Natural experiments in urban air quality: lessons from car-free days and COVID-19 lockdowns in Kigali, Rwanda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A lack of long-term air quality monitoring data in African countries such as Rwanda poses a significant challenge as urbanization leads to declining air quality. This study uses four years of data on particulate matter air pollution (PM2.5) to understand the current drivers of air pollution, the success of current interventions and the potential for further actions. PM2.5 data were collected using low-cost and reference monitors in two sites in Kigali. Results show that PM2.5 levels in Kigali exceeded the recommended WHO air quality guidelines. Using the COVID-19 lockdown as a natural experiment, we find that reduced travel activity of over 80% led to PM2.5 levels declining by 33%, suggesting that transport may account for a smaller share of particulate emissions than is assumed in government literature. We also find that a program to encourage non-motorized transport in Kigali called ‘Car-Free Days’ reduced by PM2.5 15% when it was held between 2017 and 2020. This reduction is expected to have resulted in more than 200 disability-adjusted life years saved in Kigali annually, about 150 hospital visits, and 600 lost working days being avoided. We conclude by reflecting on the policies for improving air quality in Kigali City.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it