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VERSION:2.0
PRODID:-//EMPOWER: Education Model Program on Water-Energy Research - ECPv4.7.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:EMPOWER: Education Model Program on Water-Energy Research
X-ORIGINAL-URL:https://empower.syr.edu
X-WR-CALDESC:Events for EMPOWER: Education Model Program on Water-Energy Research
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20161111T153000
DTEND;TZID=America/New_York:20161111T163000
DTSTAMP:20260419T071707
CREATED:20161003T192302Z
LAST-MODIFIED:20161010T203917Z
UID:1038-1478878200-1478881800@empower.syr.edu
SUMMARY:Nathaniel Chien to present at WAGGS
DESCRIPTION:Nathan Chien\, a new MS student in Earth Sciences and EMPOWER trainee\, will be discussing his research at WAGGS. His presentation is tentatively entitled “Fingerprinting sources of salinity in shallow groundwater using discriminant analysis in regions impacted by deicers and natural gas production.” \nFull abstract: Development of unconventional gas resources (e.g. high-volume hydraulic fracturing) may pose contamination risks to shallow groundwater. However\, assessment of such contamination can prove difficult due in part to other potential sources of groundwater contamination in shale gas basins. In previous work\, we developed a statistical model that uses linear discriminant analysis to identify the most probable source of salinity in groundwater samples based on their geochemical fingerprints. Here\, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al.\, Illinois State Geol. Survey\, Open File Series 2005-1 and Hwang et al.\, Environ. & Eng. Geosci.\, 11: 75-90 (2015). By predicting the source of salinity in groundwater samples for which sources of contamination are known\, we were able to validate model predictive accuracy. Different combinations of solutes and saline end members were considered. Results show high classification success (>85%) for groundwater samples impacted by formation brines and road salt\, with diminishing success for septic effluent and animal waste due in large part to the indistinguishable chemical fingerprints of these contaminants. These results indicate that this model could effectively be transferred to other shale gas basins\, with potential to be used for assessment and accountability measures. \n  \n
URL:https://empower.syr.edu/event/nathaniel-chien-present-waggs/
LOCATION:210 Heroy Geology Lab
CATEGORIES:Of Interest
ORGANIZER;CN="Deanna%20McCay":MAILTO:dhmccay@syr.edu
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