Impacts of 2023 Canadian Wildfires on Air Quality in the Lake Michigan Region During AGES+
Bibliographic record
Abstract
The historic 2023 Canadian wildfire season degraded air quality in the United States due to smoke transport across international borders. This study evaluates the impact of these fires on surface ozone (O3) and fine particulate matter (PM2.5) air quality in the Lake Michigan region during the AGES+ (AEROMMA+CUPiDS, GOTHAAM, EPCAPE, STAQS, and others) field campaign period. We conducted simulations with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) driven by chemical boundary conditions with and without Canadian wildfire emissions and analyzed them in conjunction with ground-based measurements, satellite observations, and in situ aircraft observations. Results show that the diurnal cycle and magnitude of PM2.5 concentrations were largely influenced by wildfire smoke transport and that a majority of the PM2.5 exceedance days that occurred during the study period can be attributed to wildfire smoke. Further analysis revealed that the first of two O3 exceedance periods, from 23–25 July 2023, was impacted by near-surface wildfire smoke, while the second period, from 1–3 August 2023, was not. Comparing simulations with and without Canadian wildfire emissions, we found that the aerosol shading effect on surface O3 production occurred. However, modeled aerosol optical depth values were underestimated compared to satellite observations, indicating that this phenomenon was likely stronger than suggested by the model simulations. As the frequency of forest fires is projected to increase due to climate change, this research highlights the growing impacts of wildfire smoke on air quality in the Lake Michigan region.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".