Validation And Applications Protocol For Source Apportionment Of Volatile Organic Compounds And Fine Particles Eric M. Fujita [email protected] John G. Watson Desert Research Institute University and Community College System of Nevada Reno, Nevada 18th AWMA/EPA Symposium on - PowerPoint PPT Presentation
Validation And Applications Validation And Applications Protocol For Source Apportionment Protocol For Source Apportionment Of Volatile Organic Compounds And Of Volatile Organic Compounds And Fine Particles Fine Particles Eric M. Fujita [email protected]John G. Watson Desert Research Institute University and Community College System of Nevada Reno, Nevada 18th AWMA/EPA Symposium on Measurement of Toxic and Related Air Pollutants September 1-3, 1998
Transcript
Validation And Applications Protocol For Validation And Applications Protocol For Source Apportionment Of Volatile Source Apportionment Of Volatile
Organic Compounds And Fine ParticlesOrganic Compounds And Fine Particles
• Describe steps in the CMB applications and validation protocol
• Demonstrate CMB model outputs and diagnostics
Protocol for Applying and Validatingthe CMB Model
1. Assess general applicability
2. Configure with source types, source profiles, and chemical species from receptor
3. Examine model statistics and diagnostics.
4. Determine compliance with model assumptions
5. Modify model configuration to better comply with assumptions
6. Test the consistency and stability of CMB results
7. Evaluate the validity of model results
Step 1. General Applicability
• Sufficient data to determine excessive pollutant levels
• Samples amenable to or have been chemically speciated
• Potential source contributors identified
• Source profiles measured or approximated
• More receptor species than source types
Step 2. Model Configuration
• Receptor species:– A value and uncertainty is needed for each species
– Only one measurement of a given species should be included in the solution
– Values below lower quantifiable limits may be included if uncertainty is set to LQL
• Source type selection:– Common area sources
– Natural sources
– Point sources in emissions inventory
– Sources identified preliminary data analyses such as PCA
Step 2. Model Configuration (Continued)
• Source profiles in CMB solution:– Upwind point sources
– Seasonal emitters
– Non-collinear profiles
CMB8 Application Options
Step 3. Outputs and Diagnostics
Output/Statistic/Code Abbreviation Description
Source ContributionEstimate
SCE Primary output: Source contribution(µg/m3 or ppbC).
Standard Error STDERR Primary output: The variance of theSCE. [Target: << SCE]
t-statistic T-STAT Statistic: The ratio of the SCE to itsSTDERR. A high T-STAT suggestsa non-zero SCE. [Target: > 2.0]
R-square R-SQUARE Statistic: Used to measure thevariance in theambient speciesconcentrations which is explained bythe calculated speciesconcentrations. Ranges from 0 to1.0. [Target: 0.8 to 1.0]
Step 3. Outputs and Diagnostics (continued)
Output/Statistic/Code Abbreviation Description
Chi-square CHI-SQUARE Statistic: Used to consider theuncertainties of the calculatedspecies concentrations. A highCHI-SQUARE suggests that themodel has not explained the speciesdata very well. [Target: 1.0 to 4.0]
Percent MassAccounted For
PERCENTMASSor % MASS
Statistic: Used to track the % ofambient mass explained by the sumof the SCEs. A % MASS near 100%can be misleading because a poor fitcan force a high % MASS.[Target: 100% ± 20%]
Step 3. Outputs and Diagnostics (Continued)
Output/Statistic/Code Abbreviation Description
Degrees of Freedom DF Statistic: Number of species in fitminus number of sources in fit.
Site/Sample SITE, etc. Status: Data being used in the fit.
Singular Values,Number of EstimableSources, andEstimable LinearCombinations
CollinearityMeasures
Diagnostic: Summary of singularvalue decomposition analysis. Itshows clusters of sources which themodel cannot easily distinguishbetween and that are likely to beinterfering with the model’s ability toprovide a good set of SCEs.[Target: Estimable Sources=Numberof Sources; No inestimable sourceprofiles for 20% uncertainty]
Estimates the sum of SCEs ofinestimable profiles and the standarderror of the sum. Shows theuncertainty reduction that might beachieved by using a single profile torepresent the sum of both sourcecontributions.
Source Contribution Estimates
SOURCE CONTRIBUTION ESTIMATES - SITE: WELBY DATE: 01/17/97 CMB8(97350)SAMPLE DURATION 6 START HOUR 06 SIZE: F R SQUARE .92 PERCENT MASS 92.9 CHI SQUARE .61 DF 71 B and L: No SRC ELIM: No WEIGHTS: CHISQR 1.000 R SQR 1.000 PCMASS 1.000 FRCEST 1.000 SOURCE EST CODE NAME SCE(UG/M3) STD ERR TSTAT ---------------------------------------------------- YES N001 NVNSP 3.32177 2.04933 1.62090 Cold Start Gas
YES N007 NVNSP2 .92356 .41105 2.24682 Normal Gas
YES N010 NVSM 6.60239 3.71219 1.77857 High Emitter Gas
RATIO C/M Diagnostic: Shows extent to whichcalculated equals measuredconcentation
[Target: Within one or twouncertainty intervals of 1.0
Ratio of Differencebetween Calculatedand MeasuredConcentration toUncertainty ofDifference
RATIO R/U Diagnostic: Determines thesignificance of differences incalculated and measuredconcentrations. The sum of squaresof these ratios divided by the DF isthe CHI-SQUARE. [Target: 0.0 withan acceptable range between –2.0and +2.0]
• Measurement uncertainties random, uncorrelated, and normally distributed
Step 5. Adjust Model Inputs
• Increase uncertainties of profiles or provide different composites
• Linearize profiles with chemical theory
• Identify and characterize missing sources
• Measure additional species at source and receptor.
• Stratify samples by meteorological regime
• Test for effect of deviation with randomized data from non-normal distributions
Step 6. Verify Consistency and Stability
• Substitute different profiles for the same source type
• Add or drop species
• Examine source contributions to species
• Examine modified psuedo inverse matrix
Step 7. Evaluate and Reconcile
• Compare source contributions among nearby sites
• Compare source contribution variations over time with expected emissions and meteorological variations
• Apply other receptor methods and compare results
• Apply dispersion models and compare results
Protocol for Reconciling Differences AmongReceptor and Dispersion Models
1. Compare CMB and DM results
2. Verify input data in both models
3. Recompare results
4. Refine CMB model inputs
5. Recompare result.
6. Refine dispersion model inputs
7. Recompare
8. “… if it is clearly evident that the dispersion model is not valid, the CMB estimates should be used as the basis for control strategy development. However, if the disparity is not clearly attributable to either model alone, the dispersion model should be used for control strategy development.”
Conclusions
• The applications and validation protocol results in more accurate source apportionments
• Though the protocol does not solve every problem encountered in the CMB, it does identify that a problem exists and suggests some alternatives for solving it
• Reconciliation of CMB source apportionments with other source apportionment methods yields more accurate results
