Simulation study for gaseous fluxes from an area source using computed
tomography and optical remote sensing
Ram A. Hashmonay*, Michael G. Yost*, D. Bruce Harris* and Edgar L. Thompson*
*Department of Environmental Health
University of Washington
Seattle WA, 98195
*US Environmental Protection Agency
Mail Drop 61
Research Triangle Park, NC 27711
ABSTRACT
This paper presents a new approach to quantify emissions from fugitive gaseous air pollution sources. We combine
Computed Tomography (CT) with Path-Integrated Optical Remote Sensing (PI-ORS) concentration data in a new field
beam geometry. Path integrated concentrations are sampled in a vertical plane downwind from the source along several
radial beam paths. An innovative CT technique, which applies the Smooth Basis Function Minimization (SBFM) method to
the beam data in conjunction with measured wind data, is used to estimate the total flux from an area source. We conducted
simulation study to evaluate the proposed methodology under two beam geometry and configurations. This approach was
found to be robust for a wide range of fluctuating wind directions. In the very sparse beam geometry we examined (5 beam
paths), successful emission rates were retrieved over a 70° range of wind directions.
1. INTRODUCTION
This paper applies Path Integrated Optical Remote Sensing (PI-ORS) and innovative Computed Tomography (CT)
techniques to determine fluxes an area gaseous air pollution sources. The described methodology is designed to provide an
applicable PI-ORS monitoring approach in sparse beam geometries for estimating the total emission rate directly from the
measured concentration and wind data, without employing any dispersion model dependent assumptions.
Several methods have been developed and applied'1"*' in the past to estimate emission rates from fugitive sources, such as
landfills131, coal mines15"6', or water treatment plants'7"8', using PI-ORS technologies. All previous methodologies combine
downwind Path Integrated Concentration (PIC) data, wind measurements, and plume dispersion modeling assumptions to
retrieve the total emission rate. The ideal approach for measuring the flux from an upwind emission source would be to
directly measure the integrated concentration across the entire cross-wind vertical plane located downwind from the
emission source. Multiplying by the averaged wind speed (weighted average by heights if several wind monitors are
mounted in different elevations) component in the normal direction to the vertical plane provides the flux flowing through
this plane, and therefore the upwind source's emission rate. In most situations, it is impractical to directly measure the
plane integrated concentration using PI-ORS methods due to the complex beam configuration needed to cover all of the
relevant plane'2'7"8'. Alternatively, another technique such as Differential Absorption LIDAR (DIAL) could be applied to
directly measure a spatially resolved map of the contaminant concentrations'10'. Integrating the concentration over the
whole map provides the required plane integrated concentration. Cost, a limited number of potentially detectable target
contaminants, high detection limits, and calibration difficulties so far have prevented the wide application of DIAL
technology.
Recently our Computed Tomography (CT) efforts have focused on developing an algorithm which will allow reconstructior
of a concentration field in a plane, based on non-overlapping PIC data scanned in radial alternating path lengths"11. The
authors and others'11"111 have shown that the Smooth Basis Functions Minimization (SBFM) approach"31 can provide the
desired field of concentration with a relatively sparse beam geometry. We suggested applying this innovative SBFM
approach to reconstruct the smoothed field of concentration in the downwind vertical plane."41 This study allowed us to
calculate correctly the plane integrated concentration and thus the emission flux from a point source under different
meteorological conditions and simulated error. With this SBFM approach, the plume dimensions can be directly
Part of the SPIE Conference on Environmental Monitoring and Remediation Technologies
Boston. Massachusetts • November.1996 " 405
SPIE Vol. 3534 • 0277-786X/99/S10.00
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reconstructed based on the acquired PIC data, as opposed to previous methods which are dispersion model dependent.
Applying a sparse beam geometry with the SBFM algorithm to the vertical plane should allow more accurate calculation of
the desired plane integrated concentration.
In this paper, two beam geometries are evaluated for emission rate from an area source. This study is part of the designing
process of a collaborative field study underway to validate this innovative method.
