Characterization of Ammonia, Methane, and Nitrous Oxide Emissions

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PageEnvironmental 1 of 37 Science & Technology

ACS Paragon Plus Environment

Environmental Science & Technology

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Characterization of ammonia, methane, and nitrous oxide emissions from concentrated

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animal feeding operations in northeastern Colorado

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Scott J. Eilerman1,2,*, Jeff Peischl1,2, J. Andrew Neuman1,2,*, Thomas B. Ryerson2, Kenneth C.

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Aikin1,2, Maxwell W. Holloway2,3, Mark A. Zondlo4, Levi M. Golston4, Da Pan4, Cody

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Floerchinger5,†, Scott Herndon5

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1

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Boulder, 216 UCB, Boulder, CO 80309, USA.

Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado

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2

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Broadway, Boulder, CO 80305, USA.

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3

Contract with Science and Technology Corporation, Hampton, VA 23666, USA.

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Department of Civil and Environmental Engineering, Princeton University, E209A

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Olden Street, Princeton, NJ 08544, USA.

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NOAA Earth System Research Laboratory (ESRL) Chemical Sciences Division, 325

Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821-3976, USA.

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† Now at Department of Earth and Planetary Sciences, Harvard University, 20 Oxford St,

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Cambridge, MA 02138, USA.

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*Corresponding Authors:

21 22 23 24 25 26 27

Scott Eilerman 325 Broadway, R/CSD7 Boulder, CO 80305 Phone: 303-497-4379 Fax: 303-497-5126 Email: [email protected]

28 29 30 31

J. Andrew Neuman 325 Broadway, R/CSD7 Boulder, CO 80305 Phone: 303-497-7872


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Fax: 303-497-5126 Email: [email protected]


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Abstract

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Atmospheric emissions from animal husbandry are important to both air quality and

38

climate, but are hard to characterize and quantify as they differ significantly due to management

39

practices and livestock type and they can vary substantially throughout diurnal and seasonal

40

cycles. Using a new mobile laboratory, ammonia (NH3), methane (CH4), nitrous oxide (N2O),

41

and other trace gas emissions were measured from four concentrated animal feeding operations

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(CAFOs) in northeastern Colorado. Two dairies, a beef cattle feedlot, and a sheep feedlot were

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chosen for repeated diurnal and seasonal measurements. A consistent diurnal pattern in the NH3

44

to CH4 enhancement ratio is clearly observed, with midday enhancement ratios approximately

45

four times greater than nighttime values. This diurnal pattern is similar, with slight variations in

46

magnitude, at the four CAFOs and across seasons. The average NH3 to CH4 enhancement ratio

47

from all seasons and CAFOs studied is 0.17 (+ 0.13 / - 0.08) mol/mol, in agreement with

48

statewide inventory averages and previous literature. Enhancement ratios for NH3 to N2O and

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N2O to CH4 are also reported. The enhancement ratios can be used as a source signature to

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distinguish feedlot emissions from other NH3 and CH4 sources, such as fertilizer application and

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fossil fuel development, and the large diurnal variability is important for refining inventories,

52

models, and emission estimates.

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1. Introduction Concentrated animal feeding operations (CAFOs) represent a large and relatively under-

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sampled source of several atmospheric trace gases including methane (CH4), nitrous oxide

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(N2O), carbon dioxide (CO2), and ammonia (NH3). CH4, N2O, and CO2 are greenhouse gases,

58

with CH4 and N2O having 34 and 298 times the 100-year Global Warming Potential of CO2,

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respectively1. The United States Environmental Protection Agency (EPA) estimates that 31% of

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U.S. anthropogenic CH4 emissions and 4% of N2O emissions come from animal husbandry

61

(enteric fermentation and manure management)2. NH3 is the primary base compound in the

62

atmosphere and contributes to particle formation in the presence of nitrate and sulfate. Fine

63

particulate matter has negative health effects on humans, animals, and plants, and can also lead

64

to decreased visibility3–5. NH3 can also directly deposit on local ecosystems, causing

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eutrophication, soil acidification, and changes to nitrogen-sensitive plant species6,7. Quantifying

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the emissions of these gases is important for understanding the atmospheric abundance of

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greenhouse gases and criteria pollutants and for developing effective policies that address

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climate and air quality.

