Home |  English version |  Mappa |  Commenti |  Sondaggio |  Staff |  Contattaci Cerca nel sito  
Istituto sull'inquinamento atmosferico

Torna all'elenco Contributi in volume anno 2016

Contributo in volume

Tipo: Contributo in volume

Titolo: Bayesian Spatiotemporal Modeling of Urban Air Pollution Dynamics

Anno di pubblicazione: 2016

Formato: Elettronico Cartaceo

: Capitolo

Autori: Simone Del Sarto, M. Giovanna Ranalli, K. Shuvo Bakar, David Cappelletti, Beatrice Moroni, Stefano Crocchianti, Silvia Castellini, Francesca Spataro, Giulio Esposito, Antonietta Ianniello, Rosamaria Salvatori

Affiliazioni autori: S.D. Sarto Department of Economics, University of Perugia, Perugia, Italy M.G. Ranalli Department of Political Sciences, University of Perugia, Perugia, Italy K.S. Bakar Deparment of Statistics, Yale University, New Haven, CT, USA D. Cappelletti B. Moroni S. Crocchianti S. Castellini Department of Chemistry, Biology and Biotechnologies, University of Perugia, Perugia, Italy F. Spataro G. Esposito A. Ianniello R. Salvatori Institute of Atmospheric Pollution Research (IIA), CNR, Roma, Italy

Autori CNR:

  • GIULIO ESPOSITO
  • ANTONIETTA IANNIELLO
  • ROSAMARIA SALVATORI
  • FRANCESCA SPATARO

Lingua: inglese

Sintesi: This work deals with the spatiotemporal analysis of urban air pollution dynamics in the town of Perugia (Central Italy) using high-frequency and size resolved data on particular matter (PM). Such data are collected by an Optical Particle Counter (OPC) located on a cabin of the Minimetro, a public transport system that moves on a monorail on a line transect of the town. Hierarchical Bayesian models are used that allow to model a quite large dataset and include an autoregressive term in time, in addition to spatially correlated random effects. Models are fitted for three response variables (fine and coarse particle counts, nitric oxide concentration) and using covariate information such as temperature and humidity. Results show a large temporal autocorrelation, relatively larger for particle counts; moreover, all variables show a significant spatial correlation, with larger ranges for fine PM rather than for coarse PM and nitric oxide concentration.

Lingua sintesi: eng

Pagine da: 95

Pagine a: 103

Pagine totali: 9

Titolo del volume: Studies in Theoretical and Applied Statistics, Topics on Methodological and Applied Statistical Inference

Curatore/i del volume: Tonio Di Battista, Elas Moreno, Walter Racugno (Editors)

ISBN: 978-3-319-44093-4

DOI: 10.1007/978-3-319-44093-4

Editore: Springer International Publishing, Switzerland (CHE)

Referee: S: Internazionale

Stato della pubblicazione: Published version

Parole chiave:

  • Particular matter
  • Nitric Oxide Concentration
  • Hierarchical Bayesian models
  • PMetro project

Strutture CNR:

Moduli:

Allegati: Bayesian Spatiotemporal Modeling of Urban Air Pollution Dynamics (application/pdf)

 
Torna indietro Richiedi modifiche Invia per email Stampa
Home Il CNR  |  I servizi News |   Eventi | Istituti |  Focus