CHARACTERIZING EMISSIONS FROM AGRICULTURAL BURNING
IN A PILOT-SCALE REACTOR AND IN THE FIELD
PROJECT NUMBER A-04-02
KIRK L. WENDEL, UNIVERSITY OF UTAH
DAVID A. WAGNER, UNIVERSITY OF UTAH
KERRY E. KELLY, UNIVERSITY OF UTAH
GEOFFREY D. SILCOX, UNIVERSITY OF UTAH
PORFIRIO CABALLERO MATA, INSTITUTO TECNOLÓGICO Y DE ESTUDIOS
SUPERIORES DE MONTERREY
GERARDO MANUEL MEJIA-VELÁSQUEZ, INSTITUTO TECNOLÓGICO Y DE ESTUDIOS
SUPERIORES DE MONTERREY
NARRATIVE SUMMARY
The burning of agricultural crop residues is of concern due the low combustion
efficiency, difficulty in regulation, and the fact that emissions are released at ground
level. Concern over the health and environmental effects from crop burning in the U.S.Mexican boarder region has led to field studies of the emissions. Due to the expense
and difficulty of field-testing, laboratory studies are an attractive alternative. Laboratory
testing is less expensive, allows more complete analysis, and gives more control of the
test environment.
A two-level factorial design was chosen for the preliminary pilot-scale experiments. The
variables included crop residue loading (kilogram [kg]/meter [m]2), wind speed, and
moisture content in the residue. Crop residue was collected from Mexicali, Baja
California and El Centro, California, and burned in a pilot-scale facility. Field emission
measurements were also performed in El Centro. Emission factors (EF), based on lab
testing for the Mexicali and El Centro residues, were determined for carbon dioxide
(CO2), carbon monoxide (CO), nitrous oxide (NO), and particulate matter (PM10), and
were in the same range as previous field and laboratory measurements. Emission
factors for twelve polycyclic aromatic hydrocarbons (PAH) for the El Centro field and lab
data were determined by high-pressure liquid chromatography (HPLC). Similar PAH
results for residues collected in Mexicali were not obtained because analysis was by
gas chromatography/mass spectrometry (GC/MS) and the concentrations were below
the detection limits.
The field PAH emissions showed fairly consistent patterns with respect to the relative
quantities of twelve compounds for all fuels tested. This suggests that PAH might be a
suitable marker for identifying agricultural sources of particulate in ambient air samples.
The laboratory PAH data showed similar patterns but there was some variation between
the field and lab values. The lab and field emission factors for CO are in better
agreement than those for PAH.
CHARACTERIZING EMISSIONS FROM AGRICULTURAL BURNING
IN A PILOT-SCALE REACTOR AND IN THE FIELD
PROJECT NUMBER A-04-02
KIRK L. WENDEL, UNIVERSITY OF UTAH
DAVID A. WAGNER, UNIVERSITY OF UTAH
KERRY E. KELLY, UNIVERSITY OF UTAH
GEOFFREY D. SILCOX, UNIVERSITY OF UTAH
PORFIRIO CABALLERO MATA, INSTITUTO TECNOLÓGICO Y DE ESTUDIOS
SUPERIORES DE MONTERREY
GERARDO MANUEL MEJIA-VELÁSQUEZ, INSTITUTO TECNOLÓGICO Y DE ESTUDIOS
SUPERIORES DE MONTERREY
INTRODUCTION
Advances in science and medicine have led to a greater understanding of the different
health effects, including heart disease (Pope et al. 2004, Henneberger et al. 2005) and
cancer, which result from products of incomplete combustion. Emissions from the
burning of agricultural waste are of particular concern due to the low combustion
efficiency of open burning, difficulty in regulation, and the fact that emissions are
released at ground level. Large-scale crop burning takes place in the U.S.-Mexican
border region. Burning agricultural waste has long been practiced to prepare land for
planting, return nutrients to the soil, increase harvests, and control pests. The pollutants
that are released into the atmosphere include carbon monoxide (CO), nitrogen oxides
(NOx), sulfur oxides (SOx), and particulate matter (PM) consisting primarily of ash,
polycyclic aromatic hydrocarbons (PAH), and soot. PAH are a class of compounds that
form from incomplete combustion of hydrocarbons. They attach themselves to PM,
primarily soot, and can enter the body through inhalation, ingestion, or the skin. They
are classified by the U.S. Department of Health and Human Services as “reasonably
anticipated as human carcinogens” and are found in several known carcinogens such
as coke oven emissions, coal tars, and soot. To better characterize the emissions from
agricultural burning, field measurements are often made. Due to the difficulty of these
measurements and their uncertainty, laboratory testing is an attractive option. The
laboratory setting allows for better control of conditions and more complete
characterization of the process.
RESEARCH OBJECTIVES
The primary objectives of this study were to (1) characterize emission factors from
burning agricultural material and (2) compare lab and field data to determine if the
laboratory approach could accurately replace field data. Several parameters were
examined in the lab including wind speed, crop moisture content, and drop residue
density. The experiments have provided useful emission factors for PAH and suggest
2
that patterns in the relative concentrations of PAH could be used for source
apportionment from agricultural burns.
RESEARCH METHODOLOGY/APPROACHES
Fuels
The residues used in this study included domestic wheat, Mexican wheat, barley,
canola, flax, Bermuda grass, Klein grass, and asparagus. Domestic wheat was burned
initially as a trial for the test facility and to systematically examine the effects of
moisture, wind speed, and density (kilogram [kg]/meter [m]2) as described below. The
Mexican wheat, barely, canola, and flax were collected from the Mexicali, Baja
California, region in conjunction with a previous Southwest Consortium for
Environmental Research and Policy project (A-03-02). A chemical analysis of these
crops is given in Table 1. Bermuda grass, Klein grass, and asparagus were sampled
and collected in El Centro, California, and their chemical analysis is not available.
