Bibliographic record
Abstract
Very limited research has been conducted to study odour and toxic gas emissions (e.g. NO2, SO2, H2S, VOCs) from oil refineries in Canada similar to everywhere else in the world. The main goal of this thesis was to study odour and toxic gases impact of an oil refinery on surrounding environment in Western Canada. The performance of a recently developed portable olfactometer, SM 100 (IDES Inc., Toronto, ON, Canada) was evaluated by comparison its results with results of a standard dynamic laboratory olfactometer on odour concentrations of n-butanol and poultry barn exhausted air. Results demonstrated that the difference between odour concentrations measured by SM 100 and the lab olfactometer was not significant (P>0.05). Results also demonstrated that odour concentrations measured by SM 100 and the standard dynamic lab olfactometer were close as all 16 measured odour samples fit within 20% margin of the parity plot comparing log odour concentrations measured by two devices. Odour properties including odour concentration (OC), odour intensity (OI), hedonic tone (HT), odour character, and relationships among them were investigated for the oil refinery odour through two field measurement campaigns conducted in radius of 7 km from the oil refinery in summer and spring. Results showed that odour could be detected in further distances in spring (up to 6.7 km) than summer (2.3 km) due to more stable atmospheric in spring. An exponential relationship were established between OC and OI (R2=0.8). It was found that there was a linear relationship between HT and OI (R2=0.81) in which odour character did not have a main effect. Although, HT is defined independent from OI, this study demonstrated that panelists were stimulated by intensity of odour rather than solely by its pleasantness and unpleasantness. Odour impact of the oil refinery on surrounding environment was investigated using the developed OERs, five years meteorological data and odour complaint data recorded by the plant leading to developing source-specific odour impact criteria for the oil refinery. Results demonstrated that odour limit of 1 OU with the odour-free occurrence frequency of 99.9% could be an appropriate criterion in managing odour problems posed by oil refineries for densely populated residential area with sensitive spots that provides them with a setback distance of 2.7 to 5.2 km. Odour limit of 2 OU with the odour-free occurrence frequency of 99.9% could be an appropriate option for scarcely populated residential area without sensitive spots and urban commercial which results in a setback distance range of 1.8 to 4 km. Odour limit of 4 OU is suggested for industrial land use resulting in a setback distance of 0.8 to 2.5 km. Finally, the odour limit of 6 OU with the odour-free occurrence frequency of 99.9% is suggested for agricultural land uses which leads to a setback distance range of 0.5 to 2.2 km. Toxic gas dispersions from the oil refinery was investigated using plume measurements by portable gas analyzers, data obtained from the air quality monitoring stations and dispersion modeling by AERMOD. Results demonstrated that benzene and H2S concentrations originated from the oil refinery could violate ambient air quality limits in adjacent area. SO2 and NO2 were found the other two most important pollutants emitted from the refinery since ambient NO2 and SO2 concentrations originated from the oil refinery could reach up to 50% of the standard levels even at far locations from the refinery. Results also demonstrated that the refinery did not have significant health impact in terms of xylenes and toluene emissions as they could reach to 8 to 10% of the strictest criteria. The refinery did not seem to affect the air quality of surrounding environment by ethylbenzene and CO emissions as predicted values of ambient concentrations for these pollutants are well below the air quality standards. Gases dispersion modeling results along with the measured odour concentration and character around the oil refinery revealed that toluene, xylenes, H2S, SO2 play important roles in posing odour problems by the oil refinery. A multilinear regression model which related OC to these odorant concentrations were developed using 70% of data and validated using 30% of data (r=0.48). The study concluded that these odorants along with ethyl and methyl mercaptans (emission data for them was not available) could be considered as odour indicators for the oil refinery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.288 | 0.168 |
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".