Coal-Fired Power Plant's Air Quality Impact and Policy Implications - What Does the Ontario's Historical Data Tell Us ?
Bibliographic record
Abstract
Coal-fired power plants, as important electricity sources, accounted for 38 percent of global electricity and 9 percent of Canada’s electricity in 2017. However, the combustion of coal produces a lot of air pollutants and greenhouse gases that impose serious air quality, climate change and health risks on human beings and natural environment. Using Ontario’s historical dataset from 2010 to 2014, this paper finds that the increase of 100 Megawatt’s electricity from coal plants per hour would increase the hourly PM2.5 concentration by 0.06 ug/m3, NOx and SO2 by 0.13 ppb and 0.057 ppb significantly. It also finds that during the hours when the wind blows from the coal plants to the air pollutant monitors, the PM2.5 and NOx level would be higher. Compared to the further areas, the monitors within 50km from the coal plants have more serious PM2.5 and SO2 concentrations. This paper estimates that if Ontario would still have operated the three coal plants included in this study in 2015, it would have caused 219 heart disease deaths and 12 stroke deaths due to PM2.5 in the populous cities within 100km from the plants, in condition that the three plants generate electricity in the average hourly output level. Facing with coal plants’ air quality and health impacts, this paper suggests that for the provinces in Canada that still have coal plants, it is necessary to think about controlling the air pollutants from coal by shutting down the coal plants in the long-term, or at least relocating the plants or installing some control devices in the short-term, depending on different conditions each province has.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".