DMUG 2016 - Scott Hamilton, Ricardo Energy & Environment

CALPUFF for odour assessment
A recent case study
Scott Hamilton, PhD
Technical Lead
Urban Air Quality Modelling
April 19th 2016

2© Ricardo-AEA LtdRicardo Energy & Environment in Confidence
This presentation……
• CALPUFF for odour impacts in the literature
• Methodology
• WRF
• CALWRF
• CALMET
• CALPUFF modelling
• Some results
• Comparison with Warren Springs model
• Pros and cons

CALPUFF in the literature for odour impacts
“Despite the differences between CALPUFF and field inspection estimates, their general
agreement suggests that both methods provide reasonable estimates of the real odor nuisance, so
that their applied use is justified.”

CALPUFF in the literature for odour impacts
“...the odour impacts resulting from the application of the field inspection turned out to be quite
comparable with those obtained by simulating the dispersion of emissions by means of a suitable
dispersion model (CALPUFF), thus indicating that both approaches may be effective and
complementary for odour impact assessment purposes.”

CALPUFF can work well in the near field
“On the basis of the combined results of the four-part validation (i.e., weight of evidence),
the performance of CALPUFF was judged to be superior to that of AERMOD.”

• Modelled meteorology (WRF and CALMET)
• Nested WRF grid, using ARW core
• Boundary meteorology from NCEP Climate Forecast System Reanalysis
• Terrain and coastal effects included
• Detailed consideration of building effects (BPIP)
• One year modelled meteorology
• CALPUFF model to explore effect of increasing stack height and velocity on odour
dispersion
• Advantages of CALPUFF include treatment of coastal effects, non-steady state
meteorology, build up of pollutants over time, and handling of calm wind
conditions (important for odour)
Main aspects of methodology

The site
Large manufacturing facility with waster
water treatment and odour control unit.
Short stack with very cool emissions.
History of odour complaints from nearby
towns and villages.
Client was interested in knowing the
emissions limits that would avoid future
complaints.
We back calculated the emissions
required to comply with 98%ile 1.5
ou/m3 limit at residential locations.
The computed values were then used to
provide design values for a new OCU

Stack parameters modelled
Base case
• 1) Height- 14m from ground level
• 2) Efflux temperature- 15 to 25oC
• 3) Stack diameter- 1.3 m with an
exit nozzle of 1 m
• 4) Efflux flow rate- 15000 m3/h
• 5) Efflux velocity- 5.3 m/s
• 6) Design velocity- >15 m/s
Scenarios (from client)
1) 14 m stack with stack velocity set to:
a. 5 m/s
b. 15 m/s
c. 25 m/s
a. 5 m/s
b. 15 m/s
c. 25 m/s
a. 5 m/s
b. 15 m/s
c. 25 m/s

The Weather Research and Forecasting model (WRF)
The Weather Research and Forecasting (WRF)
Model is a next-generation mesoscale numerical
weather prediction system designed to serve both
atmospheric research and operational forecasting
needs.
We used the WRF-EMS tool (available from
http://strc.comet.ucar.edu/software/newrems/),
which at the time was based on WRF 3.4.1. WRF-
EMS comes pre-set with useful defaults form
NOAA’s meteorologists.
WRF has a large worldwide community of
registered users (over 25,000 in over 130
countries)

WRF model domain
The domain was designed so as to
provide data in a format similar to that
provided by commercial vendors of
WRF data.
The WRF model domain was prepared with
nested grid configuration 25 x 25 cells in
each direction with boundary and initialisation
conditions provided by the Climate
Forecasting System Reanalysis* datasets.
The model was set with 30 pressure levels
which are more densely arranged near the
earth’s surface.
* http://cfs.ncep.noaa.gov/cfsr/downloads/

January d01- 300 x 300 km ,
winds and temperature
Visualisation prepared in UCAR’s IDV
package
WRF modelling

January domain (d02)- 50
x 50 km- winds and
temperature
Visualisation prepared in UCAR’s
IDV package
WRF modelling

July domain 1- winds and
temperature
WRF modelling

July d02- winds and
temperature
WRF modelling

Processing the WRF data
• The WRF model provided a series of netCDF files each of which contained
one day of meteorology for the Irvine bay area.
• The modelling yielded about 20GB of daily files which were then processed
using the CALWRF code to derive suitable fields for use in the CALMET
model.
• CALWRF produces CALMET.DAT files which form the input to the next
stage of the model, the CALMET diagnostic meteorological model.

