Aerial emission sampling of four natural gas boiler stack plumes was
conducted using an unmanned aerial system (UAS) equipped with a lightweight
sensor–sampling system (the “Kolibri”) for measurement of nitrogen oxide
(NO), and nitrogen dioxide (NO2), carbon dioxide (CO2), and carbon
monoxide (CO). Flights (n= 22) ranged from 11 to 24 min in duration at
two different sites. The UAS was maneuvered into the plumes with the aid of
real-time CO2 telemetry to the ground operators and, at one location, a
second UAS equipped with an infrared–visible camera. Concentrations were
collected and recorded at 1 Hz. The maximum CO2, CO, NO, and NO2
concentrations in the plume measured were 10 000, 7, 27, and 1.5 ppm, respectively. Comparison of the NOx emissions between the stack
continuous emission monitoring systems and the UAS–Kolibri for three boiler
sets showed an average of 5.6 % and 3.5 % relative difference
for the run-weighted and carbon-weighted average emissions, respectively. To
our knowledge, this is the first evidence of the accuracy performance of
UAS-based emission factors against a source of known strength.
Introduction
Aerial measurement of plume concentrations is a new field made possible by
advances in unmanned aircraft systems (UASs, or “drones”), miniature
sensors, computers, and small batteries. The use of a UAS platform for
environmental sampling has significant advantages in many scenarios in which
access to environmental samples is limited by location or other factors.
Hazards to equipment and personnel can also be minimized by the mobility of
UASs and their ability to be remotely operated away from hazardous
sources. UAS-based emission samplers have been used for measurement of area
source gases (Neumann et al., 2013; Rosser et al., 2015; Chang et al.,
2016; Li et al., 2018), point source gases (Villa et al., 2016),
aerosols (Brady et al., 2016), black carbon particles (Craft et al.,
2014), volcanic pollutants (Mori et al., 2016), particle mass
(Peng et al., 2015), and particle number concentrations
(Villa et al., 2016).
UAS-based emission measurements are particularly suited for area source
measurements of fires and can be used to determine emission factors, or the
mass amount of a pollutant per unit of source operation, such as the mass of
particulate matter (PM) per mass of fuel (e.g., biomass) burned. These
values can be converted into emission rates, such as the mass of pollutant per
unit of energy (e.g., g NOx kJ-1). These determinations typically
rely on the carbon balance method in which the target pollutant is
co-sampled with the major carbon species present, and, with knowledge of the
source's fuel (carbon) composition, the pollutant-to-fuel ratio or an
emission rate and/or factor can be calculated.
For internal combustion sources that have a process emission stack, downwind
plume sampling can use the same method. When combined with the source fuel
supply rate and stack flow rates (to determine the dilution rate),
measurements comparable to extractive stack sampling may be possible. To our
knowledge, determination of emission factors from a stack plume using a
UAS-borne sampling system has not been previously demonstrated. The goal of
this effort was to compare NOx measurements obtained by UAS-borne
emission samplers to those from concurrent continuous emission monitoring
(CEM) measurements. While not
necessarily obviating the need for CEM for regulatory compliance, the use
of UAS-based measurements could provide a safe and fast method of checking
emissions that does not require personnel and equipment to access elevated
stacks for periodic CEM verification. More importantly, however, the
comparison of UAS-based emission measurements against a source of known
CEM-determined concentration allows the accuracy of this new type of
measurement to be assessed. Demonstrating the efficacy of these measurements
would then open their applicability to other less understood sources that
are not amenable to conventional CEM sampling, such as open fires,
industrial flares, and gas releases.
The feasibility of downwind plume sampling using a sensor-equipped UAS was
tested on industrial boilers at the Dow Chemical Company (Dow) facilities in
Midland, Michigan (MI), and St. Charles, Louisiana (LA). The sensor system
was designed and built by the EPA's Office of Research and Development, and
the UAS was owned and flown by the Dow Corporate Aviation Group. To
determine the comparative accuracy of the measurements, the UAS-based
emission factor was compared to the stack continuous emission monitoring
systems (CEMSs). The target pollutants were nitrogen oxide (NO) and nitrogen
dioxide (NO2) to mimic the stack CEMS measurement methods. Carbon as
carbon dioxide (CO2) and carbon monoxide (CO) was measured on the UAS
for the carbon balance method.
