Evidence map›Paper›PMID 30361116›Full record

ArticleJournal of contaminant hydrology2018

Source strength functions from long-term monitoring data and spatially distributed mass discharge measurements.

Michael C Brooks, A Lynn Wood, Jaehyun Cho, Christine A P Williams, William Brandon, Michael D Annable

Abstract read
In one paragraph

Article in Journal of contaminant hydrology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael C BrooksNational Risk Management Research Laboratory, U.S. Environmental Protection Agency, Ada, OK 74820, United States. Electronic address: brooks.michael@epa.gov.
A Lynn WoodRetired, National Risk Management Research Laboratory, U.S. Environmental Protection Agency, Ada, OK 74820, United States.
Jaehyun ChoInterdisciplinary Program in Hydrologic Sciences, Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL 32611, United States.
Christine A P WilliamsFederal Facility Superfund Section, U.S. Environmental Protection Agency, Boston, MA 02109, United States.
William BrandonFederal Facility Superfund Section, U.S. Environmental Protection Agency, Boston, MA 02109, United States.
Michael D AnnableInterdisciplinary Program in Hydrologic Sciences, Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL 32611, United States.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Source strength functions (SSF), defined as contaminant mass discharge or flux-averaged concentration from dense nonaqueous phase liquid (DNAPL) source zones as a function of time, provide a quantitative model of DNAPL source-zone behavior. Such information is useful for calibration of screening-level models to assist with site management decisions. We investigate the use of historic data collected during long-term monitoring (LTM) activities at a site in Rhode Island to predict the SSF based on temporal mass discharge measurements at a fixed location, as well as SSF estimation using mass discharge measurements at a fixed time from three spatially distributed control planes. Mass discharge based on LTM data decreased from ~300 g/day in 1996 to ~70 g/day in 2012 at a control plane downgradient of the suspected DNAPL source zone, and indicates an overall decline of ~80% in 16 years. These measurements were compared to current mass discharge measurements across three spatially distributed control planes. Results indicate that mass discharge increased in the downgradient direction, and was ~6 g/day, ~37 g/day, and ~400 g/day at near, intermediate, and far distances from the suspected source zone, respectively. This behavior was expected given the decreasing trend observed in the LTM data at a fixed location. These two data sets were compared using travel time as a means to plot the data sets on a common axis. The similarity between the two data sets gives greater confidence to the use of this combined data set for site-specific SSF estimation relative to either the sole use of LTM or spatially distributed data sets.

Indexed as

Water Pollutants, ChemicalEnvironmental MonitoringHumansWater Pollutants, ChemicalLong-term monitoring dataMass dischargeMass fluxPassive flux metersSource strength functionSource zone characterization

Identifiers

PMID30361116
PMCPMC7390023

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.