2. METHODOLOGY
The following section describes our general approach to this estimation problem. The proposed methodology uses a two-
dimensional SBFM-CT technique applied to PI-ORS data to reconstruct the cross-wind concentration map in a vertical
plane. The plane integrated concentration from this reconstruction along with the averaged wind data provide an estimate
of the total flux from the emission source. The key to this methodology is a rather simple and sparse PI-ORS beam
geometry, that allows reconstruction of smoothed concentration maps in a downwind vertical plane. An Example showing
the suggested radial non-overlapping beam geometry and an upwind area source is illustrated in Figure l.This beam
geometry includes up to eight beam-paths approximately in the cross-wind vertical plane. Four beam-paths are on the
ground level with different path-lengths and the other four are elevated in two locations along the cross-wind direction. We
suggest scanning the PI-ORS instrument among the beam paths for at least twenty minutes to allow a build up of an
approximate Gaussian plume. Wind speed and direction data would be collected and averaged over the same time interval.
Figure 1 - Simulated field configuration and ORS beam geometry. The beam geometry is in a vertical plane 25 meters
downwind from a near end of an area source emission simulator. The beam geometry consist of eight beam paths, four
scanning the ORS device to ground level (2 meters height) retrorefleetors and four slanted beam paths scanning the ORS
device to elevated (6 and 10 meters height) retrorefleetors mounted on two towers. We also applied a beam geometry
consisting of five beam paths eliminating, the three circled retrorefleetors and a tower.
In order to develop a reliable time-averaged plume profile, it is desirable to get as many repeats as possible at each beam
path. Therefore the sampling time at each retroreflector should be as short as practical limits allow. For example, when
applying Open Path Fourier Transform Infra-Red instruments (OP-FIIR) this sampling time for each beam path is limited
by spectral resolution and the desired detection limit. Typically a ten second averaging time, resulting in high detection
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limits for most compounds, is a practical limit for OP-FOR. This relatively short time will allow the PI-ORS device to aim
back about ten times to the same retroreflector over a twenty minute interval, allowing about five seconds for moving
between retroreflectors. Then the PIC data should be time averaged for each beam path prior to the application of the CT
reconstruction method.
We used the SBFM reconstruction approach with a two-dimensional smooth basis function (bi-variate Gaussian) in order to
estimate the total emission rate. In the SBFM approach, a known smooth basis function or a superposition of such functions,
with unknown parameters are assumed to describe the distribution of concentrations. Instead of trying to find the
concentration value in each pixel, the search is for the unknown parameters of the basis functions. Since our interest is in
the plane integrated concentration and not the exact map of concentrations in the plane, we suggest to fit only one smoothed
basis function (one bi-variate Gaussian) to reconstruct the smoothed map. However, it is important to emphasize that we do
not assume in this methodology that the true distribution of concentration in the vertical plane is a bi-variate Gaussian. We
show in this study that one can fit a single bi-variate Gaussian to a skewed plume generated by an area source and still
retrieve a reasonably good estimate of the plane integrated concentration.
In each iterative step of the SBFM-CT search procedure, the measured PIC values are compared with assumed PIC values
calculated from the new set of parameters. In order to compute the assumed PIC values one has to integrate the basis
function along the beam paths. Therefore, in the radial beam geometry it is more convenient to express a bi-variate
Gaussian distribution in polar coordinates, G(r,Q): ;
0)
G(r,e) = -
*exp^-
2(1-Py
(r-cos6-myf 2p,2(r-cos9-my)(r-sine -mj
where r is the distance from the PI-ORS instrument; 0 is the angle created between the beam and the horizontal plane
intersecting the PI-ORS instrument location. As shown in Equation 1, each bi-variate Gaussian has six unknown
independent parameters: (1) A - normalizing coefficient which adjusts for the volume under the bi-variate surface; (2) fsl2
correlation coefficient which defines the direction of the distribution independent variations in relation to the Cartesian
directions y and z (p;2=0 means that the distribution variations overlap the Cartesian coordinates); (3) m, and mz - peak
locations in Cartesian coordinates; (4)
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In most of the previous methodologies OP-FTIR instruments were used as the PI-ORS device mainly due to its simultaneous
chemical analysis capability. However, when only a few species are of interest it might be more efficient to employ other
laser based PI-ORS technologies like Tunable Diode Laser (TDL) or Path Integrated DIAL which have longer ranges and
faster scanning capabilities.
