Imperial College London
Alternate

ProfessorMartaBlangiardo

Faculty of Medicine, School of Public Health

Chair in Biostatistics
 
 
 
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Contact

 

m.blangiardo Website

 
 
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Location

 

705School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Baerenbold:2023:10.1002/env.2763,
author = {Baerenbold, O and Meis, M and MartínezHernández, I and Euán, C and Burr, WS and Tremper, A and Fuller, G and Pirani, M and Blangiardo, M},
doi = {10.1002/env.2763},
journal = {Environmetrics},
title = {A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution},
url = {http://dx.doi.org/10.1002/env.2763},
volume = {34},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.
AU  - Baerenbold,O
AU  - Meis,M
AU  - MartínezHernández,I
AU  - Euán,C
AU  - Burr,WS
AU  - Tremper,A
AU  - Fuller,G
AU  - Pirani,M
AU  - Blangiardo,M
DO  - 10.1002/env.2763
PY  - 2023///
SN  - 1180-4009
TI  - A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
T2  - Environmetrics
UR  - http://dx.doi.org/10.1002/env.2763
UR  - https://onlinelibrary.wiley.com/doi/10.1002/env.2763
UR  - http://hdl.handle.net/10044/1/99860
VL  - 34
ER  -
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