69

NH3 measurement capability has increased in recent years8, and new measurements have

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begun to quantify NH3 emissions from CAFOs that can vary widely based on climate, livestock

71

type, manure management techniques, feed, and other practices9–11. These NH3 measurements

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are needed for comparison with process-based models and for inventory refinement10–13. For

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example, recent studies have shown that current inventories underestimate NH3 emissions from

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dairies in California’s South Coast Air Basin by a factor of 3-2014. Semi-arid regions in

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particular are notably under-sampled15.

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Northeastern Colorado is an interesting and complicated source region consisting of large


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agricultural, urban, and fossil fuel sources. The Denver-Julesburg Basin is a heavily developed

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oil and gas basin with over 25,000 wells and numerous compressors and processing plants16. In

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addition, over 75 CAFOs housing over 900,000 cattle and sheep are located within the basin17.

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These sources lie within 100 km of the Denver metropolitan area, which has a population of

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approximately 3.3 million people18. Figure 1 provides a geographic overview of this region, with

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several of the larger sources indicated.

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Numerous measurements of CH4 emissions associated with oil and gas extraction and

84

processing have been conducted in the region19–22; however, animal husbandry is also a

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significant source of CH4 and needs to be properly accounted for when apportioning emissions

86

by source sector. Furthermore, nitrogen deposition and the ecological damage it causes in nearby

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Rocky Mountain National Park is the topic of several studies23–28, and a statewide program is

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aimed at reducing deposition to sub-critical levels by 2032 to avoid further changes to the

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ecosystem29. Measurements from a mobile laboratory that samples emissions from individual

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sources can be used to quickly and accurately assess the effectiveness of emission control

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strategies.

92 93 94

2. Instrumentation This measurement campaign utilized a new mobile laboratory constructed by the

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National Atmospheric and Oceanic Administration (NOAA) in June 2014. The mobile laboratory

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is a converted 15 passenger van outfitted with a suite of instruments including a Picarro G2103

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NH3 cavity-ringdown spectrometer8, a Picarro G1301-m CH4/CO2 cavity-ringdown

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spectrometer30, a Los Gatos Research N2O/CO integrated cavity output spectrometer31, and a

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Droplet Measurement Technologies wideband integrated bioaerosol sensor32. Meteorological


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sensors (Airmar 200WX, R.M. Young 85004) measure pressure, temperature, relative humidity,

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and horizontal wind speed and direction. Position, heading, and speed are determined using a

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differential GPS (ComNav G2B) and accelerometer. The uncertainty of the trace gas instruments

103

was determined by comparison with a gas standard or permeation tube with known emission

104

rate. Detection limits, uncertainty, and time resolution of the trace gas measurements are

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summarized in Table 1.

106

The rack-mounted instruments are powered by a 2 kW inverter (Magnasine MS2012) that

107

generates 120 V AC from four 12 V, 100 A-h Lithium iron phosphate batteries (Stark Power SP-

108

12V100-EP). When the engine is running, the batteries are charged via a second alternator

109

(Nations Starter & Alternator 8292HP-270A), allowing continuous, unlimited operation while

110

the van is driving and 2 to 4 hours of instrument operation, depending on the load, with the van

111

engine off. The instrument power system can be connected to stationary electrical outlets to

112

provide instrument power and charge batteries without running the van engine. A computer

113

monitor mounted in front of the passenger seat allows real-time data display and analysis.

114

Air is sampled 46 cm above the roof of the van through Teflon, Synflex, and stainless

115

steel sampling lines (for the NH3/H2O, CO2/CH4, and N2O/CO instruments, respectively). The

116

NH3 inlet is heated to reduce adsorption on the sampling line33 and periodically overfilled with

117

scrubbed ambient air to determine background signal levels due to NH3 desorption from the

118

sampling line or instrument interior. A silicon phosphate scrubber34 is used to selectively

119

remove NH3 from the ambient air during the overfill. An instrument background value

120

determined by interpolating between consecutive scrubbed air overfill periods was subtracted

121

from the total signal to determine ambient NH3 mixing ratios. The NH3 signal from these

122

background determinations varied from 1 to 22 ppbv with an average of 7.5 ppbv.


