Composition and Toxicity of Biogas Produced from DifferentFe.docx

Composition and Toxicity of Biogas Produced from Different
Feedstocks in California
Yin Li,† Christopher P. Alaimo,† Minji Kim,† Norman Y.
Kado,§ Joshua Peppers,‡ Jian Xue,†
Chao Wan,† Peter G. Green,† Ruihong Zhang,‡ Bryan M.
Jenkins,‡ Christoph F. A. Vogel,§
Stefan Wuertz,∥ Thomas M. Young,† and Michael J.
Kleeman*,†
†Department of Civil and Environmental Engineering,
‡Department of Biological and Agricultural Engineering, and
§Department of
Environmental Toxicology and Center for Health and the
Environment, University of California − Davis, Davis,
California 95616,
United States
∥ Singapore Center for Environmental Life Sciences
Engineering, Nanyang Technical University, Singapore 637551
*S Supporting Information
ABSTRACT: Biogas is a renewable energy source composed
of methane, carbon dioxide, and other trace compounds
produced from anaerobic digestion of organic matter. A
variety of feedstocks can be combined with different digestion
techniques that each yields biogas with different trace
compositions. California is expanding biogas production
systems to help meet greenhouse gas reduction goals. Here,
we report the composition of six California biogas streams
from three different feedstocks (dairy manure, food waste, and

municipal solid waste). The chemical and biological
composition of raw biogas is reported, and the toxicity of
combusted biogas is tested under fresh and photochemically
aged conditions. Results show that municipal waste biogas
contained elevated levels of chemicals associated with volatile
chemical products such as aromatic hydrocarbons, siloxanes,
and
certain halogenated hydrocarbons. Food waste biogas contained
elevated levels of sulfur-containing compounds including
hydrogen sulfide, mercaptans, and sulfur dioxide. Biogas
produced from dairy manure generally had lower concentrations
of
trace chemicals, but the combustion products had slightly higher
toxicity response compared to the other feedstocks.
Atmospheric aging performed in a photochemical smog chamber
did not strongly change the toxicity (oxidative capacity or
mutagenicity) of biogas combustion exhaust.
1. INTRODUCTION
Biogas is a renewable fuel produced from the anaerobic
digestion of organic feedstocks including municipal waste, farm
waste, food waste, and energy crops. Raw biogas typically
consists of methane (50−75%), carbon dioxide (25−50%),
and smaller amounts of nitrogen (2−8%). Trace levels of
hydrogen sulfide, ammonia, hydrogen, and various volatile
organic compounds are also present in biogas depending on
the feedstock.1 Life cycle assessment studies have shown that
deploying biogas technologies can effectively reduce green-
house gas (GHG) emissions and, therefore, reduce the climate
impact of energy consumption.2−4 Biogas production and
utilization practices also help diversify energy systems while
simultaneously promoting sustainable waste management
practices.1,5 California is promoting biogas utilization by
mandating the low carbon fuels, offering grants to develop
biogas production facilities, and providing assistance in

accessing pipeline infrastructure.6−8
There are many environmental factors to consider when
developing biogas energy sources including the potential for air
quality impacts. California is home to 7 of the 10 most polluted
cities in the United States9 and so the widespread utilization of
any new fuel must be carefully analyzed for effects on the air
quality and human health. The concentrations of minor
chemical and biological components in biogas differ from those
found in other fuels. Some of these components have the
potential to be toxic to human health and the environment, to
form toxic substances during the combustion process, or to
form toxic substances after photochemical aging in the
atmosphere.
The California Air Resources Board (CARB) and the Office
of Environmental Health Hazzard Assessment (OEHHA)
compiled a list of 12 trace components potentially present in
biogas at levels significantly above traditional fossil natural gas
including carcinogens (arsenic, p-dichlorobenzene, ethyl-
Received: May 20, 2019
Revised: August 31, 2019
Accepted: September 3, 2019
Published: September 3, 2019
Article
pubs.acs.org/estCite This: Environ. Sci. Technol. 2019, 53,
11569−11579
© 2019 American Chemical Society 11569 DOI:
10.1021/acs.est.9b03003
Environ. Sci. Technol. 2019, 53, 11569−11579

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pubs.acs.org/est

http://pubs.acs.org/action/showCitFormats?doi=10.1021/acs.est.
9b03003
http://dx.doi.org/10.1021/acs.est.9b03003
benzene, n-nitroso-di-n-propylamine, vinyl chloride) and
noncarcinogens (antimony, copper, hydrogen sulfide, lead,
methacrolein, mercaptans, toluene). A limited dataset of
measurements is available to characterize levels of these biogas
components in California. Measurements of landfill biogas
composition have been made over many decades around the
world to identify sources of odor, to reduce ground level
volatile organic compound (VOC) contamination, and to
optimally recover biogas as an energy source.10 Trace
components identified in the landfill biogas include halocar-
bons, aromatic hydrocarbons, and siloxanes.11−16 Animal
waste
has significant biogas potential in California but often contains
sulfur compounds that must be removed prior to use.17,18
Food waste is a relatively new feedstock that has only been
analyzed for biogas plant performance.19,20 Previous studies
have tested biogas or simulated biogas burning in engines or
turbines, focusing on engine/turbine performance, NOx and
small hydrocarbon emissions,21−26 but these studies did not
examine trace chemical compounds in the engine combustion
exhaust that could pose environmental and human health
concerns.
Here, we report the composition and toxicity of biogas
produced and directly used for electricity production at five
different facilities in California. Samples at each site were
collected over 3 separate days spanning a range of environ-
mental conditions. Comprehensive measurements were
performed for 273 different features including major biogas
chemical components, a variety of different organic and

inorganic trace components, trace elements, and micro-
organisms. Concentrations were compared to previously
reported measurements and to the regulatory limits specified
by OEHHA and the California Division of Occupational Safety
and Health (Cal/OSHA). A standard biomarker assay was
used to evaluate the oxidative capacity of biogas combustion
exhaust and the associated short-term inflammatory response.
A carcinogen screening mutagenicity bioassay was used to
evaluate the probability that biogas combustion exhaust will
damage DNA, leading to increased cancer risk over longer time
periods. These comprehensive measurements help to under-
stand the potential air quality impacts of widespread biogas
production and combustion for electricity generation across
California.
2. MATERIALS AND METHODS
2.1. Biogas Sources. A total of 18 sets of samples were
collected from five biogas facilities: (1) dairy waste biogas
produced by a flushed manure collection and covered lagoon
system, (2) dairy waste biogas produced by a scraped manure
collection and digester system, (3) food waste biogas, (4) food
waste biogas mixed with nearby landfill gas, (5) biogas
produced by the core portion of a regional landfill, and (6)
biogas produced from the perimeter of the same regional
landfill. The biogas production and utilization technologies
used at each site are summarized in Table 1. A map showing
the locations of all biogas facilities studied is present in Figure
S1. All of the facilities generate electricity on-site using
engines
or turbines tuned to operate on biogas.
2.2. Chemical Analysis. Biogas is a complex matrix
containing hundreds of trace chemical compounds that cover a
broad range of functional groups with different volatilities.
Multiple sampling and analysis techniques are employed to

measure the full range of compound classes. Common
sampling methods include collecting high volatility compounds
in Tedlar (poly(vinyl fluoride)) bags or in metal canisters,
enriching lower volatility compounds onto solid sorbent tubes,
and stripping polar compounds using liquid sorbents in glass
impingers. The widely used analysis procedures include
compound separation using gas or liquid chromatography
optionally coupled with a desorption unit followed by
detectors that may be compound-specific or general mass
spectrometers. Detection limits are typically tens to hundreds
of parts per billion by volume for different com-
pounds.10,11,13−15,27−29
The current study employed sampling and analysis
techniques following the practices summarized above as
published by the EPA (TO-15,29 8081b,30 8270d,31 8082a,32
2933) and ASTM (D1945,34 D622835) standard laboratory
methods. Tedlar sample bags were collected under the positive
system pressure or using a “Vac-U-Chamber” (SKC-West,
Inc.) vacuum sampling apparatus if the biogas pressure was
negative. Each Tedlar bag sample analysis included a pure
nitrogen system blank and calibration standards. Tedlar sample
bags were directly connected to the instruments summarized in
Table S1 and analyzed for the 119 compounds listed in Table
S2. Semivolatile and/or reactive chemical compounds were
collected on three different types of sorbent tubes: XAD-2
sorbent tubes, coconut charcoal sorbent tubes, and DNPH-
Table 1. Summary of Biogas Production Sites Characterized in
This Study
biogas
streams type of feedstock biogas production technology gas end
use

1 Dairy farm cow manure (flushed).
1200 cows total. Surface water.
Covered lagoon. Lower mesophilic
(20−30 °C). Retention time 100 days
Fuel for internal combustion engine to generate electricity
2 Dairy farm cow manure (scraped).
1200 cows total. Ground water.
Single continuously stirred digester.
Mesophilic (35−40 °C). Retention time
50 days
Fuel for internal combustion engine to generate electricity
3 Food waste 25 tons per day Three-stage digester Thermophilic
(a) Fuel for internal combustion engine to generate
electricity, (b) upgrade to biomethane using membrane
system
4 Varying amount of food waste, animal
bedding and waste, municipal organic
waste
Three-stage digester. Thermophilic
Fuel for microgas turbines to generate electricity
Landfill 15 acres since early 1970s
5 Landfill (core part) Residential and commercial waste since