Comparison of modelled and measured winds
Observed wind direction

Comparison of modelled and measured wind speed
Observed wind direction

Comparison of modelled and measured wind speed

Most complaints in early summer, this
seems to coincide with a drop in dilution.

CALMET modelling- post CALWRF
process
Prognostic met data nested grid 60km
CALMET domain
16km @200m res.

Terrain
Gridded terrain elevation for the
modelling domain were derived from the
3 arc-second digital elevation models
(DEMs) produced by the United States
Geological Survey (USGS). Elevations in
the DEM files are in metres relative to
sea level.
The spacing of the elevations along each
profile is 3 arc-seconds which
corresponds to approximately 90 metres.

Land use

CALMET modelling
CALMET domain
16km
With terrain height and wind field from CALMET at 200m resolution

CALMET modelling
CALMET domain
16km
Close up of wind field around site at 200m resolution

CALMET modelling
CALMET domain
16km
Geophysical data example- surface roughness

CALMET modelling
3D wind fields

CALPUFF modelling
CALMET domain
16km
Site buildings included in the run

CALPUFF modelling
CALMET domain
16km
Nested receptor grids

Receptor set up

Plume tracking over 48 hours

Results
Outputs from the dispersion
model were processed using the
CALRANK algorithm.
CALRANK extracts the relevant
percentile values for the
receptors from the hourly time
series (CONC.DAT) CALPUFF
output file for each case.
The assessment is based around
the 98th percentile
The 1000 oue/m3 baseline emission factor
is set to test the dispersion of a unit
emission in the domain around the GSK
site.
To facilitate decision making we also
provided results based on 5000, 10000 and
20000 oue/m3cases- based on the same
flow rate (4.17m3/s) this corresponds to
emissions of 4170, 20850, 83400 and
208500 ou/s from the OCU stack.

14m stack, 5ms velocity

30m stack, 5ms velocity

Base case- allowable odour units to eliminate complaints
14m high, 5m/s velocity

Case 2

Case 3

Agrees quite well with the CALPUFF results

Conclusions of the work
The on-site and boundary odour profile at the site would be improved significantly with a
taller stack. This is evidenced by the dramatic reduction in concentrations around the site
across runs 1 to 4, 5 to 8, and 9 to 12 (each set of runs corresponding to 14, 20, 25 and
30 m stacks with the groupings representing velocity).
The results suggest that odour concentrations are likely to be quite insensitive to release
velocity enhancements at the distances where complaints are noted.
The impact of increasing the stack height is much less pronounced at more distant
receptors. This is evidenced by the lack of sensitivity of the size of the 1.5 oue/m3 contour
line for cases where the stack height is stepped up from 14m to 30m.
A design value of around 60,000 oue/m3 should be enough to prevent complaints at
nearby properties.

Conclusions of the work
A balanced approach of engineering solutions would appear to offer improvements to both of
these situations (on site and off site), which as explained are quite distinct. Reducing
emissions will improve the odour profile at residential receptors, whilst increasing the stack
height will improve the odour profile on and very close to the site.
To inform the engineering decisions that will follow, we provided numerical results across 37
receptors around the site. These results essentially provide estimates of the carrying capacity
of the local dispersion environment and the receptors within it to emissions from the OCU.
The results of the work were used to inform the specification for odour abatement at the
facility, the engineering work is now underway.
Interestingly the Warren Springs calculation provided quite similar results!!

Pros and cons of the approach
PROS
• The modelling framework is set up now and can be turned to other applications at the same site
• Using prognostic data avoids issues of data quality at surface stations- data capture is 100%
• We learned a lot during the project
• The agreement between the modelled and measured meteorology was pretty good.
• Client was happy that unusual met had been considered- “we covered all bases”
CONS
• Extremely data intensive- project folder is about 50GB of data. This presents problems for
archiving if we do a lot of this sort of work.
• Probably takes about 5-10x longer from start to finish than using either AERMOD or ADMS
• Quite difficult operationally when things don’t work as they should- not many people to turn to!

Scott Hamilton, PhD
Ricardo Energy & Environment
scott.hamilton@ricardo.com

DMUG 2016 - Scott Hamilton, Ricardo Energy & Environment

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DMUG 2016 - Scott Hamilton, Ricardo Energy & Environment