Materials and methods
Plume sampling tests were conducted on two natural-gas-fired industrial
boilers located at Dow's Midland, Michigan, and St. Charles, Louisiana,
facilities. The Midland boilers are fire-tube-type boilers using low-pressure
utility-supplied natural gas. They are equipped with low NOx burners
and utilize flue gas recirculation to reduce stack NOx concentrations.
The Midland facility burned natural gas with a higher heating value (HHV) of
9697 kcal m-3 (1089 BTU ft-3: British Thermal Units per cubic foot). The two
tested stacks are 14 m above ground level and 7 m apart. To avoid sampling
overlapping plumes, only a single boiler was operating during the testing.
The St. Charles boilers are D-type water package boilers using natural gas
fuels (high-pressure fuel gas – HPFG; low-pressure off-gas – LPOG). They
are equipped with low NOx burners with flue gas recirculation to reduce
stack NOx concentrations. The boiler stacks are about 20 m apart and
reach over 20 m in height above ground level. The St. Charles facility
burned natural gas under steady-state conditions with a composition of 77.12 % CH4, 2.01 % C2H6, and 19.91 % H2, with an HHV of
7845 kcal m-3 (881 BTU ft-3). Both boilers were operational
during aerial sampling, but the wind direction and UAS proximity to the
target stack precluded co-mingling of the plumes.
a Non-dispersive infrared. b Hz – hertz. c Zero (0) cal. gas: air.
Air sampling was accomplished with an EPA–ORD-developed sensor–sampler
system termed the “Kolibri”. The Kolibri consists of real-time gas sensors
and pump samplers to characterize a broad range of gaseous and particle
pollutants. This self-powered system has a transceiver for data transmission
and pump control (Xbee S3B, Digi International, Inc., Minnetonka, MN, USA)
from the ground-based operator. For this application, gas concentrations
were measured using electrochemical cells for CO, NO, and NO2 and a
non-dispersive infrared (NDIR) cell for CO2
(Table 1). All sensors were selected for their
applicability to the anticipated operating conditions of concentration level
and temperature as well as for their ability to rapidly respond to changing
plume concentrations due to turbulence and entrainment of ambient air. Each
sensor underwent extensive laboratory testing to verify performance and
suitability prior to selection for the Kolibri. Tests included sensor
performance (linearity, drift, response time, noise, detection limits) in
response to anticipated field temperatures, pressure, humidity, and
interferences. Additional information from the manufacturers on sensor
performance is available from the links in Table 1.
In anticipation of temperatures as low as 0 ∘C at the Midland site and
to avoid daily temperature fluctuations, insulation was added to the Kolibri
frame, and the sampled gases were preheated prior to the sensor with the use
of a heating element and micro-fan inside the Kolibri. All sensors were
calibrated before each sampling day under local ambient conditions. After
sampling was completed, the sensors were similarly tested to assess
potential drift.
Concentration data were stored by the Kolibri using a Teensy USB-based
microcontroller board (Teensy 3.2, PJRC, LLC, Sherwood, OR, USA) with an
Arduino-generated data program and secure digital data card. All four sensors underwent
pre- and post-sampling two- or three-point calibration using gases
(Calgasdirect Inc., Huntington Beach, CA, USA) traceable to National
Institute of Standards and Technology (NIST) standards.
The NO sensor (NO-D4) is an electrochemical gas sensor (Alphasense, Essex,
UK) that measures concentration by changes in impedance. The sensor has a
detection range of 0 to 100 ppm with a resolution of < 0.1 root mean square (rms) noise
(parts per million equivalent) and linearity error within ±1.5 ppm at full scale.
The NO-D4 was tested to have a response time to reach 95 % of the reference concentration
(t95) of 6.3 ± 0.52 s and a noise level of 0.027 ppm. The
temperature and relative humidity (RH) operating range is 0 to +50 ∘C and 15 % to 90 % RH, respectively.
The NO2 sensor (NO2-D4) is an electrochemical gas sensor (Alphasense,
Essex, UK) that likewise measures by impedance changes. It has an NO2
detection range of 0–10 ppm with a resolution of 0.1 rms noise (parts per million
equivalent) and linearity error of 0 to 0.6 ppm at full scale. Its t95 was measured as 32.3 ± 3.8 s with a noise level of 0.015 ppm.
The temperature and RH operating range is 0 to +50 ∘C and 15 % to
90 % RH, respectively.
