Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate.

ABSTRACT: Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO₃^-)). The mechanism between ε(NO₃^-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO₃^-). Here we introduce a supervised machin

SUBMITTER: Xu B 

PROVIDER: S-EPMC10661687 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature