Field Evaluation of a Method for Estimating Gaseous Fluxes
from Area Sources Using Open-Path FTIR
Ram A. Hashmonay, David F. Natschke, and Keith Wagoner
ARCADIS Geraghty & Miller, Inc., P.O. Box 13109, Research Triangle Park, NC 27709
D. Bruce Harris and Edgar L. Thompson
NRMRL, U.S. EPA, Mail Drop 61, Research Triangle Park, NC 27711
Michael G. Yost
Department of Environmental Health, Box 357234
University of Washington, Seattle WA 98195
ABSTRACT
This paper describes preliminary results from a field experiment designed to evaluate a new
approach for quantifying gaseous fugitive emissions of area air pollution sources. The approach
combines path-integrated concentration data acquired with any path-integrated optical remote
sensing (PI-ORS) technique and computed tomography (CT) technique. In this study, an open-path
Fourier transform infrared (OP-FTER) instrument sampled path-integrated concentrations along five
radial beam paths, in a vertical plane downwind from the source. A meteorological station
collected measurements of .wind direction and wind speed. Nitrous oxide (NiO) was released from
a controlled area source simulator. The innovative CT technique, which applies the smooth basis
function minimization method to the beam data in conjunction with measured wind data, was used
to estimate the total flux from the simulated area source. The ne%v approach estimates agreed with
the true emission rates in unstable atmospheric conditions and consistently overestimated the true
emission rate in neutral atmospheric conditions. This approach is applicable to many types of
industrial areas or volume sources, given the use of an adequate PI-ORS system.
INTRODUCTION
This paper describes preliminary results from a field experiment to validate a technique for
determining fluxes from fugitive gaseous air pollution sources. The method is designed as an
applicable path-integrated optical remote sensing (PI-ORS) monitoring approach for estimating the
total emission rate directly from the measured concentration and wind data. Moreover, this
approach is independent of dispersion model assumptions. Several methods have been developed
and applied1'9 in the past to estimate emission rates from fugitive sources such as landfills13', coal
mines "6, or water treatment plants7"8, using PI-ORS technologies. All previous methodologies
combine downwind path-integrated concentration (PIC) data, wind measurements, and plume
dispersion modeling to retrieve the total emission rate. The ideal approach for measuring the flux
from an upwind emission source would directly measure the integrated concentration across the
entire crosswind vertical plane located downwind from the emission source. Multiplying by the
averaged wind speed component, in the normal direction to the vertical plane (weighted average by
heights if several wind monitors are mounted in different elevations), provides the flux flowing
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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 vertical plane2'7"8. In recent
methods3'4, an open-path Fourier transform infrared (OP-FTIR) instrument was scanned diagonally
from a fixed ground level location to retroreflectors at different heights to partially cover the
vertical plane. Dispersion modeling was then applied to interpolate and extrapolate the measured
data, and generate the plane integrated concentrations. Differential absorption lidar (DIAL) could
directly measure a spatially resolved map of the contaminant concentration10 and, by integrating the
concentration over the whole map, provide the required plane-integrated concentration. Cost, a
limited number of potentially detectable target contaminants, and calibration difficulties so far have
prevented the wide application of the DIAL technology.
In earlier simulation studies11"14, we suggested and tested the application of similar radial beam
geometry and the smooth basis functions minimization (SBFM)1 computed tomography (CT)
approach to reconstruct the smoothed field of concentration in a plane. The relevant beam
geometry is slightly different from the previous slanted beam geometry method by adding several
ground level retroreflectors in alternating pathlengths. This allows us to directly fit a bivariate
Gaussian function to the measured PIC and a smoothed mass equivalent concentration map is
generated for the vertical plane. Estimates of the emission flux are retrieved in the same way as
previous methods suggested, by multiplying the relevant wind speed at each height level.
METHODOLOGY
The proposed methodology uses a two-dimensional SBFM-CT technique applied to PI-ORS data to
reconstruct the crosswind-smoothed concentration map in a vertical plane. The plane-integrated
concentration from a reconstructed mass equivalent concentration map, along with the averaged
wind data, provides an estimate of the total flux from the upwind 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. The setup of the beam geometry and
upwind area source in the field experiment is illustrated in Figure 1. This beam geometry includes
five beam paths approximately in the crosswind vertical plane. Three beam paths are on the ground
level with different path lengths, and two retroreflectors are elevated above the longest path by the
tower. When using an OP-FTIR instrument, we suggest scanning among the beam paths for at least
2Q minutes to compensate for the relatively slow data acquisition capabilities and to allow a buildup
of an approximate Gaussian plume. Wind speed and direction data are collected and averaged over
the same time interval.
In order to develop a reliable time-averaged plume profile, it is desirable to get as many repeated
measurements as possible at each beam path. Therefore the sampling time at each retroreflector
should be as short as practical limits allow. For example, we used 1 minute averaging in this field
study. This relatively short time will allow the PI-ORS device to aim back 4 times to the same
retroreflector over the about 20-minute interval. Then the PIC data are averaged for each beam
path prior to application of the CT reconstruction method.
We used the SBFM reconstruction approach with a two-dimensional smooth basis function
(bivariate Gaussian) in order to reconstruct the smoothed mass equivalent concentration map. In
the SBFM approach, a smooth basis function is assumed to describe the distribution of
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concentrations, and the search is for the unknown parameters of the basis function. Since our
interest is in the plane integrated concentration and not the exact map of concentrations in the plane,
we fit only one smoothed basis function (one bivariate Gaussian) to reconstruct the smoothed map.
»Scanning
OP-FTIR
Figure 1 - Field configuration and OP-FTIR beam geometry. The beam geometry is in a vertical
plane 50 m downwind from a controlled emission area source. The beam geometry
consists of five beam paths, three scanning the OP-FTIR device to ground level (2 m
height) retroreflectors, and two slanted beam paths scanning to elevated (7 and 13 m
height) retroreflectors mounted on a tower.
However, this methodology does not assume that the true distribution of concentration in the
vertical plane is a bivariate Gaussian. Earlier computational studies13"14 showed that one may fit a
single bivariate Gaussian function to many kinds of skewed distributions and still retrieve a
reasonably good estimate of the plane-integrated concentration. We also examined the fit of a
single bivariate Gaussian function to a multiple mode distribution and found that the reconstructed
plane integrated concentration to conserve fairly well the test input 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
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assumed PIC values, the basis function is integrated along the beam path's direction and path-
length,
In our beam geometry it is convenient to express the smooth basis function G in polar coordinates r
and 9. To fit the unknown parameters of the smooth basis function to the PIC data, one has to
define an error function for minimization. The Sum of Squared Errors (SSE) function is defined in
our study as:
r;
SSE(A,pn,my,m:,(Jy,ai) = £ «C, -J
Where PIC represents the measured PIC values and the index i is for the different beams. The
bivariate Gaussian has six unknown independent parameters: A - normalizing coefficient which
adjusts for the peak value of the bivariate surface; pn - 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); my and mz - peak
locations in Cartesian coordinates; and o"v and a, - standard deviations in Cartesian coordinates.
•^ »j~
The SSE function is minimized using an iterative minimization procedure, such as the Simplex
method, to solve for the unknown parameters.
As mentioned earlier, our interest is in the plane-integrated concentration; therefore, we fit one
bivariate Gaussian surface to match the volume under the underlying true concentration distribution
surface. This volume is highly conserved in the fitting procedure, which emphasizes agreement
over the five path integrals. Six independent beam paths are sufficient to determine one bivariate
Gaussian that has six independent unknown parameters.
Some reasonable assumptions also may be made when applying the SBFM-CT method to this
problem, to reduce the number of unknown parameters to four; e.g., setting the correlation factor
p/2 parameter equal to zero. This assumes that the reconstructed bivariate Gaussian is limited only
to changes in the vertical and crosswind directions. One also can fix the peak location in the
vertical direction to the ground level when ground level emissions are known to exist, as in our
field experiment. However in this methodology, there is no requirement to apply a priori
information on the source location and configuration.
