A presentation on using CALPUFF for odor modeling.

1. Environmental solutions delivered uncommonly well
CALPUFF in Odor Modeling: State of
the Practice, Recent Developments
and Future Improvements
Prepared By:
JB. Douglas Reeves
TRINITY CONSULTANTS
12700 Park Central Drive
Suite 2100
Dallas, TX 75251
+1 (972) 661-8881
trinityconsultants.com
October 4,2006

2. B. Douglas Reeves
Emerging Issues in Air Quality Modeling - Canada
October 4,2006
CALPUFF in Odor Modeling:
State of the Practice, Recent
Developments and Future
Improvements

3. Overview
• Background
• State of the Practice
– US
– Canada
• Potential Improvements
– CALPUFF Modeling System
– Meteorological Data
– Population Exposure Statistics
• Q&A

4. First, An Apology (or two)
• I am not Christine Otto
• I do not spell “odor” with a “u”
• I do not have lots of pretty graphs to show
you
• I have drawn the dreaded “just after-lunch”
time slot
• The topic of this presentation stinks

5. Odor Background
• Odor results in a disconnect between
sources, community, and regulators.
Why?
• What is an unacceptable impact?
• How do you measure / predict odor impacts?
• Can agencies initiate enforcement actions for
odor complaints where there is no standard?

6. Odor Background
• Odor is hard to quantify
• Subjective – individual variation
• Lack of good instrumentation
• Artificial noses – research stage
• Human odor panels are not portable!
• Frequently complaint driven
• How does an inspector determine a violation?
• Inability to add monitoring to support
complaints

8. Odor Vs. Criteria Pollutants
• Instantaneous effect
• Averaging period ~ 30 sec for outside
• Building air exchange increases averaging time for
indoor sources
• Inversion break-ups correlated with complaints
• Puffs accumulate
• Convective action mixes atmosphere
• Local meteorological impacts
• Calm periods
• Persistence of puff over receptor

9. Odor Regulation in the US
• No real Federal rules
• State rules vary, but almost all are
complaint driven
• Citizens complain to the agency
• Inspector comes out to assess the odor, often
finds something different than complainant
• Enforcement is contentious
• No real way to determine
or assure compliance

10. Sample Provincial Requirements
(or, is it better in Canada?)
• Quantify odor emission rate
• Model odor impacts
• AERMOD is Provincial Model of Choice
• AERMOD gives hourly avg. odor; multiply by
1.65 to get 10 min avg.
• Permit evaluation is made on frequency of
exceeding 1 OU at “sensitive receptors”
• Typically underpredicts extent
and frequency of odor

11. Quantifying Odor
• Use “Odor Panel” technique
refined by ORTECH
• Dilute stack gas and store in a
Tedlar bag
• Present various dilutions to a
“panel” of people whose
sensitivity is tracked over time
• Threshold is determined
statistically
• Calculate emission rate in
odor units (OU) per seconds

12. Gaussian Models and Odor
• Plume meander
• Plume varying from the centerline due to
horizontal turbulence
• Instantaneous odor sensing means areas of
plume meander will cause odor impacts
• Addressed in AERMOD using a broadening
factor based on horizontal turbulence

13. Gaussian Models and Odor
• Eddy Separation
• Significant horizontal turbulence causes puffs
or eddies to separate from the plume
• All areas where these puffs hit may cause
odor sensing
• Not addressed in AERMOD

14. Gaussian Models and Odor
• Calm Periods
• Persistence of odor during calms can cause
extended periods of odor exposure – more
likely to cause complaints
• Not addressed by AERMOD
• Can Lagrangian models do better?
• CALPUFF
• SCIPUFF

15. Lagrangian Models and Odor
• Improved description of meteorology
• Spatial variation of meteorology
• Can incorporate direct measurements of
turbulence and vertical structure via CALMET
• Improves predictions of plume meander only
when used with shorter period met data
• Improved descriptions of inversion breakups if
vertical profile data is available(?)

16. Lagrangian Models and Odor
• Puff tracking nature
• Better characterization of puffs, particularly
with short-term meteorology and turbulence
measurements – this can improve plume
meander
• Improved prediction of separated puffs?
• Improved handling of calm periods

18. CALPUFF vs SCIPUFF
• CALPUFF
• Lagrangian, EPA “Guideline” model
• SCIPUFF
• Lagrangian, EPA “Alternative” model
• Second-order characterization of turbulence
(Sykes) better suited for odor
• Handles both plume meander and eddy
separation
• Designed for short term timesteps, met data
• Good agreement with field observations

19. Improved Meteorological Data for
Better Odor Modeling
• Shorter averaging periods
• Improved prediction of plume meander
• Takes advantage of puff tracking
• Little to no additional cost
• Direct measurements of turbulence
• Improved prediction of plume meander
• Addresses micrometeorology
• Improve minimum wind speed
thresholds

20. Improved Meteorological Data for
Better Odor Modeling
• Direct measurement of vertical structure
• Multi-level towers or radar profilers
• May improve prediction of / validate inversion
formation and breakup
• Expensive and requires data reduction

21. Improved Odor Metrics
• Common exposure metric
• Histogram for “sensitive receptors”
• Does not describe community-wide
impacts
• What is important to community and
regulators?
• Number of people exposed?
• Duration?
• Frequency?

22. Context Matters

23. Candidate Odor Metrics
• Total population exposure within area
exposed to > 1 OU
– Allow some number of allowable
exceedances of 1 OU?
• Total number of odor sensings
– Count of population within 1 OU contour,
summed over all timesteps
• Parallels to risk assessment metrics

24. Potential New Odor Metrics

25. Potential New Odor Metrics

26. Potential New Odor Metrics

27. Conclusions
• It’s not practical to quantify odor events in
the field, so we must model it.
Enforcement alone won’t work.
• Lagrangian models should provide more
realistic odor results
– SCIPUFF appears better suited to model odor
• Short-term met data should improve
prediction of odor impacts when coupled
to Lagrangian models

28. Conclusions
• Vertical profile data may be helpful to
improve characterization of inversions
• New population exposure metrics are
presented for evaluating odor impacts
– Frequency histograms alone are not sufficient
– Regulators, sources and the community must
decide what kinds of impacts are important
– Parallels to risk assessment

29. Questions?
Contact B. Douglas Reeves
(403) 290-1575
Mobile (403) 606-0341
dreeves@trinityconsultants.com