Rethinking heat in thousands of school classrooms through continuous monitoring and novel exposure metrics

Abstract

As global temperatures rise, heat exposure in classrooms is becoming a growing concern for the millions of students attending school, in particular those learning in buildings without air conditioning (AC). With limited resources and competing interests, school decision-makers need health-related data-based approaches to inform cooling solutions and prioritize investments. In collaboration with a large school district in Northeastern United States (US), we analyzed minute-level temperature data in > 3600 classrooms across 125 school buildings during the 2023 hot season. Using a first-of-its-kind commercial-grade indoor sensor network and data science methods, we quantified heat exposure through novel heat metrics capturing intensity, frequency, and duration, and characterized spatial variability within and across buildings with three types of AC. On average, intra-building temperature variability was 2.3 degrees Celsius (°C), with a maximum value of 14.3°C. On a hot day, classrooms exceeded extreme caution thresholds by 0.1 %, 1.1 %, and 8.4 % in schools with central, window, and no AC, respectively. Classrooms on the top floor were 0.3°C, 0.5°C, and 5.7°C warmer than classrooms on the first floor, for central, window, and no AC groups, respectively. Novel and traditional heat exposure metrics were weakly correlated, with implications for school rankings. Findings identified schools with the greatest cooling needs and investigated key predictors of classroom overheating. Our results underscore the need for continuous temperature monitoring in all classrooms and highlight the importance of access to mechanical cooling in locations that have historically not been prepared for extreme heat. Our work shows how data analyses informed by researcher-school partnerships can support critical climate resilience needs in schools.