1967 (1084 acres)
Fuel for gas turbines to generate electricity
6 Landfill (perimeter) Residential and commercial waste since
1967 (1084 acres)
Flared
Environmental Science & Technology Article
DOI: 10.1021/acs.est.9b03003
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treated silica gel tubes. Flow through each sorbent tube was
controlled at 1.0 L·min−1 using a calibrated variable area flow
meter with a built-in stainless steel valve followed by a
downstream Teflon diaphragm pump (R202-FP-RA1, Air
Dimensions Inc.). All sorbent tubes were sealed until just
prior to sampling and immediately capped at the conclusion of
sampling. Each sample analysis run included a system blank,
two sample blanks, and calibration standards. A multipoint

calibration curve generated from the calibration standard was
used to quantify the target compounds. Table S3 lists
collection times, extraction methods, and analysis methods
for each sorbent tube. A comprehensive list of target chemical
compounds in each sampling/analysis pathway is presented in
Tables S4−S6 (102 + 33 + 13 = 148 compounds total). Biogas
samples for metals analysis were collected using three serial
glass impingers that each contained 20 mL of 5% nitric acid
and 10% hydrogen peroxide in double deionized (18.2 MΩ·
cm) water. Liquid solutions from each of the impingers were
transferred into separate capped vials and then analyzed with
inductively coupled plasma mass spectrometry (Agilent 7500i
ICP-MS, operated with a Glass Expansion AR50 MicroMist
nebulizer).
2.3. Biological Analysis. Samples for biological analysis
were collected on two 47 mm polycarbonate filters (0.4 μm
pore size) to support analysis for cultivable microorganisms
and corrosion-inducing bacteria DNA. Sample flow rates
ranged from 1 to 5 L·min−1 over times ranging from 2 to 4 h.
Condensate transported along with biogas was also collected.
Individual filters were placed in 50 mL Falcon tubes containing
15 mL of phosphate-buffered saline (PBS). Filters were eluted
in PBS by vortexing the Falcon tube for 5 s followed by manual
shaking for 2 min in a biosafety cabinet. Cultivable aerobic and
anaerobic bacteria in eluates and condensates were enumerated
by propagation in different growth media using the most
probable number (MPN) tests.36,37 Positive samples in the
MPN tests were further characterized using DNA sequencing
by conducting polymerase chain reaction (PCR) targeting 16S
rRNA.38 This allowed simultaneous identification of different
bacteria species in each sample. Nucleic acids in eluates and
condensates were extracted using the FastDNA SPIN Kit for
Soil (MP Biomedicals, Irvine, CA) following the manufac-
turer’s protocol. Five qPCR assays targeting total bacteria and
corrosion-inducing bacteria including sulfate reducing bacteria

(SRB), iron oxidizing bacteria (IOB), and acid producing
bacteria (APB) were selected from the literature.39−43
2.4. Toxicity Analysis. Samples of biogas combustion
exhaust were collected from five different electricity generators
summarized in Table 1 after dilution with the precleaned
background air. A 5.5 m3 Teflon photochemical reaction
chamber (0.051 mm NORTON FEP fluoropolymer film)
installed in a 24 ft mobile trailer was used to simulate
atmospheric aging of diluted exhaust under both light (daytime
UV = 50 W m−2) and dark (nighttime) conditions at each test
facility. The reaction chamber was flushed 3 times before each
test with the air that was precleaned using granulated activated
carbon to remove background gases followed by a high
efficiency particulate arrestance (HEPA) filter to remove
background particles. Dark aging tests started by filling the
reaction chamber to 50% capacity with the clean air, followed
by injecting combustion exhaust through a 1/2 in. diameter
insulated stainless steel transfer line at a flow rate of 26 L·
min−1 (50−55 °C) for 255 s. The reaction chamber was then
filled to 100% capacity with the clean air within 90 s, yielding a
well-mixed system at a dilution ratio of approximately 50:1.
The diluted exhaust was aged in the chamber for 3 h before
collection onto 47 mm Teflon filters (Zefluor, 2 μm pore size)
at 20 L·min−1 for 3.5 h. Light aging tests followed the same
experimental protocol with the exception that 100 L of VOC
surrogate gas (1.125 ± 0.022 ppmv m-xylene and 3.29 ± 0.07
ppmv n-hexane in the air, Scott Marrin, Inc.) was injected into
the chamber immediately after the combustion exhaust,
creating a final VOC concentration of 90 ppbv. Hydroxyl
radical concentrations during the light aging tests were
calculated to be (5−6) × 106 molecules cm−3 based on the
decay rate of the m-xylene, and final ozone concentrations at
the end of the 3 h experiment were measured to be 110−125
ppb.

The expression of in vitro pro-inflammatory markers was
measured in human U937 macrophage cells (American Tissue
Culture Collection, Manassas, VA). Macrophages are the first
line of defense in human lungs, and substances in engine
exhaust sample may interact with the macrophage cells though
the Toll-Like Receptors (TLR), Aryl hydrocarbon Receptor
(AhR), and the NF-κB protein complex to induce inflamma-
tory responses. Biomarkers checked in this study include
Cytochrome P450 monooxygenase (CYP1A1: marker for
polycyclic aromatic hydrocarbons), interleukin 8 (IL-8: marker
for inflammation), and cyclooxygenase (COX-2: a key enzyme
for the production of prostaglandins mediating pain and
inflammation). After a 6 h treatment of the exhaust filter
extract, mRNA was isolated from U937 macrophage cells and
reverse-transcribed into cDNA for quantitative expression
analysis using qPCR. Results were normalized to housekeeping
gene β-actin expression and expressed as fold increase of
mRNA in treated cells relative to untreated cells.44 The
mutagenicity bioassay was carried out via a microsuspension
modification of the Salmonella/microsome Ames assay45,46
that
is 10 times more sensitive than the standard plate
incorporation test. Frame-shift mutations of Salmonella
typhimurium tester strain TA98 were observed. TA98 requires
exogenous histidine (His−) for growth; however, substances in
exhaust samples could cause deletion or addition of
nucleotides in the DNA sequence of TA98 histidinegene
(frame-shift), resulting in TA98’s ability to manufacture
histidine (His+). The resultant colonies are referred to as
“Revertants”, as the DNA sequence is changed back to its
original correct form. Liver homogenate (S9) from male
Aroclor-induced Sprague Dawley rats (Mol Tox, Boone, NC)
was added to the assay to provide metabolic activation of the
sample.

3. RESULTS AND DISCUSSION
3.1. Biogas Composition. Concentrations of major biogas
components (methane, carbon dioxide, nitrogen, and oxygen)
are shown in Figure 1. Methane (CH4) content of the different
biogas streams varied from 49.5 to 70.5% with the exception of
perimeter landfill biogas (biogas 6), which contained only
35.4% methane due to high levels of air intrusion into the gas
extraction system. Biogas produced from flushed dairy waste
using a covered lagoon (biogas 1) had the highest measured
methane concentration. In contrast, biogas produced from
scraped dairy waste in an anaerobic digester (biogas 2) had a
much lower methane concentration of 51.3%. Similar trends
were reported by Saber et al., who measured the average
methane concentration in a covered lagoon dairy biogas facility
in the western US to be 67.6%, while the methane
DOI: 10.1021/acs.est.9b03003
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concentration in a complete mixed dairy biogas digester in the
western US was only 60.5%.47 In addition, the biogas 2 facility
adds iron chloride to digester slurry. Iron chloride is known to
inhibit anaerobic digestion processes, resulting in lower biogas
methane content.48 Core landfill biogas had an average

methane concentration of 49.5%, which fell into the range
reported by previous studies conducted in US and
Europe.11,12,14 The carbon dioxide content in the biogas
ranged from 20.2% in lagoon dairy biogas (biogas 1) to 46.9%
in food waste/landfill biogas (biogas 4). Concentrations of
CH4 and CO2 observed in this study fall in the range
commonly reported for biogas. Another important GHG
formed during the life cycle of organic waste management is
nitrous oxide (N2O). N2O is known to account for more than
20% of the total global warming potential (100 year scale)
associated with GHGs emissions from organic waste storage
practices,49,50 but N2O is unlikely to form in the anaerobic
digestion process that produces biogas.51
Small amounts of nitrogen (N2, <8%) and oxygen (O2,
<0.5%) were measured in biogas streams one through four.
The air is commonly injected into the anaerobic digestion
process at a rate of 2−6% to inhibit the formation of hydrogen
sulfide.52 The rate of anaerobic methane production does not
decrease and may even increase when a small amount of
oxygen is introduced, while the rate of hydrogen sulfide
production is strongly reduced.53 Higher concentrations of the
air are entrained into the landfill biogas by blowers that create
a negative pressure in porous collection pipes leading to air
intrusion through the soil into the biogas stream. Air intrusion
rates were higher at the perimeter of the landfill because less
biogas was produced in this region, requiring more air
intrusion to supply the extracted gas volume.
3.1.1. Sulfur-Containing Compounds. Figure 2 shows the
concentration of sulfur-containing compounds and their
speciation. The amount of total sulfur-containing compounds
varied significantly between different biogas facilities,
reflecting
the impact of both feedstock composition and primary sulfur

