Characterization of Ammonia, Methane, and Nitrous Oxide Emissions

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PageEnvironmental 1 of 37 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:

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

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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

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

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

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

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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

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

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

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

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

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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,

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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,

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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

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

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

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

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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.

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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

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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

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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

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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

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

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

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summarized in Table 1.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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determined by interpolating between consecutive scrubbed air overfill periods was subtracted

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

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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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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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

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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

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

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

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

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

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

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

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

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

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

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

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

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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

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according to Equation 1: ∆ =

∮( ) −    ∮ 

(1)

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

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

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

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

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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

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

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

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

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

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

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

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

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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

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

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

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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

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emission ratios.

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

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

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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

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

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however, no snow was present during our measurements.

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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

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

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

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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

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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-

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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

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

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

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downwind from the feedlot during any of our measurements.

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

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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,

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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

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

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

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

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

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

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

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

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

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California dairies43.

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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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

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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

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

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

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given to all times.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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section 4.3.

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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

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

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

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

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

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

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

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facility type.

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

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

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

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

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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

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

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# can be calculated as57,58:

#($) =

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

(3)

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

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

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

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

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

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

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

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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

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increase by a factor of 64 from summer to winter.

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78 9

exp :−

;< 9

?. These scaled

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

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

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

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

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

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

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

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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

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

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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

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

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

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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

References:

369

(1)

370

Myhre, G.; Shindell, D.; Bréon, F.-M.; Collins, W.; Fuglestvedt, J.; Huang, J.; Koch, D.; Lamarque, J.-F.; Lee, D.; Mendoza, B.; et al. 2013: Anthropogenic and Natural Radiative

ACS Paragon Plus Environment

17

Page 19 of 37

Environmental Science & Technology

371

Forcing. In Climate Change 2013: The Physical Science Basis. Contribution of Working

372

Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate

373

Change; 2013; pp 659–740.

374

(2)

375 376

United States Environmental Protection Agency. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2014 – Main Text. 2016.

(3)

Pope, C. A.; Dockery, D. W. Health Effects of Fine Particulate Air Pollution: Lines that

377

Connect. J. Air Waste Manage. Assoc. 2006, 56 (6), 709–742 DOI:

378

10.1080/10473289.2006.10464485.

379

(4)

Schwarze, P. E.; Øvrevik, J.; Låg, M.; Refsnes, M.; Nafstad, P.; Hetland, R. B.; Dybing,

380

E. Particulate matter properties and health effects: consistency of epidemiological and

381

toxicological studies. Hum. Exp. Toxicol. 2006, 25 (10), 559–579 DOI:

382

10.1177/096032706072520.

383

(5)

Davidson, C. I.; Phalen, R. F.; Solomon, P. A. Airborne Particulate Matter and Human

384

Health: A Review. Aerosol Sci. Technol. 2005, 39 (8), 737–749 DOI:

385

10.1080/02786820500191348.

386

(6)

387 388

Nadelhoffer, K. J. The Impacts of Nitrogen Deposition on Forest Ecosystems. In Nitrogen in the environment; 2008; pp 463–482.

(7)

Fenn, M. E.; Haeuber, R.; Tonnesen, G. S.; Baron, J. S.; Grossman-Clarke, S.; Hope, D.;

389

Jaffe, D. A.; Copeland, S.; Geiser, L.; Rueth, H. M.; et al. Nitrogen Emissions,

390

Deposition, and Monitoring in the Western United States. Bioscience 2003, 53 (4), 391

391

DOI: 10.1641.

392 393

(8)

von Bobrutzki, K.; Braban, C. F.; Famulari, D.; Jones, S. K.; Blackall, T.; Smith, T. E. L.; Blom, M.; Coe, H.; Gallagher, M.; Ghalaieny, M.; et al. Field inter-comparison of eleven

ACS Paragon Plus Environment

18

Environmental Science & Technology

Page 20 of 37

394

atmospheric ammonia measurement techniques. Atmos. Meas. Tech. 2010, 3 (1), 91–112

395

DOI: 10.5194/amt-3-91-2010.

396

(9)

Hristov, A. N.; Hanigan, M.; Cole, A.; Todd, R.; Mcallister, T. A.; Ndegwa, P. M.; Rotz,

397

A. Review : Ammonia emissions from dairy farms and beef feedlots. Can. J. Anim. Sci.

398

2011, 91 (1), 1–35 DOI: 10.4141/CJAS10034.