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In addition to the NOAA mobile laboratory measurements, some of the data presented

124

were collected by mobile laboratories from Princeton University35 and Aerodyne Research, Inc.36

125

The Princeton mobile laboratory used wavelength modulation spectroscopy to measure NH3 and

126

CH4 with open-path quantum cascade laser (QCL) and LICOR sensors, respectively. NH3, CH4,

127

and N2O were measured by Aerodyne Research, Inc. using closed-path QCL instruments.

128

Additional compounds were measured by Princeton and Aerodyne but are not interpreted here.

129 130 131

3. Methodology An initial survey in northeastern Colorado identified several large CAFOs that were

132

suitable for effective emission characterization studies. The four sites detailed in Table 2 were

133

chosen for intensive study due to their isolation, public road access surrounding the facility,

134

variation in facility type (e.g. dairies, beef cattle feedlots, sheep feedlots), and large animal

135

populations that resulted in plume mixing ratios that far exceeded background levels.

136

Characterization of emissions from a single facility must account for the heterogeneous

137

NH3, CH4, and N2O emission sources on each site. Figure 2(a) shows a drive track around one of

138

the dairies, with mixing ratios of NH3, CH4, and N2O shown in Figure 2(b) as a function of

139

distance around the CAFO. The mixing ratios of each compound were enhanced at different

140

locations, indicating that the sources of each species are not co-located. A majority of the

141

downwind feedlot plumes were measured within 500 m of the CAFO so that emissions from a

142

single facility can be identified unambiguously; however, the slower time response of the NH3

143

instrument and the heterogeneity of the sources reduces the correlation between compounds for

144

measurements obtained very close (~ 20-500 m) to the CAFO. Further downwind, the emissions

145

may mix so that all compounds are correlated, but additional uncertainty may arise as a


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consequence of emissions from additional nearby sources, reduced mixing ratio enhancements

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due to dispersion, and differential loss of emitted species. Local deposition of NH3 can vary

148

dramatically based on atmospheric conditions and the structure of nearby canopy,37 but is

149

generally expected to be less than 10% at these distances38,39. Ammonium nitrate formation and

150

transport is believed to be negligible compared to the measured gas-phase NH3 and is discussed

151

further in Section 4. Since our measurements are taken along the CAFO fence line, we consider

152

them to be representative of net emissions from the site, including re-emission of NH3 deposited

153

within the feedlot while excluding NH3 or ammonium deposition that remains on site.

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All measurements were obtained at a single height approximately 3 m above ground

155

level, and the lack of knowledge about vertical mixing prevents a quantitative flux calculation.

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Wind direction and speed were determined using a 2-D sonic anemometer and corrected to

157

account for the mobile laboratory’s speed and heading. Low wind speeds, particularly at night,

158

further complicate flux estimations using a mass balance approach. The uncertainty in the wind

159

speed measurement was approximately +/- 0.5 m/s, and thus wind direction could not be

160

determined accurately when wind speeds were less than 0.5 m/s, which was typical most nights.

161

In lieu of an emission flux calculation, emissions are characterized using enhancement

162

ratios (ERs). Using ratios removes many of the confounding effects of mixing and dilution so

163

that emission trends can be identified. Enhancement ratios are determined using the following

164

sampling and analysis methodology, which accommodates meteorological variability, non-

165

colocated sources, and variations in background levels. The mobile laboratory circled each site at

166

a uniform speed of ~25 mph (~40 km/h). The speed was chosen to achieve the highest possible

167

spatial resolution without compromising safety by impeding traffic. Each site was circled at least

168

twice before moving on to the next site to ensure repeatability in the measurement. For each lap


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around the site, a loop-integrated mixing ratio enhancement was calculated for each trace gas

170

according to Equation 1: ∆ =

∮( ) − ∮

(1)

171

where ∆ is the loop-integrated mixing ratio enhancement, C(x) is the 1-second mixing ratio, Cbg

172

is the background mixing ratio, and dx is the distance traveled each second. The background

173

mixing ratio is chosen by finding the location that minimizes a cost function F that compares the

174

relative enhancement of each species to the full enhancement range of that species in a given lap.

