1 research outputs found
A Data Mining Approach to Predict In Situ Detoxification Potential of Chlorinated Ethenes
Despite advances
in physicochemical remediation technologies, in
situ bioremediation treatment based on <i>Dehalococcoides mccartyi</i> (<i>Dhc</i>) reductive dechlorination activity remains
a cornerstone approach to remedy sites impacted with chlorinated ethenes.
Selecting the best remedial strategy is challenging due to uncertainties
and complexity associated with biological and geochemical factors
influencing <i>Dhc</i> activity. Guidelines based on measurable
biogeochemical parameters have been proposed, but contemporary efforts
fall short of meaningfully integrating the available information.
Extensive groundwater monitoring data sets have been collected for
decades, but have not been systematically analyzed and used for developing
tools to guide decision-making. In the present study, geochemical
and microbial data sets collected from 35 wells at five contaminated
sites were used to demonstrate that a data mining prediction model
using the classification and regression tree (CART) algorithm can
provide improved predictive understanding of a site’s reductive
dechlorination potential. The CART model successfully predicted the
3-month-ahead reductive dechlorination potential with 75.8% and 69.5%
true positive rate (i.e., sensitivity) for the training set and the
test set, respectively. The machine learning algorithm ranked parameters
by relative importance for assessing in situ reductive dechlorination
potential. The abundance of <i>Dhc</i> 16S rRNA genes, CH<sub>4</sub>, Fe<sup>2+</sup>, NO<sub>3</sub><sup>–</sup>, NO<sub>2</sub><sup>–</sup>, and SO<sub>4</sub><sup>2–</sup> concentrations, total organic carbon (TOC) amounts, and oxidation–reduction
potential (ORP) displayed significant correlations (<i>p</i> < 0.01) with dechlorination potential, with NO<sub>3</sub><sup>–</sup>, NO<sub>2</sub><sup>–</sup>, and Fe<sup>2+</sup> concentrations exhibiting precedence over other parameters. Contrary
to prior efforts, the power of data mining approaches lies in the
ability to discern synergetic effects between multiple parameters
that affect reductive dechlorination activity. Overall, these findings
demonstrate that data mining techniques (e.g., machine learning algorithms)
effectively utilize groundwater monitoring data to derive predictive
understanding of contaminant degradation, and thus have great potential
for improving decision-making tools. A major need for realizing the
predictive capabilities of data mining approaches is a curated, open-access,
up-to-date and comprehensive collection of biogeochemical groundwater
monitoring data