Challenges and opportunities in using low-cost sensors for PM2.5 monitoring in urban transport microenvironments: a study case in Barranquilla Metropolitan Region, Colombia

Air pollution constitutes a significant environmental justice challenge, particularly affecting vulnerable communities in low- and middle-income countries (LMICs). Urban mobility plays a substantial role in personal exposure to pollutants, notably PM_2.5, exacerbating health disparities among transport users. Effective monitoring of these exposures is essential for understanding and addressing these inequities; however, traditional air quality measurement infrastructures are often inadequate in LMIC contexts. The growing availability of low-cost sensors (LCS) presents a promising avenue for bridging data gaps in urban air quality monitoring. Nonetheless, the reliability and applicability of these sensors in dynamic urban transport environments require thorough evaluation. This study, executed as part of an interdisciplinary collaboration among experts in urban policy, air quality, and transport studies, investigates the deployment of an LCS for PM_2.5 monitoring in Soledad, Colombia. The research aims to assess the potential of LCS to capture exposure disparities among various modes of transport while addressing associated technical and logistical challenges. The primary focus is to evaluate the feasibility of utilizing LCS to measure personal exposure to PM_2.5 in urban transport microenvironments, emphasizing calibration accuracy, adaptability to local conditions, and the potential to inform equitable transport policies. The AirBeam3 sensor was employed across motorized three-wheelers, buses, and private cars during predetermined urban routes. A rigorous 15-day calibration process against a reference-grade station was conducted to ensure data accuracy, achieving a correlation coefficient of R² = 0.87. Data collection strategies were tailored to account for transport-specific dynamics, including variations in ventilation, proximity to emission sources, and traffic conditions. The study encountered several challenges, including adaptation to high humidity, protection of equipment in high-risk environments, and correction of measurement biases. Notably, the sensor identified significant PM_2.5 exposure disparities among transport modes, with motorized three-wheeler users exhibiting the highest exposure levels. Adjusted data indicated that environmental conditions, traffic density, and vehicle type emerged as critical determinants of exposure. Despite certain limitations, the LCS provided robust, high-resolution exposure data, demonstrating its suitability for capturing real-world variability in LMIC contexts. This research underscores the challenges and opportunities presented by the deployment of LCS for air quality monitoring in resource-constrained urban settings. While technical hurdles, such as calibration and environmental sensitivity, persist, the affordability and accessibility of LCS render them invaluable tools for addressing environmental justice issues. The findings emphasize the potential of LCS to enhance local air quality initiatives, inform sustainable transport policies, and promote equitable health outcomes through data-driven interventions.

Keywords: low-cost sensors, air quality, PM_2.5, urban mobility, personal exposure, environmental justice, LMICs.