175

For example, when comparing NH3 and CH4, we find the point x that minimizes:

( ) − ( ) − ( ) = + − −

(2)

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where and are the minimum and maximum NH3 and CH4 mixing ratios during that

177

lap. The background locations determined using this method were always upwind of the CAFO

178

and background mixing ratios of NH3 and CH4 were always a small fraction of the enhancement.

179

Upwind measurements were typically within 2 km of each feedlot. The average background

180

mixing ratios of NH3, CH4, and N2O were 18 ppbv, 2107 ppbv, and 330 ppbv, respectively,

181

whereas the average peak mixing ratios in the plumes were 860 ppbv, 8860 ppbv, and 350 ppbv,

182

respectively. By taking multiple measurements and determining and subtracting the background

183

for each plume measurement, the enhancement ratios faithfully represent emissions ratios40.

184

Completely circling each site ensure that the plume from the feedlot is captured

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regardless of wind direction. Integrating each trace gas measurement around the loop gives a

186

total enhancement for the site and reduces the effects of non-colocated sources and differing

187

instrument time responses. The mixing ratio enhancement of a single species depends heavily on

188

meteorology, but enhancement ratios between various species (e.g. ∆ ⁄∆ ) represent ACS Paragon Plus Environment

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emissions ratios that can be meaningfully compared to inventories and other studies. We

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interpret the observed loop-integrated enhancement ratios to be representative of the site-wide

191

emission ratios.

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The four sites chosen for intensive study were measured consecutively for 24-48 hour

193

periods to capture the diurnal variations in the enhancement ratios. Each site was circled twice

194

before moving to the next site; including travel time between sites, this resulted in a pair of

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measurements at each site every two hours. The first diurnal measurement period was in August

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2014, and similar measurements were repeated in November 2014, January 2015, and May 2015

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to examine seasonal variations. Additional daytime data were taken in August 2015 to ensure

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year-to-year repeatability. All data were collected during periods of little to no precipitation and

199

temperatures were characteristic of the season. Snow cover may affect emissions in the winter;

200

however, no snow was present during our measurements.

201

Data from August 11-13, 2014, include contributions from Aerodyne Research, Inc.,

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Princeton University, and NOAA. All three mobile laboratories measured NH3 and CH4, whereas

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only the NOAA and Aerodyne vans reported N2O. Carbon monoxide, carbon dioxide, water

204

vapor, ethane, and bioaerosols were measured by some of the mobile labs, but those data are not

205

interpreted in this work. Stationary and mobile intercomparisons were performed to verify the

206

accuracy and consistency of the different instruments. Because the three mobile laboratories

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operated during different periods of the diurnal cycle, NOAA collected additional data on August

208

20, 2014 to ensure there were no systematic differences in the data produced by the different

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groups. Despite differences in the time response of the NH3 instruments, comparison of the loop-

210

integrated enhancement ratios for a given time of day did not show any significant biases

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between data collected by different mobile laboratories.


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Active oil and natural gas wells located along the roadways around sites 3 and 4

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frequently produced large, localized CH4 enhancements. Since the focus of this work is feedlot

214

emissions, all data taken within 100 m of these oil and gas wells were removed before

215

integrating around each site. The wells (and thus the areas of removed data) were not directly

216

downwind from the feedlot during any of our measurements.

217

NH3 may also be emitted by non-feedlot sources such as automobile catalytic

218

converters41 or fertilized fields42. The feedlots studied were all located in rural areas with little

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automobile traffic, and no significant NH3 plumes were correlated with carbon monoxide,

220

demonstrating that NH3 from combustion sources did not influence our data. Further, all loops

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were driven in a clockwise pattern, which ensured automobile traffic never came between the

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CAFO and the mobile lab. NH3 emissions from fertilized fields fall off dramatically within days

223

of application9,42, and there was no evidence of nearby field fertilization (e.g. NH3 enhancements

224

that were downwind of fields and not feedlots) during our study.

