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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Santos, FM (Autor) Outra; Gomez Losada, A (Autor) Outra; Pires, JCM (Autor) FEUP

Revista

Título: Journal of Hazardous Materials

Vol. 419

ISSN: 0304-3894

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Projetos Associados

LEPABE - Projeto Estratégico do LEPABE - Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia

IF/01341/2015 - Configurações de fotobiorreatores para cultivo de microalgas: modelação de bioprocessos e avaliação de sustentabilidade

Outras Informações

ID Authenticus: P-00V-4T4

DOI: 10.1016/j.jhazmat.2021.126386

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10