calpuff in odor modeling: state of the practice, recent developments and future improvements
TRANSCRIPT
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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
B. Douglas ReevesEmerging Issues in Air Quality Modeling - Canada
October 4,2006
CALPUFF in Odor Modeling:State of the Practice, Recent
Developments and Future Improvements
Overview
• Background• State of the Practice
– US– Canada
• Potential Improvements– CALPUFF Modeling System– Meteorological Data– Population Exposure Statistics
• Q&A
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
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?
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
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
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
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
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
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
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
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
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(?)
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
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
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
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
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?
Context Matters
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
Potential New Odor Metrics
Potential New Odor Metrics
Potential New Odor Metrics
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
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
