Reactive Organic Gas Emissions

from Livestock Feed Contribute

Significantly to Ozone Production in

Central California

C O D Y J . H O W A R D , † A N U J K U M A R , †,‡

I R I N A M A L K I N A , § , | F R A N K M I T L O E H N E R , §

P E T E R G . G R E E N , † R O B E R T G . F L O C C H I N I , ‡

A N D M I C H A E L J . K L E E M A N * , †

Department of Civil and Environmental Engineering, Crocker

Nuclear Laboratory, and Department of Animal Science,

University of California at Davis, 1 Shields Avenue, Davis

California 95616

Received September 21, 2009. Revised manuscript received

February 7, 2010. Accepted February 12, 2010.

The San Joaquin Valley(SJV) in California currently experiences

some of the highest surface ozone (O3) concentrations in

the United States even though it has a population density that

is an order of magnitude lower than many urban areas with

similar ozone problems. Previously unrecognized agricultural

emissions may explain why O3 concentrations in the SJV have

not responded to traditional emissions control programs. In

the present study,the ozoneformation potentials(OFP) of livestock

feed emissions were measured on representative field

samples using a transportable smog chamber. Seven feeds

were considered: cereal silage (wheat grain and oat grain),

alfalfa silage, corn silage, high moisture ground corn (HMGC),

almond shells, almond hulls, and total mixed ration (TMR )

55% corn silage, 16% corn grain, 8% almond hulls, 7% hay, 7%

bran + seeds, and 5% protein + vitamins + minerals). The

measured short-term OFP for each gram of reactive organic gas

(ROG) emissions from all livestock feed was 0.17-0.41 g-O3

per g-ROG. For reference, OFP of exhaust from light duty gasoline

powered cars under the same conditions is 0.69 ( 0.15 g-O3

per g-ROG. Model calculations were able to reproduce the ozone

formation from animal feeds indicating that the measured

ROG compounds account for the observed ozone formation

(i.e., ozone closure was achieved). Ethanol and other alcohol

species accounted for more than 50% of the ozone formation for

mosttypes offeed. Aldehydes were also significant contributors

for cereal silage, high moisture ground corn, and total

mixed ration. Ozone production calculations based on feed

consumption rates, ROG emissions rates, and OFP predict that

animal feed emissions dominate the ROG contributions to

ozone formation in the SJV with total production of 25 ( 10 t

O3day-1

. The nextmostsignificantROGsource of ozone production

in the SJV is estimated to be light duty vehicles with total

production of 14.3 ( 1.4 t O3 day-1

. The majority of the animal

feed ozone formation is attributed to corn silage. Future work

should be conducted to reduce the uncertainty of ROG

emissions from animal feeds in the SJV and to include this

significant source of ozone formation in regional airshed models.

1. Introduction

Ozone (O3) is a persistent public health problem with serious

economic consequences in the United States. In the years

2005-2007, more than 400 counties had 8 h average O3

concentrations higher than 75 ppb (the most recent healthbased National Ambient Air Quality Standard) (1). Three of

six counties with the highest O3 concentrations were located

in California’s San Joaquin Valley (SJV), while the remaining

“top six” counties were located in Southern California (2).

The severity of the O3 problem in the SJV counties is puzzling

given that they have a combined population of only 2.1 M

compared to 14 M residents in the top Southern California

counties. Higher temperatures, less summer cloud cover,

and longer periods of stagnation in the SJV explain part of

this trend, but even the most sophisticated computer models

that account for all of these effects predict that O3 concentrations in the SJV should be decreasing faster than currently

observed in response to emissions control programs.

Ozone is produced by the photochemical reaction of

oxides of nitrogen (NOx) and reactive organic gases (ROGs).

Lower ozone concentrations generally result from reductions

in ROG emissions in urban areas. NOx control is a more

effective means to decrease ozone concentrations in regions

where biogenic and other natural sources account for the

majority of the ROG emissions. Photochemical model results

based on current emissions inventories predict that NOx

control is a more efficient method for ozone reduction in the

SJV, but that conclusion is subject to review as new ROG

emissions sources are discovered. One possible cause for

unexpected O3 formation in the SJV ismissing ROG emissions

associated with the intensity of agricultural activities in the

region. Almost 10% of the agricultural output for the entire

United States comes from the SJV (3). The California Air

Resources Board recently estimated that reactive organic gas

(ROG) emissions from dairy cattle waste are the second largest

source of O3 formation in the SJV (with motor vehicle exhaust

being the largest source) (4). Direct testing suggests that this

initial estimate for dairy cattle wasteis overstated since animal

emissions do not contain ROGs with high ozone formation

potential (OFP) (5, 6). Nevertheless, the OFPs of many other

agricultural ROGs have not yet been tested, making agricultural emissions a high priority for further analysis.

