Work

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

Year: 2024

Type: article

Abstract: The pursuit of more scientific, refined, and precise ozone and air quality control continues to present significant challenges. Data visualization and random forest (RF) algorithms were applied to figure out the temporal distribution of atmospheric pollutants and interrelationship among O3 concentration and its influential factors with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions mostly belonged to NOx-sensitive and transition zone. Extremely high O3 concentration mostly occurred at conditions of T above 30°C, and RH between 30–60%. NO2, RH and T were identified as the top 3 important factors, and O3 concentration have better linearly relationship to RH and T, while stronger nonlinearly relationship to NO2. Applying optimized RF model controlling consistent mild and high reaction atmospheric conditions, the O3 concentration response to the change of individual influencing factors was acquired. The O3 concentration increased and then decreased in response to the increasing NO2 concentration, displaying a characteristic inflection point at 10 μg·m-3. More reactive radicals produced at higher VOCs concentration and continuing NOx cycle at lower NO2 concentration, resulting in the acceleration in the direction of producing more O3. Therefore, the significant different O3 response to variation of VOCs and NOx concentration between mild and high reaction atmospheric conditions, as well as the existing of oxidant elevation should be considered in local air quality control. The study demonstrates the efficacy of ML methods in simulating nonlinear response of ozone, strongly supports the understanding of local O3 formation and quick guidance for precise air pollution control strategies.

Source: Heliyon

DOI: 10.1016/j.heliyon.2024.e36303