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Record W7125884489

CHALLENGES IN TRACKING THE DEGRADATION OF LANDFILLED MSW THROUGH LABORATORY AND NUMERICAL MEANS

2025· article· en· W7125884489 on OpenAlexaboutno aff
Tyler JP Casavant

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMunicipal solid wasteMethaneLandfill gasBioreactor landfillLeachateDegradation (telecommunications)Waste disposalTracking (education)
DOInot available

Abstract

fetched live from OpenAlex

Landfilling is one of the primary methods utilized for the long-term disposal and treatment of municipal solid waste (MSW) in Canada. The decay of landfilled MSW will govern several other ongoing landfill processes, such as heat generation, gas generation and biologically induced landfill settlement. Thus, a measurement of the remaining biological potential of in situ waste can assist with the understanding and prediction of these ongoing landfill process, while also providing a means of assessing the degree of (biological) landfill stabilization. The research presented here will focus on the assessment of the remaining biological potential of MSW through laboratory testing of exhumed samples, and through numerical simulation of in situ landfill temperature behaviour. The biochemical methane potential (BMP) test is the primary method used to determine the anaerobic biological decay potential of MSW. Several aspects of the BMP test methodology such as the long test duration, unstandardized nature, and low sample capacity can be problematic when testing MSW samples. Thus, an investigation into BMP test methodology with respect to MSW samples was undertaken. A novel method of analyzing test completion based on changes in relative gas production was proposed. It was found that the recommended range of ISR presented in the literature (2 – 4) was higher then required for MSW samples and depending on PS an ISR of 0.5 – 1.3 was acceptable. It was found that neither ISR nor PS had a meaningful effect on the final measured BMPult value but they did have an substantial effect on the succusses rate of test replicates. Several other testing methods were explored as potential rapid BMP surrogate test methods. None of the surrogate method investigated produced a viable surrogate test or provided any significant insights into how the properties of an MSW sample changed over anaerobic digestion. The cumulative errors associated with the creation and sub-sampling of the MSW samples was substantial and appeared to be greater than the trend being measured in all cases. Numerical modelling of landfill behaviour provides an alternative means of studying in situ waste behaviour. As previously discussed, the anaerobic biological decay of landfilled MSW causes significant heat generation and the resultant elevated landfill temperatures. Thus, the modelling work conducted herein focused on the simulation of landfill temperature behaviour as a means of studying biological decay and the coupled temperature inhibition of biological decay occurring in cold weather climates. To meet these research objectives a novel cold weather landfill modelling framework, the python finite difference (PFD) model was proposed. The PFD modeling framework allows for the coupled simulation of thermal, biological, and hydraulic effects while also simulating the phase change. The general applicability of the PFD modeling framework and the importance of a coupled biological temperature factor (TF) were both demonstrated through the simulation of a generic landfill in a Saskatoon (cold-weather) and a New Mexico (warm weather) climate. The implementation of a TF had a significant effect on both the temperature and biological behaviour of the cold-weather landfill simulations. At shallow depths, the TF had substantial effects on RMP behaviour but minimal effects on temperature behaviour. At depth, the relative effects of the TF were proportional to the exposure duration of that layer to cold weather prior to burial and the temperature at the time of burial. 6 years of temperature data with depth collected from numerous locations across the landfill were used to conduct 23 near surface simulations. It was found that the Loraas Landfill had an average nthaw = 1.1 and a nfroz = 0.8. Following the near surface simulations, 4 full-scale Loraas Landfill simulations were conducted, and the resultant simulated temperatures were compared against observed temperature data. At all four locations the predicted temperature values aligned with the observed temperature values within a tolerance of < 5 °C (top 5 m) for the near surface zone and within < 2 °C below the near surface zone. Each location simulated experienced significant but varying degrees of cold weather temperature inhibition, yet the PFD simulations were able to closely predict temperature trends and values at nearly all depths for all locations. This result strongly indicates that the PFD modelling framework proposed herein, can simulate the key coupled relationships which govern cold weather landfill temperature behaviour. The fitted biological and heat generation properties from the calibrated simulations were compared against the expected literature ranges and both the simulated k values (0.11 – 0.15 yr-1) and the simulated ΔH values (45 – 52 kJ/molCH4) fell within expected ranges. For all 4 simulations a TF with k90 at 20 °C produced the best alignment between simulated and observed behaviours which was significantly less intense (more biological activity occurring over 10 to 30 °C) than the TFs used currently in the literature. The comparison of measured laboratory BMPult values against simulated RMP values showed mixed results. For the middle and bottom samples there was a moderate degree of alignment but for the top samples the fit was quite poor.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.183
Teacher spread0.169 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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