Laboratory Combustion Facility
All of the laboratory testing was done in a steel and glass enclosure referred to as “the
Burn Box”. The 16-foot [ft]-by-16-ft-by-14.67-ft (4.9-by-4.9-by-4.17-meters [m]) structure
is shown schematically in Figure 1. The biomass was placed on a one-inch [in] (2.54centimeter [cm]) thick refractory board measuring 3-ft-by-4-ft (0.91-m-by-1.22-m). The
board rested on a scale to continuously measure the weight. Typical weight loss data
are shown in Figure 2 for domestic wheat. The scale rested on a steel frame that was 8ft-by-8-ft-by-1.5 ft (2.45-m-by-2.45-m-by-0.46 m) located in the center of the burn box.
Twelve removable panels, 20-in-by-32 in (0.51-m-by-0.81 m), were located along the
bottom of the box to allow different airflow patterns. Only one panel was removed to
achieve a direct air flow across the burning waste. A pitot tube, placed just upwind of
the biomass, measured the wind speed. The flowing air was channeled over the burning
waste by a 12-ft-by-8-ft (3.7-m-by-2.4-m) sheet metal panel. A fan pulled air through the
14-inch (0.36-m) exhaust duct at the top of the burn box. The flow rate was adjusted
using a damper in the duct. The duct contained a pitot tube, a thermocouple, and ports
for gas and particulate sampling.
Laboratory Burning Technique
From 0.55 to 1.65 pounds (lb) of residue was evenly spread over the refractory board to
give densities ranging from 0.0512 to 0.153 lb/ft2 (0.25 to 0.75 kg/m2). This range was
based on field estimates from Jenkins et al. (1996). The fuel was ignited with a propane
torch on the upwind edge and the flame was drawn by the wind across the board.
Combustion gases were drawn through the exhaust duct. The fuel was allowed to
smolder for 10 to 20 minutes until CO concentrations returned to ambient levels.
Laboratory Sampling
The gas and particle sampling equipment is shown schematically in Figure 1. Solids
were collected on a quartz filter for subsequent analysis using gas chromatography/
mass spectrometry (GC/MS) or high pressure, liquid chromatography (HPLC). A
separate particulate sample was analyzed for black carbon (BC). The stream for PM
was first passed through an eductor which was looped to a dilution manifold that also
3
cooled the gas. A photoacoustic analyzer containing a tuned laser and sensitive
microphones determined the amount of BC (Arnott et al. 1999, Arnott, Moosmüller, and
Walker 2000). A portion of this particulate stream was also sent to a TSI DusTrak™ to
measure the amount of PM10 using a gravimetric impact technique. Finally, a stream
was removed from the exhaust duct, cooled to remove moisture, and analyzed for CO 2,
CO, NO, and O2. No analysis was performed for SO2 because the experiments were
becoming too complex to manage.
Field Sampling
Field studies were performed in cooperation with the Imperial County Air Pollution
Control District in El Centro, California. The crops included asparagus, Bermuda grass,
and Klein grass. The crops had been harvested and the growers were waiting for
permission to burn the stubble to prepare for the next planting. The burns were
supervised by the Air Pollution Control District to ensure safety and proper burning
conditions.
The equipment available for the field sampling included a DusTrak to measure PM10, a
TSI IAQ-Calc™ CO/CO2 analyzer, and a filter sampler for collecting particulate. This
equipment was assembled on a backpack with a battery as shown in Figure 3. A
sampling probe and pump were part of system. The 1.5-m-long, PVC sampling probe
was held 12-16 inches above the smoldering residue. The high temperatures
occasionally caused the probe to droop. This was corrected by rotating it 180 degrees.
Sampling from flaming portions of the fields was not possible due to the extreme
temperatures.
GC/MS and HPLC Analysis
Filter samples collected during the burns were sent to the Instituto Tecnológico y de
Estudios Superiores de Monterrey (ITESM) for analysis. The filters were extracted in a
soxhlet for 15 hours using dichloromethane as solvent according to EPA Method
3540C. Extracts were concentrated to 1 mL in a rotor evaporator.
The extracts were analyzed for semi-volatile organic compounds (SVOC) by GC/MS
following EPA Method 8270. Complex mixtures of signals were identified in the extracts
that included PAH, fatty acids, hydrocarbons, and small chain oxidized hydrocarbons.
Since PAH signals from the GC/MS were small and difficult to quantify, their
quantification was by HPLC analysis with a fluorescence detector according to EPA
Method 8310. The HPLC was calibrated with a certified standard solution, TCL
Polynuclear Aromatic Hydrocarbons Mixture, from Supelco, lot number LB20553.
Emission Factor Calculations
The results in this study are reported entirely in terms of emission factors (EF).
Calculating emission factors involves finding the ratio of the amount of a specified
component, such as CO2, that is found in the exhaust gas to the amount of dry fuel
consumed in combustion. According to Lemieux et al. (2000) the emission factor of
component i is:
4
EFi 
Ci  Q  t
m
(1)
where
EFi = emission factor [grams [g/kg dry fuel]
Ci = concentration in flue gas [gi/L]
Q = flow rate of combustion gases [L/min]
t = burn time [min]
m = mass of dry fuel [kg]
Because Q is constant and Ci varies with time, the emission factors are actually
calculated using
Q
EFi   Cik tk
m k
where t is the sampling interval. Figure 4 shows, for example, how the concentration of
CO varies with time. Almost30 minutes are required for the CO levels to fall to zero.