Once the parameters of the function were found for a specific run, we calculated the concentration
values for every 4x4 m square elementary unit in a vertical domain size of 250x24 m. Then, we
integrated these values, incorporating wind speed data at each height level to compute the flux. In
this stage we converted the concentration values from parts per million by volume to grams per
cubic meter, considering the molecular weight of NaO and ambient temperature. This enables us to
calculate directly the flux in grams per second using wind speed data in meters per second.
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 much faster scanning
capabilities.
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FIELD STUDY
Study design and Methods
The objective of the field study was to evaluate the feasibility of the suggested beam geometry and
the CT algorithm to estimate the total emission rate under realistic changing meteorological
conditions. Figure 1 presents the experimental layout for this work, which was performed at the
Oxford-Henderson Airport in North Carolina.
A monostatic Midac OP-FTIR was used with optical paths consisting of five retroreflectors in a
two-dimensional vertical array as shown in Figure 1. PLX, Inc. 20-cube retroreflectors were used
in this study. A 12 m scissors jack supported mirrors 3,4, and 5. Mirrors 1 and 2 were mounted on
tripods at a height of 2 m above the ground, at different distances from the OP-FTIR instrument.
The OP-FTIR instrument was mounted on a scanner, custom built by the modeling and electronics
shop of EPA's Air Pollution Prevention and Control Division. Horizontal and vertical rotations
were performed under computer control through a pair of Yuasa stepper-controllers.
Meteorological data were collected every second using a Climet, Inc. system consisting of a
Campbell data logger, two Tacmet sensing heads, and a radio frequency (RF) modem. Wind
direction, wind speed, temperature, and relative humidity data were collected with this system. The
Tacmet heads w,ere located at heights of 2 and 10 m. The 2 m head was located on a tripod in the
vicinity of the scissors jack. The 10 m head was mounted on top of the scissors jack. This system
data are stored internally in the data logger and then downloaded directly to a computer via the
modem.
Nitrous oxide (NaO), supplied by Air Products, was released upwind of the optical path. The gas
tank was fitted with a regulator, electric heat tape, and a Variac variable-voltage transformer. Since
the gas is in liquid form in the tanks, there was significant expansive cooling, and the tank valve
and regulator were heated with electric tape to prevent freezing. The NaO was released from an
area source located about 50 m upwind of the optical path. This area source, 8 by 23 m, was
constructed as an "H" pattern using porous rubber hosing. This hosing is a consumer grade product
manufactured from recycled shredded rubber and is available as 0.75 in. diameter by 50 ft rolls.
NaO flow rate was measured with a self-calibrated Gilson tapered-tube flowmeter.
Data presented here were collected during 2 days: October 15 and 19, 1999. Seven successfully
completed runs were executed during the 2 days (three on the 15* and four on the 19th) this field
study took place. Four plume traverses were considered as a complete successful run. Each plume
traverse was completed after monitoring events were acquired at each of the five beam paths. A
monitoring event was an average of 18 scans of the OP-FTIR instrument, with a spectral resolution
of 0.5 cm"1. Therefore the duration of a monitoring event was approximately 1 minute and the
duration of each run was about 20 minutes in this study. In all runs the emission rate was set up to
a nominal rate of 60 SLPM (standard liters per minute). This yields an emission rate of 2 g/s in
standard conditions. All seven runs consisted of a total of 28 plume traverses or 140 monitoring
events, all of which were analyzed and quantified for NaO PIC. Absorbance spectra were generated
using synthetic background spectra as described in the OP-FTIR guidance document17. Classical
least squares (CLS) fit was performed using the Midac Autoquant software and Infrared Analysis,
Inc. reference spectra. Each day, background spectra were collected for the five measurement
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paths. Background spectra were acquired before the N2O release started, and we quantified these
spectra for N2O to evaluate the background concentration, which was subtracted from all the
quantified concentrations for the same day. After background subtraction, PIC values in each beam
path were averaged for each run. As described in the previous section we applied SBFM-CT with
the Simplex minimization algorithm, to the measured PIC values to solve for the bivariate Gaussian
function unknown parameters. We assumed that the correlation factor was equal to 0 and that the
peak height and the source were at ground level. The latter is based on the fact that PIC data on the
lower retroreflector were always the highest (Table 1). We did not apply any restrictions for the
other four unknown parameters.
