Low-cost IoT sensors reveal adverse health outcomes associated with air pollution exposure
Bibliographic record
Abstract
Abstract Background Air pollution is a major public health threat and has been linked to various adverse health effects including respiratory and cardiovascular diseases. Fine particulate matter (PM2.5) and coarse particulate matter (PM10) are two major components of air pollution that are significant contributors to these health outcomes. With the advent of low-cost sensors, accurate measurement of exposure has become possible, allowing for better assessment of the impact of air pollution exposure on human health. Methods To investigate the association between air pollution exposure and illnesses resulting in hospital visits, IoT sensors were deployed in Ulaanbaatar, Mongolia, to measure PM2.5 and PM10 levels from 2018-2021. Health data was obtained from the Mongolian National Center for Public Health. Multivariable logistic regression models were used to estimate overall odds ratios for the associations between daily PM2.5 and PM10 exposure and the occurrence of illnesses, adjusting for age and sex. Results The unadjusted odds ratio for the association between PM10 exposure and acute respiratory illnesses was 1.3, while the unadjusted odds ratio for PM2.5 exposure was 1.2; decreasing to 1.2 and 1.1 respectively after adjusting for age and sex (p < 0.05). These findings are consistent with previous studies and highlight the significant impact of air pollution on the occurrence of acute respiratory illnesses. The use of IoT sensors allowed for accurate assessment of air pollution exposure in the study population. Conclusions This study provides further evidence of the detrimental effects of air pollution on human health, particularly on the occurrence of acute respiratory illnesses. These findings highlight the importance of continued efforts to reduce air pollution levels. Accurate measurement of exposure rates using IoT sensors can aid in identifying and addressing sources of air pollution to improve the quality of life for individuals and communities affected by this health threat. Key messages • This study shows the significant impact of air pollution on acute respiratory illnesses, highlighting the importance of reducing air pollution levels. • Accurate measurement of air pollution exposure using IoT sensors can aid in identifying and addressing sources of air pollution to improve public health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".