Recent studies haveidentified animal feeds as one possible

ROG source of agricultural OFP (7, 8). The ROG fluxmeasured

from silage and total mixed ration (TMR) was 2 orders of

magnitude higher than comparable fluxes from animal waste

(7). Chamber measurements confirm that animal feed ROG

emissions are significantly higher than animal waste emissions and several of the animal feed ROG compounds have

potentially high OFP (8). Neither of these previous studies

directly quantified the OFP from animal feed or performed

total ozone closure experiments, leaving the contribution of

this source to regional ozone formation unknown.

The purpose of the present study is to directly measure

the OFP of commonly used animal feeds and to estimate the

importance of this ROG source for O3 formation relative to

other common ROG sources. A transportable smog chamber

was used to measure OFP from seven feed types including

one feed mixture under realistic agricultural conditions.

Measured ROG emissions from feed placed into an envi-

* Corresponding author phone: (530) 752-8386; fax: (530) 752-

7872; e-mail: mjkleeman@ucdavis.edu. † Department of Civil and Environmental Engineering.

‡ Crocker Nuclear Laboratory.

§ Department of Animal Science.

| Current address: California Air Resources Board, 1001 “I” Street,

P.O. Box 2815, Sacramento, California 95812.

Environ. Sci. Technol. 2010, 44, 2309–2314

10.1021/es902864u  2010 American Chemical Society VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 2309

Published on Web 03/01/2010
ronmental chamber were used toinitializemodel calculations

of O3 formation that were compared to measured values

(ozone closure experiments). Finally, total emissions rates

of ROGs from animal feeding operations were estimated for

the SJV so that the importance of this source could be judged

relative to other common ROG sources that contribute to O3

production.

2. Materials and Methods

2.1. Field Experiments.The OFP of sources too complicated

to reproduce in the laboratory can be measured directly in

the field using transportable smog chambers (5, 9). Ozone

formation is measured by introducing a source gas into a

well mixed chamber that contains background NOx and

reactive organic gases (ROG) that represents conditions in

the region of interest. The background NOx and ROG produce

ozone when it is exposed to ultraviolet (UV) radiation. The

OFP for the target source is defined to be the additional ozone

that is formed when emissions from that source are added

to the background mixture. The one drawback to transportable chambers is that they are usually smaller than laboratory

chambers. The reduced size limits experiments to shorter

times and the larger surface to volume ratios require extra

care when accounting for wall effects. The benefits of making

ozone measurements directly from complex sources far

outweigh these limitations.

In the present study, a mobile ozone chamber assay

(MOChA) was used to directly measure OFP from livestock

feeds. The MOChA consists ofa1m3 Teflon film reaction

chamber housed within a wooden enclosure sitting on top

of a modified trailer. The inner surface of the enclosure is

covered with highly reflective aluminum sheeting, which

helps to maximize UV irradiation of the reaction chamber.

The UV irradiation is supplied by up to 26 UV lamps (model

no. F40BL, Sylvania) with peak intensity at a wavelength of

350 nm. The lamps are mounted approximately 50 cm from

the reaction chamber. The number of lamps was adjusted

to produce ∼50 ( 2 W/m2 of UV output, which is typical for

conditions in Central California during the summer months.

The intensity of UV irradiation was measured before and

after each experiment using a photometer (model no. PMA2111, Solar Light Co. Inc., Glenside, PA).

During a typical experiment, the reaction chamber was

filled with source air using a Teflon diaphragm pump. The

target concentration of background NOx was added from a

high pressure cylinder as a 95% NO2/5% NO mixture by

volume. The background ROG used in the present study

consisted of a 55 ( 1% ethene, 33 ( 1% n-hexane, and 12

( 1% xylenes mixture by volume that was designed to

simulate background ROG concentrations in the SJV during

stagnation events. The composition of the background ROG

was chosen to represent diluted urban plumes based on the

“mini-surrogate” developed by Carter et al. (10). A grab

canister sample (11) of the ROG concentrations was collected,

the lights were turned on, and a three-hour ozone formation

experiment was performed. Ozone, NOx, relative humidity,

and temperature measurements were made at regular

intervals and logged to a computer. A second grab sample

of ROG concentrations was collected at the end of the

experiment, the lights were turned off, and the bag was

evacuated and flushed using a clean air generator (model

no. ZA-750-12, Perma Pure Inc., Toms River, NJ). Further

details of the MOChA standard operating procedures and

initial validation experiments are provided elsewhere (5).