225 226 227

4. Results Using the mobile lab to measure along the feedlot fence lines often allows identification

228

of specific trace gas emission sources within the CAFOs. In the example shown in Figure 2, NH3

229

mixing ratios are largest downwind of the animal pens and milking parlor, while the largest CH4

230

plume is downwind of the wastewater lagoons. The highest mixing ratios of N2O (shown in

231

Figure 2(b)) are downwind of the manure composting piles. The associations of NH3, CH4, and

232

N2O with animal pen, lagoon, and manure sources within a feedlot are similar to those found for

233

California dairies43.

234

The mixing ratios of all three species downwind of the feedlot are significantly higher


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than upwind background levels. The large mixing ratios of NH3 can be particularly important for

236

ammonium nitrate formation by gas to particle conversion, since particulate nitrate formation is

237

proportional to the concentration product of NH3 and nitric acid (HNO3) under appropriate

238

meteorological conditions44–46. HNO3 in this area is typically less than 1 ppbv22 and NH3 mixing

239

ratios in these plumes were approximately three orders of magnitude greater than HNO3. Since

240

NH3 was in great excess in these plumes, ammonium nitrate formation will be limited by HNO3

241

availability, and NH3 conversion to ammonium will be a small fraction of plume NH3.

242 243 244

4.1 Diurnal variability in enhancement ratios The ERs obtained during each season and at each study site showed substantial diurnal

245

variability that was similar for all seasons and CAFOs. Data from all seasons and sites were

246

combined into one hour bins, and the box plot in Figure 3 shows the diurnal pattern observed in

247

the ∆ ⁄∆ ER. Each box represents, on average, 17 ER determinations from individual

248

laps around one of the four sites. There is a clear diurnal pattern with mean ER values that range

249

from 0.1 mol/mol in early evening and early morning to greater than 0.4 mol/mol in the early

250

afternoon. This diurnal pattern in emission ratios is similar to the diurnal pattern of NH3

251

emissions determined from previous measurements47–49 and from air quality modeling in the

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California central valley50. Since repeated measurements within a given 1-hour bin were

253

performed under varying wind conditions, some variability in the ER determination is expected

254

due to differing dilution of the non-colocated sources. Sporadic CAFO activities such as feed

255

processing and manure scraping or flushing also contribute to the observed variability, and

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measurements were made regardless of these management practices. The geometric mean and

257

standard deviation of the ER over the entire diurnal cycle is 0.17 (+ 0.13 / - 0.08) mol/mol. The


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geometric standard deviation (a multiplicative factor) has been converted into an additive factor

259

for ease of interpretation. Because some one-hour bins had more measurements than others, the

260

geometric mean was calculated using the medians of the one-hour bins so that equal weight was

261

given to all times.

262

For comparison, we calculated a statewide NH3/ CH4 CAFO emissions ratio using NH3

263

data from the livestock waste sector of the 2011 EPA National Emissions Inventory (NEI)51 and

264

the 2010 CH4 emissions data from the manure management and enteric fermentation sectors of

265

the Colorado Department of Public Health and Environment Greenhouse Gas Inventory52. Based

266

on these inventories, the statewide NH3/ CH4 emissions ratio is 0.165 mol/mol, in agreement

267

with our experimental findings. Additional emission ratio determinations were calculated from a

268

collection of published studies that simultaneously measured NH3, CH4, and/or N2O and are

269

summarized in Table 3. Some variation is expected based on the waste handling procedures and

270

environmental conditions (e.g. soil moisture) at the various sites studied; for example, the Idaho

271

dairy studied by Leytem et al.15 runs some of its wastewater through an anaerobic digester which

272

decreases CH4 emissions from the wastewater pond but increases NH3 emissions. However, there

273

is generally good agreement between the ERs calculated in this study and those from previous

274

studies of similar facilities. CAFOs for non-ruminant animals such as chickens or swine are

275

expected to emit in different ratios, as those animals do not produce CH4 via enteric

276

fermentation.

