Imperial College London
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ProfessorSamirBhatt

Faculty of Medicine, School of Public Health

Professor of Statistics and Public Health
 
 
 
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Contact

 

+44 (0)20 7594 5029s.bhatt

 
 
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Location

 

G32ASt Mary's Research BuildingSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Forlani:2020:10.1002/env.2644,
author = {Forlani, C and Bhatt, S and Cameletti, M and Krainski, E and Blangiardo, M},
doi = {10.1002/env.2644},
journal = {Environmetrics},
pages = {1--17},
title = {A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA},
url = {http://dx.doi.org/10.1002/env.2644},
volume = {31},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources, such as dispersion model outputs, ground observations and covariates, leads to more accurate predictions of air pollution at grid level. We consider nitrogen dioxide (NO2) concentration in Greater London and surroundings for the years 20072011, and combine two different dispersion models. Different sets of spatial and temporal effects are included in order to obtain the best predictive capability. Our proposed model is framed in between calibration and Bayesian melding techniques for data fusion. Unlike other examples, we jointly model the response (concentration level at monitoring stations) and the dispersion model outputs on different scales, accounting for the different sources of uncertainty. Our spatiotemporal model allows us to reconstruct the latent fields of each model component, and to predict daily pollution concentrations. We compare the predictive capability of our proposed model with other established methods to account for misalignment (e.g. bilinear interpolation), showing that in our case study the joint model is a better alternative.
AU  - Forlani,C
AU  - Bhatt,S
AU  - Cameletti,M
AU  - Krainski,E
AU  - Blangiardo,M
DO  - 10.1002/env.2644
EP  - 17
PY  - 2020///
SN  - 1099-095X
SP  - 1
TI  - A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
T2  - Environmetrics
UR  - http://dx.doi.org/10.1002/env.2644
UR  - https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2644
UR  - http://hdl.handle.net/10044/1/81056
VL  - 31
ER  -
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