Large Scale Air Pollution Estimation Method Combining Land Use Regression and Chemical Transport Modeling in a Geostatistical Framework

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also...

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Bibliographic Details
Published in:Environmental science & technology 2014-04, Vol.48 (8), p.4452-4459
Main Authors: Akita, Yasuyuki, Baldasano, Jose M, Beelen, Rob, Cirach, Marta, de Hoogh, Kees, Hoek, Gerard, Nieuwenhuijsen, Mark, Serre, Marc L, de Nazelle, Audrey
Format: Article
Language:English
Subjects:
Air Pollutants - analysis
Air pollution
Air Pollution - analysis
Air quality
Air. Soil. Water. Waste. Feeding
Aire
Applied sciences
Atmospheric pollution
Biological and medical sciences
Catalunya
Chemical Transport Models
Contaminació atmosfèrica
Degradació ambiental
Desenvolupament humà i sostenible
Environment. Living conditions
Environmental Monitoring - methods
Estimating techniques
Exact sciences and technology
Geography
Geostatistical
Geostatistics
Land use
Land Use Regression
Mathematical models
Maximum entropy method
Measurement
Medical sciences
Mesurament
Modeling
Models matemàtics
Models, Theoretical
Nitrates - analysis
Outdoor air quality
Pollutants physicochemistry study: properties, effects, reactions, transport and distribution
Pollution
Public health. Hygiene
Public health. Hygiene-occupational medicine
Qualitat
Regression Analysis
Reproducibility of Results
Spain
Statistics as Topic
Àrees temàtiques de la UPC
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Summary:In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.
ISSN:0013-936X
1520-5851
DOI:10.1021/es405390e