Ozone formation experiments were performed on seven

different types of feed obtained from a commercial local

dairy. Those tested feeds included cereal silage (wheat grain

and oat grain), alfalfa silage, corn silage, highmoisture ground

corn (HMGC), almond shells, almond hulls, and total mixed

ration (TMR ) 55% corn silage, 16% corn grain, 8% almond

hulls, 7% hay, 7% bran+seeds, and 5% protein + vitamins

+ minerals on a as-fed basis). Alfalfa silage was tested under

two conditions: <1 week of fermentation and ∼1 month of

fermentation.

Feed samples were collected from trench silos on the dairy

farm andmoved to the testing facilityin large double wrapped

plastic bags. For cereal, alfalfa, and corn silage, a section of

the silage face was removed so that the entire feed sample

was collected from the anaerobic region. Air was removed

from the plastic bags and they were sealed for transportation

to the UC Davis Department of Animal Science where

experiments were conducted. The test chamber was a 4.4 ×

2.8 × 10.5 m sealed room with mechanically controlled

ventilation. A detailed description of this facility can be found

elsewhere (6). Feed samples were weighed and then placed

in a circular bin that set on the floor of the chamber. The

circular bin ensured that each feed type had the same exposed

surface area (2.63 m2

) during an experiment. The effective

density of each of the feed types in kg per m3 was: corn silage

(300 ( 40), alfalfa silage (260 ( 30), cereal silage (300 ( 35),

HMGC (640 ( 70), almond shells (150 ( 20), and almond

hulls (160 ( 20). After six minutes (the air residence time in

the chamber), MOChA air samples were drawn from the

ventilation outlet of the testing room through a 10 m Teflon

tube. Canister samples, DNPH-silica cartridges (model no.

037500, Waters Corp, MA), and sorbent tube (model no.

226-119, SKC Inc., Eighty-Four, PA) samples were also

collected inside the testing facility for supplemental ROG

analysis. DNPH cartridges were eluted with acetonitrile and

analyzed using high performance liquid chromatography

(HPLC), while sorbent tube and canister samples were

analyzed using gas-chromatographymass-spectrometry (GCMS) (11–14). The ozone formation of each feed type was

measured under two background ROG conditions: with

background ROG added to the system and without background ROG. Initial NOx concentrations were 50 ( 5 ppb.

2.2. Model Calculations. Model calculations were used

to perform ozone closure experiments and to estimate OFP

under ROG/NOx ratios other than those tested during

experiments. Ozone closure experiments attempt to reconcile

ozone measurements at the end of an experiment with ozone

predictionsmade using only the ROG and NOx concentrations

measured at the beginning of an experiment. Extensive

under-predictions of ozone formation would suggest the

presence of unidentified ROG compounds with significant

OFP (no such under-predictions were detected in the current

study). Simulations were carried out using a modified version

of the Caltech Atmospheric Chemistry Mechanism (CACM)

(15).Modifications weremade to CACM in order to accurately

represent ethanol and acetaldehyde chemistry in rural

conditions and to better simulate the spectrum of UV

radiation emitted by theMOChA lamps (5).Model predictions

for OFP were found to be in good agreement with previous

OFP measurements for animal waste sources (5, 9). Likewise,

in the present study model predictions are able to reproduce

OFP for animal feed sources (see the Supporting Information

(SI)).

3. Results and Discussion

A detailed list of the chemical species measured across all

feed types and their lumped model category is provided in

the SI. Alkanes (ALKL + ALKH), alkenes (OLEL + OLEH), and

ketones (KETL+KETH) are lumped into two categories based

on the number of carbon atoms in each molecule. Esters are

lumped into one of the two ketone categories. Alcohols

(ALCH) are represented with a single lumped category with

the exception of explicit treatment for ethanol (ETOH).

