Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.
A Joint Control Model Based on Emission Rights Futures Trading for Regional Air Pollution That Accounts for the Impacts on Employment