399

(10)

Pinder, R. W.; Adams, P. J.; Pandis, S. N.; Gilliland, A. B. Temporally resolved ammonia

400

emission inventories: Current estimates, evaluation tools, and measurement needs. J.

401

Geophys. Res. Atmos. 2006, 111 (16), D16310 DOI: 10.1029/2005JD006603.

402

(11)

Pinder, R. W.; Pekney, N. J.; Davidson, C. I.; Adams, P. J. A process-based model of

403

ammonia emissions from dairy cows: Improved temporal and spatial resolution. Atmos.

404

Environ. 2004, 38 (9), 1357–1365 DOI: 10.1016/j.atmosenv.2003.11.024.

405

(12)

Pinder, R. W.; Strader, R.; Davidson, C. I.; Adams, P. J. A temporally and spatially

406

resolved ammonia emission inventory for dairy cows in the United States. Atmos.

407

Environ. 2004, 38 (23), 3747–3756 DOI: 10.1016/j.atmosenv.2004.04.008.

408

(13)

Pinder, R. W.; Walker, J. T.; Bash, J. O.; Cady-Pereira, K. E.; Henze, D. K.; Luo, M.;

409

Osterman, G. B.; Shephard, M. W. Quantifying spatial and seasonal variability in

410

atmospheric ammonia with in situ and space-based observations. Geophys. Res. Lett.

411

2011, 38 (4), L04802 DOI: 10.1029/2010GL046146.

412

(14)

Nowak, J. B.; Neuman, J. A.; Bahreini, R.; Middlebrook, A. M.; Holloway, J. S.;

413

McKeen, S. A.; Parrish, D. D.; Ryerson, T. B.; Trainer, M. Ammonia sources in the

414

California South Coast Air Basin and their impact on ammonium nitrate formation.

415

Geophys. Res. Lett. 2012, 39 (7), L07804 DOI: 10.1029/2012GL051197.

416

(15)

Leytem, A. B.; Dungan, R. S.; Bjorneberg, D. L.; Koehn, A. C. Greenhouse Gas and

ACS Paragon Plus Environment

19

Page 21 of 37

Environmental Science & Technology

417

Ammonia Emissions from an Open-Freestall Dairy in Southern Idaho. J. Environ. Qual.

418

2013, 42 (1), 10–20 DOI: 10.2134/jeq2012.0106.

419

(16)

Colorado Department of Natural Resources Oil & Gas Conservation Commission.

420

Colorado Oil and Gas Information System (COGIS)

421

http://cogcc.state.co.us/data.html#/cogis (accessed Apr 26, 2016).

422

(17)

Colorado Department of Public Health and Environment. CAFO permit database. 2014.

423

(18)

MetroDenver Economic Development Corporation. Metro Denver Population

424 425

http://www.metrodenver.org/do-business/demographics/population/. (19)

Pétron, G.; Frost, G.; Miller, B. R.; Hirsch, A. I.; Montzka, S. A.; Karion, A.; Trainer, M.;

426

Sweeney, C.; Andrews, A. E.; Miller, L.; et al. Hydrocarbon emissions characterization in

427

the Colorado Front Range: A pilot study. J. Geophys. Res. Atmos. 2012, 117 (4), 1–19

428

DOI: 10.1029/2011JD016360.

429

(20)

Pétron, G.; Karion, A.; Sweeney, C.; Miller, B. R.; Montzka, S. A.; Frost, G. J.; Trainer,

430

M.; Tans, P.; Andrews, A.; Kofler, J.; et al. A new look at methane and nonmethane

431

hydrocarbon emissions from oil and natural gas operations in the Colorado Denver-

432

Julesburg Basin. J. Geophys. Res. Atmos. 2014, 119 (11), 6836–6852 DOI:

433

10.1002/2013JD021272.

434

(21)

Lafranchi, B. W.; Petron, G.; Miller, J. B.; Lehman, S. J.; Andrews, A. E.; Dlugokencky,

435

E. J.; Hall, B.; Miller, B. R.; Montzka, S. A.; Neff, W.; et al. Constraints on emissions of

436

carbon monoxide, methane, and a suite of hydrocarbons in the Colorado front range using

437

observations of 14CO2. Atmos. Chem. Phys. 2013, 13 (21), 11101–11120 DOI:

438

10.5194/acp-13-11101-2013.

