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Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

The alarming increase of ambient ozone (O3) levels across China raises an urgent need in understanding underlying mechanisms of regional O3 events for highly urbanized city clusters. Sichuan Basin (SCB) situated in southwestern China has experienced severe O3 pollution at times in summer from 2013 to 2020. Here, we use the WRF-CMAQ model with the Integrated Source Apportionment Method (ISAM) to investigate the evolution mechanism and conduct source attribution of an extreme O3 episode in the SCB from June 1 to 8, 2019. This typical summer O3 episode is associated with the synoptic-driven meteorological phenomenon and transboundary flow of O3 and precursors across the SCB. Weak ventilation in combination with stagnant conditions triggered the basin-wide high O3 concentrations and enhanced BVOC emissions substantially contribute up to 57.9 μg/m3 MDA8 O3. CMAQ-ISAM indicates that precursor emissions from industrial and transportation have the largest impacts on elevating ambient O3 concentrations, while power plant emissions exhibit insignificant contributions to basin-wide O3 episodes. These results improve the understanding of the summertime O3 episode in the SCB and contribute insights into designing O3 mitigation policy.