Air pollution in the atmosphere derives from complex non-linear relationships that involve anthropogenic and biogenic precursor emissions. Due to this complexity, Decision Support Systems (DSSs) are important tools, to help Environmental Authorities to improve air quality reducing human and ecosystems pollution impacts in a cost efficient way. In this work seasonal air quality surrogate models (to be used in a DSSs) are presented. These surrogate models are able to model the nonlinear relation between emissions and air quality indexes considering also sub-yearly aggregation time horizons, usually not considered in integrated assessment models.

NEURAL NETWORK SURROGATE MODELS IN THE FRAME OF AIR QUALITY PLANNING AT REGIONAL SCALE

GUARISO, GIORGIO;
2013-01-01

Abstract

Air pollution in the atmosphere derives from complex non-linear relationships that involve anthropogenic and biogenic precursor emissions. Due to this complexity, Decision Support Systems (DSSs) are important tools, to help Environmental Authorities to improve air quality reducing human and ecosystems pollution impacts in a cost efficient way. In this work seasonal air quality surrogate models (to be used in a DSSs) are presented. These surrogate models are able to model the nonlinear relation between emissions and air quality indexes considering also sub-yearly aggregation time horizons, usually not considered in integrated assessment models.
2013
File in questo prodotto:
File Dimensione Formato  
accent2013_poster.pdf

Accesso riservato

: Altro materiale allegato
Dimensione 3.51 MB
Formato Adobe PDF
3.51 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/862553
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact