Pages that link to "Item:Q5128962"
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The following pages link to Large-scale Bayesian spatial modelling of air pollution for policy support (Q5128962):
Displaying 8 items.
- Spatial-temporal modellization of the \(\mathrm{NO}_2\) concentration data through geostatistical tools (Q290365) (← links)
- European population exposure to airborne pollutants based on a multivariate spatio-temporal model (Q321458) (← links)
- Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model (Q5085648) (← links)
- (Q5236401) (← links)
- A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data (Q5327276) (← links)
- A Bayesian Kriged Kalman Model for Short-Term Forecasting of Air Pollution Levels (Q5757753) (← links)
- A Bayesian multivariate receptor model for estimating source contributions to particulate matter pollution using national databases (Q6139091) (← links)
- Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring (Q6183910) (← links)
