Landfarming of petroleum hydrocarbons, development and evaluation of methods to monitor their impact on the atmosphere
Bibliographic record
Abstract
The emission of trace gases during refinery and bioremediation landfarming is investigated in this dissertation. Field and lab-scale methods were developed and used to quantify emissions and evaluate their impact. A flux gradient and aerodynamic mass balance micrometeorological method are described in detail along with measured total hydrocarbon (THC) and CO2 fluxes collected during measurements at 2 active refinery landfarms in Ontario. The methods worked well and allowed the unobtrusive real-time measurement of fluxes during the spreading of oily liquid wastes and dewatered biosolids, and during cultivating of the landfarm surface. Static chamber methods were developed and used to measure trace gas fluxes from a remotely located diesel-fuel contaminated soil landfarm. A modified static chamber method measured THC fluxes in real-time. Data could be collected in 10 minutes or less, minimizing the influence of the chamber's presence on the measured fluxes. Experiments were successfully performed to characterize landfarm emissions and to evaluate the impact of cultivation on emissions. A mesocosm-scale simulator facility was designed and constructed to measure trace gas fluxes from contaminated soils in real-time. Five simulators were integrated with a data acquisition system and trace gas analyzers; allowing automated sampling and calculation of THC, CO2 and water vapour fluxes. N2O fluxes were calculated using a discrete sampling method. Experiments were performed to evaluate the impact of simulated cultivation and rainfall on trace gas fluxes. Fluxes measured using the simulators were similar in magnitude to field results and effects observed during field studies were reproduced. Results from the conducted studies are used to assess the impact of landfarm emissions and recommend strategies for mitigation of emissions. A Gaussian areal source dispersion model was used to estimate airborne concentrations of hydrocarbons. Maximum concentrations were found to occur on the downwind edge of the landfarms. Maximum concentrations were at least an order of magnitude smaller than published exposure limit values for specific hydrocarbons, suggesting a minimal inhalation risk to humans. Recommendations are provided for landfarm operators to aid in minimization of volatile emissions while maintaining soil conditions for waste reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".