control methods. The dairy biogas facility with covered lagoon
(biogas 1) had the lowest total sulfur concentration composed
mostly of sulfur dioxide with very little hydrogen sulfide. In
contrast, the dairy biogas facility with the digester (biogas 2)
had the second highest total sulfur, nearly half of which (by
volume) was hydrogen sulfide. Both of these dairy facilities
used simple air injection to reduce H2S production, and the
digester dairy facility also added iron chloride to the digester to
further control H2S. This suggests that the covered lagoon
dairy achieved optimum operating conditions for anaerobic
digestion and biological desulfurization, with an effective
combination of hydraulic retention time, lagoon temperature,
pH, and air injection rates. Different feedstocks at the two
dairy facilities may also contribute to different biogas sulfur
contents. A dairy biogas study in the eastern US found that
differences in water sulfur concentration explained differences
in the biogas sulfur concentration from some facilities.18 In the
current study, biogas facility 1 used lagoon water to flush the
dairy stalls with periodic dilution using surface water sources.
Biogas facility 2 did not have access to surface water sources
and so used ground water exclusively. Landfills with active
blower systems inevitably have air intrusion in the biogas,
which helps reduce the sulfur content. The fraction of sulfur
dioxide in the perimeter landfill biogas stream is higher than
that in the core landfill biogas stream, indicating a more
oxidized environment in the perimeter part compared to the
core. Biogas 4 included contributions from a nearby landfill,
which produced a sulfur profile similar to that of the core
landfill. Biogas 3 had the highest concentration of total sulfur-
containing compounds. Levels of H2S (77.7 ppm) and
mercaptans (42.8 ppm) in biogas 3 both exceeded OEHHA
risk management trigger levels (22 ppm for H2S and 12 ppm
for mercaptans) but were still well below the lower action level
(216 ppm for H2S and 120 ppm for mercaptans).
5 This

suggests that no health risk concerns have been identified, but
routine monitoring of biogas sulfur content is advisable.
Overall, the total sulfur concentrations measured in the current
study fell into the lower end of the range reported in previous
studies.18,47
Figure 1. Major component concentrations by volume in dry
biogas
streams.
Figure 2. Total sulfur-containing compound concentration
(ppmv) and speciation in different biogas streams.
DOI: 10.1021/acs.est.9b03003
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3.1.2. Halocarbons. Figure 3 shows that the two landfill
biogas streams (5 and 6) had the highest total halocarbon
concentrations, while the dairy waste and food waste biogas
facilities (1−3) produced biogas with lower total halocarbon
concentrations. Biogas 4 had intermediate halocarbon
concentrations because it was a mixture of food waste biogas
and landfill gas. These trends reflect the halocarbon content of
different feedstocks. Dairy biogas produced from the digester
(biogas 2) had more chlorinated compounds than biogas
produced from the covered lagoon (biogas 1) possibly due to
the addition of iron chloride to the slurry, providing an
additional source of chlorine. Chlorofluorocarbons (CFCs)

commonly used in the past as refrigerants were present in
landfill biogas. Larger chloroalkenes (trichloroethene and
tetrachloroethene) commonly used as degreasers were also
detected in landfill biogas, along with smaller chloroalkenes
that are likely breakdown products of the anaerobic digestion
process.54 Biogas 2 (dairy digester) unexpectedly contained
chloroethene, suggesting that there were some cleaning
processes involved in the operation of this digester. Table S7
compares selected halocarbon species with available data from
previous studies on landfill biogas, together with the Cal/
OSHA permissible exposure levels (PELs) and OEHHA risk
management trigger levels (if available). Levels of halocarbons
Figure 3. Total halocarbon concentration (ppmv) and speciation
in different biogas streams.
Figure 4. Total BTEX concentration (ppmv) and speciation in
different biogas streams.
Figure 5. Total siloxane concentration (ppmv) and speciation in
different biogas streams L2 (hexamethyldisiloxane), L3
(hexamethylcyclo-
trisiloxane), L4 (decamethyltetrasiloxane), L5
(dodecamethylpentasiloxane), D4
(octamethylcyclotetrasiloxane), D5 (decamethylcyclopentasilox-
ane), D6 (dodecamethylcyclohexasiloxane).
DOI: 10.1021/acs.est.9b03003
11573

fall in the wide concentration range reported by previous
landfill studies and are well below the PELs, indicating
negligible occupational health concern. Studies have shown
that halocarbons form corrosive products during combustion
in engines, resulting in earlier failure of engine parts. However,
total halocarbon concentrations found in biogas from all
different streams in this study are safely below the level that
might cause early engine part corrosion.11
3.1.3. Benzene, Toluene, Ethylbenzene, and Xylene
(BTEX). Benzene, toluene, ethylbenzene, and xylene (BTEX)
compounds are regulated as “hazardous air pollutants” by the
US EPA. Benzene is a known human carcinogen, while
toluene, ethylbenzene, and xylenes are harmful to the human
nervous system and can cause eye and throat irritation during
high-level exposure. Consumer products such as paints, rubber,
adhesives, cosmetics, and pharmaceuticals are major sources of
BTEX.55 Figure 4 shows that landfill biogas had much higher
BTEX than food waste and dairy waste biogas because
municipal solid waste contains many consumer products.
Biogas 4 had intermediate BETX concentrations because it is a
mixture of food waste and landfill biogas. Table S8 lists
average
concentrations of BTEX in each biogas stream, together with
the PELs given by Cal/OSHA and risk management trigger
levels by OEHHA. All of the biogas-averaged BTEX
compound concentrations were below the 8 h averaged PEL.
Concentrations of benzene in landfill biogas were just below
the PEL, indicating that routine monitoring of BTEX
concentrations is advisable at landfills.
3.1.4. Siloxanes. Figure 5 presents siloxane concentrations
from different biogas streams. Biogas 4−6 had high total

siloxanes because of the many siloxane-containing compounds
in the landfill feedstock including personal care products, fabric
softeners, and surface treatment formulas. Notably, although
biogas 4 was a mixture of food waste biogas and landfill biogas,
it contained even more siloxanes than the pure landfill biogas
streams (5 and 6). This indicated that the landfill site, which
contributed to biogas 4, had received more siloxane-containing
products compared to the landfill site producing biogas 5 and
6. Biogas 1−3 had low siloxane concentrations made up mostly
by D4 (decamethyltetrasiloxane) and D5 (dodecamethylpen-
tasiloxane) species. Table S9 lists the concentration of each
siloxane species in the top three high-siloxane biogas streams
(biogas 4−6), together with measured values from previous
landfill studies. Siloxane concentrations in landfill gas are
variable but L2 (hexamethyldisilocane), D4, and D5 are
consistently found to be the most abundant species. Although
siloxanes are not considered to be directly toxic to the
environment or human health, siloxane combustion forms
silica (SiO2) nanoparticles (Dp < 100 nm). Silica nanoparticles
can degrade engine performance and increase CO emission by
abrading engine parts, depressing spark plug functionality, and
deactivating emission control systems.14,15,56 Engine
manufac-
turers typically set siloxanes concentration limits ranging from
10 to 28 mg·m−3.57 All of the biogas streams measured in the
current study met this requirement.57 Nanoparticles are also
known to be toxic due to their large surface-to-volume ratio
and ability to translocate in the human body, but the exact
short- and long-term effects of Si-based nanoparticles are not
yet completely understood.58
Table 2. Concentration of Arsenic (As), Antimony (Sb), Lead
(Pb), Copper (Cu), and Aluminum (Al) in different biogas
streams (μg·m−3)a,b

element As Sb Pb Cu Al
LOD 0.005 0.005 0.1 0.005 0.2
biogas 1 0.315 ± 0.432 0.259 ± 0.184 1.7 ± 2.4 0.000 ± 0.000
0.0 ± 0.0
biogas 2 0.012 ± 0.018 0.006 ± 0.009 7.4 ± 10.5 0.000 ± 0.000
0.0 ± 0.0
biogas 3 0.230 ± 0.380 0.310 ± 0.170 0.0 ± 0.0 0.005 ± 0.008
0.0 ± 0.0
biogas 4 1.600 ± 1.400 1.600 ± 1.800 0.1 ± 0.3 0.000 ± 0.000
2.2 ± 5.0
biogas 5 8.500 ± 3.400 12.500 ± 12.500 0.8 ± 0.8 0.000 ± 0.000
0.0 ± 0.0
biogas 6 4.200 ± 2.300 1.300 ± 2.000 0.7 ± 1.1 0.200 ± 0.340
5.6 ± 9.6
Cal/OSHA 8 h average PEL64 10 500 50 100 5000
OEHHA trigger level64 19 600 75 60
aResults expressed in (average value ± 1 standard deviation).
bConcentrations below limit of detection (LOD) are denoted as
0 ± 0.
Table 3. Results of Biological Analysis (Cultivation and qPCR)
parametera biogas 1 biogas 2 biogas 3 biogas 4 biogas 5 biogas
6
Cultivation Analysis (MPN m−3)b
live aerobic bacteria 34 ± 7 (2/3) <SLOD (0/3) <SLOD (0/4) 87
± 39 (2/7) <SLOD (0/3) <SLOD (0/3)
live anaerobic bacteria <SLOD (0/3) <SLOD (0/3) <SLOD (0/4)
<SLOD (0/7) <SLOD (0/3) <SLOD (0/3)
live aerobic spore bacteria <SLOD (0/3) 22 ± 10 (1/3) 32 ± 14
(2/4) 32 ± 13 (2/7) <SLOD (0/3) 29 ± 19 (1/3)
live anaerobic spore bacteria <SLOD (0/3) <SLOD (0/3) <SLOD

