PM2.5 Exposure in Public Primary Schools Kampala, Uganda
Bibliographic record
Abstract
Background Worldwide, 93% of the world’s children under 15 years of age are exposed to ambient fine particulate matter (PM2.5) levels above WHO defined air quality standards of 25 μg/m3 (24-hour mean). In low- and middle-income countries around the world, 98% of all children under 5 are exposed to PM2.5 levels above WHO suggested three interim air quality targets. In comparison, in high-income countries, 52% of children under 5 are exposed to levels above WHO air quality guidelines. We measured daily levels of ambient PM2.5 concentrations, temperature, relative humidity in public primary schools in Kampala City Uganda. Methods We conducted the Ambient PM2.5 Concentration measurements using the E-samplers centrally installed in four public primary schools in divisions of Kawempe, Central, Makindye and Lubaga in Kampala City, Uganda. The E-Samplers generated real-time, data for PM2.5 concentrations, Relative Humidity and Ambient Temperature. Data analysis was done using Excel 2016 to generate daily averages of PM2.5 concentrations for the Month of June 2018. Results The results showed the lowest and highest average daily PM2.5 concentrations for St. Paul Primary School Minimum 17 µg/m3 and Maximum 44 µg/m3); Central - Buganda Rd. Primary School Minimum 14 µg/m3 and Maximum 45 µg/m3); Makindye - Kibuli Demonstration Primary School Minimum 9 µg/m3 and Maximum 46 µg/m3); Rubaga - Queen of Peace Primary School Minimum 9 µg/m3 and Maximum 110 µg/m3). Studies have shown that exposure to high levels of PM2.5 concentrations results increased morbidity and mortality from cardiovascular and respiratory conditions. Conclusions There was an observed increase in ambient PM2.5 Concentration beyond World Health Organization defined limit of 25 μg/m3 and US EPA 35 μg/m3 (24-hour mean). Funding Sources: NIH Fogarty International Center, NIEHS, CDC/NIOSH, Canada’s IDRC, GACC Grant # 5R24 TW009552 [AAU]; 5R24 TW009548 [USC]; 1U01TW010094 [AAU]; 1U2RTW010125 [USC]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".