Impact on air quality and health due to the Saddleworth Moor fire in northern England
Bibliographic record
Abstract
Abstract On June 24th 2018 one of the largest UK wildfires in recent history broke out on Saddleworth Moor, close to Manchester, in north-west England. Since wildfires close to large populations in the UK have been relatively small and rare in the past, there is little knowledge about the impacts. This has prevented the development of effective strategies to reduce them. This paper uses a high-resolution coupled atmospheric-chemistry model to assess the impact of the fires on particulate matter with a diameter less than 2.5 µ m (PM 2.5 ) across the region and the impact on health from short-term exposure. We find that the fires substantially degraded air quality. PM 2.5 concentrations increased by more than 300% in Oldham and Manchester and up to 50% in areas up to 80 km away such as Liverpool and Wigan. This led to one quarter of the population (2.9 million people) in the simulation domain (−4.9–0.7°E and 53.0–54.4°N) being exposed to moderate PM 2.5 concentrations on at least one day, according to the Daily Air Quality Index (36–53 µ g m −3 ), between June 23rd and 30th 2018. This equates to 4.5 million people being exposed to PM 2.5 above the WHO 24-hour guideline of 25 µ g m −3 on at least one day. Using a concentration-response function we calculate the short-term health impact, which indicates that in total over the 7-day period 28 (95% CI: 14.1–42.1) deaths were brought forward, with a mean daily excess mortality of 3.5 deaths per day (95% CI: 1.8–5.3). The excess mortalities from PM 2.5 from the fires represented up to 60% of the total excess mortality (5.7 of 9.5 excess deaths), representing an increase of 3.8 excess mortalities (165% increase) compared to if there were no fires. We find the impact of mortality due to PM 2.5 from the fires on the economy was also substantial (£21.1 m).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".