The gas analysis equipment reported concentration measurements in units of ppm; a
conversion to grams/liter was performed:
Ci 
ppmi P

 MWi
10 6 R  T
(2)
where
Ci = concentration [g/L]
ppmi = concentration [ppm by volume]
P = atmospheric pressure [atm]
L  atm 
R = 0.08206 

mol  K 
T = temperature [K]
MW i = molecular weight [g/mol]

The pitot tube in the exhaust duct yielded the flue gas velocity (V). The calculation of
volumetric flow rate (Q) was based on the ideal gas law for density (), a plug flow
assumption, and the cross-sectional area of the duct (A):
5
Q  AV
The amount of fuel consumed, m, was measured with the scale. For the laboratory
tests, the ash was analyzed by hot foil loss on ignition (HFLOI) to determine the amount
of unburned char. About 8%(by weight, wet basis) of the waste remained as ash and
the loss on ignition of the ash was typically 40% (by weight, dry basis).
Emission factors are also estimated using emission ratios (ER) when the amount of fuel
burned is unknown, as it is in the field measurements discussed below. Emission ratios
allow the emission factor of a species that is difficult to measure to be estimated from a
carbon balance. This method bases the EF of component i on a reference emission
factor of another well quantified component j and an ER. Reference emission factors,
EFj,ref , based on laboratory data were used. The following relationships between ER
and EF were used to analyze field data (Lemieux, Lutes, and Santoianni 2004). Note
that concentrations Ci and Cj are both measured in the field.


ERi j 

Ci
Cj
EFi  ERi j  EFj,ref
where
(3)
(4)


ERi/j = mass emission
ratio of species i with respect to species j, field
C = concentration [g/L], field
EFref = emission factor [g/kg dry fuel], lab
EFi = emission factor, field
For flaming periods, CO2 is most often used as component j. For smoldering periods,
CO is more accurate.
Factorial Design and Key Variables
The Burn Box provided a controlled environment that allowed determination of the
effects of loading, wind speed, and moisture on emissions. A two-level factorial design
was initially used to determine the main effects of these three variables and their
interactions for domestic wheat. The values for each variable are summarized in Table
2 and the two-level design is shown in Tables 3 and 4. Each variable was given a high
(+) and a low (-) value for a total of eight tests. The loading and moisture content were
chosen based on field levels reported by Jenkins et al. (1996). The high wind speed
was low enough to avoid extinguishing the fire. Domestic wheat was chosen for these
tests because large amounts were available. Each test was repeated three or four times
to determine the average emission factors and to develop statistics.
6
The moisture content (wet basis) in the domestic wheat was determined by measuring
the weight loss after drying it in an oven. The moisture content at ambient conditions
was used as the low value and for the wheat samples this was typically 9%. To increase
the moisture content of the wheat straw, it was placed on a rack in a 55 gallon,
polyethylene barrel, about eight inches (20 cm) above the bottom. A humidifier was
place in the bottom and the residues were mixed every 15 minutes for an hour.
PROBLEMS/ISSUES ENCOUNTERED
Field sampling of sugar cane burns in Texas were originally part of the experimental
plan. The Sugar Cane Growers Association denied permission to sample from their
fields. For this reason the field sampling was moved to Southern California (El Centro).
The Imperial County Air Pollution Control District was helpful.
Analysis of filter samples for PAH, from both field and lab experiments, by GC/MS was
unsuccessful. The concentrations of PAH were too low. Subsequent efforts to quantify
PAH by HPLC were successful. This suggests that future SCERP efforts to measure
PAH in emissions or ambient air should focus on HPLC techniques.
A portion of the waste collected in El Centro was not reserved for ultimate and
proximate analysis. The analysis is not expected to be significantly different from the
domestic and Mexicali samples that are summarized in Table 1.
The emission factors for PAH measured in the lab and field differ by as much as a factor
of four. There are several possible reasons for this. The cumulative volumes passed
through the filters are inaccurate, particularly in the field, because as the filter cake
accumulated, air flows rapidly dropped. Additional field measurements would be helpful
now that the sensitivity of flow rates to cake thickness is better understood. In addition,
the field measurements were based only on the smoldering stage of combustion,
whereas the lab measurements included burning and smoldering.
RESEARCH FINDINGS
Laboratory Results with Domestic Wheat and Factorial Design
The factorial design was applied only to laboratory experiments with domestic wheat.
The factorial design showed that the emission factors for PM10, CO, and NO were
sensitive to loading (kg/m2), moisture, and wind. In what follows, the uncertainties are all
standard errors. The average emission factor for PM10 was 7.3 ± 0.5 g/kg dry fuel.
Increasing the loading from 0.25 to 0.75 kg/m2 caused an increase by almost 60% to
11.4 ± 1.0 g/kg. An increase in moisture content increased EFPM10 to 11.3 ± 1.0 g/kg dry
fuel.
Density and moisture also increased the emission factor for CO. The average emission
factor of 44.4 ± 0.9 g/kg dry fuel increased with increasing density by 25% to 55.5 ± 1.8
and with increasing moisture by 18% to 52.3 ± 1.8 g/kg. A two-factor interaction was
seen when both the density and wind were increased causing EFCO to increase by 11%
to 49.5 ± 1.8 g/kg dry fuel. The average EFNO of 0.39 ± 0.05 g/kg dry fuel dropped below
7
our detection limit, because of dilution and lower temperatures, when the wind speed
was increased to 7 mph.