3. COMPUTATIONAL STUDY
The objective of the synthetic study was to evaluate the feasibility of a reduced beam geometry (5 beams) and CT
algorithm for total emission rate estimation from an area source under realistic changes in wind direction. We used a
Gaussian dispersion model (PAL)1"1 only for generating the time averaged concentration test maps in a vertical plane 25
meters downwind from the near end of a ground level area source (Figure 1). Concentration values where calculated for
every 4x4 meters square elementary unit in a vertical domain size of 100x24 meters. We calculated test maps every 10
degrees of wind direction up to ± 40 degrees from the normal situation in Pasquill's stability1"1 category B; 11 test maps
were created overall. The test map of the normal cross-wind situation is somewhat close to a symmetric bi-variate
Gaussian distribution. However all the other maps are non-symmetric and the larger the wind shift angle, the more skewed
is the distribution (Figure 2).
•-
9
4 90 86 82 78
f
muss.
3M»-
=
=x
§ «*g s *g sags m
— —
•^— — .
M_m
••KS
~~«
— •«•
1ft
I
====±_:
74 70 66 62 58 54 50 46 42 38 34 30 26 22 18 14 10 6 2
Y[m]
D 0.00&-00-7.00E-04 D7.00E-04-1.40E-03 Q1.40E-03-2.10E-03
H2.10E-03-2.80&03 • 2.80&03-3.50E-03 • 3.50E-03-4.20E-03
Figure 2 - Example of modeled concentration contour maps calculated for the downwind vertical plane with wind direction
of 270° using the PAL dispersion model. The five beam paths are overlaid on the concentration map.
Simulated PIC values were computed for each beam path for a given test map by integrating the projected length of the
beam through each elementary square unit concentration and along the whole path-length. Using relatively large
elementary square units for the numerical integration procedure introduced some error into the simulated PIC values.
Therefore, the calculated PIC values are not truly "noiseless." As described in the previous section we applied SBFM-CT
with the Simplex minimization algorithm, to the simulated PIC values to solve for the bi-variate Gaussian unknown
parameters. We assumed that the correlation factor is equal to 0 and restrict the peak to the ground. Therefore, the search
was for 4 unknown parameters. As a first guess we substitute the following values: A=5; m=50 m; G=10 m; m=2 m; a<=5
m. We chose these values arbitrarily and applied them to all reconstructions executed in this study. Then, since the input
wind speed was 1 m/s, the reconstructed emission rate was calculated by numerical integration of the reconstructed bi-
variate Gaussian followed by multiplication of the latter by the cosine of the wind shift angle.
We compared the beam geometry consisting of five paths scanning to one far tower, with a geometry of eight beam paths
scanning to two towers as illustrated in Figure 1. In the perpendicular situation, the reconstructed emission rate only
reached the value of 0.9 (ideally should be 1) for both beam geometries. This could be explained by the limited number of
408
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beams in all these configurations. The small diamond data marks in Figure 3 show the change in reconstructed emission
rate as a function of azimuth angle in wind direction for the eight beam geometry. The five beam geometry yielded
reasonably good reconstructed emission rate values compared to the eight beam geometry, but only when the wind shifts
were in the range of 230°-300° (large square data marks in Figure 3). Therefore, it appears from our results that the
suggested beam geometry (5 beams) with one tower in Figure 1 yielded robust estimates in our study, but over a smaller
range of angles.
42 3
2 2.5
Emission Rate
P —
., o in -• In rvj
. . «».
r-
,.B
X3
"*" * **•*•* i
i
JO 240 250 260 270 280 290 300 310
Wind
-- H-- 5 beams — *— 8 beams
Direction Azimuth [degrees]
Figure 3 - Estimated emission rate as a function of the wind direction azimuth angle for the five (one tower) and eight (two
towers) beam geometries, calculated for stability class B and modeled total emission rate of 1 g/s. The location of towers
projected in the angular domain is shown with the thick gray vertical lines (it is the angle created by a line connecting the
SW comer of the source and the tower and by the normal to the plane).
4. CONCLUSIONS
We found the CT SBFM approach to be robust for estimating the emission rate from an area source, scanning only among
five beams (one tower), as long as the average wind direction range is about 70°. If a wider range of wind direction is
desired one can move back the PI-ORS device to compensate.
As in our previous simulation study"4', the simpler local search like the Simplex algorithm was found to be sufficient.
In the near future, the experimental validation of a PI-ORS monitoring system for estimating emission rates is expected.