439

(22)

National Oceanic and Atmospheric Administration. SONGNEX 2015: Shale Oil and

ACS Paragon Plus Environment

20

Environmental Science & Technology

440 441

Page 22 of 37

Natural Gas Nexus http://esrl.noaa.gov/csd/projects/songnex/. (23)

Malm, W. C.; Schichtel, B. A.; Barna, M. G.; Gebhart, K. A.; Rodriguez, M. A.; Collett, J.

442

L.; Carrico, C. M.; Benedict, K. B.; Prenni, A. J.; Kreidenweis, S. M. Journal of the Air &

443

Waste Management Association Aerosol species concentrations and source apportionment

444

of ammonia at Rocky Mountain National Park Aerosol species concentrations and source

445

apportionment of ammonia at Rocky Mountain National Park. J. Air Waste Manage.

446

Assoc. 2013, 6311 (11), 1245–1263 DOI: 10.1080/10962247.2013.804466.

447

(24)

Benedict, K. B.; Day, D.; Schwandner, F. M.; Kreidenweis, S. M.; Schichtel, B.; Malm,

448

W. C.; Collett, J. L. Observations of atmospheric reactive nitrogen species in Rocky

449

Mountain National Park and across northern Colorado. Atmos. Environ. 2013, 64 (2013),

450

66–76 DOI: 10.1016/j.atmosenv.2012.08.066.

451

(25)

Benedict, K. B.; Carrico, C. M.; Kreidenweis, S. M.; Schichtel, B.; Malm, W. C.; Collett,

452

J. L. A seasonal nitrogen deposition budget for Rocky Mountain National Park. Ecol.

453

Appl. 2013, 23 (5), 1156–1169 DOI: 10.1890/12-1624.1.

454

(26)

Rodriguez, M. A.; Barna, M. G.; Gebhart, K. A.; Hand, J. L.; Adelman, Z. E.; Schichtel,

455

B. A.; Collett Jr., J. L.; Malm, W. C. Modeling the fate of atmospheric reduced nitrogen

456

during the Rocky Mountain Atmospheric Nitrogen and Sulfur Study (RoMANS):

457

Performance evaluation and diagnosis using integrated processes rate analysis. Atmos.

458

Environ. 2011, 45 (1), 223–234 DOI: 10.1016/j.atmosenv.2010.09.011.

459

(27)

Gebhart, K. A.; Schichtel, B. A.; Malm, W. C.; Barna, M. G.; Rodriguez, M. A.; Collett, J.

460

L. Back-trajectory-based source apportionment of airborne sulfur and nitrogen

461

concentrations at Rocky Mountain National Park, Colorado, USA. Atmos. Environ. 2011,

462

45 (3), 621–633 DOI: 10.1016/j.atmosenv.2010.10.035.

ACS Paragon Plus Environment

21

Page 23 of 37

463

Environmental Science & Technology

(28)

Gebhart, K. A.; Malm, W. C.; Rodriguez, M. A.; Barna, M. G.; Schichtel, B. A.; Benedict,

464

K. B.; Collett, J. L.; Carrico, C. M. Meteorological and back trajectory modeling for the

465

rocky mountain atmospheric nitrogen and sulfur study II. Adv. Meteorol. 2014, 2014, 1–

466

19 DOI: 10.1155/2014/414015.

467

(29)

468 469

Colorado Department of Public Health and Environment. Rocky Mountain National Park Initiative: 2012 Nitrogen Deposition Milestone Report; 2014.

(30)

Peischl, J.; Ryerson, T. B.; Holloway, J. S.; Trainer, M.; Andrews, A. E.; Atlas, E. L.;

470

Blake, D. R.; Daube, B. C.; Dlugokencky, E. J.; Fischer, M. L.; et al. Airborne

471

observations of methane emissions from rice cultivation in the Sacramento Valley of

472

California. J. Geophys. Res. Atmos. 2012, 117 (23), D00V25 DOI:

473

10.1029/2012JD017994.

474

(31)

Baer, D.; Gupta, M.; Leen, J. B.; Berman, E. Environmental and atmospheric monitoring

475

using off-axis integrated cavity output spectroscopy (OA-ICOS). Am. Lab. 2012, 44 (10),

476

20–23.

477

(32)

Perring, A. E.; Schwarz, J. P.; Baumgardner, D.; Hernandez, M. T.; Spracklen, D. V.;

478

Heald, C. L.; Gao, R. S.; Kok, G.; McMeeking, G. R.; McQuaid, J. B.; et al. Airborne

479

observations of regional variation in fluorescent aerosol across the United States. J.

