Subsurface Transport of Sulfidated Nano Zero Valent Iron and In Situ Biogeochemical Transformation of Chlorinated Solvents: A Field Study
Bibliographic record
Abstract
In situ chemical reduction of chlorinated volatile organic compounds (cVOCs) by nano zero-valent iron (nZVI) has been widely applied in the past 20 years, but with limited effectiveness for bare nZVI due to rapid particle settling, short lifespan, and low reactivity. Stabilization and sulfidation of nZVI have improved its mobility and longevity, increased reactivity towards cVOCs, and reduced toxicity to microbes (Nunez Garcia et al. 2021). In the first-ever CMC-S-nZVI field trial, nZVI sulfidated by dithionite (S-nZVI) and stabilized by carboxymethyl cellulose (CMC) was injected into the subsurface of a site contaminated with a wide range of cVOCs. Multi-level wells were installed to monitor the transport of the CMC-S-nZVI suspension and its remedial performance for two years. Short-term (0-17 days) monitoring demonstrated a good transport of the suspension in the down- and up-gradient wells, in terms of total iron, boron, and sulfides which were major constituents of CMC-S-nZVI. Changes in concentrations of parent compounds, intermediates, and ethene showed effective dechlorination of high-chlorinated VOCs such as tetrachloroethene (PCE) and carbon tetrachloride (Nunez Garcia et al. 2020a, Nunez Garcia et al. 2020b). Long-term (157-729 days) performance was evaluated through temporal analyses of microbial communities, total iron, boron, and cVOCs in groundwater samples. Microbial populations, including organohalide-respiring bacteria, increased by >1 order of magnitude; with Geobacter being the most abundant. This long-term enrichment can be attributed to the low toxicity of CMC-S-nZVI and biostimulation by CMC and perhaps Fe 3+ . Non-metric multidimensional scaling analysis was carried out on microbial data grouped by depth range and proximity to the injection well. At locations that clearly received CMC-S-nZVI, there was a significant shift in microbial communities that was sustained for the long term. Iron concentrations increased substantially in long-term samples while boron concentrations decreased, suggesting that this iron did not come from CMC-S-nZVI. Microbial dissolution of iron minerals might have contributed to the increased iron content (Jones et al. 2006). Excess dithionite in CMC-S-nZVI would also have reductively dissolved native iron from the soil, as successfully demonstrated in the in situ redox manipulation (ISRM) technology wherein subsurface Fe 3+ in soil was reduced to Fe 2+ for long-term remedial purposes (Vermeul et al. 2000). Long-term changes in concentrations of lesser-chlorinated VOCs and hydrocarbons suggest that PCE was degraded via both the microbially-mediated sequential hydrogenolysis as well as the abiotic β-elimination. The intermediate vinyl chloride (VC) surprisingly did not accumulate in the current study, in contrast to the significant VC accumulation in a previous un-sulfidated CMC-nZVI trial at the same location (Kocur et al. 2016). Excess dithionite injected in this study might have avoided VC accumulation, as previously reported for ISRM treatment of a cVOCs-contaminated site (Vermeul et al. 2000). Additionally, the identified bacterial populations might have utilized sulfur species (from dithionite decomposition) and iron to form iron sulfides, which could dechlorinate cVOCs via in situ biogeochemical transformation (Kennedy et al. 2006). In summary, this study has demonstrated the long-term efficiency of CMC-S-nZVI for cVOCs removal through a combination of abiotic, biotic, and biostimulatory processes in the subsurface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".