Laboratory calibration testing prior to field measurements on both the NO-D4
and NO2-D4 sensor outputs showed their responses to be linearly
proportional (R2> 0.99) over the range of four- and five-point
calibration gas concentrations. The response times of both sensors were
derived using the maximum reference concentration of 47.81 ppm for NO and
10.46 ppm of NO2. The times to reach 95 % of the reference
concentration, t95, were 6.3 and 32.3 s (relative standard deviation: RSD 8.2 % and 11.8 %),
respectively, for the NO-D4 and NO2-D4 sensors. These response times are
both shorter than those measured simultaneously in the laboratory with CEM
(Ametek 9000 RM, Pittsburgh, PA, USA) at 37 and 50 s, respectively,
for NO and NO2.
The CO2 sensor (CO2 Engine® K30 Fast Response,
SenseAir, Delsbo, Sweden) is an NDIR gas sensor, and the voltage output is
linear from 400 to 10 000 ppm. The temperature and RH operating range is 0
to +50 ∘C and 0 % to 90 % RH, respectively. The CO2-K30
sensor was measured to have a t95 response time at 6000 ppm CO2
of 9.0 ± 0.0 s and a noise level of 1.6 ppm. The response
time was 4 s longer than compared to CO2 measured by a portable gas
analyzer (LI-820, LI-COR Biosciences, Lincoln, NE, USA). The sensor and the
LI-820 showed good agreement, as the measurements showed an R2 of 0.99
and a slope of 1.01.
The CO sensor (e2V EC4-500-CO, SGX Sensortech Ltd, High Wycombe,
Buckinghamshire, UK) is described more fully elsewhere (Aurell et al.,
2017; Zhou et al., 2017). In previous sensor evaluation tests with
laboratory biomass burns (Zhou et al., 2017) with CO ranging between 0 and
250 ppm, the sensor was compared to simultaneous measurements by a CO continuous emission monitor
(CAI model 200, California Analytical Instruments Inc., Orange, CA, USA).
The concentration measurements had an R2= 0.98 and a slope of
1.04, indicating the level of agreement between the two devices. The
t90 was measured as 18 s, while the time-integrated CO
concentration differences with the CAI-200, rated at t90< 1 s,
were only 4.9 %.
Variations of the Kolibri sampling system allow for measurement of
additional target pollutants. These include particulate matter (PM),
polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs), including carbonyls, energetics, chlorinated organics, metals from filter
analyses, and perchlorate (Aurell et al., 2017; Zhou et al., 2017).
At both facilities the aviation team from Dow flew their DJI Matrice 600
UAS, a six-motor multicopter (hexacopter), into the plumes with an EPA–ORD
Kolibri sensor–sampler system attached to the undercarriage
(Fig. 1). In this configuration of sensors, the
Kolibri system weighed 2.4 kg. Typical flight elevations at Midland and St. Charles were 21 and 32 m above ground level (a.g.l.), respectively, and flight
durations ranged from 9 to 24 min. At the St. Charles location, the UAS
pilot was approximately 100 m from the center point of the two stacks,
easily allowing for line-of-sight operation. A telemetry system on the
Kolibri provided real-time CO2 concentration and temperature data to
the Kolibri operator, who in turn advised the pilot on the optimum UAS
location.
Dow UAS with Kolibri attached to the undercarriage.
CEMSs on the boiler stacks produced a continuous record of NOx emissions
and O2 concentrations. Stack and CEMS types located at the Midland
and St. Charles facilities are shown in Table 2. The stack NOx analyzer
uses a chemiluminescence measurement with a photomultiplier tube and is
capable of split concentration range operation: low (0–180 ppm) and high
(0–500 ppm). Its response time is reported as 5 s. The O2 analyzer
uses a zirconium oxide cell with a measurement range of 0 % to 25 % and a
reported t95 of < 10 s.
CEMS instruments at both Dow locations.
Gas measuredMidland CEMSSt. Charles CEMSO2Gaus model 4705ABB/Magnos 106NOxThermo model 42i-HLABB/Limas 11
The plant CEMSs undergo annual relative accuracy audit testing (NSPS Subpart
Db, Part 70) using US EPA Method 7E (2014) for NOx and US EPA
Method 3A (2017) for O2. Calculation of NOx
emissions uses the appropriate F factor, a value that relates the required
combustion gas volume to fuel energy input, as described in US EPA Method 19 (2017). Flue gas analysis for O2 and CO2 is performed in
accordance with US EPA Method 3A (2017) using an infrared
analyzer to allow for calculation of the flue gas dry molecular weight.
