Do sweat it: understanding of multiscale sweat evaporation dynamics in the context of adapting to extreme heat

“Inferno” refers to the student section at Arizona State University athletic events, but it is also a fitting description of local weather conditions and their implications for human health, well-being, and productivity. In 2024 alone, Phoenix—the hottest city in the United States—experienced over 600 heat-related deaths and 2,000 hospitalizations, while extreme heat also disrupted economic and recreational activities. Understanding how various individuals are exposed to and physiologically respond to extreme heat can inform adaptation strategies, from heat action plans and emergency responses to urban design improvements and wearable technologies.

To this end, my group is developing three physical-digital human twins that merge environmental sensors with thermophysiological models to provide real-time heat strain predictions and quantification of how that risk can be reduced with various interventions. In the thermoregulation models, sweat evaporation rate—the primary avenue of heat dissipation—is calculated by assuming the Lewis heat and mass transfer analogy and treating sweat as an isothermal thin film at skin temperature. In this presentation, I will describe testing of these assumptions through theoretical analysis and innovative experimental methods, including an outdoor sweating thermal manikin, an infrared-transparent “mini-wind tunnel,” and even a lie detector. Our efforts revealed new insights across length scales, from single pores to the whole-body level. At the microscale, we found that the lateral stratum corneum hydration rate and salt deposits control wetting and shifts between porewise, transition, and filmwise sweating modes. At the macroscale, we uncovered strong impacts of humidity-induced buoyancy in free convection regime and of turbulent air flow characteristics in forced convection regime.
Together, these findings advance our understanding of sweat evaporation dynamics and provide a quantitative basis for predicting human heat illness risks.