Human activities constantly produce air pollutants, which may greatly
impact climate change. Elucidating the relationship between air quality
and temperature change is essential to gain a better understanding of
climate change. Up until now, machine learning algorithms have been
deployed to big data analysis in various fields. Here, we use the
machine learning algorithms to analyze temperature and air quality data
of different cities across China. Multiple linear regression and
tree-based methods, including bagging, boosting and random forest, are
used to train the model. With the tree-based methods, the factors highly
associated with temperature change will be elucidated, which indicate
their significant impact on temperature change. The results in this
study demonstrate the possibility of using machine learning in
atmospheric science field to predict air quality and temperature change,
and how different algorithms perform regarding temperature and air
quality predictions, which is informative for future air quality
prediction research. The relationship between air quality and
temperature change can also provide guidance to policymakers.