A hierarchical Bayesian approach to the spatio-temporal modeling of air quality data

Riccio, Angelo; Barone, G.; Chianese, Elena; Giunta, Giulio

doi:10.1016/j.atmosenv.2005.09.070

Cited by 36 publications

(29 citation statements)

References 28 publications

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“…Hierarchical methods have been successfully applied in other fields (Riccio et al, 2006;Gelman, A. and Hill, J., 2002;Lehuger et al, 2009). A general summary and the application to uncertainty analysis can be found in Cressie et al (2009).…”

Section: A L Ganesan Et Al: Uncertainty Quantification In Trace Gamentioning

confidence: 99%

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

et al. 2014

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Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF 6 ) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF 6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.

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Section: A L Ganesan Et Al: Uncertainty Quantification In Trace Gamentioning

confidence: 99%

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

et al. 2014

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“…These data are often coupled with a host of topographic and satellite derived variables through a spatial regression model to predict short-and long-term weather conditions. Recently, several investigators used these data to illustrate the virtues of a Bayesian approach to spatial prediction (see Diggle and Ribeiro Jr 2002;Paciorek and Schervish 2006;Riccio et al 2006).…”

Section: Atmospheric and Weather Modelingmentioning

confidence: 99%

Bayesian Spatial Regression for Multisource Predictive Mapping

Finley

Banerjee

2016

Encyclopedia of GIS

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“…McKendry, 2002), evaluation of air quality models (e.g. Riccio et al, 2006), systematically missing data (e.g. Le and Zidek, 2006) and trends assessment (e.g.…”

Section: Background Chemistry Of Ozonementioning

confidence: 99%

Mixed deterministic statistical modelling of regional ozone air pollution

Kalenderski

Steyn

2011

Environmetrics

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We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest.

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A hierarchical Bayesian approach to the spatio-temporal modeling of air quality data

Cited by 36 publications

References 28 publications

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

Bayesian Spatial Regression for Multisource Predictive Mapping

Mixed deterministic statistical modelling of regional ozone air pollution

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