Acetaldehyde (ALD1) is also represented explicitly, while the

rest of the aldehydes are grouped into two lumped categories

representing higher molecular weight aldehydes (ALD2) and

2310 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 7, 2010
cyclic aldehydes (BALD). Biogenics (BIOL + BIOH) and

aromatic species (AROL + AROH) are lumped according to

their SOA yield, whereas phenol (PHEN) is represented

explicitly. A more detailed description of the CACM lumping

scheme is provided elsewhere (5, 15), as are emissions rates

for each of the chemical species (8).

Figure 1 depicts the differences between ROG species

concentrations measured in the MOChA vs direct measurements in the feed testing facility. Each graph represents either

an individual chemical species or a lumped chemical species

category tracked by model calculations (see SI Table S1) with

direct measurements of ROG on the x-axis and MOChA

measurements of ROG on the y-axis. Regression analysis (see

SI Table S2) was performed on MOChA vs direct measurements and the results show that those lumped species with

average concentrations greater than 2 ppb had R2 values

above 0.84. The two species with the highest average

concentration, ethanol (650 ppb) and acetaldehyde (60 ppb),

had R2 values of 0.91 and 0.98 respectively and the regression

slope fell within one standard deviation of the 1:1 line (0.94

(0.27 and 1.04(0.13, respectively). Four of the eight lumped

categories with average concentrations above 2 ppb (ALCH,

OLEL, OLEH, and KETL) had regression slopes <0.68 with

95% confidence intervals below the 1:1 line consistent with

losses to surfaces in the ventilation ducts and sampling lines.

The two lumped species right at the 2 ppb threshold (BIOH

and PAH) had regression slopes >1.21 but closer inspection

shows that this result was driven by a single data point in

each case. The corresponding 95% confidence intervals for

the regression slopes are therefore very broad. Likewise, there

was significant scatter for lumped species measured at

concentrations <2 ppb, which resulted in lower correlation

coefficients and broader confidence intervals for the regression slopes. The lower detectable concentration of the ROG

measurementmethod was 1 ppb which explains the scattered

behavior of measurements approaching this limit.

Figure 2 illustrates the ozone formation (ppb-O3) due to

emissions from each animal feed vs the ozone formation

predicted using CACM (ozone closure experiment). The figure

depicts ozone formation under controlled conditions, where

surface area of feed, ventilation rate in the chamber, and

volume sampled remain constant across all feed types. By

controlling these variables, the emissions from a feed type

can be attributed to the actual flux from that feed. Simulations

were conducted using the ROG profiles measured in the

MOChA and the ROG profiles measured directly from the

feed testing facility. For almost every feed type, the model

predictions for ozone formation based on the MOChA ROG

profiles are within uncertainty estimates to measured ozone

formation in the MOChA. Ozone formation from corn silage,

high moisture ground corn (HMGC), and almond hulls

predicted using ROG profiles measured directly from the

feed testing facility are higher than predictions based on

FIGURE 1. MOChA ROG canisters sample concentrations vs Direct ROG (from the test chamber) canister sample concentration for

each lumped chemical species (concentrations in ppb). Note that each graph is from 0 to 100% of the maximum concentration,

which is displayed in parentheses next to the species type.

FIGURE 2. Ozone formation (ppb O3) measured in each

experiment vs model predictions using ROG samples from

MOChA and Direct ROG samples from the feed testing facility.

VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 2311
MOChA ROG measurements. Concentrations of alcohol

species were higher in the direct sample than the MOChA

sample by a factor of 1.5, 5, and 10 for corn silage, almond

hulls, and HMGC, respectively. Alcohol concentrations (ALCH

+ ETOH) account for roughly half of the ozone formation for

these feed types. Multiplying the increased alcohol concentration by the expected ozone formation yields the difference

in ozone formation between direct and MOChA samples for

these three feed types (25% increase for corn silage, 300% for

almond hulls, and 500% for HMGC). The influence of

sampling line losses on these compoundsmust be considered

when predicting the atmospheric ozone formation associated

with these feeds.

Figure 3 illustrates the contribution that each lumped

ROG categorymakes to ozone formation for each of the feeds.

