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(3) 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(3) formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O(3) tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO(2)) leads to a larger decrease capacity in O(3) concentration. More importantly, there is a local reachable maximum incremental reactivity (MIR(L)) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O(3) concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O(3) and the precursors control.