The increases seen in EFPM10 and EFCO due to higher density, wind, and moisture were
probably due to less efficient combustion and lower combustion temperature. This was
also evident in the lower EFNO since NOx formation decreases with decreasing
temperature. Based on these observations, the laboratory tests were performed at
intermediate and low values: density of 0.10 lb/ft2 (0.5 kg/m2), wind speed of 4.5 mph
(2.0 m/s), and moisture level of nine percent (wet basis).
Laboratory Results with Crop Residues Collected in Border Areas
The emission factors for CO, CO2, NOx, and PM10 from laboratory tests of the Mexicali
and El Centro crops are shown in Tables 5 and 6. These values were calculated using
(1). Standard errors are reported for all values. The two sets of data are fairly consistent
for EFCO2, EFCO, and EFNO. For asparagus, however, the emission factor for black
carbon is over six times that for Bermuda and Klein grasses and the Mexicali crops.
This may be due to elevated entrainment of ash and char particles due to the light, lacy
nature of asparagus residue and ash. All three of the El Centro crops also had much
higher emission factors for PM10 than the Mexicali crops, averaging 7.09 ± 1.48 versus
2.16 ± 0.58 g/kg dry fuel. This may again be due to the lacy nature of the grasses from
El Centro.
The backpack-mounted IAQ-Calc CO/CO2 analyzer was placed in the burn box to
compare emission factors based on the lab and field sampling equipment. Samples for
the portable unit were drawn above the burning material at the trailing edge of the wind
panel. The results are summarized in Table 7. Using the ERCO/CO2 from (3) and (4)
along with an EFCO2 reference of 1029 g/kg dry fuel (average EFCO2 of Mexicali
biomass) the average emissions of CO measured by the IAQ-Calc were within the
standard error of the average measured by the laboratory equipment. A direct
comparison of CO concentrations was not possible because the lab instrument drew its
sample from the flue, while the portable unit was operated in a manner that was similar
to that in the field.
Filter samples from the laboratory burning of El Centro crops were analyzed for PAH
using HPLC. Table 8 summarizes the emission factors for the 12 PAH that were
detected. Naphthalene, acenaphthylene, acenaphthene, and fluorene were below the
limits of detection. These four compounds have the lowest boiling points and may not
have been collected on the filters. The filters had a temperature of approximately 40C
in the lab and field experiments.
PAH emission factors were calculated using (1) and ranged from 0.12 – 50 mg/kg dry
fuel. Asparagus consistently had higher emission factors than the Klein and Bermuda
grasses - as much as 78 times higher for benzo[g,h,i]perylene. The lacy structure of the
asparagus residues may have contributed to the high emissions. The emission factors
are compared in Figure 5. The three highest PAH for Bermuda and Klein grass were
pyrene, fluoranthene, and phenanthrene. Pyrene and fluoranthene were also the
8
highest for the asparagus but benzo[b]fluoranthene was five times higher than
phenanthrene.
Field Results for El Centro Crops
The loading (kg/m2) of the crop residue in the El Centro fields was not measured
because of the difficulty of obtaining representative samples. For this reason the
emission factors for the field experiments were calculated using ERCO/CO2 (from the field
measurements) and the average EFCO2 of 1030 g/kg dry fuel from the laboratory studies
of Mexicali residues. Table 9 summarizes the Bermuda and Klein grass CO emission
factors: 49 ± 16 and 55 ± 7 g/kg dry fuel. These values are close to the laboratory
values in Table 6. The EFCO for asparagus was 75 ± 25 g/kg dry, considerably higher
than the grasses and also much higher than the EF for asparagus based on lab data.
The lacy structure of the asparagus residues may have contributed to the high
emissions.
Due to the high concentration of PM in the field and the unavailability of dilution air, the
DusTrak could not accurately measure PM. Emission factors for PM10 are not reported
for the field samples.
The extraction of filter samples from the El Centro fields and analysis of the extracts by
HPLC identified 16 PAH and quantified 12 of them with concentrations above the limits
of detection. To calculate emission factors for the field data, the laboratory ER PAH/CO
from (3) and the EFCO of 45.8 g/kg dry fuel (average EFCO from Mexicali lab data in
Table 5) were used in (4) along with a multiplicative correction factor. The need for the
correction may be due to differences in sampling between the lab and field. In the field
the sampling probe was held just inches above the smoldering residue. In the lab the
probe was located in the exhaust duct. However, there is also a large inconsistency
between EFPAH based on (1) and EFPAH based on (3) and (4) for the laboratory data.
The correction factor, fc, is the ratio of the EF based on (1) with lab data, to the EF
based on the ER ((3) and (4) with lab data). It was calculated for each type of residue
using:
 EF 
fc   i,1 
EFi,34 lab
(5)
where
 factor using equation 1, lab PAH
EFi,1 = Emission
EFi,34 = Emission factor using equations 3 and 4, lab PAH
The correction factors are summarized in Table 10 and range from 131 for the Bermuda
grass to 338 for asparagus. Equation 4 was modified with the correction factor f c to
calculate the emission factors for the field data:
9
EFPAH,field  ERPAH,field / CO,field  EFCO,lab  fc
(6)
The resulting PAH emission factors for the fields burned in El Centro are shown in Table
11. They range from 0.336 – 18.9 mg/kg dry fuel. The data are plotted in Figure 6 and
several trendsare apparent. First, asparagus and Klein grass have consistently higher
emission factors than the Bermuda grass. There is no correlation between the PAH field
emissions and the PM10 lab emissions shown in Table 6. All three crops show the
highest emissions for pyrene, fluoranthene, and phenanthrene. Figure 7 shows that all
three crops display the same decreasing emission pattern with respect to the PAH.