ROG contributions to ozone formation were calculated by

removing the ROG from the feed profile and observing the

reduction in predicted ozone production. This method

assumes simple additive behavior (linear approximation) that

does not completely describe the nonlinear photochemical

system. The measured ozone formation and predicted ozone

formation (sum of the individual ROG contributions) are

displayed after the subtitle for each feed to convey the

uncertainty introduced by the linear approximation. The

relative error introduced by the linear approximation is<20%

for feeds that produce >50 ppb O3 under the experimental

conditions (corn, alfalfa, cereal, TMR) with larger errors for

feeds that produce <50 ppb of O3 under the study conditions

(HMGC, almond shells, almond hulls). Ethanol and especially

larger alcohol species (ALCH) account for >50% of the ozone

formation for most types of feed. Alkene species (OLEL) were

significant contributors for corn silage andimportantin alfalfa

silage and TMR. Acetaldehyde accounts for 25-30% of the

ozone formation in cereal silage, TMR, and HMGC. Phenols

account for significant ozone production for HMGC, almond

shells, and almond hulls. Other important contributors to

total ozone formation include the second lumped aldehyde

category (ALD2), biogenic species, and aromatic species.

Model simulations were conducted to explore OFP of

animal feeds under pollutant conditions expected in the SJV.

Figure 4 displays the calculated ozone formation potential

for feed in grams of ozone produced per gram of ROG emitted

using the emissions measured in the current study. These

values can be compared to the OFP of light duty gasolinepowered vehicle exhaust (LDV). The error bars in Figure 4

represent the range of conditions considered for each feed

type, while the large bar represents the average between the

estimates. The upper estimate represents urban concentrations in the SJV (NOx ) 75 ppb, ROG ) 125 ppb), while the

lower estimate represents rural conditions in the SJV (NOx

) 25 ppb, ROG ) 62.5 ppb) (2). OFP is typically calculated

using incremental reactivity, which compares the ozone

formation of a reference mixture to the ozone formation of

the reference mixture plus a small concentration of source

ROG. Incremental reactivity can be defined for any point on

an ozone isopleth, but at low NOx and ROG conditions it is

best to use the equal benefit incremental reactivity (EBIR),

which is the point on the ozone isopleth where ROG and

NOx controls contribute equally to ozone reduction (16).

Fortunately, the reference estimates for the SJV fall along

this EBIR line for the NOx conditions considered. The three

silage feed types used in the experiments had OFP ranging

from 0.17 to 0.29 g-O3 per g-ROG. Total mixed ration, which

contains both silage and other feeds, had the sixth highest

OFP at 0.26 ( 0.11 g-O3 per g-ROG. High moisture ground

corn had the third highest OFP (0.36 ( 0.15 g-O3 per g-ROG),

almond shells had the second highest OFP (0.37 ( 0.16 g-O3

per g-ROG), and almond hulls had the highest OFP (0.41 (

0.21 g-O3 per g-ROG). The OFP of LDV at EBIR conditions

was calculated using CACM to be 0.69 ( 0.15 g-O3 per g-ROG

using published ROG emission estimates (17). These results

demonstrate that under representative NOx conditions, the

OFP of feed sources are potentially important compared to

LDV OFP.

Ozone formation potential quantifies the reactivity of each

gram of ROG, but total emission estimates are needed to

calculate total ozone formation within a region. Animal feed

ROG emissions originate from storage silos and from feed

placed in front of animals for their consumption. ROG

FIGURE 3. Contribution to total ozone formation from each

lumped model species assuming additive behavior. Ozone

formation associated with each species is calculated by

removing that species from the ROG profile and observing the

net reduction in ozone formation. The amount of ozone

produced under the experimental conditions is listed after each

subtitle (ppb O3). The first value represents the measured total

ozone formation, while the second value represents the

predicted total O3 formation using the sum from individual ROG

subfractions. See the Supporting Information for an explanation

of lumped model species codes.

FIGURE 4. Calculated average O3 formation potential (OFP) of

the ROG emissions from animal feed sources and light duty

gasoline-powered vehicles (LDV) expressed as g-O3 produced

per g-ROG emitted based on background NOx and ROG

concentrations. Uncertainty bars represent the range of

conditions considered (see text).

2312 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 7, 2010
emission rates from the exposed face of storage silos and

from feed placed in front of animals are calculated based on

exposed feed surface area and measured flux rates (g ROG

day-1 m-2

). SI Table S3 summarizes the flux emissions rates

for different feeds inferred from test chamber measurements

in the current study. Test chamber measurements were

converted to flux rates using the following equation:

where C is the measured concentration in the chamber, V

is the chamber volume, τ is the time scale for air exchange

in the room, and A is the surface area of exposed feed.

