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Record W2738775128 · doi:10.2134/jeq2017.03.0119

Greenhouse Gas Emissions after Application of Landfilled Paper Mill Sludge for Land Reclamation of a Nonacidic Mine Tailings Site

2017· article· en· W2738775128 on OpenAlexafffund
Patrick Faubert, Simon Durocher, Normand Bertrand, Rock Ouimet, Philippe Rochette, Pascal Tremblay, Jean‐François Boucher, Claude Villeneuve

Bibliographic record

VenueJournal of Environmental Quality · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsMinistère des Ressources naturelles et des ForêtsAgriculture and Agri-Food CanadaUniversité du Québec à Chicoutimi
FundersMitacsUniversité du Québec à Chicoutimi
KeywordsTailingsEnvironmental scienceGreenhouse gasTonneLand reclamationPaper millEnvironmental engineeringWaste managementEffluentChemistryEngineeringGeologyEcology

Abstract

fetched live from OpenAlex

Large areas of mine tailings are reclaimed by applying organic amendments such as paper mill sludge (PMS). Although mining industries can use PMS freshly generated by paper mills, operational constraints on paper industries make temporary landfilling of this material an unavoidable alternative for the paper industries, creating the most prominent PMS source for mining industries. This study aimed to quantify soil greenhouse gas (GHG) emissions (N2O, CO2, and CH4) after application of landfilled PMS (LPMS; i.e., excavated from a landfill site at a paper mill) and LPMS combined with a seeding treatment of white clover (Trifolium repens L.) on nonacidic mine tailings site prior to reforestation. Soil N2O, CO2, and CH4 fluxes were measured after applications of 50 and 100 Mg dry LPMS ha−1 during two consecutive snow‐free seasons on two adjacent sites; LPMS was applied once in the first season. The LPMS application increased N2O emissions (7.6 to 34.7 kg N2O‐N ha−1, comprising 1.04 to 2.43% of applied N) compared with the unamended control during the first season; these emissions were negligible during the second season. The LPMS application increased CO2 emissions (∼5800 to 11,400 kg CO2–C ha−1, comprising 7 to 27% of applied C) compared with the unamended control on both sites and in both seasons. Fluxes of CH4 were negligible. White clover combined with LPMS treatments did not affect soil GHG emissions. These new GHG emission factors should be integrated into life‐cycle analyses to evaluate the C footprint of potential symbioses between the mining and paper industries. Future research should focus on the effect of PMS applications on soil GHG emissions from a variety of mine tailings under various management practices and climatic conditions to plan responsible and sustainable land reclamation. Core Ideas Applied PMS for mine tailings reclamation affected GHG emissions. PMS application increased N2O and CO2 emissions, whereas CH4 fluxes were negligible. White clover combined with PMS treatments did not affect soil GHG emissions. Mine and paper industries can integrate these GHG emissions into industrial symbioses.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.287
Teacher spread0.269 · 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 designObservational
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".

Quick stats

Citations13
Published2017
Admission routes2
Has abstractyes

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