The uncertainties in the PAH emission factors could not be determined because of the
combination of multiple samples to provide adequate sample sizes for the HPLC
analyses.
The pattern in Figure 7 does not correlate with the relative volatilities of the PAH. Data
that relates relative amounts of PAH to combustion conditions, particularly for field data,
is not available. Jenkins et al. (1996) note that PAH emissions increase with increasing
particulate emissions. Our data do not support that correlation based on the PM 10
values from our laboratory experiments. Field levels of PM10 could not be measured.
Comparison of Laboratory and Field Emissions
Lab and field emission factors of CO and PAH were compared. Particulate emissions
were not compared because they could not be measured in the field. The average EF CO
for all wastes burned in the laboratory was 45 ± 6 g/kg, and for the field was 60 ± 14
g/kg dry fuel. The EFCO for the lab may be lower due to more efficient combustion. The
dry, flat, insulating board on which the waste burned in the lab may have increased
combustion efficiencies and temperatures relative to the rough, uneven ground in the
field.
The emission factors for PAH for the lab and the field are quite different. Figure 8
compares the asparagus data. The field emission was 40% higher for phenanthrene
and 10% higher for anthracene, but the laboratory emissions for the other PAH were
three to six times higher than those in the field. Figure 9 compares the Klein grass data.
In this case the field and laboratory PAH emissions were much closer than for the
asparagus. The field emissions were higher with the exception of benz[a]anthracene.
The benzo[k]fluoranthene and benzo[g,h,i]perylene field emissions were 4 and 8 times
higher than the laboratory values. The other field PAH values averaged 1.5 times the
lab emissions. Figure 10 compares the Bermuda grass data. The field emission of PAH
was consistently higher, ranging from two to five times higher.
As shown in Figure 11, most of the lab and field PAH emission data followed the same
descending pattern. If the asparagus laboratory emission factors and those of
benz[a]anthracene are excluded, all of the PAH results follow the same decreasing
pattern. This suggests that PAH may serve as a suitable class of compounds to identify
the source of ambient particulate. However, laboratory studies show that PAH is rapidly
oxidized in sunlight with half-lives of a few hours (Haynes 1991).
10
Comparison with Literature
The emission factors of CO, CO2, NOx, and PM10 of field and laboratory tests were
within the range of values found by other studies for similar crops (Jenkins et al. 1996,
Lemieux, Lutes, and Santoianni 2004, Andreae and Merlet 2001). Asparagus, however,
had an EFCO of 75 ± 25 g/kg dry fuel which was moderately higher than expected, but
the standard deviation is large. The El Centro biomass had lab EFPM10 values that were
approximately three times higher (7.1 ± 1.5 g/kg dry fuel) than the Mexicali biomass (2.2
± 0.6 g/kg dry fuel); however, they are still lower than those reviewed by Lemieux,
Lutes, and Santoianni (2004), which reports 11 g/kg burned material.
The PAH emission factors are of the same order of magnitude as those of Jenkins et al.
(1996); however, those results do not show the descending pattern shown in Figure 11.
A notable difference was seen with respect to naphthalene. It was not detected in this
study, but it was the dominant PAH in Jenkins’ study. Note that Jenkins suspected
possible contamination due to the breakdown of XAD-2 sorbent in his sampling system.
CONCLUSIONS
The emission factors for CO, CO2, and PAH, determined in the laboratory, were similar
to those measured in the field and were reasonably close to values reviewed by
Lemieux, Lutes, and Santoianni (2004) and Andreae and Merlet (2001). Emission
factors for PM10 in the field were not measured because the particulate concentrations
exceeded the range of the analytical equipment. The laboratory measurement of
emission factors for black carbon ranged from 0.113 – 1.02 g/kg dry fuel. The
agreement between PAH emission factors from the laboratory and the field is not as
close as that for CO and CO2; they sometimes differ by a factor of six. The overall
agreement between lab and field data suggests that properly designed laboratory
experiments can provide reliable data that approximates what happens in the field.
Analysis of filter samples for PAH, from both field and lab experiments, by GC/MS was
unsuccessful. The concentrations of PAH were too low. Subsequent efforts to quantify
PAH by HPLC were successful. This suggests that future SCERP efforts to measure
PAH in emissions or ambient air should consider HPLC techniques.
The analysis and comparison of the PAH data from the lab and field was complicated by
sampling issues. A correction factor, based entirely on laboratory data, was created to
adjust the emission ratio approach represented by (4) so that it agreed with results from
(1). This correction factor was then used to calculate PAH emission factors for all of the
field data using emission ratios per (5).
The lab and field PAH emissions showed a fairly consistent descending pattern with
respect to the relative quantities of twelve compounds for all fuels tested. This suggests
that PAH might be a suitable marker for identifying agricultural sources of particulate in
ambient air samples. The laboratory PAH emission factor data show similar patterns.
However, laboratory studies show that PAH is rapidly oxidized in sunlight with half-lives
of a few hours.
11
RECOMMENDATIONS
The emission factors for PAH from the lab and field are somewhat different. The
reasons for this are not known. It is possible that the sample volumes measured for the
filters in the field were inaccurate because as the filter cake accumulated, air flows
rapidly dropped. This was not a significant problem in the lab. Additional field
measurements would be helpful given our awareness of the sensitivity of flow rates to
cake thickness.