Chamber measurements made at time ) τ were still

increasing to steady state values (achieved after time ) 3τ)

and so the flux values are approximately 37% lower than the

true initial emissions rates from the animal feeds. Continuous

emissions flux measurements for corn silage made over a

24 h period indicate that steady state emissions decreases

over time (18). A decrease of 37% from the initial emissions

rate is achieved after approximately 4-5 h have passed.

Hence, the emissions flux measurements are appropriate

for an exposure time of 4-5 h. The corn silage emissions flux

rates in the current study (1.66 ( 0.18 ROG g hr-1 m-2

) are

in excellent agreement with direct flux rate measurements

described by other investigators (1.8 ( 0.1 g ROG hr-1 m-2

)

(7).

Total corn silage ROG emissions in the SJV were calculated

assuming that almost all of the corn silage used in California

is fed to dairy cattle and that most of the corn silage is kept

in trench silos (not tower silos). The total daily feed

consumption was estimated using statistics from the U.S.

Department of Agriculture (19) (see SI Table S1).

ROG emissions from the exposed face of the trench silo

(Eface) were calculated using the following equation:

where Mfeed is the total mass of silage feed consumed in the

SJV each year (1.0 × 1010 ( 5.0 × 108 kg) (19), F is the density

of silage in the pile (300 ( 40 kg m-3

) (20), Vpile is the volume

of a representative silage pile (1.0 × 104 ( 100 m3

) (20), Aface

is the representative area of the silage pile face (90 ( 4.5 m2)

(20), and flux is the ROG emissions flux appropriate for 4-5

h of exposure time (40 ( 2 g ROG day-1 m-2

) (measured this

study).

Fugitive ROG emissions from corn silos (Espoilage) were

calculated assuming that all of the ROG contained in the

spoiled silage is released to the atmosphere using the

following equation:

where Mspoil is the total amount of feed lost in the silo due

to air spoilage (10% of total mass ) 1.0 × 109 ( 5.0 × 107 kg

yr-1

) (21), DM is the fraction of the silage that is dry matter

(30%) (22), fEtOH_DM is the ratio of ethanol to dry matter in the

feed (1.2%) (22), and fEtOH_ROG is the fraction of the ROG

attributed to ethanol (EtOH) (55%) (8). This methodology

predicts that fugitive ROG emissions can be calculated as

0.65% of the spoiled silage mass.

The ROG emission rate from feed placed in front of the

animals (Emanger) was calculated assuming that the feed is

available to the cows twenty-four hours a day using the

equation:

where Scow is the representative surface area of feed in front

of each cow, Ncow is the number of cows in the SJV (1.9 × 106

( 1.9 × 104) (7), fsilage is the fraction of the feed composed

of silage (50%) (see previous discussion of TMR composition),

and flux is the ROG emissions flux appropriate for 4-5 h of

exposure time (40 ( 2 g ROG day-1 m-2

) (measured this

study). The Emanger was calculated using measurements from

a typical dairy in the SJV (1200 cows, two barns each housing

600 cows, total length of feed line is 750 m, and effective

cross-sectional width of feed line is 2.2 m). The result gives

the average exposed feed surface area of 1650 m2 for 1200

cows or 2.7 × 106 m2 for 1.9 million cows in the SJV (23).

Again, fsilage reduces the resulting surface area by half to

account for approximately 50% corn silage used in TMR. All

of the values needed to apply eqs 2-4 are summarized in SI

Table S6 along with references for data sources.

The total ROG emissions from corn silage calculated using

eqs 2-4 were 12.3 ( 1.9 t day-1 (storage face) + 18.4 ( 1.8 t

day-1 (fugitive emission) + 53.1 ( 6.0 t day-1 (feed in front

of animals) yielding a total emissions rate of 83.8 ( 6.6 t

day-1

. Multiplying ROG emissions by the OFP of corn silage

(0.27 ( 0.11 g-O3 per g-ROG) gives total ozone production

in the SJV as 23 ( 9.5 t day-1

. Similar calculations of ozone

production from the other feed sources were performed and

the result is summarized in Figure 5. The estimated ozone

formation from LDVs is also displayed in Figure 5 using

published emissions estimates for this source (4). Traditional

emissions inventory estimates have identified LDVs as the

largest anthropogenic ROG source of ozone production in

the SJV. The present calculations suggest that ozone production from animal feed ROG (25 ( 10 t day-1

) is nearly two

times larger than ozone production from LDV ROG (14.3 (

1.4 t day-1

) in this heavily polluted region. Corn silage

accounts for 93% of the feed ozone production in the SJV.

