An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia
Bibliographic record
Abstract
Epidemiologic studies have consistently reported associations between outdoor fine particulate matter (PM 2.5 ) air pollution and adverse health effects. Although Asia bears the majority of the public health burden from air pollution, few epidemiologic studies have been conducted outside of North America and Europe due in part to challenges in population exposure assessment. We assessed the feasibility of two current exposure assessment techniques, land use regression (LUR) modeling and mobile monitoring, and estimated the mortality attributable to air pollution in Ulaanbaatar, Mongolia. We developed LUR models for predicting wintertime spatial patterns of NO 2 and SO 2 based on 2-week passive Ogawa measurements at 37 locations and freely available geographic predictors. The models explained 74% and 78% of the variance in NO 2 and SO 2 , respectively. Land cover characteristics derived from satellite images were useful predictors of both pollutants. Mobile PM 2.5 monitoring with an integrating nephelometer also showed promise, capturing substantial spatial variation in PM 2.5 concentrations. The spatial patterns in SO 2 and PM, seasonal and diurnal patterns in PM 2.5 , and high wintertime PM 2.5 /PM 10 ratios were consistent with a major impact from coal and wood combustion in the city’s low-income traditional housing (ger) areas. The annual average concentration of PM 2.5 measured at a centrally located government monitoring site was 75 μg/m 3 or more than seven times the World Health Organization’s PM 2.5 air quality guideline, driven by a wintertime average concentration of 148 μg/m 3 . PM 2.5 concentrations measured in a traditional housing area were higher, with a wintertime mean PM 2.5 concentration of 250 μg/m 3 . We conservatively estimated that 29% (95% CI, 12–43%) of cardiopulmonary deaths and 40% (95% CI, 17–56%) of lung cancer deaths in the city are attributable to outdoor air pollution. These deaths correspond to nearly 10% of the city’s total mortality, with estimates ranging to more than 13% of mortality under less conservative model assumptions. LUR models and mobile monitoring can be successfully implemented in developing country cities, thus cost-effectively improving exposure assessment for epidemiology and risk assessment. Air pollution represents a major threat to public health in Ulaanbaatar, Mongolia, and reducing home heating emissions in traditional housing areas should be the primary focus of air pollution control efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".