Predicting Abiotic Reduction Rate Constants of Munition Compounds in Soils

We report an empirical poly-parameter linear free energy relationship (LFER) for estimating the mass-normalized rate constants for the abiotic reduction of munition compounds (MC) in soil. A total of 131 kinetic experiments were performed, using three classes of MC (nitroaromatic [TNT, DNAN], nitramine [RDX], and azole [NTO]) and 11 soils having highly varied organic carbon and iron contents and reduced with dithionite to different electron contents. The LFER has the same form as that for MC reduction by Fe<sup>III</sup> (oxyhydr)oxide-Fe<sub>aq</sub><sup>II</sup> redox couples and predicts MC reduction rate constants to within an order of magnitude, using only the aqueous-phase one electron reduction potential (<i>E</i><sub>H</sub><sup>1</sup>) of the MC and the pe and pH of the soil. As previously shown for azoles, which exhibited markedly higher reactivity toward iron than toward carbon reductants relative to all neutral MC, NTO reduction rate depended on soil composition and hence a correction to model prediction was necessary at soil iron-to-carbon mass ratios ≲1. This is the first successful attempt to predict the reduction kinetics of structurally diverse nitro compounds in compositionally complex soils based on their thermodynamic properties. The LFER would be useful in the management/restoration (e.g., natural or enhanced attenuation) of soils impacted by MC or other nitro pollutants.

Authors

Institutions

Publication Details

Journal
Environmental Science & Technology
Published
2025-02-06
DOI
https://doi.org/10.1021/acs.est.4c12872
Citations
3
Primary Topic
Coal and Its By-products
Type
article
Field-Weighted Citation Impact
4.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting Abiotic Reduction Rate Constants of Munition Compounds in Soils

Herbert E. Allen, Paula A. Cárdenas-Hernández, Jimmy Murillo‐Gelvez, Richard F. Carbonaro et al.
3 citations
Environmental Science & Technology
Coal and Its By-products
4.01
article

Predicting Abiotic Reduction Rate Constants of Munition Compounds in Soils

Herbert E. Allen, Paula A. Cárdenas-Hernández, Jimmy Murillo‐Gelvez, Richard F. Carbonaro, Pei C. Chiu, Dominic M. Di Toro, Juan C. Rincón-Rodríguez
article en
3 citations

Abstract

We report an empirical poly-parameter linear free energy relationship (LFER) for estimating the mass-normalized rate constants for the abiotic reduction of munition compounds (MC) in soil. A total of 131 kinetic experiments were performed, using three classes of MC (nitroaromatic [TNT, DNAN], nitramine [RDX], and azole [NTO]) and 11 soils having highly varied organic carbon and iron contents and reduced with dithionite to different electron contents. The LFER has the same form as that for MC reduction by Fe<sup>III</sup> (oxyhydr)oxide-Fe<sub>aq</sub><sup>II</sup> redox couples and predicts MC reduction rate constants to within an order of magnitude, using only the aqueous-phase one electron reduction potential (<i>E</i><sub>H</sub><sup>1</sup>) of the MC and the pe and pH of the soil. As previously shown for azoles, which exhibited markedly higher reactivity toward iron than toward carbon reductants relative to all neutral MC, NTO reduction rate depended on soil composition and hence a correction to model prediction was necessary at soil iron-to-carbon mass ratios ≲1. This is the first successful attempt to predict the reduction kinetics of structurally diverse nitro compounds in compositionally complex soils based on their thermodynamic properties. The LFER would be useful in the management/restoration (e.g., natural or enhanced attenuation) of soils impacted by MC or other nitro pollutants.

Environmental Science & TechnologyVol. 59(6)
Manhattan University (US), University of Delaware (US)
Strategic Environmental Research and Development Program
Climate action
Openalex Percentile: Top 9%
Coal and Its By-products
4.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.