Air quality and attributable mortality among city dwellers in Kampala, Uganda: results from 4 years of continuous PM2.5 concentration monitoring using BAM 1022 reference instrument
Bibliographic record
Abstract
Abstract Background Air pollution is a known risk factor for non-communicable diseases that causes substantial premature death globally. Rapid urban growth, burning of biomass and solid waste, unpaved sections of the road network, rising numbers of vehicles, some with highly polluting engines, contribute to the poor air quality in Kampala. Objective To provide evidence-based estimates of air pollution attributable mortality in Kampala city, with focus on ambient fine particulate matter (PM2.5). Methods We utilized a time series design and prospectively collected data on daily ambient PM2.5 concentration levels in micrograms per cubic meter (μg/m3) using a Beta Attenuation Monitor (BAM-1022) in Kampala city, Uganda. We combined the PM2.5 data with all-cause mortality data obtained from the Uganda Bureau of Statistics and the Ministry of Health in Kampala. We calculated attributable risk estimates for mortality using the WHO AirQ+ tools. Results Overall, the annual average concentration for PM2.5 for the period of 4 years, 2018–2021, was 39 μg/m3. There was seasonal variation, with the rainy season months (March–June and October–December) having lower values. PM2.5 concentrations tend to be highest in the morning (09.00 h) and in the evening (21.00 h.) likely due to increased vehicular emissions as well as the influence of weather patterns (atmospheric temperature, relative humidity and wind). Saturday has the most pollution (daily average over 4 years of 41.2 μg/m3). Regarding attributable risk, we found that of all the deaths in Kampala, 2777 (19.3%), 2136 (17.9%), 1281 (17.9%) and 1063 (19.8%) were attributable to long-term exposure to air pollution (i.e., exposure to PM2.5 concentrations above the WHO annual guideline of 5 μg/m3) from 2018 to 2021, respectively. For the 4 years and considering the WHO annual guideline as the reference, there were 7257 air pollution-related deaths in Kampala city. Impact Our study is the first to estimate air pollution attributable deaths in Kampala city considering the target as the WHO annual guideline value for PM2.5 of 5 μg/m3. Our monitoring data show that fine particulate matter air pollution in Kampala is above the WHO Air Quality Guideline value, likely resulting in substantial adverse health effects and premature death. While further monitoring is necessary, there is a clear need for control measures to improve air quality in Kampala city.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".