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| Titel |
Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations |
| VerfasserIn |
J. Hu, H. Zhang, Q. Ying, S.-H. Chen, F. Vandenberghe, M. J. Kleeman |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 6 ; Nr. 15, no. 6 (2015-03-30), S.3445-3461 |
| Datensatznummer |
250119582
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| Publikation (Nr.) |
 copernicus.org/acp-15-3445-2015.pdf |
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| Zusammenfassung |
For the first time, a ~ decadal (9 years from 2000 to 2008) air quality
model simulation with 4 km horizontal resolution over populated regions and
daily time resolution has been conducted for California to provide air
quality data for health effect studies. Model predictions are compared to
measurements to evaluate the accuracy of the simulation with an emphasis on
spatial and temporal variations that could be used in epidemiology studies.
Better model performance is found at longer averaging times, suggesting that
model results with averaging times ≥ 1 month should be the first to be
considered in epidemiological studies. The UCD/CIT model predicts spatial and
temporal variations in the concentrations of O3, PM2.5, elemental
carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard
modeling performance criteria when compared to monthly-averaged measurements.
Predicted sulfate concentrations do not meet target performance metrics due
to missing sulfur sources in the emissions. Predicted seasonal and annual
variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional
biases that meet the model performance criteria in 95, 100, 71, 73, and
92% of the simulated months, respectively. The base data set provides an
improvement for predicted population exposure to PM concentrations in
California compared to exposures estimated by central site monitors operated
1 day out of every 3 days at a few urban locations.
Uncertainties in the model predictions arise from several issues. Incomplete
understanding of secondary organic aerosol formation mechanisms leads to OC
bias in the model results in summertime but does not affect OC predictions
in winter when concentrations are typically highest. The CO and NO (species
dominated by mobile emissions) results reveal temporal and spatial
uncertainties associated with the mobile emissions generated by the EMFAC
2007 model. The WRF model tends to overpredict wind speed during stagnation
events, leading to underpredictions of high PM concentrations, usually in
winter months. The WRF model also generally underpredicts relative
humidity, resulting in less particulate nitrate formation, especially during
winter months. These limitations must be recognized when using data in
health studies. All model results included in the current manuscript can be
downloaded free of charge at
http://faculty.engineering.ucdavis.edu/kleeman/ . |
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