Public health impacts of naturally-derived particulate matter : a case study of Asian dust in southwestern British Columbia
Bibliographic record
Abstract
The adverse public health impacts of anthropogenically-derived particulate matter have been well documented, with measureable increases in both morbidity and mortality rates associated with high particulate matter pollution events. Most current research has focused on the health impacts of anthropogenically-derived particulate matter, and there is a distinct scarcity of literature that examines the role of naturally-derived particulate matter and adverse health impacts in the urban context. This study of a Gobi desert dust event in British Columbia, Canada, in spring of 1998 provided a unique opportunity to identify the adverse health effects related to naturally-derived particulate matter in a large urban setting. Respiratory and cardiac hospitalizations were examined for a three-year period (January 1997 to December 1999), with the Gobi dust event occurring in late April 1998. A meteorological analogue was identified for spring 1997 in order to identify the public health impacts associated with anthropogenically-derived particulate matter and those impacts associated with the presence of the Gobi desert dust. Results indicate that this Gobi dust event was not associated with an excess of hospitalizations for the Fraser Valley region. Peak particulate matter concentrations of Gobi desert dust in the airshed were only associated with an additional two or three hospitalizations for respiratory and cardiac illnesses, and these increases were not distinguishable from the 'normal' variability in hospitalization rates. Despite high particulate matter concentrations, fine particle size, presence of heavy metals in the dust and extended exposure periods, it appears that the Gobi desert dust event was not associated with significant risk to public health in the Fraser Valley, British Columbia. Therefore it is concluded that naturally-derived particulate matter is more benign than particulate matter of anthropogenic origin, and thus poses a low risk to health for the general public.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".