quality data

1997 ◽  
Vol 36 (5) ◽  
pp. 337-348 ◽  
Author(s):  
Paul H. Whitfield ◽  
Kathleen Dohan
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Data Sets ◽  
Water Quality Data ◽  
Periodic Processes ◽  
Small Streams ◽  
Transient Events ◽  
Identification And Characterization

Two wavelet transform techniques for identifying water quality transients are applied to example data sets from two small streams. Temperature and conductance represent the range of properties from periodic processes to transient events. Both methods were successful in identifying the location, duration and magnitude of the transient events in these data sets. The methods may be refined to automate the detection and classification of transient events.

Sign in / Sign up

Export Citation Format

Share Document