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Record W3093275191

Removal of Arsenic and Metals from Mine-impacted Groundwater Using Organic Carbon and Zero-valent Iron in Passive Remediation Systems

2020· dissertation· en· W3093275191 on OpenAlexaboutno aff
Joanne U. Angai

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsZerovalent ironEnvironmental remediationArsenicGroundwaterEnvironmental chemistryEnvironmental scienceTotal organic carbonContaminated groundwaterGroundwater remediationWaste managementEnvironmental engineeringMining engineeringChemistryContaminationMetallurgyGeologyMaterials scienceEngineeringAdsorptionGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Arsenic (As) is a wide-spread contaminant, often encountered in drainage associated with Au mining. The oxidation state controls the mobility and toxicity of As in water. Passive remediation is a potential management approach for removing As and other contaminants from mine waters. Permeable reactive barriers (PRBs) are a passive management technology that utilizes reactive material to target the contaminant of interest through chemical interactions, including precipitation, reduction, and adsorption. The Long Lake field site is an abandoned Au mine located near Sudbury, ON, characterized by acidic conditions and high concentrations of As, Fe, SO42-, and metals in the tailings porewater. This project aims to evaluate the potential for passive remediation to remove As and other contaminants from mine drainage at the Long Lake field site. \nA series of laboratory column experiments were conducted to determine the potential of a reactive mixture, containing organic carbon substrates (OC), granular zero-valent iron (ZVI), limestone, and silica sand, to remove As and increase the pH of the water. Groundwater was collected directly from the Long Lake site and used as the influent solution for the column experiments. Results indicated an increase in pH and removal of As within the first 3 cm of reactive material. Removal of As in the three treatment columns represented 99.9% of the total As in the water. A decrease in Eh, the production of H2S, a decline in SO42- concentrations, an enrichment in δ34S, and the presence of microbial communities, indicated the presence of bacterially-mediated SO42- reduction. The percentage of total reads that were sulfate-reducing bacteria (SRB) ranged from 2.4 – 10.0%. Sulfate reduction rates ranged from 0.18 to 0.20 mg L-1 d-1 g-1 dry wt. % OC for the three treatment columns. Synchrotron-radiation bulk S X-ray absorption near-edge structure (XANES) indicated that accumulation of reduced S phases including pyrite and elemental S occurred in the solid material during the experiments. Geochemical modelling results further indicate that precipitation of sulfides including mackinawite, greigite, pyrite, sphalerite, and chalcopyrite, was favoured. Removal of metals, including Cu, Ni, and Zn, is attributed to the precipitation of low-solubility metal sulfides following SO42- reduction. Synchrotron As µXANES indicated that As was present in secondary precipitates in both the reduced phase, as realgar, orpiment, and arsenopyrite, and in the oxidized phase, as As(V) sorbed onto ferrihydrite. The addition of OC contributed to the development of sulfate-reducing conditions and resulted in bacterially-mediated SO42- reduction. The presence of ZVI led to the formation of ferrous iron and ZVI corrosion products, providing additional surface sites for As adsorption. \nTwo separate field-reaction cell (30 cm inner diameter by 99 cm length) trials, were conducted at the Long Lake mine site, one in the summer (mean air temperature of 19 ℃) and one in the autumn (mean air temperature of -1 ℃), to evaluate the effect of temperature on As removal. A reactive mixture containing ZVI, OC, limestone, and pea gravel (at similar proportions to the column experiments) was utilized. The results from the summer field cell were similar to those observed in the laboratory column experiments. A decrease in As, metals, SO42-, and acidity, were observed within the first 9 cm of reactive media. Reactions contributing to metal and As removal include precipitation of low-solubility metal sulfides and adsorption on ZVI corrosion products. The results from the autumn field cell indicated that the development of bacterially-mediated SO42- reduction was limited, with lower percentages of SRB observed in the autumn cell compared to the summer cell and laboratory column experiments. Removal of As, metals, and an increase in pH was observed in the autumn cell, however, aqueous chemistry results did not show a decline in SO42- concentrations or an enrichment in δ34S. Optical microscopy indicated the presence of pyrite and pyrrhotite in the autumn cell material, but abundance was lower in the autumn cell than in the other two experiments. The results from bulk S XANES indicated the accumulation of sulfides in the solid material was also limited. The difference in results between the summer cell and autumn cell may be attributed to colder outside temperatures during the field trial or the shorter duration of the experiment. \nThe results from all three experiments indicate that the addition of OC to the reactive mixture was important for the development of sulfate-reducing conditions and the growth and activity of SRB. The addition of ZVI further enhanced the removal of As, metals, and Fe from the water through the formation of corrosion products and metal sulfide precipitation. Removal of As and metals and an increase in pH was observed in all three experiments despite varying flow rates and fluctuating temperatures. These results indicate that a mixture of OC and ZVI will likely be effective at removing As and metals from mine drainage waters under a range of flow rates and temperature conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.189
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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