An area source simulator was constructed with the same dimensions of the simulated area source in our study. An 18 m
cranked-up tower was purchased to the retroreflectors on. This technology should provide fairly robust estimates of the
total emission from many kinds of fugitive sources along with the selection of an adequate PI-ORS device. Based on the
experimental data of the validation study we will explore ways for such a monitoring system to provide estimates with
better time resolution.
Such a system could be applied in a continuous monitoring mode to function also as an alarm for departure from normal
working conditions of many kinds of area and volume emission sources. In this case the system's beam geometry should be
located between the emission source and the concerned neighboring communities or workers' location to alert them to a
potential emergency. Reconstructed maps should be able to provide measures of the initial near field dispersion
parameters. These parameters serve as atmospheric stability condition indicators, along with the estimated flux could be
input into a dispersion model to calculate in near real time the downwind field of concentrations.
5. ACKNOWLEDGMENTS
This research was partially supported by the Consortium for Risk Evaluation with Stakeholder Participation (CRESP)
through Department of Energy Cooperative Agreement #DE-FC01-95EW55084. This support does not constitute an
endorsement by DOE of the views expressed in this article. This research was partially supported by the National Institute
for Occupational and Safety Health (NIOSH) grant RO1OH02660.
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6. REFERENCES
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Environmental and Process Monitoring. VIP-55 San Francisco 1995, Pittsburgh, Pa,, (1996), 393.
4. Piccot, S.D.; Masemore, S.S.; Lewis-Sevan, W.; Ringler, E.S.; Harris, D.B. "Reid assessment of a new method for
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5. Piccot, S.D.; Masemore, S.S.; Ringler, E.S.; Srinivasan, S.; Kirchgessner, D.A.; Herget, W.F. "Validation of a method
for estimating pollution emission rates from area sources using open-path FTIR spectroscopy an dispersion modeling
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University Press, Cambridge MA., 1992
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Sources, (EPA/600/8-87/009; NTIS PB87-168787), U.S. Environmental Protection Agency, Research Triangle Park,
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Atmospheric Diffusion, Technical Information Center, U.S. Department of Energy, 1982; 25-35.
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N RMRL-RTF-P-539
TECHNICAL REPORT DATA
{Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA/600/A-00/063
2.
3. R6CIP1
4. TITLE AND SUBTITLE
Simulation Study for Gaseous Fluxes from an Area
Source Using Computed Tomography and Optical
Remote Sensing
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7.AUTHOR(ss
A. Hashmonay and M. G. Yost (U. WA); and
D.B.Harris and E. L. Thompson (EPA)
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
University of Washington
Dept. of Environmental Health
Seattle, WA 98195
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO,
NA
12. SPONSORING AGENCY NAME AND ADDRESS
EPA, Office of Research and Development
Air Pollution Prevention and Control Division
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD. COVEREE
Published paper; 9/97-9/98
14. SPONSORING AGENCY CODE
EPA/600/13
is.SUPPLEMENTARY NOTES APPCD project officer is D. Bruce Harris, Mail Drop 61, 919/
541-7807. Presented at SP1E Conference on Environmental Monitoring and Reme-
diation Technologies, Boston, MA, 11/98.
IB.ABSTRACTrj-j^ paper presents a new approach to quantifying emissions from fugitive
gaseous air pollution sources, Computed tomography (CT) and path-integrated opti-
cal remote sensing (PI-QRS) concentration data are combined in a new field beam
geometry. Path-integrated concentrations are sampled in a vertical plane downwind
from the source along several radial beam paths. An innovative CT technique, that
applies the smooth basis function minimization (SBFM) method to the beam data in
conjustion with measured wind data, is used to estimate the total flux from an area
source. A simulation study was conducted to evaluate the proposed methodology
under two-beam geometry and configurations. This approach was found to be robust
for a wide range of fluctuating wind directions. In the very sparse beam geometry
examined (five paths), successful emission rates were retrieved over a 70-degree
range of wind directions.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Pollution
Gases
Fluxes
Optics
Remote Sensing
Measurement
Pollution Control
Stationary Sources
Tomography
Fugitive Emissions
13 B
07D
11G
20F
14B
14G
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (This Report}
Unclassified
21. NO. OF PAGES
2O. SECURITY CLASS {Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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