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

Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles
Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles
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
0160-4120
56-64
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Best, Nicky
d7d79436-e852-41b3-8007-c4a2d7871b9c
Blangiardo, Marta
410dbbc0-9c77-43f9-8edf-000bde88d013
Liverani, Silvia
d18c93e9-7873-46ff-b384-ba1fcf4144c9
Atkinson, W. Richard
d37b6f0b-a18a-4381-a6c5-43ed93e18a76
Fuller, W. Gary
db733f1a-4d37-46db-a196-d45a22b801a3
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Best, Nicky
d7d79436-e852-41b3-8007-c4a2d7871b9c
Blangiardo, Marta
410dbbc0-9c77-43f9-8edf-000bde88d013
Liverani, Silvia
d18c93e9-7873-46ff-b384-ba1fcf4144c9
Atkinson, W. Richard
d37b6f0b-a18a-4381-a6c5-43ed93e18a76
Fuller, W. Gary
db733f1a-4d37-46db-a196-d45a22b801a3

Pirani, Monica, Best, Nicky, Blangiardo, Marta, Liverani, Silvia, Atkinson, W. Richard and Fuller, W. Gary (2015) Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles. Environment International, 79, 56-64. (doi:10.1016/j.envint.2015.02.010).

Record type: Article

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

Other
1-s2.0-S0160412015000379-main.pdf__tid=4486760c-cf11-11e4-a6c0-00000aab0f6c&acdnat=1426863559_af7f5125ddbfd13f3ecb9a88ad75b2ce - Version of Record
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Accepted/In Press date: 19 March 2015
Published date: June 2015
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 375363
URI: http://eprints.soton.ac.uk/id/eprint/375363
ISSN: 0160-4120
PURE UUID: 55c3c296-b5c9-4bc6-bc56-650fa1936db8

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Date deposited: 20 Mar 2015 15:00
Last modified: 14 Mar 2024 19:24

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Contributors

Author: Monica Pirani
Author: Nicky Best
Author: Marta Blangiardo
Author: Silvia Liverani
Author: W. Richard Atkinson
Author: W. Gary Fuller

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