(0/4) <SLOD (0/7) <SLOD (0/3) <SLOD (0/3)
qPCR Analysis (gene copies m−3)
total bacteria <SLOD (0/3) <SLOD (0/3) <SLOD (0/4) <SLOD
(0/7) 3600 ± 2300 (1/3) 4300 ± 4000 (1/3)
sulfate reducing bacteria (SRB) <SLOD (0/3) <SLOD (0/3)
<SLOD (0/4) <SLOD (0/7) <SLOD (0/3) <SLOD (0/3)
iron oxidizing bacteria (IOB) 450 ± 440 (1/3) 29 ± 23 (1/3) 190
± 190 (1/4) 170 ± 100 (2/7) 430 ± 550 (1/3) 23 ± 24 (1/3)
acid producing bacteria (APB) 180 ± 180 (1/3) <SLOD (0/3)
160 ± 100 (2/4) 800 ± 560 (2/7) <SLOD (0/3) <SLOD (0/3)
aResults shown are means ± standard errors. Data below sample
limits of detection (SLODs) were assumed to be the half of the
SLODs for mean
calculation. The median SLODs of cultivation tests were 23
MPN per m3. The median SLODs of qPCR were 3200, 22, 140,
13, and 16 gene copies
per m3 for total bacteria, SRB, IOB, and APB, respectively. The
number of detects out of total samples tested is shown in
parenthesis. Condensate
water data were combined with raw biogas data if applicable.
bMPN, most probable number.
DOI: 10.1021/acs.est.9b03003
11574

3.1.5. Metals. A total of 24 different elements were analyzed
in the biogas streams with all measured values reported in
Table S5. Concentrations of arsenic (As), antimony (Sb), lead
(Pb), copper (Cu), and aluminum (Al) are summarized in
Table 2. Concentrations of antimony, lead, copper, and
aluminum in biogas fall well below the Cal/OSHA PELs and
OEHHA risk management trigger levels. Arsenic concen-
trations in some landfill biogas samples slightly exceeded the
Cal/OSHA 8-h PEL, but the average concentrations were
below the PELs as well as the risk management trigger level,
indicating negligible potential health risks. Possible sources of
arsenic in biogas include groundwater, semiconductor
electronic devices deposited into landfills or pesticides/
herbicides that make it into the organic waste stream.
3.1.6. Bacteria. Table 3 summarizes the biological entities
measured in the biogas samples. Cultivable (spore-forming)
bacteria were detected 2 times in 3 samples (biogas 1, dairy), 1
time in 3 samples (biogas 2, dairy), and 6 times in 11 samples
(biogas 3, food waste). Cultivable biologicals were less
commonly found in landfill biogas streams (2 out of 7 samples
for biogas 4 and 1 out of 6 samples for biogas 5 and 6). Basic
Logical Assignment Search Tool (BLAST) database analysis
determined that cultivable biologicals were closely related to
Bacillus spp. or Paenibacillus spp., which are Gram-positive,
spore-forming bacteria found in a variety of environments
including soil, water, and rhizosphere. Approximately, 10 to
100 MPN per m3 were measured in the current study, which is
comparable to results from previous studies reporting
cultivable bacteria concentrations in biogas.59,60
Total bacteria concentrations assessed by qPCR were below
sample limits of detection (SLODs) in most biogas streams
except for the landfills. Sulfate reducing bacteria (SRB) target
genes were not detected in any samples, consistent with the
results from previous studies on dairy and landfill biogas.37,59

Iron oxidizing bacteria (IOB) was found only once in most
biogas streams except for biogas 4 (twice in 7 samples). DNA
sequencing of qPCR amplicons revealed that IOB were closely
related to Gallionella capsiferriformans and Leptothrix spp.
Acid
producing bacteria (APB) target gene (buk) was detected in
biogas 1, 3, and 4. One-way ANOVA analysis showed that the
mean values of IOB and APB were not statistically different
from their SLODs (p > 0.05). Therefore, IOB and APB will
not likely reduce the service life of biogas facilities
characterized in the current study.
3.2. Biogas Engine Combustion Exhaust. Figure 6a−d
shows bioassay results measured from the particulate matter
collected on filters for on-site biogas engine/turbine exhaust
aged under dark and light conditions. Panels a−c present levels
of biomarker expression (CYP1A1, IL-8, and COX-2,
respectively) in U937 human macrophages after a 6 h
treatment with the biogas engine exhaust extract. Results are
expressed as fold increase above blank levels. Overall, the
biomarker responses from biogas electricity generators at sites
1−5 were similar under dark conditions. Photochemical aging
did not appear to strongly influence these results, with the
exception that biogas 2 engine exhaust induced notably greater
expression of the monooxygenase enzyme CYP1A1 and pro-
inflammatory signaling protein IL-8 under light conditions.
These samples likely contained polycyclic aromatic hydro-
Figure 6. Bioassay results of on-site biogas engine/turbine
exhaust aged under dark and light conditions: (a) fold increase
of CYP1A1 per m3 of
engine exhaust, (b) fold increase of IL-8 per m3 of engine
exhaust, (c) fold increase of COX-2 per m3 of engine exhaust,
and (d) number of TA98
net revertants per m3 of engine exhaust.

DOI: 10.1021/acs.est.9b03003
11575
carbons, which could be metabolized into carcinogens by
CYP1A1 and materials that could lead to inflammatory
responses when inhaled.
Figure 6d shows the result of the mutagenic bioassay
(Salmonella/microsome Ames assay) under dark and light
conditions. The number of TA98 revertants from a field blank
sample (clean air and surrogate VOC gases aged in the
photochemical reaction chamber) was subtracted from the
biogas test results. No activity over spontaneous background
was observed for biogas 2 dark, biogas 4 light, and biogas 5
under both dark and light conditions. Exhaust from biogas 1
showed higher mutagenicity concentrations than other biogas
streams. Photochemical aging did not strongly affect the
mutagenicity of exhaust, suggesting that photochemical
reactions will likely not change the genotoxic properties of
the particulate matter exhaust from biogas engines.
Overall, engine exhaust from dairy biogas (biogas 1 and 2)
showed slightly higher bioassay activity than exhaust from
other biogas sources, especially after aging under simulated UV
light. A previous study indicated that the particulate matter
from dairy farms can induce pro-inflammatory responses with

toxic and immunogenic substances such as histamine,
endotoxins, different antigens, and microorganisms.44 The
observed higher activities in dairy biogas combustion exhaust
may actually be driven by the dairy farm background air drawn
into the engines during the combustion process, rather than
the combustion products of biogas itself. Moreover, a previous
study by Xue et al. showed that ultrafine particle emission from
biogas-fueled engines is influenced more strongly by the engine
and combustion technology than by the fuel composition.61
The relationships between the properties of the fuel, the
properties of the gas-phase combustion exhaust, the properties
of the PM in the combustion exhaust, and the toxicity of the
PM are not fully understood in the current study, but the
current results suggest that the toxicity of the dairy biogas
combustion exhaust merits further investigation.
4. IMPLICATIONS
Calculated emission factors (EFs) of SO2 and selected organic
compounds are summarized in Tables 4 and 5 to support
future predictions of the aerosol formation potential of biogas
burning in engines. SO2 EFs (
− −
−
g SO (or mg SO )
m biogas
2 2
3 or
−lb SO
10 Btu energy