The backpack was useful for collecting filter samples to be analyzed for PAH. Due to
the buildup of particulate on the filter, the air flow rates changed with time and this
added considerable uncertainty to the total volume sampled. Future configurations of
the backpack should include a totalizer for determining the cumulative volume sampled.
Analysis of filter samples for PAH, from both field and lab experiments, by GC/MS was
unsuccessful. The concentrations of PAH were too low. Subsequent efforts to quantify
PAH by HPLC were successful. This suggests that future SCERP efforts to measure
PAH in emissions or ambient air should consider HPLC techniques.
RESEARCH BENEFITS
This study suggests that laboratory experiments to measure emission factors can
provide values that are reasonably close to those measured in the field. The emission
factors reported for PAH and PM10 are an important addition to the available data, and
they focus on crops that are significant in the U.S.-Mexican border region. The emission
factors should help regulators access risk and design regulations to minimize exposure
to PAH and particulate emissions in the border region.
A preliminary version of this report was presented by Geoff Silcox at the 9th International
Congress on Combustion By-Products and their Health Effects, “Characterizing
Emissions from Agricultural Burning with Attention to the U.S.-Mexico Border Region,”
June 12-15, 2005, Tucson, Arizona.
The primary author of this report was Kirk Wendel and this report constitutes his senior
thesis. Kirk graduated Spring 2006 with a Bachelor of Science Degree in Chemical
Engineering from the University of Utah. He performed most of the laboratory work and
assisted David Wagner in preparing for the field experiments. Kirk learned a great deal
about designing experiments, analyzing data, making measurements, and report
writing.
All of the analysis of filter samples for PAH was performed by Porfirio Caballero Mata
and his colleagues at ITESM. This fruitful collaboration made the calculation of emission
factors for PAH possible.
ACKNOWLEDGMENTS
This work was sponsored by the Southwest Consortium for Environmental Research
and Policy (SCERP) through a cooperative agreement with the U.S. Environmental
12
Protection Agency. SCERP can be contacted for further information through
www.scerp.org and scerp@mail.sdsu.edu.
REFERENCES
Andreae, M. O., and P. Merlet. 2001. “Emission of Trace Gases and Aerosols from
Biomass Burning.” Global Biogeochemical Cycles 15 (4): 955-966.
Arnott, W. P., H. Moosmüller, C. F. Rogers, T. Jin, and R. Bruch. 1999. “Photoacoustic
Spectrometer for Measuring Light Absorption by Aerosol: Instrument Description.”
Atmospheric Environment 33: 2845-2852.
Arnott, W. P., H. Moosmüller, and J. W. Walker. 2000. “Nitrogen Dioxide and KeroseneFlame Soot Calibration of Photoacoustic Instruments for Measurement of Light
Absorption by Aerosols.” Review of Scientific Instruments 71 (7): 4545-4552.
Haynes, B. S. 1991. “Soot and Hydrocarbons in Combustion,” in Fossil Fuel
Combustion: A Source Book, William Bartok and Adel F. Sarofim, Editors, p. 261, John
Wiley & Sons, Inc., New York.
Henneberger, A., W. Zareba, A. Ibald-Mulli, R. Rückerl, J. Cyrys, J. Couderc, B. Mykins,
G. Woelke, H. E. Erich Wichmann, and A. Peters. 2005. “Repolarization Changes
Induced by Air Pollution in Ischemic Heart Disease Patients.” Environmental Health
Perspectives 113: 440-446.
Jenkins, B. M., A. D. Jones, S. Q. Turns, and R. B. Williams. 1996. “Emission Factors
for Polycyclic Aromatic Hydrocarbons from Biomass Burning.” Environmental Science &
Technology 30 (8): 2462-2469.
Lemieux, P. M., C. C. Lutes, and D. A. Santoianni. 2004. “Emissions of organic air
toxins from open burning: a comprehensive review.” Progress in Energy and
Combustion Science 30 (1): 1-32.
Lemieux, P. M., C. C. Lutes, J. A. Abbott, K. M. Aldous. 2000. “Emissions of
Polychlorinated Dibenzo-p-dioxins and Polychlorinated Dibenzofurans from the Open
Burning of Household Waste in Barrels.” Environmental Science & Technology 34 (3):
377-384.
National Toxicology Program (NTP), Department of Health and Human Services. 2005.
“The Report on Carcinogens, Eleventh Edition: Polycyclic Aromatic Hydrocarbons, 15
Listings.” (cited 16 December),
http://ntp.niehs.nih.gov/ntp/roc/eleventh/profiles/s150pah.pdf.
Pope III, C. A., M. L. Hansen, R. W. Long, K. R. Nielsen, N. L. Eatough, W. E. Wilson,
and D. J. Eatough. 2004. “Ambient Particulate Air Pollution, Heart Rate Variability, and
Blood Markers of Inflammation in a Panel of Elderly Subjects.” Environmental Health
Perspectives 112 (3): 339-345.