The next most significant category of feed is alfalfa silage

contributing 2% to the SJV total.

All of the calculations described above are preliminary.

Further refinements are needed to account formeteorological

variables such as temperature, wind speed, and humidity.

The relative importance of NOx vs ROG control on ozone

formation in the SJV must also be considered. The natural

approach to evaluate these factors is the application of a

regional air quality model that includes the newly recognized

animal feed ROG emissions and then perturbs the system to

consider the effectiveness of NOx vs ROG emissions controls.

The preliminary calculations shown in the present study

clearly indicate that animal feed emissions are a significant

source of ozone precursors in the SJV at current NOx levels.

Ozone control strategies in the SJV currently focus on NOx

control because previous calculations (without animal feed

flux ) CV

τA (1)

Eface ) MfeedAface(flux)

FVpile

(2)

Espoilage ) MspoilDMfETOH_DM

fETOH_ROG

(3)

Emanger ) ScowNcowfsilageflux (4)

FIGURE 5. Total ozone production in metric tons per day for the

various animal basic feed types vs light duty vehicles (LDV) in

the SJV. Note that the y-axis is log scale. Calculations are

based on OFP and total ROG emissions (see SI Table S1 for a

summary of corn silage calculations).

VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 2313
ROG emissions) predicted this to be the most efficient

strategy. If some measure of ROG control is deemed to be

worthwhile when these new emissions are recognized, then

future research should study how ROG emissions can be

reduced from these essential animal feeds.

Acknowledgments

This research was funded by USDA Grant TM No. 2004-

06138 with additional funding provided by the California Air

Resources Board and the San Joaquin Valleywide Air Pollution

Study Agency under contract 2000-05PM. The statements,

opinions, findings, and conclusions of this paper are those

of the authors and do not necessarily represent the views of

the California Air Resources Board. The authors thank Chris

Alaimo for his help with sample collection and analysis. Prof.

Tom Young made available HPLC and GC-MS instruments.

We are especially grateful to the cooperating dairy farm for

providing animal feeds.

Supporting Information Available

Table S1 describes the assignment of individual ROG

species to lumped CACM species, Table S2 summarizes

regressions between MOChA vs Direct ROG measurements,

Table S3 displays the emission flux rates for the various

feed types in the test chamber, Table S4 lists the mass of

feed used in chamber experiments, Table S5 lists the pH

and total solids content of corn and alfalfa silage, Table

S6 summarizes the data needed to calculate corn silage

ROG emissions in the SJV, Figure S1 illustrates agreement

between CACM predictions and MOChA measurements,

Figure S2 illustrates calculated O3 isopleths, Figure S3

illustrates the amount of each feed type used in California

each year, Figure S4 displays measured concentrations of

ROG species at or below the quantification threshold, and

Figure S5 illustrates a picture of the MOChA apparatus.

This material is available free of charge via the Internet at

http://pubs.acs.org.

Literature Cited

(1) Proposed Revisions to Ozone National Standards; U.S. Environmental Protection Agency: Washington, DC, 2007.

(2) California Air Resources Board, pollutant summary. http://

www.arb.ca.gov/adam/cgi-bin/db2www/adamtop4b.d2w/

start (accessed March 2009).

(3) California Agricultural Statistical Review; Department of Food

and Agriculture: Sacramento, CA, 2009.

(4) California Air Resources Board, Emissions Inventory Data.

http://www.arb.ca.gov/ei/emissiondata.htm (accessed March

2009).

(5) Howard, C. J.; Yang, W.; Green, P. G.; Mitloehner, F.; Malkina,

I. L.; Flocchini, R. G.; Kleeman, M. J. Direct measurements of

the ozone formation potential from dairy cattle emissions using

a transportable smog chamber. Atmos. Environ. 2008, 42 (21),

5267–5277.

(6) Shaw, S. L.; Mitloehner, F. M.; Jackson, W.; Depeters, E. J.; Fadel,

J. G.; Robinson, P. H.; Holzinger, R.; Goldstein, A. H. Volatile

organic compound emissions from dairy cows and their waste

as measured by proton-transfer-reaction mass spectrometry.

