Smart air: Informing Driver Behavior through Dynamic Sensing and Smart Messaging
High concentrations of energy use from fossil fuels can lead to poor air quality, resulting in adverse health effects as well as economic consequences. A prime example is found where large numbers of idling vehicles congregate (e.g., schools and hospital drop-off/pick-up zones), leading to microclimates of unhealthy air. Workers, such as valet parking attendants, can spend their entire workday in these microenvironments, and children passing through these zones can experience up to 60% higher levels of pollution than adults, because of their height. These vehicle-caused, poor-air-quality microclimates offer a compelling opportunity for communities to engage with emerging technologies to take ownership of their air and the behaviors that impact its quality. This project sociotechnical approach, called SmartAir, will synergistically integrate dynamic air-quality information with social-norm feedback to positively influence decisions that affect the well-being of vulnerable individuals working in or passing through polluted microenvironments. The feedback approach for decreasing idling mirrors the feedback provided by digital speed displays, which has been shown to positively influence driver behavior (reduced speeding) and thus reduce health-impacts of that behavior (reduced traffic accidents). The proposed pilot demonstrations will take place in Northern Utah, a region that periodically experiences the poorest air quality in the country. The project SmartAir employs a comprehensive community engagement approach — from the development of the sensing and display technologies to cocreation of culturally sensitive messaging, cooperatively conducted pilot studies, and efficacy evaluation.
SmartAir will produce novel technological and behavioral-science developments. First, this project will develop wearable, calibrated, low-cost air quality sensing nodes that will support members of smart and connected communities to minimize pollution exposure. Second, this project will enable the rapid integration of sensor measurements with local meteorological information and data-screening algorithms to dynamically provide feedback to individuals about idling behavior and to workers that seek to minimize pollution exposure. Third, the SmartAir system will be integrated into behavior-change experiments and the co-creation of community-crafted messaging to influence individual choices. Comprehensive involvement of the community partners will be critical to co-develop and pilot solutions to address poor air quality and ultimately ensure a highly scalable and sustainable system. The broader impacts of this work are multifold, including the following. SmartAir will serve as a framework for closing the loop between air quality measurements and individual decision making. It will also help drive institutional decisions that reduce worker pollutant exposure and improve worker performance, career longevity, and job satisfaction. Anonymized data will be made available to support numerous personal and community-driven needs, such as health-effects studies, anti-idling campaigns, school drop-off policies, and urban/traffic planning. Additionally, this project will have a substantial outreach effort that involves community members in message crafting, data collection, and interpretation.
This research effort is funded by NSF CMMI award number #1952008.