Dynamic Public Perception of Air Quality in China: Implications for Adaptive Environmental Governance

While the health impacts of air pollution are established, how public perception evolves with air quality remains unclear. Here, we analyze 180 million geotagged Weibo posts from China (2013–2023) using a natural language processing model to quantify public satisfaction with air quality and examine its relationship with monitored pollutant levels. We find that air quality improved significantly (PM2.5 concentration decreased by 51%), but public satisfaction increased only marginally (8.2%). This reflects progressively stricter subjective standards over time. Regional disparities reveal that economically developed areas exhibit lower tolerance for pollution, driven by heightened public awareness and media exposure. Annually, air pollution triggered negative emotions in 80 million people, influencing governance priorities. The findings underscore the dynamic interplay between air quality, public perception, and socioeconomic factors, advocating for adaptive policies integrating behavioral metrics to align with evolving public expectations. This work highlights the need for perception-aware environmental governance globally.