CHESS deploys sensors in homes, schools, and neighborhoods to collect high-resolution data on temperature, air quality, and other environmental factors. This data helps researchers identify patterns and risks that traditional metrics often miss.

To analyze these large, complex datasets, CHESS relies on research computing resources, including high-performance clusters at BU and MGHPCC. These tools enable advanced modeling, time-series analysis, and the development of novel exposure metrics that inform public health interventions and policy. CHESS exemplifies how computational power and community-based research can work together to promote environmental justice and health equity.

A recent paper from the group explores how heat exposure in school classrooms affects student health and learning, using continuous temperature monitoring and novel metrics to better capture real-world conditions. Researchers deployed sensors in Boston-area schools to collect high-resolution indoor temperature data over time, revealing that traditional metrics like daily maximum temperature often miss critical patterns—such as prolonged exposure to moderate heat or temperature fluctuations during school hours.

To analyze these complex datasets, the team leveraged high-performance computing resources at MGHPCC, enabling them to process large volumes of time-series data and develop new exposure metrics that reflect students’ actual thermal environments. These metrics offer a more accurate basis for assessing health risks and informing building design and policy decisions.

By combining environmental monitoring with computational modeling, the study provides a framework for data-driven climate resilience in schools, emphasizing the importance of indoor heat exposure in public health and educational equity.