480

Geophys. Res. D Atmos. 2015, 120 (3), 1153–1170 DOI: 10.1002/2014JD022495.

481

(33)

Neuman, J. A.; Huey, L. G.; Ryerson, T. B.; Fahey, D. W. Study of Inlet Materials for

482

Sampling Atmospheric Nitric Acid. Environ. Sci. Technol. 1999, 33 (7), 1133–1136 DOI:

483

10.1021/es980767f.

484 485

(34)

Nowak, J. B.; Neuman, J. A.; Kozai, K.; Huey, L. G.; Tanner, D. J.; Holloway, J. S.; Ryerson, T. B.; Frost, G. J.; McKeen, S. A.; Fehsenfeld, F. C. A chemical ionization mass

ACS Paragon Plus Environment

22

Environmental Science & Technology

486

spectrometry technique for airborne measurements of ammonia. J. Geophys. Res. 2007,

487

112 (D10), D10S02 DOI: 10.1029/2006JD007589.

488

(35)

Page 24 of 37

Tao, L.; Sun, K.; Miller, D. J.; Pan, D.; Golston, L. M.; Zondlo, M. A. Low-power, open-

489

path mobile sensing platform for high-resolution measurements of greenhouse gases and

490

air pollutants. Appl. Phys. B 2015, 119 (1), 153–164 DOI: 10.1007/s00340-015-6069-1.

491

(36)

Herndon, S. C.; Jayne, J. T.; Zahniser, M. S.; Worsnop, D. R.; Knighton, B.; Alwine, E.;

492

Lamb, B. K.; Zavala, M.; Nelson, D. D.; McManus, J. B.; et al. Characterization of urban

493

pollutant emission fluxes and ambient concentration distributions using a mobile

494

laboratory with rapid response instrumentation. Faraday Discuss. 2005, 130, 327 DOI:

495

10.1039/b500411j.

496

(37)

Loubet, B.; Asman, W. A. H.; Theobald, M. R.; Hertel, O.; Tang, Y. S.; Robin, P.;

497

Hassouna, M.; Dammgen, U.; Genermont, S.; Cellier, P.; et al. Ammonia deposition near

498

hot spots: Processes, models and monitoring methods. In Atmospheric Ammonia:

499

Detecting Emission Changes and Environmental Impacts; 2009; pp 205–267.

500

(38)

Fowler, D.; Pitcairn, C. E. R.; Sutton, M. A.; Flechard, C.; Loubet, B.; Munro, R. C. The

501

mass budget of atmospheric ammonia in woodland within 1 km of livestock buildings. In

502

Environmental Pollution; 1998; Vol. 102, pp 343–348.

503

(39)

Dennis, R. L.; Mathur, R.; Pleim, J. E.; Walker, J. T. Fate of ammonia emissions at the

504

local to regional scale as simulated by the Community Multiscale Air Quality model.

505

Atmos. Pollut. Res. 2010, 1 (4), 207–214 DOI: 10.5094/APR.2010.027.

506

(40)

Yokelson, R. J.; Andreae, M. O.; Akagi, S. K. Pitfalls with the use of enhancement ratios

507

or normalized excess mixing ratios measured in plumes to characterize pollution sources

508

and aging. Atmos. Meas. Tech. 2013, 6 (8), 2155–2158 DOI: 10.5194/amt-6-2155-2013.

ACS Paragon Plus Environment

23

Page 25 of 37

509

Environmental Science & Technology

(41)

Bishop, G. A.; Peddle, A. M.; Stedman, D. H.; Zhan, T. On-road emission measurements

510

of reactive nitrogen compounds from three California Cities. Environ. Sci. Technol. 2010,

511

44 (9), 3616–3620 DOI: 10.1021/es903722p.

512

(42)

Van Der Weerden, T. J.; Jarvis, S. C. Ammonia emission factors for N fertilizers applied

513

to two contrasting grassland soils. Environ. Pollut. 1997, 95 (2), 205–211 DOI:

514

10.1016/S0269-7491(96)00099-1.

515

(43)

Miller, D. J.; Sun, K.; Tao, L.; Pan, D.; Zondlo, M. A.; Nowak, J. B.; Liu, Z.; Diskin, G.;

516

Sachse, G.; Beyersdorf, A.; et al. Ammonia and methane dairy emission plumes in the San

517

Joaquin Valley of California from individual feedlot to regional scales. J. Geophys. Res.

518

Atmos. 2015, 120 (18), 9718–9738 DOI: 10.1002/2015JD023241.

