Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bakar, K.S., Sahu, S.K.: spTimer: spatiotemporal bayesian modelling using R. J. Stat. Softw. 63(15), 1–32 (2015)
Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, Boca Raton (2004)
Banerjee, S., Gelfand, A.E., Finley, A.O., Sang, H.: Gaussian predictive process models for large spatial data sets. J. R. Stat. Soc. B 70, 825–848 (2008)
Gelfand, A.E., Ghosh, S.K.: Model choice: a minimum posterior predictive loss approach. Biometrika 85, 1–11 (1998)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Chapman & Hall/CRC, Boca Raton (2004)
Laud, P., Ibrahim, J.: Predictive model selection. J. R. Stat. Soc. B 57, 247–262 (1995)
Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009)
Sahu, S.K., Bakar, K.S.: A comparison of Bayesian models for daily ozone concentration levels. Stat. Method. 9, 144–157 (2012a)
Sahu, S.K., Bakar, K.S.: Hierarchical Bayesian autoregressive models for large space time data with applications to ozone concentration modelling. Appl. Stoch. Models Bus. Ind. 28, 395–415 (2012b)
Sahu, S.K., Gelfand, A.E., Holland, D.M.: Spatiotemporal modeling of fine particulate matter. J. Agric. Biol. Environ. Stat. 11, 61–86 (2006)
Sahu, S.K., Gelfand, A.E., Holland, D.M.: High-resolution space-time ozone modeling for assessing trends. J. Am. Stat. Assoc. 102, 1221–1234 (2007)
Sahu, S.K., Mardia, K.V.: A Bayesian kriged kalman model for short-term forecasting of air pollution levels. J. R. Stat. Soc. C 54, 223–244 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sarto, S.D. et al. (2016). Bayesian Spatiotemporal Modeling of Urban Air Pollution Dynamics. In: Di Battista, T., Moreno, E., Racugno, W. (eds) Topics on Methodological and Applied Statistical Inference. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-44093-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-44093-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44092-7
Online ISBN: 978-3-319-44093-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)