# Air–Noise Pollution Linkages: Testing Innovative Community-Based Adaptation and Mitigation Strategies in Kenya

## TL;DR

This study explores how air and noise pollution affect mental health in perinatal adolescents in Kenya, using community-based monitoring and training.

## Contribution

The study introduces a community-based model for environmental monitoring to address mental health in vulnerable populations.

## Key findings

- Air quality monitoring showed PM2.5 levels in Nairobi and Kilifi often exceeded WHO guidelines.
- Noise levels at healthcare centers exceeded recommended thresholds, indicating potential stressors for patients.
- Household monitoring revealed challenges in sensor deployment and data consistency, highlighting the need for better strategies.

## 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.