Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models

Ozone (O-3) is a reactive oxidant that causes chronic effects on human health, vegetation, ecosystems and materials. This study aims to create O-3 isopleths in urban and suburban environments, based on machine learning with air quality data collected from 2001 to 2017 at urban (EA) and suburban (CC) monitoring stations from Madrid (Spain). Artificial neural network (ANN) models have powerful fitting performance, describing correctly several complex and nonlinear relationships such as O-3 and his precursors (VOC and NOx). Also, ANN learns from the experience provided by data, contrary to mechanistic models based on the fundamental laws of natural sciences. The determined isopleths showed a different behaviour of the VOC-NOx-O-3 system system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NOx concentrations, O-3 concentrations decreased with VOC concentrations in the ANN model. Considering the difficulty to model all the phenomena (and acquired all the required data) that influences O-3 concentrations, the statistical models may be a solution to describe this system correctly. The applied methodology is a valuable tool for defining mitigation strategies (control of precursors' emissions) to reduce O-3 concentrations. However, as these models are obtained by air quality data, they are not geographical transferable.

Automated summary

This study utilized machine learning to generate O-3 isopleths in urban and suburban environments, demonstrating the powerful fitting performance of ANN models in describing complex relationships between O-3 and its precursors. The findings provide valuable insights for defining mitigation strategies to reduce O-3 concentrations.