277 278 279 280

To examine whether the underlying NH3 or CH4 emissions are responsible for the diurnal behavior of the ∆ ⁄∆ ER, we also examined the diurnal patterns of the ∆ ⁄∆! "

and ∆! " ⁄∆ ERs (Figure 4). The ∆ ⁄∆! " ER exhibits a diurnal pattern similar to the ∆ ⁄∆ ER. Although the ∆! " ⁄∆ ER has increased variability that limits analysis,


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it does not appear to have a significant diurnal pattern. Diurnal variation in emission of all three

282

gases is expected due to a number of factors including daytime increases in temperature, wind

283

speed, and animal or feedlot activity53–56. The stronger diurnal variation we observed in ERs

284

containing NH3 implies that the relative variation of NH3 is larger than CH4 or N2O. This may be

285

due to the strong temperature dependence of NH3 volatilization from solution, examined in

286

section 4.3.

287 288 289

4.2 Seasonal and facility-type variation in enhancement ratios The ERs from each study site and each season follow diurnal trends similar to those

290

shown in Figures 3 and 4. To determine the seasonal variability, data from all sites were binned

291

first by time of day (to eliminate unintentional weighting due to number of measurements) and

292

then by season. Similarly, to determine whether different facility types produce different ERs,

293

data from all seasons were binned first by time of day and then by facility type. These data are

294

summarized by the box plots in Figure 5. Overall, the diurnal variability (roughly indicated by

295

the whiskers of each box) has a much stronger influence on the ER than either the season or

296

facility type.

297

Figure 5(b) shows that the sites that focus primarily on animal feeding (the beef cattle

298

and sheep feedlots) have slightly higher median ERs compared to the dairy facilities. Assuming

299

similar CH4 emissions, this is consistent with previous findings that dairies emit less NH3 per

300

animal than feedlots because some of the input nitrogen is retained in milk (whereas beef cattle

301

excrete more nitrogen as urine)9.

302 303

4.3 Temperature dependence of enhancement ratios


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Because air temperature has a diurnal variation similar to the observed ∆ ⁄∆ ER,

we have also examined the temperature dependence of the ∆ ⁄∆ ER during each season

(Figure 6). Like the diurnal pattern, the ∆ ⁄∆! " ER has a temperature dependence similar

307

to the ∆ ⁄∆ ER, while the ∆! " ⁄∆ ER does not (not shown). Volatilization of

308

NH3 increases at higher temperature, and the temperature dependence of the compensation point

309

# can be calculated as57,58:

#($) =

161,500 10,380 1NH4+ 5 exp -− 0 1H + 5 $ $

(3)

310

where T is the temperature in K and [NH4+] and [H+] are the concentrations of ammonium and

311

hydrogen ions in the solution. The compensation point can vary dramatically based on the pH of

312

the solution9,59–61, which is unknown in this experiment. However, the exponential coefficient

313

−10,380 is derived from the enthalpy change of dissolution of NH3 and acid dissociation constant

314

of ammonium58 and should be relatively constant across temperature and pH. The relationship

315

between the observed ∆ ⁄∆ ER temperature dependence and NH3 volatilization is

316

examined by comparing the data for each season with the exponential function in Equation 3

317

scaled by a multiplicative fitting coefficient A0, i.e. 6($) =

318

versions of the volatilization exponential function are shown in Figure 6 for values of A0 that

319

increase by a factor of 64 from summer to winter.

320

78 9

exp :−

;< 9

?. These scaled

Although we do not have sufficient supporting data to conclusively explain this seasonal

321

variation in temperature dependence, we offer several possible reasons for the differences seen in

322

Figure 6. First, the temperature of the animal waste / soil solution likely differs substantially

323

from the ambient air temperature. The ER data in each season may be uniquely dependent on

324

solution temperature, which is not known here. Additionally, the pH of the solution may change


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325

from season to season based on recent precipitation and temperature-dependent biological

326

processes (such as fermentation of feed and production of organic acids62,63). Finally, there is

327

likely a diurnal component to the animals’ activity level and waste production that is correlated

328

with temperature. These effects may also explain why the averaged seasonal ERs in Figure 5(a)

329

exhibit an inverted temperature trend.

330

CH4 emissions also vary with temperature, and in this simple model, those effects are

331

wrapped into the temperature dependence of the A0 coefficient. However, previous studies64–66

332

indicate that CH4 emissions should only change by a factor of 2-4 over these temperature ranges

333

and therefore cannot fully explain the factor of 64 difference observed here.

