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Environ Int. 2015 Jun;79:56-64. doi: 10.1016/j.envint.2015.02.010. Epub 2015 Mar 19.

Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles.

Author information

1
MRC-PHE Centre for Environment and Health, King's College London, Division of Analytical and Environmental Science, Franklin-Wilkins Building, 150 Stamford Street, SE1 9NH, London, UK. Electronic address: monica.pirani@kcl.ac.uk.
2
MRC-PHE Centre for Environment and Health, Imperial College London, Department of Epidemiology and Biostatistics, 526 Norfolk Place, W2 1PG London, UK. Electronic address: n.best@imperial.ac.uk.
3
MRC-PHE Centre for Environment and Health, Imperial College London, Department of Epidemiology and Biostatistics, 526 Norfolk Place, W2 1PG London, UK. Electronic address: m.blangiardo@imperial.ac.uk.
4
Brunel University, Department of Mathematics, UB8 3PH Uxbridge, London, UK; MRC Biostatistics Unit, Institute of Public Health, Forvie site, Robinson Way, CB2 0SR Cambridge, UK; Imperial College London, Department of Epidemiology and Biostatistics, 526 Norfolk Place, London W2 1PG London, UK. Electronic address: silvia.liverani@brunel.ac.uk.
5
MRC-PHE Centre for Environment and Health, St. George's University of London, Population Health Research Institute, Cranmer Terrace, SW17 0RE London, UK. Electronic address: atkinson@sgul.ac.uk.
6
MRC-PHE Centre for Environment and Health, King's College London, Division of Analytical and Environmental Science, Franklin-Wilkins Building, 150 Stamford Street, SE1 9NH, London, UK. Electronic address: gary.fuller@kcl.ac.uk.

Abstract

BACKGROUND:

Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis.

METHODS:

We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002-2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005.

RESULTS:

The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of -3.5% (95% CI: -0.12%, -5.74%).

CONCLUSIONS:

We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.

KEYWORDS:

Airborne particles; Bayesian inference; Dirichlet process mixture model; Respiratory mortality; Time series

PMID:
25795926
PMCID:
PMC4396698
DOI:
10.1016/j.envint.2015.02.010
[Indexed for MEDLINE]
Free PMC Article

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