Table 1 - Summary of PIC data (ppm-m)
Run#
10/15 - 1
10/15 - 2
10/15-3
10/19 - 1
10/19 - 2
10/19 - 3
10/19-4
N2O PIC at
Retro 1
17.2
28.4
28.8
8.4
2.0
3.7
4.1
N20 PIC at
Retro 2
53.1
53.5
64.5
219.8
177.9
207.8
190.5
N20 PIC at
Retro 3
57.9
48.1
66.1
234.9
178.6
243.9
330.0
N2O PIC at
Retro 4
58.6
35.7
45.6
57.2
75.7 •
74.2
56.6
N2O PIC at
Retro 5
44.5
38.2
35.2
51.9
56.6
48.4
76.4
As a first guess, we substituted the following values: the peak concentration was 5 times the PIC of
the third retroreflector; crosswind peak location was 150 m from the OP-FITR; the standard
deviation of the plume's dimension, in the crosswind direction, was 10 m; and the standard
deviation of the plume's dimension, in the vertical direction, was 2.6 m. We chose these values
arbitrarily and applied them to all reconstructions executed in this study. Running the SBFM-CT
procedure with a different set of first guessed parameters did not change the resulting reconstructed
fluxes. The reconstructed emission rate was calculated by numerical integration of the
reconstructed bivariate Gaussian function weighted by multiplication of the interpolated wind speed
and the cosine of the wind shift angle at each relevant height.
Results and Discussion
The 2 days were very different in atmospheric stability as reflected in the collected wind data in
Table 2. The standard deviations in wind directions, 25 degrees)18.
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Table 2 - Summary of wind data
Run # Wind Speed Wind Speed Wind Direction Wind Direction ae at 10 m
at 2 m [m/s] at 10 m [m/s] at 2 m [deg] at 10 m [deg] [deg]
10/15
10/15
10/15
10/19
10/19
10/19
10/19
- 1
-2
-3
-1
-2
-3
-4
1.9
2.1
2.0
2.2
2.4
1.7
1.7
2.5
2.9
2.7
2.8
3.2
2.2
2.5
117.1
71.2
70.9
7.8
13.8
22.6
20.3
62.0
48.4
48.3
22.9
27.8
39.4
31.6
88.5
31.9
31.6
10.8
9.3
18.4
12.1
On 10/19/99, the standard deviations in wind direction are between 9 and 18 degrees, which are
compatible with categories D and C. These categories (A-D) cover the range of the daytime
atmospheric stability conditions18.
Table 1 presents the PIC of N2O after background subtraction and shows a significant difference in
the NaO PIC values between the 2 days of the field study. In the calculation of the flux, these
differences are compensated for by the vertical and cross-sectional dimensions of the plume in
which the integration is performed, and by taking the normal component of the wind speed to the
vertical plane.
Figure 2 illustrates the reconstructed plume dimensions and levels for the three runs on 10/15/99. It
also shows the average reconstructed plume where the PIC values for the three runs were averaged
prior to the reconstruction. The estimated emission flux is shown in the title of each graph, and the
averaged emission flux is 2.2 g/s for that day. This slightly overestimated the true released
emission rate, which was 2.0 g/s. The calculated emission from the reconstructed plume of the
averaged PIC was even a little lower (2.1 g/s). Generally, the reconstructed plume's dimensions on
the 15th are much larger then the reconstructed plume's dimensions on the 19th, as should be
expected for the unstable atmospheric conditions. It is obvious that the variability in wind direction
for the first run should be much larger than in runs 2 and 3, since the reconstructed plume in run 1 is
much larger. Thus, ere in run 1 is 89 degrees compared to 32 degrees in runs 2 and 3 as in Table 2.
Figure 3 illustrates the reconstructed plume dimensions and levels for the four runs on 10/19/99. It
also shows the average reconstructed plume where the PIC values were averaged prior to the
reconstruction. The estimated emission flux is shown in the title of each graph, and the averaged
emission flux is 3.0 g/s for that day.
-------
concentrations sir© in mg/m
*$*$$$
.•<'>
-------
based on preliminary PIC results, and more thorough quantification is underway. Nevertheless,
these results show robust emission reconstruction data that are consistent with atmospheric
conditions.
