Comparing the Cardiopulmonary Health Implications of Wildfire vs. Non-wildfire PM2.5 Particles in New York City
Bibliographic record
Abstract
Abstract RATIONALE: The extreme wildfire season in Canada in 2023, combined with unusual meteorological conditions, resulted in elevated levels of fine particulate matter air pollution (PM2.5) to be carried into the Eastern United States. In New York City (NYC), PM2.5 levels greatly exceeded the daily US ambient air quality standard during the wildfire episodes, obscuring visibility and causing public concerns. Given the expected increase in such wildfires from climate change, investigations are needed to evaluate whether wildfire PM2.5 health effects differ from more usual PM2.5 that the regulatory standards are based upon. METHODS: We accessed daily counts of Asthma and COPD, Other Respiratory and Cardiovascular emergency department (ED) visits from the NYC Department of Health and Mental Hygiene syndromic surveillance database in 2022 and 2023. We downloaded 2019, 2022 and 2023 PM2.5 data from monitoring stations across the city and computed composite citywide daily averages to estimate background non-wildfire PM2.5 during the wildfire episodes (defined by the New York State Air Quality alerts). The excess wildfire PM2.5 was then estimated by subtracting background PM2.5 from the measured levels. A time-series model using quasi-Poisson regression of daily ED visits on wildfire and non-wildfire PM2.5, controlling for time trend, day of week, federal holidays, was used to compare the effects of wildfire and non-wildfire PM2.5. Distributed lag segmented time-series analyses were also applied. RESULTS: We found a significant increase in total cardiopulmonary ED visit rate associated with same-day non-wildfire PM2.5 (incidence rate ratio [IRR]=1.031, 95%CI 1.019-1.042, per 10ug/m3), but not with wildfire PM2.5 (IRR=1.003, 95%CI 0.996-1.009, per 10ug/m3). Same-day non-wildfire PM2.5 was associated with higher risk for cardiovascular ED visits (IRR=1.028, 95%CI 1.017-1.040, per 10ug/m3) and other respiratory ED visits (IRR=1.035, 95%CI 1.021-1.048, per 10ug/m3) while wildfire PM2.5 was not statistically significantly associated with either. In contrast, there was a statistically significant increase specifically in Asthma and COPD ED visit rate associated with same-day wildfire PM2.5 (IRR=1.031, 95%CI 1.022-1.041, per 10ug/m3), but not with non-wildfire PM2.5 (IRR=1.010, 95%CI 0.991-1.029, per 10ug/m3). Distributed lag models found similar results, with a lagged significant adverse cardiovascular effect associated with non-wildfire PM2.5 for up to 7 days. CONCLUSION: Our study provides evidence that wildfire and non-wildfire PM2.5 particles are associated with differing cardiopulmonary health risks. Our findings suggest wildfire PM2.5 particles act primarily as local lung irritants, and not systemically. Future regulatory activities should consider developing separate guidelines and interventions for wildfire and non-wildfire air pollution.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".