Enhancing Wildfire Smoke Exposure Assessment: A Machine Learning Approach to Predict Indoor PM <sub>2.5</sub> in British Columbia, Canada
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Epidemiological studies typically model wildfire smoke exposure by predicting outdoor fine particulate matter (PM 2.5 ) concentrations, overlooking indoor environments where people spend most of their time. This discrepancy can lead to exposure misclassification for wildfire smoke and other air pollutants. We developed a machine learning (ML) model for estimating daily indoor and outdoor PM 2.5 concentrations in British Columbia, Canada, using an ensemble of nonparametric ML algorithms during the 2022 and 2023 wildfire seasons. For model training, we included daily PM 2.5 concentrations collected at 44 care facilities equipped with low-cost air quality sensors colocated indoors and outdoors. Model predictors for both indoor and outdoor PM 2.5 at the facilities included outdoor PM 2.5 and meteorological data from Canada’s National Air Pollution Surveillance Program and Purple Air sensors. The indoor and outdoor models were evaluated with cross validation and then used to compare exposure-response relationships for asthma inhaler dispensations, as an indicator of population respiratory health. Ensemble models accurately predicted PM 2.5 indoors (RMSE = 3.29 μg/m 3; R 2 = 0.71) and outdoors (RMSE = 3.80 μg/m 3; R 2 = 0.78). For the out-of-sample validation set (2023 wildfire season), the indoor model had a lower RMSE than the outdoor one (RMSE Indoor = 6.65 μg/m 3 vs RMSE Outdoor = 9.64 μg/m 3 ). The effect estimates for the relationship between indoor PM 2.5 and inhaler dispensations were higher than that for outdoor PM 2.5 . These results suggest that population-scale indoor PM 2.5 exposure assessment is feasible for wildfire smoke epidemiology research, and that using outdoor estimates may bias the true relationship toward the null.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".