2010 Progress Report: Improving Particulate Matter Source Apportionment for Health Studies: A Trained Receptor Modeling Approach with Sensitivity, Uncertainty and Spatial AnalysesEPA Grant Number: R833866
Title: Improving Particulate Matter Source Apportionment for Health Studies: A Trained Receptor Modeling Approach with Sensitivity, Uncertainty and Spatial Analyses
Investigators: Russell, Armistead G. , Klein, Mitchel , Marmur, Amit , Mulholland, James , Sarnat, Stefanie Ebelt , Sarnat, Jeremy , Tolbert, Paige
Institution: Georgia Institute of Technology , Emory University
EPA Project Officer: Ilacqua, Vito
Project Period: December 1, 2008 through November 30, 2012 (Extended to November 30, 2013)
Project Period Covered by this Report: November 1, 2009 through October 31,2010
Project Amount: $899,956
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text | Recipients Lists
Research Category: Health Effects , Particulate Matter , Air
- A receptor-based approach, trained using an ensemble of model results (including receptor and emissions-based models), can be developed that neither introduces excessive nor inhibits an appropriate level of day-to-day variability.
- The method can be applied to long-term data sets for use in acute health effects studies.
- The method can be used to temporally interpolate between observations (e.g., for data available every third day) and spatially interpolate between urban and rural monitors.
- Uncertainties can be propagated from SA model inputs to health analysis outputs, with ouputs most sensitive to source profile inputs.
- Averaging SA results, using weights based on method uncertainty, from four receptor models and one chemical transport model, the Community Multiscale Air Quality (CMAQ) model, to develop ensemble-based source impacts.
- Using the weighted source impacts (from Step 1) in an application of CMB with the Lipschitz Global Optimizer (CMB-LGO) to calculate nine ensemble-based source profiles (EBSPs); the source profiles developed include gasoline vehicles (GV), diesel vehicles (DV), dust (DUST), biomass burning (BURN), coal combustion (COAL), secondary organic carbon (SOC), SULFATE, NITRATE, and AMMONIUM.
- Using the EBSPs on a longer term data set of observations to develop improved source impacts.
We have developed a two-step method for determining source impact uncertainties. First, we average the five individual source apportionment methods and determine uncertainties of the ensemble by using propagation of errors. Next, we estimate an updated uncertainty for each SA method to be equal to the root mean square error (RMSE) between each SA method and the ensemble. Subsequently, we estimate an updated uncertainty for the ensemble using these new SA uncertainties by propagation of errors.
One major consequence of setting the updated source impact uncertainties to the RMSE for the five individual SA methods is that the daily updated uncertainties for each source and method will have the same uncertainty regardless of the magnitude of source impact. Thus, whereas traditional source apportionment results often have daily relative uncertainties that are constant, our work results in constant daily absolute uncertainties. We calculate updated uncertainties this way because square errors between each individual method and the ensemble do not, in general, correlate well with source impact, based on linear regression results.
Ensembling results in:
- reduction of zero impact days and provides results for every day of the data set, and
- reduced variability by averaging out excessively high and low source impact days.
In summer, the ensemble, when weighted, has the lowest overall relative uncertainties for BURN (45%), COAL (48%), NH4 (3%), and SOC (39%) and has the second lowest overall relative uncertainties for GV (45%), DV (21%), DUST (61%), SO4 (3%), NO3(11%); for these categories, CMB-MM had lowest overall relative uncertainties with 28%, 6%, 40%, 2% and 9%, respectively. Without weighting, the ensemble has the lowest overall relative uncertainties for DV (37%), DUST (47%), BURN (35%), and COAL (41%). For SO4 (11%), NO3 (47%) NH4 (12%) and SOC (34%) overall relative uncertainties are greater than three to four receptor models; this is due the influence of large initial uncertainties in CMAQ.
In winter, the ensemble, when weighted, has the lowest overall relative uncertainties for GV (48%), DV (38%), BURN (61%), and SOC (56%) and has the second lowest overall relative uncertainties for DUST (92%), COAL (77%), SO4 (12%), NO3 (4%), and NH4 (2%); for these categories, PMF (61%), CMAQ (59%), CMB-RG/CMB-LGO (both at 11%), CMB-MM (3%), and CMB-LGO (1%), respectively, had the lowest overall relative uncertainties. Without weighting, the ensemble ORUs do not change very much from the weighted case for primary sources and SOC: GV (46%), DV (36%), DUST (99%), BURN (53%), COAL (65%), and SOC (45%). In addition, the non-weighted ensemble did not have the lowest overall uncertainties for any category. Nevertheless, no one source in the ensemble had the highest uncertainty. For example, all four receptor models have very high ORUs for DUST, ranging from 127% to 1037%, while CMAQ had an ORU of 73%.
The ensemble ORUs are stable for primary sources and SOC and are all within a factor of 2 between the weighted and non-weighted case in both seasons. This is not true for individual SA methods, which have a number of sources that vary significantly from the weighted to non-weighted cases.