13
14
APPENDIX
Table 1. Ultimate and Proximate Analyses of Crop Residues
Domestic Wheat
Mexican Wheat
Canola
Flax
Barley
49.68
5.53
0.72
0.16
4.66
39.25
47.54
5.31
0.72
0.12
7.65
38.66
Ultimate Analysis (% dry weight)
Carbon
Hydrogen
Nitrogen
Sulfur
Ash
Oxygen*
46.27
5.03
0.64
0.20
11.19
36.67
45.99
4.91
0.52
0.63
11.73
36.22
46.06
4.83
0.68
0.28
10.01
38.14
Proximate Analysis (% dry weight)
Ash
Volatile
Fixed Carbon
11.19
74.49
14.32
11.73
73.40
14.87
10.01
74.88
15.11
4.66
77.01
18.33
7.65
77.37
14.98
Higher Heating Value (Btu/lb dry weight)
7340
*Oxygen by difference
7250
7316
8056
7600
Table 2. High and Low Values in Factorial Design
Variables
Density (kg/m2)
Moisture (%, wet basis)
Wind (mph)
High value
0.75
14
2
Low value
0.25
9
7
Table 3. Key Variables and Factorial Design
Test #
Density(kg/m2)
Moisture (%, wet basis)
Wind (mph)
1
2
3
4
5
6
7
8
0.75
0.75
0.75
0.75
0.25
0.25
0.25
0.25
9
9
14
14
9
9
14
14
2
7
2
7
2
7
2
7
Table 4. High and Low Values for Factorial Design (“+” high value, “-” low value)
Test #
Density
Moisture
Wind
1
2
3
+
+
+
+
+
-
4
5
6
+
-
+
-
+
+
7
8
-
+
+
+
16
Table 5. Emission Factors (g/kg dry fuel) for Mexicali Crops Burned in the Lab
CO2
CO
NO
BC
PM10
Abatti Wheat
South Date Wheat
Calexico Flax
1054 ± 43
974 ± 27
1089 ± 19
38.2 ± 2.5
39.6 ± 0.1
52.9 ± 3.6
1.06 ± 0.08
1.46 ± 0.05
1.41 ± 0.21
0.113 ± 0.019
0.071 ± 0.038
0.241 ± 0.076
0.91 ± 0.18
2.17 ± 0.55
2.69 ± 0.34
Calexico Canola
Calexico Wheat
964 ± 42
1066 ± 52
56.3 ± 4.2
42.1 ± 2.3
0.51 ± 0.48
1.08 ± 0.20
0.125 ± 0.024
0.147 ± 0.034
2.74 ± 1.11
2.30 ± 0.75
1029 ± 57
45.8 ± 8.2
1.10 ± 0.38
0.140 ± 0.063
2.16 ± 0.74
Average
Table 6. Emission Factors (g/kg dry fuel) for El Centro Crops Burned in the Lab
Asparagus
Bermuda Grass
Klein Grass
Average
CO2
CO
NO
BC
PM10
1144 ± 23
1269 ± 283
988 ± 44
45.2 ± 5.0
41.5 ± 8.7
46.4 ± 7.7
1.20 ± 0.06
1.76 ± 0.42
1.60 ± 0.09
1.02 ± 0.26
0.15 ± 0.05
0.13 ± 0.07
5.72 ± 0.74
7.66 ± 2.38
7.87 ± 1.32
1134 ± 131
44.3 ± 2.5
1.52 ± 0.29
0.43 ± 0.51
7.09 ± 1.19
17
Table 7. Comparison of Emission Factors for CO (g/kg dry fuel) Using the Backpack-Mounted TSI IAQ-Calc CO/CO2
Analyzer and Rack-Mounted CO/CO2 Analyzers. All Data Were Obtained in the Burn Box for this Comparison
TSI IAQ-Calc
Laboratory
Asparagus
38.0 ± 2.9
Bermuda Grass
Klein Grass
51.4 ± 5.0
58.1 ± 5.0
45.2 ± 5.0
41.5 ± 8.7
46.4 ± 7.7
49.2 ± 10.2
44.3 ± 2.5
Average
Table 8. Emission Factors for PAH (mg/kg dry fuel) for El Centro Crop Residues Burned in the Lab
Phe
An
Fla
Pyr
Asparagus 6.916 1.537 38.420 49.946
Klein
Grass
Bermuda
Grass
B[a]An
Chy
13.831
DB[ah]
B[ghi]P I[123]P
An
B[b]Fla B[k]Fla
B[a]P
14.215
35.346
19.210
12.678
1.153
9.221
9.221
4.565 0.884
5.007
7.658
3.829
2.062
2.504
0.589
0.736
0.295
0.118
0.442
3.147 0.760
4.015
5.969
1.085
1.194
2.496
0.760
0.760
0.217
0.651
0.326
Abbreviations: Phe (phenanthrene), An (anthracene), Fla (fluoranthene), Pyr (pyrene), B[a]An (benz[a]anthracene), Chy (chrysene), B[b]Fla
(benzo[b]fluoranthene), B[k]Fla (benzo[k]fluoranthene), B[a]P (benzo[a]pyrene), DB[ah]An (dibenz[a,h]anthracene), B[ghi]P (benzo[g,h,i]perylene),
I[123]P (indeno[1,2,3-cd]pyrene).
