Research on Technical Solution and Implementation Mechanism of Wechat Ecosystem-Based Air Quality Program

Amid the accelerating development of smart cities and growing public concern over environmental quality, accurate air quality prediction and early warning have become pivotal for environmental governance and public health protection. This study explores the development of an air quality prediction miniprogram within the WeChat ecosystem. By integrating real-time monitoring data from meteorological bureaus and environmental protection departments, we constructed a innovative multi-scale spatiotemporal feature fusion framework utilizing deep learning algorithms and spatiotemporal propagation network models. The framework achieves high-precision air quality forecasts ranging from 10 hours to 7 days into the future. This mini-program offers an innovative technical pathway for air quality prediction, effectively advancing the digital transformation of environmental governance in smart cities and enhancing the intelligence and precision of pollution control strategies.