Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework
Journal article
Akita, Yasuyuki, Baldasano, Jose M., Beelen, Rob, Cirach, Marta, de Hoogh, Kees, Hoek, Gerard, Nieuwenhuijsen, Mark J., Serre, Marc L. and de Nazelle, Audrey. (2014). Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework. Environmental Science and Technology. 48(8), pp. 4452 - 4459. https://doi.org/10.1021/es405390e
Authors | Akita, Yasuyuki, Baldasano, Jose M., Beelen, Rob, Cirach, Marta, de Hoogh, Kees, Hoek, Gerard, Nieuwenhuijsen, Mark J., Serre, Marc L. and de Nazelle, Audrey |
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Abstract | 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. |
Year | 2014 |
Journal | Environmental Science and Technology |
Journal citation | 48 (8), pp. 4452 - 4459 |
Publisher | American Chemical Society |
ISSN | 0013-936X |
Digital Object Identifier (DOI) | https://doi.org/10.1021/es405390e |
Scopus EID | 2-s2.0-84898872210 |
Page range | 4452 - 4459 |
Research Group | Mary MacKillop Institute for Health Research |
Publisher's version | File Access Level Controlled |
Place of publication | United States of America |
https://acuresearchbank.acu.edu.au/item/8q368/large-scale-air-pollution-estimation-method-combining-land-use-regression-and-chemical-transport-modeling-in-a-geostatistical-framework
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