18
Table 9. Emission Factors for CO (g/kg dry fuel) Measured in the Field
Field
Asparagus
Bermuda Grass
Klein Grass
75.01 ± 24.88
48.73 ± 16.22
55.45 ± 7.45
Average
19
59.7 ± 13.7
Table 10. Correction Factors for PAH Emission Factors Based on Laboratory Data for El Centro Crop Residues
Correction
Factor
Asparagus
Klein Grass
338.4
132.9
Bermuda Grass
130.8
Table 11. Emission Factors for PAH (mg/kg dry fuel) for El Centro Crop Residue Burned in the Field
Phe
An
Fla
Pyr
Chy
3.680
3.855
8.411
4.030
2.979
0.350
1.577
1.472
11.514
2.578
3.265
4.640
2.578
1.719
0.344
1.031
0.687
10.569 1.678 12.247 16.776
4.362
5.033
8.891
4.194
2.852
0.336
1.510
1.342
Asparagus 11.040 1.752 12.968 18.926
Klein
Grass
Bermuda
Grass
5.499
1.203
8.249
B[b]Fla B[k]Fla B[a]P
DB[ah]
B[ghi]P I[123]P
An
B[a]An
Abbreviations: Phe (phenanthrene), An (anthracene), Fla (fluoranthene), Pyr (pyrene), B[a]An (benz[a]anthracene), Chy (chrysene), B[b]Fla
(benzo[b]fluoranthene), B[k]Fla (benzo[k]fluoranthene), B[a]P (benzo[a]pyrene), DB[ah]An (dibenz[a,h]anthracene), B[ghi]P (benzo[g,h,i]perylene),
I[123]P (indeno[1,2,3-cd]pyrene).
20
Exhaust
Dilution manifold
Black carbon
PA analyzer
DusTrak
Particle monitor
NDIR CO/CO2
analyzer
Zirconia oxide
Oxygen analyzer
Burning
material
Chemiluminescent
NO, NO2, NOx analyzer
Filter assembly with
Pump and impactor
Air in
Figure 1. Schematic of Laboratory Facility for Burning Crop Residues
21
1.8
1.6
High loading, low moisture, low wind
Weight (lb)
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
5
10
15
20
25
30
35
Time (min)
Figure 2. Weight Loss as a Function of Time as Measured for Domestic Wheat
22
Sample
TSI-IAQ-CalcTM
CO/CO2 Monitor
Cyclone
Flowmeter
Filter
Pump
10 micron Inlet
Impactor
TSI DustTrak
PM-10 Monitor
Figure 3. Equipment Mounted on Backpack for Field Sampling
200
180
High loading, low moisture, low wind
Concentration (ppm)
160
140
CO
120
100
80
60
40
20
NO
0
0
5
10
15
20
25
30
35
Time (min)
Figure 4. CO and NO as a Function of Time for Domestic Wheat in the Burn Box
23
60
EF (mg/kg dry fuel)
50
40
Asparagus Lab
Klein Grass Lab
Bermuda Grass Lab
30
20
10
0
Phe
An
Fla
Pyr
B[a]An
Chy
B[b]Fla B[k]Fla
B[a]P
DB[ah] B[ghi]P I[123]P
An
PAHs
Figure 5. Emission Factors for PAH (mg/kg dry fuel) for El Centro Crop Residue
Burned in Laboratory Experiments
24
20
18
16
EF (mg/kg dry fuel)
14
12
Asparagus Field
Klein Grass Field
Bermuda Grass Field
10
8
6
4
2
0
Phe
An
Fla
Pyr
B[a]An
Chy
B[b]Fla B[k]Fla
B[a]P
DB[ah] B[ghi]P I[123]P
An
PAHs
Figure 6. Emission Factors for PAH (mg/kg dry fuel) for El Centro Crop Residue
Burned in the Field
25
20.0
18.0
16.0
EF (mg/kg dry fuel)
14.0
12.0
Asparagus Field
Klein Grass Field
Bermuda Grass Field
10.0
8.0
6.0
4.0
2.0
0.0
Pyr
Fla
Phe
B[b]Fla
Chy
B[k]Fla B[a]An
B[a]P
An
B[ghi]P I[123]P DB[ah]
An
PAHs
Figure 7. Emission Factors for PAH (mg/kg dry fuel) for El Centro Crop Residues
Burned in the Field, Showing Descending Emission Pattern
26
60
EF (mg/kg dry fuel)
50
40
Asparagus Field
Asparagus Lab
30
20
10
0
Phe
An
Fla
Pyr
B[a]An
Chy
B[b]Fla B[k]Fla
B[a]P
DB[ah] B[ghi]P I[123]P
An
PAHs
Figure 8. Comparison of Laboratory and Field Emission Factors for PAH (mg/kg
dry fuel) for Asparagus
27
14
12
EF (mg/kg dry fuel)
10
8
Klein Grass Field
Klein Grass Lab
6
4
2
0
Phe
An
Fla
Pyr
B[a]An
Chy
B[b]Fla B[k]Fla
B[a]P
DB[ah] B[ghi]P I[123]P
An
PAHs
Figure 9. Comparison of Laboratory and Field Emission Factors for PAH (mg/kg
dry fuel) for Klein Grass
28
18
16
EF (mg/kg dry fuel)
14
12
10
Bermuda Grass Field
Bermuda Grass Lab
8
6
4
2
0
Phe
An
Fla
Pyr
B[a]An
Chy
B[b]Fla B[k]Fla
B[a]P
DB[ah] B[ghi]P I[123]P
An
PAHs
Figure 10. Comparison of Laboratory and Field Emission Factors for PAH (mg/kg
dry fuel) for Bermuda Grass
29
20.0
18.0
16.0
EF (mg/kg dry fuel)
14.0
12.0
Asparagus Field
Klein Grass Field
Bermuda Grass Field
Klein Grass Lab
Bermuda Grass Lab
10.0
8.0
6.0
4.0
2.0
0.0
Pyr
Fla
Phe
B[b]Fla
Chy
B[k]Fla B[a]An
B[a]P
An
B[ghi]P I[123]P DB[ah]
An
PAHs
Figure 11. PAH Emission Factors for Field and Lab Data (Asparagus Lab Data
Not Included Because It Is Exceptionally High) Showing the Descending
Emission Trend Common to Most
30