519

(44)

Russell, A. G.; McRae, G. J.; Cass, G. R. The dynamics of nitric acid production and the

520

fate of nitrogen oxides. Atmos. Environ. 1985, 19 (6), 893–903 DOI: 10.1016/0004-

521

6981(85)90234-3.

522

(45)

Russell, A. G.; Cass, G. R. Verification of a mathematical model for aerosol nitrate and

523

nitric acid formation and its use for control measure evaluation. Atmos. Environ. 1986, 20

524

(10), 2011–2025 DOI: 10.1016/0004-6981(86)90342-2.

525

(46)

526 527

Seinfeld, J. H.; Pandis, S. N. Atmospheric chemistry and physics: from air pollution to climate change, Second.; J. Wiley: Hoboken, NJ, 2006.

(47)

McGinn, S. M.; Flesch, T. K.; Crenna, B. P.; Beauchemin, K. A.; Coates, T. Quantifying

528

Ammonia Emissions from a Cattle Feedlot using a Dispersion Model. J. Environ. Qual.

529

2007, 36 (6), 1585 DOI: 10.2134/jeq2007.0167.

530 531

(48)

Flesch, T. K.; Wilson, J. D.; Harper, L. A.; Todd, R. W.; Cole, N. A. Determining ammonia emissions from a cattle feedlot with an inverse dispersion technique. Agric. For.

ACS Paragon Plus Environment

24

Environmental Science & Technology

532 533

Page 26 of 37

Meteorol. 2007, 144 (1–2), 139–155 DOI: 10.1016/j.agrformet.2007.02.006. (49)

Sun, K.; Tao, L.; Miller, D. J.; Zondlo, M. A.; Shonkwiler, K. B.; Nash, C.; Ham, J. M.

534

Open-path eddy covariance measurements of ammonia fluxes from a beef cattle feedlot.

535

Agric. For. Meteorol. 2015, 213 (2015), 193–202 DOI: 10.1016/j.agrformet.2015.06.007.

536

(50)

Lonsdale, C. R.; Hegarty, J. D.; Cady-Pereira, K.; Alvarado, M. J.; Henze, D. K.; Turner,

537

M. D.; Capps, S. L.; Nowak, J. B.; Neuman, J. A.; Middlebrook, A. M.; et al. Modeling

538

the Diurnal Variability of Agricultural Ammonia in Bakersfield, California during

539

CalNex. Atmos. Chem. Phys. Discuss. 2016, No. March 2016, 1–30 DOI: 10.5194/acp-

540

2016-44.

541

(51)

United States Environmental Protection Agency. 2011 National Emissions Inventory

542

https://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-

543

data.

544

(52)

545 546

Arnold, S.; Dileo, J.; Takushi, T. Colorado Greenhouse Gas Inventory—2014 Update Including Projections to 2020 & 2030. 2014.

(53)

Leytem, A. B.; Dungan, R. S.; Bjorneberg, D. L.; Koehn, A. C. Emissions of ammonia,

547

methane, carbon dioxide, and nitrous oxide from dairy cattle housing and manure

548

management systems. J. Environ. Qual. 2011, 40 (5), 1383–1394 DOI:

549

10.2134/jeq2009.0515.

550

(54)

Harper, L. a; Flesch, T. K.; Powell, J. M.; Coblentz, W. K.; Jokela, W. E.; Martin, N. P.

551

Ammonia emissions from dairy production in Wisconsin. J. Dairy Sci. 2009, 92 (5),

552

2326–2337 DOI: 10.3168/jds.2008-1753.

553 554

(55)

Todd, R. W.; Cole, N. A.; Hagevoort, G. R.; Casey, K. D.; Auvermann, B. W. Ammonia losses and nitrogen partitioning at a southern High Plains open lot dairy. Atmos. Environ.

ACS Paragon Plus Environment

25

Page 27 of 37

Environmental Science & Technology

555 556

2015, 110, 75–83 DOI: 10.1016/j.atmosenv.2015.02.069. (56)

Leytem, A. B.; Dungan, R. S.; Bjorneberg, D. L. Case Study: Seasonal and Spatial

557

Distribution of Ambient Ammonia Concentrations Measured at a Large Open-Lot Dairy.

558

Prof. Anim. Sci. 2009, 25, 786–793.