334 335 336

4.4 Atmospheric Implications The general consistency of the ∆ ⁄∆ ER across seasons and the four measured

337

sites indicates that the ER can be a useful tool to characterize and identify feedlot emissions.

338

Particularly in an area with many potential emission sources ranging from fertilized fields

339

(producing NH3, but no CH4) to fossil fuel infrastructure (producing CH4, but no NH3),

340

comparing the ER of a measured plume to these results can help attribute the emissions to a

341

particular source sector. However, different feedlot types are not clearly distinguishable here

342

based on the ERs derived from NH3, CH4, and N2O.

343

The observed diurnal variation is also important for informing models and inventories

344

and for accurately determining emissions from observations with limited temporal coverage (e.g.

345

polar orbiting satellites). The diurnal pattern with a factor of four increase in ∆ ⁄∆ ER

346

between night and day can enable extrapolation of daily-averaged emissions from measurements

347

that do not cover the entire day. In particular, measurements taken during the mid-afternoon


16


Page 18 of 37

348

should be considered in the context of the diurnal cycle to avoid biases. The magnitude of the

349

diurnal variation covers a range that is as large or larger than seasonal variations, facility

350

variations, and differences between the studies listed in Table 3. Incorporating diurnal trends into

351

models rather than the constant values reported in inventories can greatly improve model

352

accuracy10,50. The repeatability of the diurnal trend across seasons and animal types also provides

353

some confidence in generalizing previous measurements of diurnal NH3 emission trends from a

354

single season or animal type. Lastly, we have shown that in each season the ∆ ⁄∆ ER varies with temperature

355 356

but can only be partially explained by the temperature dependence of NH3 volatilization. The

357

seasonal variation in the temperature dependence indicates that other variables, such as soil

358

temperature, pH, and animal activity also affect NH3 emissions, and that air temperature alone

359

does not predict the ∆ ⁄∆ ER.

360 361

Acknowledgements

362

The authors wish to thank Anne Perring, Ellis Robinson, Chelsea Thompson, Owen Roberts, and

363

Lei Tao for their participation in the collection of field data. This work was supported by

364

NOAA’s Climate Program Office, and M.A.Z. gratefully acknowledges NASA NNX14AT36G

365

and NNX-14AT32G for analyses and NASA DISCOVER-AQ for field support.

366 367 368

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601 602


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603 604 605

606 607 608 609 610 611

Table 1. Trace gas instrumentation on the NOAA mobile laboratory Measured Parameter

Method

Time Resolution

N2O, CO, H2O

Integrated cavity output 1s spectroscopy

Detection Limit Uncertainty N2O: 0.1 ppbv CO: 0.1 ppbv H2O: 10 ppmv

N2O: ± 1 ppbv CO: ± (1 ppbv + 0.5%) H2O: n.m.

Wavelength scanned CO2: 0.1 ppmv CO2: ± 0.2 ppmv CO2 and CH4 cavity ring-down 1s CH4: 1.4 ppbv CH4: ± 2 ppbv* spectroscopy Cavity ring-down ≥ 2 s for NH3 NH3: 1 ppbv NH3: ± 20% NH3 and H2O spectroscopy 10 s for H2O H2O: 0.05% H2O: n.m. Single particle Bioaerosols 1 min 105 particles/m3 n.m. fluorescence n.m. indicates the uncertainty was not measured * Above 2292 ppbv, uncertainty is estimated to be ±(2 ppbv + 0.01*(CH4 − 2292 ppbv)) Table 2. Animal type and maximum permitted livestock for studied sites Site Number

Site Type

1 2 3 4

Cattle feedyard Sheep feedyard Dairy Dairy

Maximum permitted livestock capacity 54,000 95,000 7,500 6,100

612 613


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614


Table 3. Comparison of molar emission ratios from inventories and previous publications NH3 / CH4 ER NH3 / N2O ER N2O / CH4 ER Study (mol/mol) (mol/mol) (mol/mol) 0.17 (+ 0.13 / – 0.08) This study*

33 + (46 / – 19)

0.006 + (0.005 / – 0.003)

CAFO type/location Colorado dairy, beef, and sheep CAFOs

0.23 (+ 0.20 / – 0.11)