SO -CO ' ^O SO _ . v -tOO -12O ','t-SO '-tBO 1€SO 2OO 22O
Figure 3 - The average reconstructed plume and levels where the PIC values for the four runs were
averaged prior to the reconstruction, and the reconstructed plumes and levels for each of
the four runs on 10/19/99. The estimated emission flux is shown in the title of each
graph.
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CONCLUSIONS
We found the CT SBFM approach for estimating the emission rate to be robust for different
atmospheric stability conditions as long as the average wind direction vector intersecting the
emission source is pointing between the first retroreflector (#1) and the tower. As in the
computational studies, the Simplex algorithm was found to be sufficient. First applying a one-
dimensional SBFM approach19 to the ground-level-segmented beam paths would allow us to
provide a very close first guess for the solution. One also could substitute values from the one-
dimensional SBFM reconstruction along the ground level as fixed parameters of the bivariate
Gaussian function to afford more degrees of freedom in the two-dimensional SBFM solution.
Based on the preliminary experimental data of this validation study, this method can provide
consistent estimates of emission flux, which are wellcorrelated to atmospheric stability conditions.
This technology provides fairly robust estimates of the total emission from many kinds of fugitive
sources along with the selection of an adequate PI-ORS device. A laser-based monitoring system
may provide estimates with better time resolution than the 20 minutes applied in this study. 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 types of area and volume emission sources.
Reconstructed maps should be able to provide measures of the initial near field dispersion
parameters. These parameters 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.
ACKNOWLEDGMENTS
The algorithm development 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.
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NRMRL-RTP-P-493
TECHNICAL REPORT DATA
1. REPORT NO.
EPA/600/A-00/051
2.
3.
4. TITLE AND SUBTITLE
Field Evaluation of a Method for Estimating Gaseous
Fluxes from Area Sources Using Open-path FTIR
S, REPORT DATE
6. PERFORMING ORGANIZATION CODE
7.AUTHOR(s> R. Hashmonay/D. Natschke/R. Wagoner (AR-
CADIS), B.Harris/E.Thompson (EPA), and
M. Yost (Univ. of WA)
8. PERFORMING ORGANIZATION REPORT NO,
i ORGANIZATION NAME AND ADDRESS
Geraghty and Miller, Inc., PO Box 13109,
Research Triangle Park, North Carolina 27709.
University of Washington, PC Box 357234,
Seattle, Washington 98195
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-C-99-201 WA 1-033 (ARC)
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 COVERED
Published paper; 10-12/99
14. SPONSORING AGENCY CODE
EPA/600/13
15.SUPPLEMENTARY NOTES APPCD project officer is D. Bruce Harris, Mail Drop 61, 919/541
7807. For presentation at AWMA meeting, Salt Lake City, UT, 6/18-22/00.
16. ABSTRACT
The paper gives preliminary results from a field evaluation of a new ap-
proach for quantifying gaseous fugitive emissions of area air pollution sources. The
approach combines path-integrated concentration data acquired with any patbrinteg- t
rated optical remote sensing (PI-CRS) technique and computed tomography (CT)
technique. In this study, and open-path Fourier transform infrared (CP-FTIR) in-
strument sampled path-integrated concentrations along five radial beam paths, in a
vertical plane downwind from the source. A meteorological station collected mea-
surements of wind direction and speed. Nitrous oxide (N2O) was released from a
controlled area source simulator. The innovative CT technique, which applies the
smooth basis function minimization method to the beam data in conjunction with mea-
sured wind data, was used to estimate the total flux from the simulated area source.
The new approach estimates agreed with the true emission rates in unstable atmos-
pheric conditions and consistently overestimated the true emission rate in neutral
atmospheric conditions.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
bJDENTIFIERS/OPEN ENDED TERMS
c. COSATl Field/Group
Pollution
Gases
Emission
Optical Measurement
Fourier Transformation
Infrared Analysis
Nitrogen .Oxide (N2O)
Flux Density
Pollution Control
Stationary Sources
T om og raphy
13B 07B
07D
14G
14B
12A
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport}
Unclassified
21. NO. OF PAGES
12
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
EPA Form 2220-1 (3-73)
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