Air–Noise Pollution Linkages: Testing Innovative Community-Based Adaptation and Mitigation Strategies in Kenya
Bibliographic record
Abstract
Introduction: Our case study was conducted across healthcare facilities in Kilifi and Nairobi, where perinatal adolescents were screened for depression. Objective: The relationship of environmental monitoring in addressing mental health needs of vulnerable perinatal adolescent populations was explored. Methods: We installed outdoor air quality sensors at two facilities in Nairobi—Kangemi and Kariobangi North health centers—and two in Kilifi—Mtwapa and Vipingo health centers—and installed sensors in two households of two perinatal adolescents. Community health workers monitored air quality and noise levels data, collecting experiential data on stress and mood from perinatal adolescents. Findings: Air quality monitoring revealed site-specific variations in PM2.5 concentrations. Kariobangi Health Center recorded the highest mean concentration of 29.45 µg/m³, exceeding the WHO 2021 annual guideline of 5 µg/m³ indicating substantially degraded air quality. Kangemi Health Center was next (21.27 µg/m³), followed by Mtwapa (15.34 µg/m³) and Vipingo (12.52 µg/m³). Noise monitoring revealed consistently elevated exposure in healthcare settings. At Kangemi Health Center, mean noise levels reached 52.2 dB (median: 53.5 dB), surpassing the WHO guideline for hospital settings (<35–40 dB). Household-level air quality monitoring highlighted significant operational challenges: sensor deployment constraints, difficulties in ensuring continuous temporal coverage, and substantial intra-day variability—underscoring the need for improved monitoring design and calibration strategies. Conclusions: We tested air and noise monitoring deployment as a lever for strengthening the health system and a strategy for improved patient care and mental well-being. We trained community health workers and youth leaders in a task-shifting model to collect environmental health data. Our approach sought to ease the deployment of environmental monitoring in a sustainable data collection process. However, both mitigation, targeting reduction in sources of pollution, and adaptation efforts focused on coping with the effects of air and noise pollution on vulnerable populations within primary care need concerted efforts.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".