Hierarchical space-time modelling of PM10 pollution

Cocchi, Daniela; Greco, Fedele Pasquale; Trivisano, Carlo

doi:10.1016/j.atmosenv.2006.08.032

Cited by 58 publications

(49 citation statements)

References 8 publications

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“…meteorological and geographical variables) as well as time and space dependence (e.g. Cocchi et al, 2007;Cameletti et al, 2011a;Sahu, 2012;Fassò and Finazzi, 2011). The main drawback of this formulation is related to the computational costs required for model parameter estimation and spatial prediction when MCMC methods are used, especially in case of massive spatio-temporal datasets.…”

Section: Meanmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

422

426

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During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method.In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

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Section: Meanmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

422

426

View full text Add to dashboard Cite

show abstract

“…Berrocal et al, 2010b;Zidek et al, 2011). The spatio-temporal model we specify here is widely adopted in the air quality literature thanks to its flexibility in modeling relevant covariates as well as correlation in space and time (Fassò and Finazzi, 2011;Cocchi et al, 2007;Cameletti et al, 2011;Sahu, 2011). Moreover, it has been already implemented in R-INLA and validated by Cameletti et al (2013).…”

Section: First Stage: Spatio-temporal No 2 Modelmentioning

confidence: 99%

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Blangiardo

Finazzi

Cameletti

2016

Spatial and Spatio-temporal Epidemiology

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Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalization or mortality data. However this approach limits the analysis to individuals characterized by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population.We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO 2 concentration integrating several data sources characterized by different spatio-temporal resolution; in the second stage we link the concentration to the β 2 -agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.

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“…PM 2.5 air pollution studies, in particular, have investigated its possible association with certain adverse health effects (Dockery et al, 1993;Pope et al, 1995;Gauderman et al, 2000;Samet et al, 2000;Tainio et al, 2005;Mascarenhas et al, 2008). Environmental organizations, regulatory groups, and local governments have launched projects that monitor general PM air pollution levels, in an effort to adequately represent their patterns across space-time under conditions of uncertainty Serre, 2000a, 2000b;Christakos et al, 2001;Kibria et al, 2002;Smith et al, 2003;Serre et al, 2004;Allshouse et al, 2006;Liao et al, 2006;Bell et al, 2007;Cocchi et al, 2007;Bogaert et al, 2009;Yu et al, 2009). The latter task calls on the need for rigorous methods that can provide informative space-time estimates and dynamic visualizations (maps) of PM 2.5 pollution throughout a geographical region.…”

Section: Introductionmentioning

confidence: 99%

Comparative spatiotemporal analysis of fine particulate matter pollution

2009

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SUMMARYThe prime focus of this work is the comparative investigation, theoretical and numerical, of spatiotemporal techniques used in air pollution studies. Space-time statistics techniques are classified on the basis of a set of criteria and the relative theoretical merits of each technique are discussed accordingly. The numerical comparison involves the applications of two representative techniques. For this purpose, the popular spatiotemporal epistemic knowledge synthesis and graphical user interface (SEKS-GUI) software of spatiotemporal statistics is used together with a dataset of PM 2.5 daily measurements obtained at monitoring stations geographically distributed over the state of North Carolina, USA. The analysis offers valuable insight concerning the choice of an appropriate spatiotemporal technique in air pollution studies.

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Hierarchical space-time modelling of PM10 pollution

Cited by 58 publications

References 8 publications

Spatial and spatio-temporal models with R-INLA

Spatial and spatio-temporal models with R-INLA

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Comparative spatiotemporal analysis of fine particulate matter pollution

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