Dynamic Heat Exposure Assessment and Rhythmic Differentiation Across Functional Spaces Under the Coupling of Thermal Environment and Population Activity: A Case Study of Zhengzhou, China

Urban heat risk depends not only on thermal environment intensity but also on the spatiotemporal distribution and diurnal rhythm of population activity. Conventional assessments based on single-phase land surface temperature or static population data cannot capture intraday exposure variation or differences among functional spaces. Using a selected high-temperature day (13 June 2025) in Zhengzhou, this paper builds a Dynamic Thermal Environment Index (DTEI) fusing a static Landsat-derived thermal baseline with intraday variation from Himawari-9. It couples DTEI with a Population Activity Index (PAI) from hourly Baidu heat-map data to construct a 500 m grid-scale Dynamic Heat Exposure Index (DHEI). DHEI’s diurnal rhythm, spatial pattern, and functional-space differences are analyzed, and a two-factor decomposition identifies the hourly relative contributions of DTEI and PAI. Results show that: (1) DTEI, DHEI, and PAI peak at 13:00, 14:00, and 18:00, with DHEI lagging DTEI by 1 h and leading PAI by 4 h; (2) high-DHEI areas concentrate in the central built-up area with a stable spatial pattern, expanding outward and rising in level over the day; (3) exposure intensity follows commercial and residential > public service > transportation, green/recreational, and employment/industrial zones, with markedly different peak timings between groups; and (4) DHEI variation is driven not by a single factor but by alternating thermal environment and population activity contributions, with the peak formed by their joint effect in early afternoon. Dynamic urban heat exposure is thus a time-varying outcome of thermal environment and population activity coupling, offering a useful reference for time- and zone-specific heat-risk management.