关键词: 冠层热岛强度, 春节, 人口流动, 不同规模城市, 人类活动

Abstract: Objective　The large-scale human migration during the Spring Festival dramatically changes urban population distribution and anthropogenic heat release, which exerts prominent influences on canopy urban heat island (CUHI) effect. Most existing relevant research focuses on surface urban heat island and megacity-scale descriptive analysis, lacking systematic quantitative comparisons of CUHI responses across different city scales under short-term population mobility, as well as quantitative identification of multi-factor driving mechanisms across diverse climatic zones. To fill these research gaps, this study quantifies the spatiotemporal differences of the CUHI intensity difference (ΔIc) between large and small cities in eastern China during Spring Festival, clarifies the divergent changing patterns of daytime and nighttime CUHI, identifies the core driving factors of ΔIc from different dimensions such as human activities, surface properties and meteorological conditions, and reveals the interactions between multiple influencing factors. The study aims to deepen the understanding of dynamic feedback of CUHI against transient population migration and provide scientific basis for urban thermal environment optimization and sustainable urban planning. Methods　Multi-source datasets from 2018 to 2023, including ground meteorological observations, PM2.5 monitoring, remote sensing products such as nighttime light (NTL), normalized difference vegetation index (NDVI), albedo and land cover, digital elevation model and population density statistics, were collected and preprocessed via Google Earth Engine and ArcGIS platforms. Forty-eight cities distributed across mid-temperate, warm temperate, northern subtropical and marginal subtropical zones in eastern China (east of Hu Huanyong Line) were selected and categorized into large and small cities according to population density percentile threshold. A simplified urban-rural classification algorithm was adopted to distinguish urban and rural meteorological stations, and CUHI intensity (Ic) was expressed as the temperature difference between urban and rural observation sites. The research period covered seven weeks surrounding the Spring Festival (three weeks before, Spring Festival week, three weeks after), with the Spring Festival week defined as experimental period and the remaining six weeks as background period. And then ΔIc was calculated as the average difference of Ic between the experimental period and the background period. Furthermore, an optimal parameter-based geographical detector model was employed to conduct single-factor detection and pairwise factor interaction analysis, while Pearson correlation analysis was used to quantify the correlation between core human activity indicators (XNTL, changes of NTL; XPM2.5, changes of PM2.5) and nighttime ΔIc of cities with different scales. Results and Discussion　 Obvious opposite evolving trends of ΔIc were found between large and small cities around the Spring Festival. Large cities presented a V-shaped CUHI variation with overall declined heat island intensity, and their average daytime ΔIc was − 0.09 ° C and nighttime ΔIc was − 0.15 ° C, driven by massive out-migration and reduced anthropogenic heat and pollutant emission. In contrast, small cities displayed a Λ-shaped trend featured by daytime warming, with daytime ΔIc reaching 0.11 °C and nighttime ΔIc close to −0.0004 °C due to massive returning population and intensified daily human activities. Spatially, ΔIc fluctuated most drastically in the marginal subtropical regions among all climate zones. Single-factor geographical detection verified that XPM2.5 (q = 0.44) and XNTL (q = 0.34), two proxies of human activity variations, were the dominant driving factors of nighttime ΔIc, and their explanatory power far exceeded that of surface parameters and meteorological elements. Pearson correlation results indicated that compared to small cities, the positive correlations between the two indicators and ΔIc in large cities (r = 0.58 for XPM2.5, r = 0.45 for XNTL) were significantly stronger. Factor interaction analysis showed that roughly 75% of pairwise interactions belonged to nonlinear or two-factor enhancement type, and the interaction between XNTL and XPM2.5 yielded the highest explanatory capacity (q = 0.938). Overall, meteorological and land surface factors could amplify the impact of human activities on CUHI through synergistic interactions. Particularly, some cities showed atypical ΔIc performances due to the influence of regional industrial layout, winter central heating and vegetation coverage expansion, further verifying the compound regulation effect of localized socioeconomic and environmental conditions on urban thermal environment. Conclusions　 It is shown that short-term large-scale population migration during the Spring Festival period dominates the divergent daytime and nighttime CUHI evolution patterns of large and small cities in eastern Chinese. Population outflow reduces anthropogenic interference and weakens the CUHI of large cities especially at night, whereas population inflow remarkably raises daytime CUHI intensity of small cities. PM2.5 and nighttime light variations, which represent changes in human activities, are the primary drivers of ΔIc heterogeneity. Their regulatory effects on CUHI can be further amplified via their combined action with surface and meteorological factors, and such driving effects are more pronounced in densely populated large cities. Climatic background has an evident spatial modulation effect on CUHI responses caused by population mobility, with the thermal environment variation in marginal subtropical cities being the most sensitive. This study quantitatively confirms the close linkage between transient human mobility and CUHI, providing data support for differentiated thermal environment management in cities with different scales. Future research can combine high-temporalresolution remote sensing and accurate population flow data to explore multi-dimensional CUHI influencing pathways and develop targeted human activity control schemes to mitigate urban heat island risks.

Key words: canopy urban heat island, Chinese New Year, massive human migration, cities of different sizes, human activities