2
6 )
were estimated for each biogas stream, assuming that all of the
Table 4. Concentration of Total S-Containing Compounds in
Biogas and Biomethane Streams and Emission Factors of SO2
from Burning These Gas Streams
biogas biomethane
total S in fuela
(ppm)
EF - SO2
(g m−3-biogas) EF - SO2 (lb/MMBtu)
total S in fuela
(ppm)
EF - SO2
(mg m−3-biomethane) EF - SO2 (lb/MMBtu)
biogas 1 0.81 ± 0.29 0.002 ± 0.001 (1.71 ± 0.74) × 10−4
biogas 2 48.28 ± 39.33 0.117 ± 0.095 (1.52 ± 0.99) × 10−2
0.648 ± 0.197 1.56 ± 0.48 (1.06 ± 0.32) × 10−4
biogas 3 138.19 ± 92.51 0.334 ± 0.223 (3.50 ± 2.32) × 10−2
1.706 ± 0.697 4.12 ± 1.68 (2.71 ± 1.12) × 10−4
biogas 4 15.95 ± 7.50 0.039 ± 0.018 (4.18 ± 1.88) × 10−3 2.583
± 1.296 6.23 ± 3.13 (4.07 ± 2.08) × 10−4

biogas 5 6.00 ± 4.96 0.014 ± 0.012 (1.81 ± 1.25) × 10−3
aConcentration results expressed as (average value ± 1 standard
deviation).
Table 5. Concentration of Selected Compounds in Diluted and
Atmospherically Aged Engine Exhaust, Their Emission Factors
from Burning in Biogas Engines, and Corresponding Emission
Factors for Natural Gas Engines
compounds concentrationa (ppb) EFa (mg m−3-biogas) EFa (lb
MMBtu−1) natural gas engines63 EF (lb MMBtu−1)
1,3-dichlorobenzene 0.003 (0−0.014) 0.007 (0−0.034) 0.78
(0−3.53) × 10−6
1,4-dichlorobenzene 0.003 (0−0.015) 0.007 (0−0.364) 0.07
(0−3.78) × 10−5
benzyl alcohol 0.008 (0−0.053) 0.014 (0−0.095) 1.50 (0−9.89) ×
10−6
m,p-cresol 0.011 (0−0.028) 0.020 (0−0.051) 2.08 (0−5.29) ×
10−6
2,4-dichlorophenol 0.009 (0−0.015) 0.025 (0−0.040) 2.55
(0−4.14) × 10−6
naphthalene 0.035 (0−0.075) 0.075 (0−0.160) 0.78 (0-1.66) ×
10−5 7.44 × 10−5
2-methylnaphthalene 0.005 (0−0.015) 0.012 (0−0.035) 1.24
(0−3.68) × 10−6 3.32 × 10−5
1-methylnaphthalene 0.010 (0−0.033) 0.024 (0−0.078) 2.47
(0−8.07) × 10−6

dimethyl phthalate 0.004 (0−0.011) 0.014 (0−0.034) 1.44
(0−3.58) × 10−6
2,4-dinitrotoluene 0.052 (0−0.139) 0.158 (0−0.423) 1.65
(0−4.40) × 10−5
diethyl phthalate 0.012 (0−0.037) 0.044 (0−0.136) 0.46 (0−1.42)
× 10−5
phenanthrene 0.002 (0−0.007) 0.005 (0−0.020) 0.55 (0−2.05) ×
10−6 1.04 × 10−5
di-n-butyl phthalate 0.002 (0−0.006) 0.007 (0−0.028) 0.76
(0−2.92) × 10−6
bis(2-ethylhexyl) phthalate 0.003 (0−0.011) 0.020 (0−0.074)
2.04 (0−7.66) × 10−6
(1-methylethyl) benzene 0.011 (0−0.090) 0.021 (0−0.181) 0.22
(0−1.89) × 10−5
1,2,4-trimethylbenzene 0.009 (0−0.064) 0.019 (0−0.128) 0.20
(0−1.33) × 10−5 1.43 × 10−5
1,3,5-trimethylbenzene 0.035 (0−0.242) 0.071 (0−0.487) 0.74
(0−5.06) × 10−5 3.38 × 10−5
decane 0.020 (0−0.346) 0.047 (0−0.822) 0.49 (0−8.55) × 10−5
tetradecane 0.013 (0−0.048) 0.044 (0−0.157) 0.45 (0−1.64) ×
10−5
hexadecane 0.017 (0−0.048) 0.064 (0−0.183) 0.67 (0−1.91) ×
10−5

octadecane 0.013 (0−0.021) 0.053 (0−0.091) 5.53 (0−9.46) ×
10−6
eicosane 0.015 (0−0.024) 0.073 (0−0.115) 0.76 (0−1.20) × 10−5
aMedian value across all biogas streams. Values inside
parentheses are minimum and maximum.
DOI: 10.1021/acs.est.9b03003
11576
S-containing compounds in the fuel are converted to SO2
under stoichiometric combustion conditions. Calculations
were carried out for both raw biogas and for upgraded biogas
(biomethane), as summarized in Table 4. SO2 EFs range from
1.71 × 10−4 lb MMBtu−1 to 3.5 × 10−2 lb MMBtu−1 in raw
biogas due to variability in fuel sulfur and methane content.
SO2 EFs for biomethane range from 1.06 to 4.07 × 10
−4 lb
MMBtu−1 because the precleaning steps for the upgrading
process remove sulfur from the fuel.62 For reference, the SO2
EF from natural gas-fired stationary reciprocating engines63 is
5.88 × 10−4 lb MMBtu−1, which is comparable to the
biomethane EFs calculated in the current study.
Concentrations of different semivolatile organic compounds,
PAHs, and extended hydrocarbons in the engine exhaust were
measured after injection into the photochemical reaction
chamber and aging under dark or light conditions. Note that

concentrations were diluted by a factor of ∼50 to represent
true atmospheric conditions, which resulted in low measured
values close to method detection limits. Concentrations vary
from site to site due to this issue and so median results across
all locations are shown rather than results for individual
locations. Table 5 summarizes concentrations of various
organic compounds in exhaust as well as the calculated EFs.
Median values are reported along with minimum and
maximum values in the parentheses. EFs for 4-stroke lean-
burn natural gas-fired reciprocating engines63 are listed in the
last column of Table 5 as a reference point. EFs of various
organic compounds from biogas-fired engines are generally
comparable or lower than EFs from natural gas-fired engines.
The current study characterizes the range of trace
composition profiles for California biogas produced from
different feedstocks. These trace component characteristics
play a central role in determining what upgrading steps are
required to enable biogas energy recovery62 and what routine
monitoring protocols are needed to protect pipeline infra-
structure and public health. Quantifying the broad array of
trace contaminants in biogas is challenging for two reasons.
First, the composition of biogas varies with feedstock, weather
condition, digester operating parameters, etc. Second, different
laboratories employ different sampling and analysis techniques
that can lead to different detection limits. Characterizing the
distribution of concentrations for each contaminant requires
repeated measurements across multiple seasons using identical
methods followed by statistical analysis. The biogas industry
should agree on a set of sampling and analysis protocols to
facilitate the intercomparison of results from different
laboratories.
No strong evidence of potential occupational health risk was
detected at any of the five California biogas sites. This study
also found no obvious differences between the toxicity of

different biogas combustion exhaust streams after atmospheric
dilution and aging. Future studies should continue to
characterize the variability of the trace chemical composition
of biogas combustion exhaust to enable a more detailed
statistical analysis of potential public health impacts of large
biogas energy recovery facilities.
■ ASSOCIATED CONTENT
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.est.9b03003.
Table S1: Tedlar bag sample analysis specifications;
Table S2: target compounds in Tedlar sample bag
analysis; Table S3: Sorbent tubes sampling and analysis
specifications; Table S4: Target compounds in XAD-2
sorbent tube analysis; Table S5: Target compounds in
charcoal sorbent tube analysis; Table S6: Target
compounds in silica sorbent tube analysis; Table S7:
Concentration range of selected halocarbons (mg·m−3)
measured at different landfill sites and permissible
occupational exposure levels by Cal/OSHA and
OEHHA; Table S8: Average concentration of BTEX
measured in different biogas streams (mg·m−3) and
permissible occupational exposure levels by Cal/OSHA
and OEHHA; Table S9: Concentration of siloxanes in
different biogas streams (mg·m−3); Table S10: Concen-
tration of metals in different biogas streams (μg·m−3);
Figure S1: Map locations of the biogas facilities studied;
Figure S2: Concentration (ppmv) of carbonyls in
different biogas streams
■ AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected] Phone: 530 752 8386.
ORCID

Jian Xue: 0000-0002-6067-2445
Thomas M. Young: 0000-0001-7217-4753
Michael J. Kleeman: 0000-0002-0347-7512
Notes
The authors declare no competing financial interest.
■ ACKNOWLEDGMENTS
This research was supported by CEC Project PIR-13-001 and
CARB Project # 13-418. The statements and conclusions in
this paper are those of the authors and not necessarily those of
the sponsors. The authors would like to thank the sponsors as
well as the biogas producers, who collaborated with us on this
study.
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Bioenergy Potential from Food Waste in California
Hanna M. Breunig,*,† Ling Jin,† Alastair Robinson,† and
Corinne D. Scown†,‡
†Energy Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States