Environ. Sci. Technol. 2007, 41 (4), 1310–1316.

(7) Alanis, P.; Sorenson, M.; Beene, M.; Krauter, C.; Shamp, B.;

Hasson, A. S. Measurement of non-enteric emission fluxes of

volatile fatty acids from a California dairy by solid phase microextraction with gas chromatography/mass spectrometry.Atmos.

Environ. 2008, 42 (26), 6417–6424.

(8) Malkina, I. L., Kumar, A., Green, P. G., Mitloehner, F. M.

Identification and quantitation of volatile organic compounds

emitted from dairy silages and other feedstuffs Atmos. Environ.

2009, submitted.

(9) Howard, C. J., Kumar, A., Mitloehner, F., Stackhouse, K., Green,

P. G., Flocchini, R. G., Kleeman, M. J. Direct measurements of

the ozone formation potential from livestock and poultry

emissions Environ. Sci. Technol. 2010, accepted for publication.

(10) Carter, W. P. L.; Pierce, J. A.; Luo, D. M.; Malkina, I. L.

Environmental chamber study of maximum incremental reactivities of volatile organic-compounds.Atmos. Environ. 1995,

29 (18), 2499–2511.

(11) Kumar, A.; Viden, I. Volatile organic compounds: Sampling

methods and their worldwide profile in ambient air. Environ.

Monit. Assess. 2007, 131 (1-3), 301–321.

(12) Grosjean, E.; Green, P. G.; Grosjean, D. Liquid chromatography

analysis of carbonyl (2,4-dinitrophenyl)hydrazones with detection by diode array ultraviolet spectroscopy and by atmospheric

pressure negative chemical ionization mass spectrometry.Anal.

Chem. 1999, 71 (9), 1851–1861.

(13) Compendium Method TO-15, Determination of Volatile Organic

Compounds (VOCs) in Air Collected in Specially-Prepared

Canisters and Analyzed by Gas Chromatography/Mass Spectrometry (GC/MS),EPA/625/R-96/010b; U.S. Environmental

Protection Agency, Office of Research and Development:

Cincinnati, OH, 1999.

(14) CompendiumMethod TO-11A, Determination of Formaldehyde

in Ambient Air Using Adsorbent Cartridge Followed by High

Performance Chromatography (Active SamplingMethodology),

EPA 625/R-96/010b; U.S. Environmental Protection Agency,

Office of Research and Development: Cincinnati, OH, 1999.

(15) Griffin, R. J., Dabdub, D., Seinfeld, J. H. Secondary organic aerosol

- 1. Atmospheric chemical mechanism for production of

molecular constituents J. Geophys. Res., [Atmos.]2002, 107 (D17).

(16) Carter,W. P. L. Development of ozone reactivity scales for volatile

organic compounds. J. Air Waste Manage. Assoc. 1994, 44 (7),

881–899.

(17) Chang, C. C.; Lo, J. G.; Wang, J. L. Assessment of reducing ozone

forming potential for vehicles using liquefied petroleum gas as

an alternative fuel. Atmos. Environ. 2001, 35 (35), 6201–6211.

(18) Zhang, R., Mitloehner, F., El-Mashad, H., Malkina, I., Rumsey,

T., Arteaga, V., Zhu, B., Zhao, Y., Goldstein, A., Matross, D.,

Hafner, S., Montes, F., Rotz, C. A. ,Process-Based Farm Emission

Model for Estimating Volatile Organic Compound Emissions from

California Dairies, Final Report to the California Air Resources

Board for Project No. 05-344; California Air Resources Board:

Sacramento, CA, 2009.

(19) Census of Agriculture, 2007; U.S. Department of Agriculture:

Washington, DC, 2007.

(20) Silage Pile Dimension Calculator; University of Wisconsin

Cooperative Extension: Madison, WI, 2009.

(21) Holmes, B. J.;Muck, R. E.,Packing Bunkers and Piles toMaximize

Forage; University of Wisconsin Cooperative Extension: Madison, WI, 2007.

(22) Sheperd, A. C.; Kung, L. Effects of an enzyme additive on

composition of corn silage ensiled at various stages of maturity.

J. Dairy Sci. 1996, 79 (10), 1767–1773.

(23) Livestock Husbandry; California Air Resources Board: Sacramento, CA, 2009.

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