The CEMS and UAS–Kolibri data were reduced to a common basis for comparison
of results. Emission factors, or the mass of NOx per mass of fuel carbon
burned, and emission rates, or the mass of NOx per energy content of the
fuel, were calculated from the sample results. The determination of emission
factors, defined as the mass of pollutant per mass of fuel burned, depends upon
foreknowledge of the fuel composition, specifically its carbon
concentration and its supply rate. The carbon in the fuel is presumed for
calculation purposes to proceed to either CO2 or CO, with the minor
carbon mass in hydrocarbons and PM ignored for this source type. Concurrent
emission measurements of pollutant mass and carbon mass (as CO2+ CO)
can be used to calculate total emissions of the pollutant from the fuel
using its carbon concentration and fuel burn rate.
The UAS–Kolibri emission factors were calculated from the mass ratio of NO + NO2, with the mass of CO + CO2 resulting in a value with
units of mg NOx kg-1 C. CO2 concentrations were corrected
for upwind background concentrations. CEMS values of O2 and fuel
flow rate were used to calculate stack flow rate using US EPA Method 19 (2017). This method requires the fuel higher heating value and an F
factor (gas volume per fuel energy content, e.g., m3 kcal-1, ft3 BTU-1) to complete the calculation. For natural gas, the F
factor is 967 m3 10-6 kcal (8710 ft3 10-6 BTU) (Table 19-2, US EPA Method 19, 2017). The concentration, stack flow rate,
and fuel flow rate data allow for the determination of NOx and C emission rates.
Results and discussion
The UAS–Kolibri team easily found the stack plumes at both locations using
the wind direction and CO2 telemetry data transmitted to the ground
operator. Use of an infrared–visible (IR: infrared) camera on a second UAS at St. Charles for some of the flights aided more rapid location of the plume and
positioning of the UAS–Kolibri. Gas concentration fluctuations were rapid
and of high magnitude as observed in a representative trace in
Fig. 2. CO2 concentrations up to 10 000 ppm were
observed; the relatively lower average CO2 concentrations reflect the
rapid mixing and entrainment of ambient air, causing dilution.
Example of UAS–Kolibri-measured plume concentrations from the St. Charles west boiler. Data reported at 1 Hz.
Midland UAS–Kolibri sampling data and emission factors. Time is indicated in US Central Standard Time (GMT-6).
Sampling data and emission factors from the UAS–Kolibri are shown in Tables 3, 4, and 5 for the Midland, St. Charles east stack, and St. Charles west
stack, respectively. Eight sampling flights were conducted at the Midland
site; five were conducted on the St. Charles east boiler and nine on the St. Charles west
boiler. Both boilers at the Midland site were operated under the same
conditions, so their results have been presented together. Flight times
averaged 14 min (10 % RSD) at the Midland
facility and just over 20 min (10 % RSD) at the St. Charles facility. The
shorter flight times in Midland were due to lower UAS battery capacity
caused by colder temperatures (the sampling temperatures in the plume
averaged 10 ± 3 ∘C). The average multi-concentration drift
for each of the sensors, tested at both locations after each sampling day,
was less than ±3 %. The NO2-D4 sensor showed higher drift (average
8.6 %) at one location for the highest concentration of its calibration
gas (10.4 ppm). This had a minimal effect on the emission factor calibrations
as the measured NO2 in the plume was actually less than 1 ppm, a range
in which the drift was much lower, and NO2 is a minor contributor to the
measured NOx species.
Average plume NOx concentrations were 0.88 ± 0.32 ppm at Midland
and 1.22 ppm and 2.41 ppm at the two St. Charles boilers, with an average RSD
of 37 %, 36 %, and 12 %, respectively. The NO emission factor was
typically 97 % of the total NOx, with the NO2 providing the
minor balance.
Table 6 presents the average O2 and NOx measurement results and
the fuel supply rate at both locations. Values for natural gas supply,
adjusted for the C2H6 and H2 composition of the St. Charles
fuel, were used to calculate the fuel carbon supply rate. These data allow
for the calculation of the emission factor, or the mass of NOx to the mass of carbon,
which is reported in Table 7.
Comparison of average NOx emission factors from CEMS and
UAS–Kolibri.