55 ( + 30 / – 19)

0.004 (+ 0.001 / – 0.001)

Beef feedlot (Site 1)

0.17 (+ 0.10 / – 0.06)

23 ( + 12 / – 8)

0.009 (+ 0.002 / – 0.002)

Sheep feedlot (Site 2)

0.14 (+ 0.13 / – 0.07)

35 ( + 35 / – 18)

0.004 (+ 0.002 / – 0.001)

Dairy (Site 3)

0.17 (+ 0.08 / – 0.05)

31 ( + 12 / – 8)

0.006 (+ 0.002 / – 0.002)

Dairy (Site 4)

NEI EPA 2011 (Colorado, livestock waste 0.17 63.1 0.003 sector), CDPHE GHG 2014 (enteric fermentation and manure management sectors) 0.10 19 0.005 Idaho open-lot dairy (site total) 0.24 34 0.007 Open lots Leytem 201153 0.018 11 0.002 Wastewater ponds 0.11 4.4 0.002 Compost facility 0.25 ----Idaho freestall dairy (site total) 15 0.18 10.3 0.018 Open freestall Leytem 2013 0.29 87.7 0.003 Wastewater ponds 0.78 136 0.006 Australia beef cattle feedlot (site total) 0.84 ----Feedlot pens Bai 201566 0.40 9.8 0.041 Manure stockpiles 1.03 ----Run-off pond *,43 Miller 2015 0.15 ± 0.03 ----California dairies 67 Ngwabie 2009 0.08 ----Sweden, inside a naturally ventilated dairy barn 68 Bjorneberg 2009 0.45 ----Idaho dairy 69 Stinn 2014 0.12 397 0.0003 Iowa swine facility† 615 * Study reported ER as a geometric mean. ERs for other studies were calculated from average emissions of individual compounds. State Inventories51,52


30


616 617

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--- N2O was not measured, data quality was poor, or concentrations did not significantly exceed ambient levels. † Because swine are not ruminant animals, methane is not produced via enteric fermentation.


31

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618

619 620 621 622 623 624 625 626 627 628

Figure 1. Northeastern Colorado hosts a diverse mix of ammonia, methane, and nitrous oxide emission sources. Gray lines depict county roads and black lines depict interstate highways. Feedlots are sized by animal units which are derived from the feedlot’s permitted maximum capacity and animal type17. For these data, 1 head of beef cattle = 0.7 dairy cattle = 2.5 swine = 10 sheep = 100 poultry.

629 630


32


631 632 633 634 635 636 637 638 639

640 641 642 643 644 645 646

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Figure 2. A sample of data around a dairy at 10:15 P.M. during autumn. The drive track (a) is colored by NH3 mixing ratio and sized by CH4 mixing ratio to show that the emission sources for each species are not co-located. The points labeled A and B mark the beginning and end of the trace gas data data shown in Figure 2(b), plotted against drive distance around the perimeter of the CAFO. The y-axis minima depict the local background for each species (1922 ppbv, 329.5 ppbv, and 6.6 ppbv, respectively). Map data: Google.

Figure 3. Diurnal profile of the ∆ ⁄∆ enhancement ratio. Box lines indicate the 75th, 50th, and 25th percentile and whiskers indicate the 90th and 10th percentile.


33

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647

648 649 650 651 652 653 654

Figure 4. Diurnal profiles of the ∆ ⁄∆! " and ∆! " ⁄∆ enhancement ratios. The ∆ ⁄∆! " ER has a similar diurnal pattern as the ∆ ⁄∆ ER, while the ∆! " ⁄∆ ER does not.


34


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655

656

657 658 659 660 661 662 663

Figure 5. The variation in the ∆ ⁄∆ ER among (a) seasons and (b) study sites. The diurnal variability (roughly indicated by the 10th and 90th percentile whiskers) is greater than the seasonal or facility type variations.

664 665 666


35

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667 668 669


Figure 6. Temperature dependence of the ∆ ⁄∆ enhancement ratio in each season. The black lines represent the temperature dependence of the ammonia volatilization process and are scaled by a multiplicative factor A0 for each season.


36

Characterization of Ammonia, Methane, and Nitrous Oxide Emissions

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