Coupling correlation analysis of air pollution inference and vegetation coverage based on Seq2Seq integrated framework: An empirical study of urban agglomeration in the Greater Bay Area of China

In the context of the increasingly deepening coupling and interaction between ecosystems and human society, the dynamic balance between atmospheric environmental quality and vegetation coverage patterns has become the core yardstick for measuring the process of sustainable development. However, the continuous deterioration of air pollution and the systematic decline in vegetation coverage have already formed a dual ecological bottleneck that restricts social development. Among the world's four major bay areas (Tokyo, Japan; New York, USA; San Francisco, USA; the Guangdong-Hong Kong-Macao Greater Bay Area, China), the Guangdong-Hong Kong-Macao Greater Bay Area, with its unique regional advantages and prominent ecological governance challenges, has become a new model for the development of global bay areas, and in view of this, this study takes this urban agglomeration as the empirical area, constructs an intelligent analysis system, focuses on the multi-step deduction of air pollution and the quantification of uncertainty, and simultaneously deeply explores its coupling mechanism with vegetation coverage. The experimental results show that the system proposed in this study outperforms the benchmark model in all performance indicators, among which the maximum reduction of the core evaluation index, mean Absolute percentage error (MAPE), is as high as 63.3 %. Furthermore, the correlation analysis for different regions shows that the degree of vegetation coverage and the degree of air pollution both present a negative correlation in multiple significance level tests, and the average P-value of the correlation analysis between the two is 0.158929259, further quantifying the linear association at the numerical level.