The UAS–Kolibri NOx emission factor for Midland is 8 % higher than
the simultaneous CEMS value. For the east and west boilers at St. Charles,
the UAS–Kolibri NOx emission factor value is < 1 % and 8 % higher, respectively, than the CEMS values. The difference for the
UAS–Kolibri in Midland may be attributed in part to the extremely cold
temperature affecting the performance of the electrochemical sensors. The
standard deviations for the CEMS data are based on the run-averaged NOx
values for each test. These values were calculated based on 10 s averaging
for the Midland tests, 60 s averaging in St. Charles, and 1 s averaging
for the UAS–Kolibri. Higher standard deviations for the UAS–Kolibri are
predictable given the rapidly changing values and wide range
(∼ 0–10 ppm) of NOx data observed in
Fig. 2. Difference testing for the CEMS and
UAS–Kolibri using α= 0.05 and assumed unequal variances indicates
that only the west boiler and UAS–Kolibri are statistically distinct.
The emission rates calculated from the UAS–Kolibri data are 5.6, 14.6, and
13.3 kg NOx× 10-3 kJ (0.013, 0.034, and 0.031 lb NOx× 10-6 BTU), respectively, for the Midland, east
St. Charles, and west St. Charles boilers, which are below the regulatory standard of
15.5 kg NOx× 10-3 kJ (0.036 lb NOx× 10-6 BTU). The emission factors were also calculated as carbon-weighted
values to reflect potential differences in plume sampling efficiency between
runs. The Midland, east St. Charles, and west St. Charles UAS–Kolibri
emission factors were 607, 1525, and 1409 mg NOx kg-1 C, respectively. These amounted to relative percent differences of 0.8 %, 1.9 %, and
7.8 % between the CEM and UAS–Kolibri values for an overall run-weighted
average difference of 5.6 %. The difference between the CEM readings and
those from the Kolibri weighted by the carbon collection amounts, reflecting
success at being within the higher plume concentrations, was 3.5 %.
Conclusions
This work reports, to our knowledge, the first known comparison of
continuous emission monitoring measurements made in a stack to downwind
plume measurements made using a UAS equipped with emission sensors.
The UAS–Kolibri system was easily able to find and take measurements from
the downwind plume of a natural gas boiler despite the lack of any visible plume
signature. The telemetry system aboard the Kolibri reported real-time
CO2 concentrations to the operator on the ground, allowing the operator
to provide immediate feedback to the UAS pilot on plume location. Comparison
of the CEM data to the UAS–Kolibri data from field measurements at two
locations showed agreement of NOx emission factors within 5.6 % and
3.5 % for time-weighted and carbon-collection-weighted measurements,
respectively. This work demonstrates the accuracy of a UAS-borne emission
sampling system for quantifying point source strength. These results also
have applicability to area source measurements, such as open fires, which
similarly employ the carbon balance method to determine source strength
emission factors.
Data availability
Data are available from the Environmental Dataset Gateway (https://edg.epa.gov/metadata/catalog/main/home.page, last access: 27 January 2021; 10.23719/1520733, Gullett, 2021) and the authors upon request. The raw, primary data on sensor voltages are processed to concentration values through time synchronization of data and user-defined, customized scripts for calibration that are a function of the gas sensor type and site-specific temperature, pressure, and relative humidity. Interested parties are welcome to contact the corresponding author for recommendations on how to customize this process to fit their specific scenarios.
Author contributions
BG was the prime author of the paper and the project
lead. JA conducted the Kolibri field testing and data analysis. WM designed
the instrument electronics. JR led the UAS group and field test
arrangements.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The views expressed in this article are those of the authors and
do not necessarily represent the views or policies of the U.S. EPA.
Acknowledgements
Dow's Corporate Aviation Group includes Laine Miller, Bryce Young,
James Waddell, Jeffrey Matthews, Chris Simmons, and Anthony DiBiase, who
conducted flights flawlessly. Dow employees Rob Seibert and Alex Kidd
provided technical data, and Amy Meskill (Dow), Jennifer DeMelo (Dow), and
Dale Greenwell (EPA–ORD) provided critical logistic support. Patrick Clark
(Montrose) reviewed the St. Charles CEMS data.
Financial support
This research has been supported by the Dow Chemical Company through a Cooperative Research and Development Agreement with the US Environmental Protection Agency.
Review statement
This paper was edited by Michel Van Roozendael and reviewed by two anonymous referees.
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