‡Joint BioEnergy Institute, Emeryville, California 94608,
United States
ABSTRACT: Food waste makes up approximately 15% of
municipal solid waste generated in the United States, and 95%
of food waste is ultimately landfilled. Its bioavailable carbon
and nutrient content makes it a major contributor to landfill
methane emissions, but also presents an important oppor-
tunity for energy recovery. This paper presents the first
detailed analysis of monthly food waste generation in
California at a county level, and its potential contribution to
the state’s energy production. Scenarios that rely on excess
capacity at existing anaerobic digester (AD) and solid biomass
combustion facilities, and alternatives that allow for new
facility construction, are developed and modeled. Potential
monthly electricity generation from the conversion of gross
food waste using a combination of AD and combustion varies
from 420 to 700 MW, averaging 530 MW. At least 66% of gross
high moisture solids and 23% of gross low moisture solids can
be treated using existing county infrastructure, and this fraction
increases to 99% of high moisture solids and 55% of low
moisture solids if waste can be shipped anywhere within the
state.
Biogas flaring practices at AD facilities can reduce potential
energy production by 10 to 40%.
■ INTRODUCTION
Inefficiencies occur at all stages of the food supply chain,
linked
to complex factors ranging from market conditions and weather
to consumer preferences, and these inefficiencies translate to an
abundance of food waste. The U.S. generated approximately 38
million tonnes of municipal food waste in 2014, approximately
95% of which was landfilled.1 An enormous amount of energy,

water, land, and other resources go into producing nutrition for
humans.2 A recent analysis of food waste estimated that $218
billion is spent in the U.S. on growing, processing, transporting,
and disposing of food and byproducts that go uneaten.3
Furthermore, because food waste biodegrades four times faster
than typical paper products and 10 times faster than wood
waste, it releases methane from landfills more quickly than
most
other organic waste, with 34−51% of generated methane
escaping typical landfill gas capture systems.4−6 Landfilling
uneaten solid organic material not only contributes to climate
change and occupies land resources, but also eliminates the
possibility of cycling the valuable nutrients and energy in food
back into the economy.
First and foremost, policy measures are necessary to ensure
source-reduction through changes in consumer behavior and
improved harvesting, processing, and transportation methods.7
However, source-reduction alone will not be a sufficient
strategy. Americans consume raw produce and livestock that
have both edible and inedible parts, from local and nonlocal
sources, and in quantities that require some level of centralized
production and distribution. These biological materials can only
maintaining the value of food is not always possible. Food
waste-to-energy strategies can help meet renewable energy
targets, greenhouse gas (GHG) reduction targets, air quality
standards, and divert waste from landfills.8
In this paper, the potential for converting California’s food
waste to electrical and thermal energy is analyzed, including
organic waste from the food supply chain: agricultural

production, postharvest handling and storage, processing and
packaging, distribution, consumption, and end-of-life. The
objectives of this study are to determine the quantity, locations,
and temporal variation in food waste generation, use these
results to model regional and subannual electricity and heat
generation potential, and gain insight into the roles of policy
and technology in overcoming challenges associated with food
waste utilization. California serves as a useful starting point for
building an analysis framework that can be applied to the U.S.
or globally because of its diversity and significance in national
food production (40% of U.S. vegetables, 20% of dairy, and
70% of fruits, tree nuts, and berry production by revenue).9,10
Although previous assessments have estimated the total annual
energy potential from food waste from retail and consumer
waste streams and from food processors in California,11−14 this
study is the first to assess food waste production at the
Received: September 9, 2016
Revised: December 23, 2016
Accepted: January 10, 2017
Published: January 10, 2017
Policy Analysis
pubs.acs.org/est
© 2017 American Chemical Society 1120 DOI:
10.1021/acs.est.6b04591
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subannual scale and to develop a spatially and temporally
explicit model that integrates feedstock production and energy

infrastructure capacity to estimate potential energy production.
By accounting for infrastructure, logistics, and storage
limitations, this study provides a more robust assessment of
potential electricity and thermal energy generation and
highlights key challenges that must be overcome to maximize
this potential.
Background and Motivation. Despite recent drought
conditions, California produced over 400 types of agricultural
commodities in 2014.15 With a state population of nearly 40
million people, a large amount of produce is processed and
consumed in-state. In 2014, 5.2 million wet tonnes of food
waste were sent to disposal facilities, up 13% from the 4.6
million wet tonnes disposed in 2008.16 Recent federal and state
regulatory action has created incentives to reduce the
generation of organic waste, including food waste, and to
divert remaining waste to composting and transformation. In
2015, the U.S. Department of Agriculture (USDA) and
Environmental Protection Agency (EPA) announced the first
national food waste reduction goal: 50% reduction in
postharvest losses at the retail- and consumer-levels by 2030.
New requirements in California for source-separation and
recycling of commercial organic waste (Assembly Bill (AB)
1826) are intended to reduce GHG emissions and create
opportunities for recycling manufacturing facilities; however,
transformation to energy is not counted toward the statewide
75% solid waste diversion goal for 2020, mandated in AB 341.
In 2006, Governor Schwarzenegger signed Executive Order S-
06-06 mandating that 20% of renewable electricity comes from
biomass; subsequent Bioenergy Action Plans have been
released to promote technology innovation and guide market
development for biobased products and energy.17
Diverting food waste for energy releases only biogenic
carbon and is therefore considered renewable. Although some
carbon in food waste would otherwise remain sequestered if the

waste is landfilled, the resulting methane emissions outweigh
this sequestration on a 100-year global warming potential
(GWP) basis.6 Multiple technologies exist for converting
organic materials, including food waste, into electricity, heat,
transportation fuels like hydrogen, and chemical products.18
Extensive reviews have been conducted on the anaerobic
digestion (AD) of the organic fraction of municipal solid
waste,18 and food waste from retailers and consumers.19−22
Anaerobic digestion generates a methane-rich biogas and a
nutrient-rich solid (digestate), the latter of which can be used as
a low-carbon fertilizer.23 The methane can be cleaned and used
onsite to generate electricity and heat in combined heat and
power systems (CHP), injected into pipelines as renewable
natural gas, or compressed into a biological natural gas
(bioCNG) transportation fuel. Life-cycle assessments of
incineration, composting, AD, and landfill treatment technol-
ogies for food waste find that AD leds to the greatest reduction
in carbon dioxide as long as biogas is captured and used for
energy.23,24 Fats, oils, and grease (FOG) can be used in AD
facilities or converted to liquid fuels. For example, yellow
grease, the used cooking oil from the food industry, is a suitable
feedstock for biodiesel.25 Not all food waste types are well
suited for anaerobic digestion. Waste with moisture content
(MC) below ∼50% and waste with high lignin content are
better suited to thermochemical processes like combustion and
gasification. Combustion of food waste like nut hulls and shells
generates heat, electricity, and a nutrient-rich ash that can be
applied to land. A number of solid biomass power plants
currently accept low moisture food waste like rice hulls and
olive pits.26,27
■ MATERIALS AND METHODS
Food waste is defined here as organic materials wasted within

the food supply chain, including food waste generated during
harvest, food processing, retail, and in eating establishments
and consumers’ homes. For example, this distinction includes
olive pits, but excludes olive tree branches and other woody/
herbaceous crop residues. In terms of transformation
technologies, our study focuses on electricity and thermal
energy generation. Direct combustion and AD of food waste
are assessed because they are the most mature conversion
technologies for food waste, are capable of handling highly
heterogeneous food waste streams, and generate products
which have established markets in California. Hydrogen or
liquid fuels could become attractive in the future with
technology advancements.28−30
Food Waste Meta-Analysis and Inventory. To deter-
mine key methodological differences and the data quality/
completeness associated with existing studies, a meta-analysis
of
food waste inventories and energy potential literature is
performed (Supporting Information (SI) Sections 1 and 2).
Assumptions used to estimate food waste yields (e.g., fraction
of MSW that is food waste) and potential electricity and heat
generation (e.g., efficiencies) vary across previous assessments
of California, and are used to recalculate and compare
results.11−14,31,32 Results from previous assessments are
compared with data from state33 and national15 agricultural
surveys, municipal solid waste databases,34,35 and personal
communication with food bioenergy program managers and
food processors36−38 to develop the assumptions and method-
ologies used in this study (SI, Sections 4.4, 4.5, 6).39,40
Waste production data is collection and disaggregation to
develop a food waste inventory by month and county of origin
for California for 2014. For ease of comparison, totals for high
moisture solid (HMS) and low moisture solid (LMS) wastes
are reported in bone dry tonnes (BDT). A bottom-up approach

is used to estimate agricultural culls, where county level
production data from the 2014 NASS33 for each type of
produce is multiplied by a “cull multiplier”, which assumes that
total available harvest is equal to the sum of reported
production and cull production (SI, Section 3.1). Planting,
harvesting, and peak harvesting dates in agricultural regions of
California are characterized through a critical review of NASS
agricultural survey and census data, and plant science literature
for each crop (SI, Section 3.6). Cull production is distributed
evenly over the harvesting time period, unless a peak harvesting
or cull collection period is identified, in which case 80% of
waste mass is distributed over the peak time period. County
level food processing waste production was reported in a 2007
survey;13 annual waste production and locations from the
survey are adjusted assuming constant waste yields, and that
2013 county level employment data can be used to scale 2007
production over time and space.41 New approaches are
developed in this study to estimate county and monthly
waste inventories for meat processors, distilleries, breweries,
commercial bakeries, tortilla manufacturers, and fruit and olive
pitters (SI, Section 3.2), while the approach developed in
Williams et al. 2015 is used to model nut and rice hullers’
waste.11
Environmental Science & Technology Policy Analysis
DOI: 10.1021/acs.est.6b04591
1121

