Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO(2), NH(3), NO, NO(2), NO(x), O(3), PM(1), PM(2.5), PM(10) and PN(10))...

Descripción completa

Detalles Bibliográficos
Autores principales: Goulier, Laura, Paas, Bastian, Ehrnsperger, Laura, Klemm, Otto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7143381/
https://www.ncbi.nlm.nih.gov/pubmed/32204378
http://dx.doi.org/10.3390/ijerph17062025

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7143381/
https://www.ncbi.nlm.nih.gov/pubmed/32204378
http://dx.doi.org/10.3390/ijerph17062025

Ejemplares similares