| Autor: |
Beckerman, Bernardo S.1 beckerman@berkeley.edu, Jerrett, Michael1, Serre, Marc2, Martin, Randall V.3, Seung-Jae Lee4, van Donkelaar, Aaron3, Ross, Zev5, Su, Jason1, Burnett, Richard T.6 |
| Předmět: |
|
| Zdroj: |
Environmental Science & Technology. 7/2/2013, Vol. 47 Issue 13, p7233-7241. 9p. |
| Abstrakt: |
Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R2 values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R2 were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S. [ABSTRACT FROM AUTHOR] |
| Databáze: |
GreenFILE |
| Externí odkaz: |
|
