Identification of response regulation governing ozone formation based on influential factors using a random forest approach.

The pursuit of enhanced scientific, refined, and precise ozone and air quality control continues to pose significant challenges. Using data visualization techniques and random forest (RF) algorithms, the temporal distribution of atmospheric pollutants and the interrelationship between O concentration and its influential factors were investigated with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions predominantly belonged to NOx-sensitive and transition zone.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors.

Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park.

Industrial parks have more complex O formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored.