Air Quality in Transportation Microenvironments

This Study measured levels of PM2.5 and PM10 inside a vehicle under different air filtration settings, using low-cost sensors and a Raspberry Pi-based data logger. The goal was to evaluate how effective various cabin filter types and ventilation modes are at reducing in-cabin particulate matter concentrations, with the aim of improving commuter health.

Repository Structure

• Docs/: Contains documentation files including project overview, methodology, results, limitations, and future work.
• Data/: Contains raw and processed data files from the monitoring campaign.
• scripts/: Contains code used on the raspberry pi for data logging.
• Notebooks/: Contains Jupyter notebooks for data analysis and visualization.
• README.md: This file, providing an overview of the project and repository structure.

Key Findings

• Using a HEPA cabin filter significantly reduced in-cabin PM2.5 and PM10 levels compared to a standard filter.
• Activating recirculation mode further decreased particulate concentrations, especially when combined with a HEPA filter.
• In-cabin particulate levels heavily depended on external traffic conditions and driving context.
• Low-cost sensors like the SDS011 can effectively track relative changes in particulate matter levels, though absolute values should be interpreted cautiously without calibration.
• Recommendations for commuters include using HEPA filters and recirculation mode during high pollution episodes to minimize exposure

Motivation & Research Questions

A large portion of daily exposure to air pollution occurs during commutes in vehicles. However, there is limited data on how different air filtration methods and settings impact in-vehicle air quality, particularly in rapidly developing regions with high traffic pollution. This study aims to address the following research questions:

• What are the typical in-cabin PM2.5 and PM10 levels in a semi-modern passenger car during normal use?
• How does ventilation mode (recirculation on/off) and cabin filter type (non-HEPA vs HEPA) affect in-cabin particulate levels?
• To what extent can simple interventions reduce in-cabin particulate matter concentrations to within WHO guideline levels?

For detailed background, motivation, and research questions addressed by this study, please refer to the Project Overview document.

Hardware and Software Setup

All hardware designs, software code, and data analysis scripts used in this study are available in this repository. The monitoring system was built using a Raspberry Pi, SDS011 particulate sensor, and BME680 environmental sensor. Data logging scripts were written in Python. Libraries used include pandas and numpy for data handling and matplotlib for visualization.

More details on hardware can be found in the Methodology document.

Data analysis and visualization were performed using Jupyter notebooks, which are included in the Notebooks/ directory.

Limitations

• Short Duration of data collection (9 days) limits statistical robustness.
• Single vehicle model used; results may differ in other vehicles.
• Low-cost sensors without local calibration; absolute PM values are approximate.
• Heavily dependent on uncontrolled external conditions (traffic, weather).
• In-cabin behaviour not systematically recorded.

Future Work

• Longer, more balanced monitoring campaign across multiple weeks.
• Co-location with reference-grade monitors for calibration.
• Deployment in multiple vehicle types.
• Addition of CO2 and VOC sensors to evaluate trade-offs.

For a detailed discussion of limitations, as well as future work, please refer to the Limitations and Future Work document.

Acknowledgements

This project was conducted as part of the MIT Emerging Talent 2025 program. Special thanks to Camila, Evan, Carlos, Megan, the entire MIT ET team, and all my fellow ET learners for their support and guidance throughout this journey.