559

(57)

Nemitz, E.; Sutton, M. A.; Schjoerring, J. K.; Husted, S.; Paul Wyers, G. Resistance

560

modelling of ammonia exchange over oilseed rape. Agric. For. Meteorol. 2000, 105 (4),

561

405–425 DOI: 10.1016/S0168-1923(00)00206-9.

562

(58)

563 564

Sutton, M.A., W. A. H. A. and J. K. S. Dry deposition of reduced nitrogen. Tellus B 1994, 46 (4), 255–273 DOI: 10.1034/j.1600-0889.1994.t01-2-00002.x.

(59)

Sander, R. Modeling atmospheric chemistry: Interactions between gas-phase species and

565

liquid cloud/aerosol particles. Surv. Geophys. 1999, 20 (1), 1–31 DOI:

566

10.1023/A:1006501706704.

567

(60)

Langford, A. O.; Fehsenfeld, F. C.; Zachariassen, J.; Schimel, D. S. Gaseous ammonia

568

fluxes and background concentrations in terrestrial ecosystems of the United States.

569

Global Biogeochem. Cycles 1992, 6 (4), 459–483 DOI: 10.1029/92GB02123.

570

(61)

Ernst, J. W.; Massey, H. F. The Effects of Several Factors on Volatilization of Ammonia

571

Formed from Urea in the Soil. Soil Sci. Soc. Am. J. 1960, 24 (2), 87–90 DOI:

572

10.2136/sssaj1960.03615995002400020007x.

573

(62)

McGinn, S. M.; Janzen, H. H.; Coates, T. Atmospheric ammonia, volatile fatty acids, and

574

other odorants near beef feedlots. J. Environ. Qual. 2001, 32 (4), 1173–1182 DOI:

575

10.2134/jeq2003.1173.

576 577

(63)

Rogge, W. F.; Medeiros, P. M.; Simoneit, B. R. T. Organic marker compounds for surface soil and fugitive dust from open lot dairies and cattle feedlots. Atmos. Environ. 2006, 40

ACS Paragon Plus Environment

26

Environmental Science & Technology

578 579

Page 28 of 37

(1), 27–49 DOI: 10.1016/j.atmosenv.2005.07.076. (64)

Gao, Z.; Yuan, H.; Ma, W.; Li, J.; Liu, X.; Desjardins, R. L. Diurnal and Seasonal Patterns

580

of Methane Emissions from a Dairy Operation in North China Plain. Adv. Meteorol. 2011,

581

2011, 1–7 DOI: 10.1155/2011/190234.

582

(65)

Saha, C. K.; Ammon, C.; Berg, W.; Fiedler, M.; Loebsin, C.; Sanftleben, P.; Brunsch, R.;

583

Amon, T. Seasonal and diel variations of ammonia and methane emissions from a

584

naturally ventilated dairy building and the associated factors influencing emissions. Sci.

585

Total Environ. 2014, 468–469, 53–62 DOI: 10.1016/j.scitotenv.2013.08.015.

586

(66)

Bai, M.; Flesch, T. K.; McGinn, S. M.; Chen, D. A Snapshot of Greenhouse Gas

587

Emissions from a Cattle Feedlot. J. Environ. Qual. 2015, 44 (6), 1974–1978 DOI:

588

10.2134/jeq2015.06.0278.

589

(67)

Ngwabie, N. M.; Jeppsson, K.-H.; Nimmermark, S.; Swensson, C.; Gustafsson, G. Multi-

590

location measurements of greenhouse gases and emission rates of methane and ammonia

591

from a naturally-ventilated barn for dairy cows. Biosyst. Eng. 2009, 103 (1), 68–77 DOI:

592

10.1016/j.biosystemseng.2009.02.004.

593

(68)

Bjorneberg, D. L.; Leytem, a B.; Westermann, D. T.; Griffiths, P. R.; Shao, L.; Pollard,

594

M. J. Measurement of atmospheric ammonia, methane, and nitrous oxide at a concentrated

595

dairy production facility in southern Idaho using open-path FTIR spctrometry.

596

Transcations Am. Soc. Agric. Biol. Eng. 2009, 52 (5), 1749–1756 DOI:

597

10.13031/2013.29137.

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Stinn, J. P.; Xin, H.; Shepherd, T. A.; Li, H.; Burns, R. T. Ammonia and greenhouse gas

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emissions from a modern U.S. swine breeding-gestation-farrowing system. Atmos.

600

Environ. 2014, 98, 620–628 DOI: 10.1016/j.atmosenv.2014.09.037.

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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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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

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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.

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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.

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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.

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

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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.

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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.

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