238 Statistical modelling of PM10 and PM2.5 exposures in poultry barns, and evaluation of electrostatic precipitators to control particulate emissions
Bibliographic record
Abstract
Objectives Particulate concentrations within poultry barns pose a potential occupational and environmental health concern. We aimed to identify determinants of PM10 and PM2.5 levels in poultry barns and evaluate the effectiveness of electrostatic precipitators (ESP) to reduce environmental air pollution. Methods PM10 and PM2.5 Harvard impactor samples were collected in “side-by-side” barns (one with an ESP, one without) at five poultry farms in British Columbia, Canada from 2008 to 2012. Measured particulate levels were analysed using multimodel inference and linear mixed-effects models after log-transformation. Random effects were added to account for clustering within farms, barns, and rearing cycles. Results A total of 234 PM2.5 and 230 PM10 valid samples were modelled. Geometric means of PM2.5 and PM10 were significantly lower in barns with ESP control in place (151 and 427 µg/m3, respectively) compared to barns with no control (334 and 969 µg/m3), resulting in unadjusted % reductions of 47% and 50% respectively. In statistical models, the fixed-effects explained 57% and 72% of the total variance in the PM2.5 and PM10 measurements, respectively. The residual (i.e. within rearing cycle) and between rearing cycle variance were the most affected by adding the fixed effects structure. Strongest determinants in the models for both dust types were ESP use (i.e. approximately halving particulate levels for both PM2.5 and PM10), bird age (i.e. 10–30 fold increase in particulate levels depending on bird and particulate type), and type of bird (i.e. approximately a 2.5 fold increase for PM2.5, and four fold increase in PM10 for turkey compared to chicken). Interactions were suggested in PM10 models between type of bird, bird age, and ESP use. Conclusions The use of ESP resulted in significant reductions in in-barn particulate even after controlling for other determinants such as bird age, type of bird, ventilation, and date.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".