| Title: | Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models |
| Author(s): | Santos FM; Gomez-Losada A; Pires JCM; |
| Address: | "LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. Departamento de Estadistica e Investigacion Operativa, Facultad de Matematicas, Universidad de Sevilla, Sevilla, Spain. LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. Electronic address: jcpires@fe.up.pt" |
| DOI: | 10.1016/j.jhazmat.2021.126386 |
| ISSN/ISBN: | 1873-3336 (Electronic) 0304-3894 (Linking) |
| Abstract: | "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 NO(x)). 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-NO(x)-O(3) system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NO(x) 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" |
| Keywords: | *Air Pollutants/analysis *Air Pollution Ecosystem Environmental Monitoring Humans *Ozone/analysis *Volatile Organic Compounds/analysis Artificial neural networks Isopleths Ozone Threshold regression VOC-NO(x)-O(3) system; |
| Notes: | "MedlineSantos, Francisca M Gomez-Losada, Alvaro Pires, Jose C M eng Research Support, Non-U.S. Gov't Netherlands 2021/06/26 J Hazard Mater. 2021 Oct 5; 419:126386. doi: 10.1016/j.jhazmat.2021.126386. Epub 2021 Jun 17" |