The mass of MSW generated in each county in 2014 is
collected by quarter from the CalRecycle disposal database (SI,
Section 3.3).16 The food waste fraction of MSW generated by
retailers and consumers at the regional level is taken from a
2014 characterization study (SI, Table S6).39 Fats, oils, and
grease production at the retail and consumer levels are
determined using per-capita annual consumption data and
waste yields from the USDA Economic Research Service (SI,
Section 3.4).42,43 U.S. Census population data from 2014 is
used to calculate total FOG generation at the county-level.
Food waste technical availability is determined using two
indicators: (1) extent of source-separation practices and hauling
networks, (2) strength of established markets for wastes
(animal feed, rendering, etc.) (SI, Section 3.5).11−13 Technical
availability is high for wastes like winery pomace, which are
collected and stored during a production process and treated as
wastes. Technical availability is very low for wastes like
almond
hulls which are used as animal feed, and low for wastes like
vegetable culls which are difficult to collect and transport.
Existing Anaerobic Digester Capacity. The EPA
estimates an excess AD capacity of 15−30% at roughly 140
wastewater treatment facilities in California.29,44 Excess
capacity
allows facilities to handle fluctuations in wastewater due to
weather, population growth, and changes that occur when

sources of wastewater and waste biomass relocate. Thus, near-
term diversion of food waste can be achieved with existing AD
infrastructure if there is sufficient capacity to codigest food
waste alongside other organic feedstocks like wastewater
solids.45,46 Specifically, high moisture solids (MC ≥ 55%),
bakery wastes, and wastewater with high-biochemical oxygen
demand (BOD) content are all candidates for codigestion. A
meta-analysis of California WWTF databases and excess
capacity estimates is included in the SI, Section 2.
High-moisture solids are expensive to transport long
distances and are challenging and costly to store, although
technologies are emerging for extending storage periods of food
materials including wastes.47 Therefore, it is critical that AD
capacity is matched to food waste production at appropriate
spatial and temporal scales. Potentially available capacity is
estimated in this study for utilizing food waste in existing
organic waste-to-energy facilities with wet, dry, or high-solids
AD systems (SI, Section 4). Excess capacity in wet AD systems
at WWTF (both municipal and private) is determined by
calculating the potential to increase flow rate if facilities
reduced
their mean cell residence time (MCRT) to the EPA 40 CRF
Part 503 regulation minimum of 15 days (eq 1).48 Adequate
digestion of solids is necessary for producing biosolids that are
stable and have low pathogen content, minimizing total
biosolids produced, and generating biogas with higher methane
content. Food waste is more biodegradable than wastewater
solids, and thus more easily broken down in 15 days, however
operators should take care when reducing their MCRT as the
impact of changes on performance will vary between facilities.
τ
= − −Q

V V
Q
(15days) dilution (1)
where Q (million liter per day MLD) is the excess volumetric
flow rate of food waste that could enter the digester, τ is the
MCRT used at the facility (days), V is the volume of the
digester (million L), and Qdilution is the volume of processing
water needed to dilute food waste to 8% total solids (TS).
Qdilution is adjusted in scenario alterative “d” (Table 1).
Not all facilities provide data on digester volume (V) and
operational MCRT (τ), so an approach is developed to
estimate flow rate increases based on average daily wastewater
flow rate (Qinfluent). Data from 16 facilities in California that
provided V, τ, and Qinfluent revealed that excess volumetric
flow
rate Q could increase Qinfluent by 0.1 to 2%.
32 These percentages
are multiplied by Qinfluent for each WWTF, to give a range in
excess capacity that is within an order of magnitude of the
values calculated using engineering principles (eq 1), making it
possible to model individual facilities, despite limited data.
Capacity at operating organic waste-to-energy facilities are
determined or approximated using available facility data on
loading rates and waste composition (SI, Section 6).39 Finally,
the excess volumetric flow rate Q [MLD] at each facility is
converted to an excess mass loading rate [BDT/d] (SI,
Equations S4−S7) and aggregated to the county and state level
Table 1. Electricity and Heat Generation Scenarios and
Constraints. HMS = High Moisture Solids; TS = Total Solids
sensitivity analysis

scenario number and description
feedstock
availability
transport.
extent parameter base case variation
variation
label
1 energy from all food waste Gross WWTF influent scaling
factor used to
estimate food waste loading rates
into AD
2%
Qinfluent
0.1%
Qinfluent
b
2 energy from technically available food waste Tech. Total
solids specification for food
waste slurry loaded into AD
8% TS no spec. c
3a energy from all food waste that can be treated
using existing California infrastructure
Gross in-state Total solids specification for food
waste slurry loaded into AD

8% TS 4% TS d
4a energy from all food waste that can be treated
using existing county infrastructure
Gross in-county HMS storage duration limit 1 month 1 year e
5a energy from all technically available food waste
that can be treated using existing California
infrastructure
Tech. in-state methane utilizationa 85% 70−95% f
6a energy from all technically available food waste
that can be treated using existing county
infrastructure
Tech. in-county
aFor facilities that report biogas utilization. Facilities without
biogas utilization are set at 0% utilization.
DOI: 10.1021/acs.est.6b04591
1122

(SI, Table S17); these rates are treated as constants. County or
state aggregated food waste production is converted to [BDT/
d] for each month or for the whole year depending on the
storage duration assumed in scenarios (alternative “f” Table 1).
Existing Combustion Capacity. It is assumed that solid
fuel biomass power plants operating at less than 90% capacity
factor (CF) are feedstock constrained and are capable of
cofiring any dry biomass to reach a CF of 90%. Capacity factors
are gathered from 2012 eGRID for all operating solid biomass
power plants in California to determine excess capacity (eq
2).27,49 Power plants are cross-referenced with facility
websites
when possible to confirm operational status and feedstock mass
loading rates. Combustion capacity and available waste biomass
are adjusted for the mass of rice hulls, fruit and olive pits, and
nut shells currently being diverted to specific solid biomass
power plants.

= − ×P CF P(0.9 )i i NPi (2)
where P is the excess capacity (MW) at facility i, CF is the
capacity factor (%), and PNP is the nameplate capacity (MW).
Biogas combustion capacity is assumed to be unconstrained.
The analysis is rerun with facility specific capacity constraints
reflected in biogas flaring practices (SI, Section 4.3).
Electricity and Thermal Energy Generation Potential.
The six scenarios used to assess food waste bioenergy potential
are summarized in Table 1. Potential energy generation from
the conversion of gross food waste generated in California is
estimated in Scenario 1. Potential energy generation from the
conversion of only technically available food waste is estimated
in Scenario 2. Scenarios 1 and 2 parallel the type of scenarios
used in previous assessments.1
̀
Scenario 3 assumes that food
waste and FOG separated out of MSW are directed to organic
waste-to-energy facilities until the statewide excess mass
loading
rate is met. Food waste and FOG not sent to organic waste-to-
energy facilities are then sent to wet AD systems at WWTFs
within the state. If excess capacity is still available, treatment
of
municipal wastes is followed by loading of state specific blends
of processor food waste and then culls. This hierarchy is based
on the following rationale: MSW is generated in urban areas
near WWTFs and have centralized collection and hauling
networks; organic waste-to-energy facilities have large upfront
capital costs and have likely established contracts with MSW
haulers; there are collection and disposal systems in place for

food processors and less so for in-field culls. Scenario 4 is the
same as Scenario 3 except food waste transportation and
treatment is constrained to the county of origin. Scenarios 5
and 6 are the same as Scenarios 3 and 4, respectively, but
evaluate energy generation from technically available food
waste. Scenario 6 is the most conservative scenario, as it
assumes energy generation competes poorly with other markets
for food waste, and that waste haulers will not export food
waste to facilities in nearby counties. Variations b-f on
Scenarios
3−6 reflect different operation practices at WWTF, and
different storage limitations on HMS, than the base case
(variation a).
Methane production in Scenarios 1 and 2 is modeled using
wet-AD methane yield factors for specific types of HMS
(including commercial bakery and tortilla wastes) (SI, Table
S8). In the other scenarios, methane yields specific to the type
of AD technologies at waste-to-energy facilities are used to
model digestion of MWS food waste and FOG (SI, Table S10).
Without in situ data, proxies are needed to estimate methane
yields from blends of food waste collected throughout the state
(Scenarios 3 and 5) or county (Scenarios 4 and 6) and sent to
wet AD systems at WWTF. To develop these proxies, waste-
specific methane yields are scaled by the volatile solids fraction
in blends of culls or blends of food processor wastes and
summed to estimate the methane yields for the blend.
Survey data is used to identify the type of CHP technology
that is or potentially would be used at different scale facilities.
Small facilities mainly use rich-burn and lean-burn engines,
whereas large scale facilities use engines as well as micro-
turbines, and fuel cells.50 Electric efficiency and average
power-
to-heat ratios are acquired from program performance

standards (SI, Section 2.2).51 A CHP capacity factor of 85%
and an energy content of 38.3 MJ/m3 (1027 BTU/ft3) methane
are assumed. Combustion turbines, steam turbines, and
combined cycles prime movers for CHP are not included as
they are uncommon at WWTF,51 and because heat demand at
WWTF reduces waste heat availability. Annual loss of methane
due to flaring and fugitive emissions is estimated for each
WWTF based on biogas utilization and flaring survey data (SI,
Section 4.3).52 Net electricity generation from LMS is
calculated based on an efficiency of 0.2, and using waste-
specific dry-basis higher heating values (HHV) (SI, Table
S7).11−13 It is assumed that LMS can be stored for up to a year
to achieve steady loading rates to solid biofuel power plants in
Scenarios 3−6. Additional details of the sensitivity analysis are
provided in Table S18.
■ RESULTS
Meta-Analysis. Recent assessments have estimated gross
production of food waste from MSW and from food processors
in California at the state and annual level, as well as the
fraction
Table 2. Previous Assessments of Food Waste Resource and
Energy Potential in California. BDT = Bone Dry Tonnes; FOG =
Fats, Oils, Grease
reference (Matteson & Jenkins 2007)12 (Williams et al. 2015)11
(Amon et al. 2012)13 (Kester 2015)32 this study (Scenario 1)
Gross Production [106 BDT/y]
culls 0.8
processors 1.2 4.0 3.2 4.4
MSW - food waste 2.0 1.3 0.9 1.1
MSW - FOG 0.01 0.004
Electricity Generation [MWe]

culls 97
processors 134 549 534 527
MSW - food waste 105 184 98
MSW - FOG 2
sum [MWe] 239 733 534 724
DOI: 10.1021/acs.est.6b04591
1123

that is technically available for energy production (SI, Section
1).11−13 With the inclusion of culled produce and new
categories of food processing wastes, this study estimates 20%
higher gross production of food waste. Previous studies have
used combustion and AD to model energy production;
however, this study is the first to model facility-specific excess
combustion and AD capacity, technology- and feedstock-
specific methane yields, and storage and transportation impacts.
Despite generation potential varying between studies due to
differences in food waste production, AD methane yields, and
methane energy content, this study estimates a very similar
generation potential from gross food waste (Scenario 1) as
William et al. (Table 2).11 The range in excess AD capacity at
WWTFs estimated in this study bounds values in previous
assessments. Kester estimates that that 75% of all food waste
from MSW could be treated in-state at WWTF (∼4100 BDT/
d). Using this estimate, it would appear that WWTF could treat
100% of technically available food waste from MSW (∼2570
BDT/d11 to ∼3030 BDT/d in this study). This study finds that
99% of technically available food waste from MSW could be
treated in-county using the most optimiztic excess capacity
assumptions. However, capacity assumptions that reflect
existing food waste treatment projects ongoing in the state
(Scenario 4b) resulted in treatment of only 23% of technically
available food waste from MSW.
■ FOOD WASTE INVENTORY
The monthly variation in HMS food waste production is less
for food waste and FOG in MSW, spent grains, and meat
residues than it is for culls and fruit and vegetable processors
(SI Figure S6, Figure S7). Production of LMS varies
significantly between harvest and nonharvest dates (SI Figure
S8) and between urban and agricultural counties (SI Figure
S9). Gross waste from food processors totals 4.4 million BDT/

y; for comparison, Williams et al. calculated a gross food
processing residue generation of 3.9 million BDT in 2013.11
Statewide, 1.1 million BDT/y of food waste and 3560 BDT/y
FOG is generated in MSW. Food waste from MSW is
generated in all 58 counties in 2014, with the lowest production
rate occurred in Alpine County in the fall (Oct-Dec) at 0.1
BDT/d and the highest production rate occurred during the
same period in Los Angeles County (813 BDT/d). Uncertainty
in the location of production is lowest for MSW and culls, and
highest for food processor wastes, while uncertainty in tonnage
is moderate for MSW and high for food processor wastes and
culls. Food waste tonnage by type, county, and month is
included in SI Section 5, Tables S13−S16 whereas statewide
annual tonnage is provided in SI Table S19.
Electricity and Thermal Energy Generation Potential.
The energy in all food waste generated in California could
supply monthly electricity generation varying from 210 MWe in
March to 1,490 MWe in September, with an annual average of
1120 MWe that is equivalent to 55% of installed biomass
electric generation capacity in California (Scenario 1, Figure
1).53 Over 22 GJ of waste heat can be generated annually in
addition to electricity. Nearly all gross HMS (99%) and 55% of
gross LMS (including 97% of all rice hulls) could be converted
to electricity and heat using only existing excess capacity, or
capacity already dedicated to treating food waste like rice hulls
(Scenario 3a). Multimonth storage of LMS evens out a majority
of the seasonality seen in treatment of gross food waste
(Scenario 1), resulting in a higher baseline at the state level.
Electricity generation from technically available food waste
(Scenario 2) is substantially lower than it is for gross food
waste
(Scenario 1), largely due to complete diversion of almond hulls
to animal feed (Figure 1). Seasonality was even less

pronounced in Scenario 5a, where technically available food
Figure 1. Total food waste converted to energy and total
electricity generation potential per month are shown for
Scenarios 1−6. Scenarios and
variations are described in the table below the figure. Months
are abbreviated in the x axis labels (J = January, A = April, J =
July, O = October).
Values are disaggregated by region and food waste type in SI,
Section 7.
DOI: 10.1021/acs.est.6b04591
1124

waste is treated using existing excess capacity, as only 10% of
culls are assumed to be diverted to bioenergy.
Surprisingly, the biogas yield from state blends of culls is
relatively stable over the year, varying from 0.29 to 0.36 m3/kg
TS (SI, Table S9). There is a slight dip in the summer due to
production of an enormous amount of tomato culls, which have
a low biogas yield, however root and tuber culls, which have a
36% higher biogas yield, are produced at the same time and
help to offset the impact. Biogas yield from the state blend of
food processing wastes stays around 0.44 m3/kg TS (high due
to meat processing residues and spent grains), except for a
noticeable dip from July through October when it drops to 0.34
m3/kg TS. This dip represents the most active season for
wineries and fruit and vegetable processors, which generate
residues with low biogas yields.
For county level treatment of gross and technically available
food waste (Scenario 4a and Scenario 6a), monthly biogas
yields are unique to each county and vary from 0.26 to 0.43 m3/
kg TS for cull blends and from 0.24 to 0.51 m3/kg TS for food
processing residue blends. Seasonal variation in biogas yields
resulting from changing composition of culls and food
processing residues is limited in some counties and pronounced
in others.
Mismatch between the location of food waste production
and the location of energy facilities resulted in ∼60% lower
average electrical generation in Scenario 4a than in Scenario 1
(Figure 2). Generation potential from HMS is further reduced
by 25% when methane losses due to facility specific flaring

practices are included (ranging from 11 to 38%). Local
combustion capacity is limited in the Central Valley, where
over
95% of gross LMS is produced; only 23% of gross LMS can be
converted to energy in Scenario 4a even with multimonth
storage.
Calculations of existing excess AD capacity is more sensitive
to changes in the food waste to wastewater volumetric loading
ratio (Scenario 3b) than the total solids specification for the
food waste slurry dilution (Scenario 3b,c). Scenario 3b reflects
the operational practices used at WWTF currently accepting
HMS; under these conditions, only food waste and FOG from
MSW are treated, generating 25 MW. Wastewater from food
processors is not included in these totals as it is unclear what
fraction is already being treated at WWTFs (SI, Section 3.2.6).
Annual wastewater from food processors contains 158410 BDT
of BOD5 and has the potential generate 56 million m
3 of
methane if 100% is codigested (20 MWe and 640 MJ waste
heat).
■ DISCUSSION
This study assesses the use of AD and direct combustion to
convert food waste into electricity and thermal energy in
California. Between 10% and 99% of gross HMS and can be
digested using state AD infrastructure and in the same month
of production, and between 10% and 66% can be digested in-
county using AD infrastructure and in the same month of
production. These large ranges reflect the uncertainty regarding
excess capacity for food waste codigestion at WWTFs and
organic waste-to-energy facilities. Accounting for technical
availability (removing losses and currently utilized fractions)
for

waste best suited for AD results in potential utilization ranging
from 37 to 100% for in-state, and 37 to 99% for in-county. Only
55% of gross LMS (including 97% of total rice hulls) can be
converted to energy using excess capacity at in-state solid
biomass power plants, while only 27% can be converted to
energy if LMS must be utilized within the county of origin. This
is concerning as over 90 MW of solid biomass installed capacity
is going offline in 2016 alone.55 Additional LMS waste from
forests, resulting from recent droughts and bark beetle
infestations, will result in even more competition at composting
and organic transformation facilities and solid biomass power
plants.56 Fuel-switching at natural gas power plants is a
possible
solution for decreasing the new capacity needed to handle
Figure 2. Facility-level electricity generation capacities for
treating food waste are mapped over county-level annual food
waste-to-energy potential
(Scenario 1e). New solid biomass combustion capacity and new
AD infrastructure (converted to biogas combustion capacity
[MWe] for ease of
comparison) needed to reach gross potentials are shown in the
maps on the right. Values reflect the assumption that facilities
with existing excess AD
capacity have unconstrained combustion capacity. Facility
addresses and counties mapped using 2016 TIGER shapefiles.54
DOI: 10.1021/acs.est.6b04591
1125

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