Introduction
Several major atmospheric sources such as temperate forest biogenic
emissions (e.g., Ortega et al., 2014), developed-world pollution from fossil
fuel use (e.g., Ryerson et al., 2013), and laboratory-simulated biomass
burning (BB) (e.g., Stockwell et al., 2014) have been sampled extensively
with a wide range of instrumentation; but many important emission sources
remain unsampled, or rarely sampled, by reasonably comprehensive efforts
(Akagi et al., 2011). As the emissions of greenhouse gases and other air
pollutants from developing countries have grown in importance for air
quality and regional–global climate studies, the need for a more detailed
understanding of these emissions has increased. For example, the diverse and
loosely regulated combustion sources of South Asia are poorly characterized
and greatly undersampled relative to their proportion of global emissions
(Akagi et al., 2011). These sources include industrial and domestic biofuel
use (e.g., cooking fires), brick kilns, poorly maintained vehicles, open
burning of garbage and crop residue, diesel and gasoline generators, and
irrigation pumps.
Approximately 2.8 billion people worldwide burn solid fuels (e.g., wood,
dung, charcoal, coal) for domestic (household) cooking and heating
(Smith et al., 2013) with the largest share in Asia. Cooking fires are the
largest source of soot in South Asia (Ramanathan and Carmichael, 2008).
Industrial solid fuel use (e.g., brick kilns) is ubiquitous but difficult to
quantify in the developing world as it is not highly regulated or
adequately inventoried and can involve a variety of fuels (e.g., coal,
sawdust, wood, garbage, tires, crop residue) (Christian et al., 2010).
Along with industrial and domestic solid fuel use, open burning of
agricultural waste and garbage, gasoline and diesel-powered generators, and
many examples of high-emitting vehicles are prevalent but grossly
undersampled in the developing world with previous field emissions
characterization usually limited to a few trace gases and a few particulate
species such as black carbon (BC) mass (Bertschi et al., 2003; Christian et
al., 2010; Akagi et al., 2011; Bond et al., 2013).
Understanding the local to global impacts of these sources is vital to
modeling atmospheric chemistry, climate, and, notably, air quality as these
sources most commonly occur indoors or near or within population centers.
Aerosols directly affect climate through both absorption and the scattering of
solar radiation and indirectly affect climate by modifying clouds (Bond and
Bergstrom, 2006). Therefore, global modeling of radiative forcing requires
(among other things) accurate information on the amount and optical
properties of aerosol emissions (Reid et al., 2005). BB is a major source of
BC in the atmosphere, but it also dominates the global emissions of
weakly absorbing organic aerosol known as brown carbon (BrC). BrC has a
contribution to the total absorption of BB aerosol that is poorly constrained but critical to determining whether the net forcing of BB aerosol is positive
or negative (Feng et al., 2013; Chen and Bond,
2010). Open burning of biomass and household-level consumption of biofuel
account for a majority of BC emissions in important regions including Asia,
but data are limited about how much BrC is emitted from biofuel and biomass
combustion (Kirchstetter et al., 2004; Chen and Bond, 2010; Hecobian et al.,
2010; Arola et al., 2011). In general, there is significant uncertainty in
emissions inventories since BrC is rarely tabulated as a separate species
though the scattering and absorption of both BC and BrC are necessary to
model radiative transfer (Clarke et al., 1987).
Additionally, the secondary formation of organic aerosol and ozone as well
as the evolution of the BC and BrC optical properties are strongly
influenced by the co-emitted gases and particles via processes such as
coagulation, evaporation, oxidation, and condensation (Alvarado et al.,
2015; May et al., 2015). Near-source measurements of light absorption and
scattering by BC and BrC and their emission factors (EFs), along with the
suite of co-emitted gas-phase precursors, are needed to better estimate the
impacts of these undersampled sources on climate, chemistry, and
local–global air quality, especially in regions that lack comprehensive
sampling.
Current reviews of global BC emissions note that global models likely
underestimate BC absorption in several important regions including South
Asia (Bond et al., 2013), making this an important area where undersampled
emission sources have critical climate and chemistry impacts. BC emissions
from South Asia may negatively impact important regional water resources
(Menon et al., 2010) and contribute significantly to the warming of the
Arctic (Allen et al., 2012; Sand et al., 2013), and emissions of volatile
organic compounds (VOCs) and nitrogen oxides (NOx) in this region were
estimated to influence global warming more significantly than similar
emissions from other Northern Hemisphere regions (Collins et al., 2013).
Thus, these sources contribute significantly to the local–global burden of
primary aerosol, greenhouse gases, and reactive trace gases. Crudely
estimating their activity and the composition of their emissions can lead to
significant errors and uncertainties in regional and global atmospheric
models (Dickerson et al., 2002; Venkataraman et al., 2005; Adhikary et al.,
2007, 2010; Akagi et al., 2011; Bond et al., 2013; Wiedinmyer et al., 2014).
The Nepal Air Monitoring and Source Testing Experiment (NAMaSTE) was a
collaborative effort with multiple goals: (1) providing detailed chemistry,
physical properties, and EFs for the trace gases and aerosols produced by
many undersampled BB sources, a poorly maintained transport sector, brick
kilns, etc.; (2) using these new emissions data to expand and update
emissions inventories including the Nepal national inventory; (3) supporting
a source apportionment for Kathmandu, Nepal; (4) enhancing regional air
quality and climate modeling; and (5) informing mitigation strategies. The
project involved the International Centre for Integrated Mountain
Development (ICIMOD, the in-country lead), MinErgy (a local contractor to
ICIMOD), the Institute for Advanced Sustainability Studies (IASS, fixed-site
support), and the universities of Drexel, Emory, Iowa (UI), California,
Irvine (UCI), Montana (UM), and Virginia (UVA) in the US.
NAMaSTE employed two strategies simultaneously in the first measurement
phase. A temporary supersite was set up at a representative suburban
Kathmandu location (Bode) to augment the ongoing monitoring that was
initiated there in 2012 (Chen et al., 2015; Lüthi et al., 2015; Putero et
al., 2015; Sarkar et al., 2016) and to provide a target receptor for the
source apportionment. Simultaneously, a well-equipped mobile team
investigated numerous undersampled emissions sources in and around the
Kathmandu Valley and in the rural Tarai region in the Indo-Gangetic
Plain (IGP) of southern Nepal. The sources represented authentic, common practices but were usually not random and were arranged by the MinErgy and ICIMOD team
before the campaign. The source and fixed-site measurements commenced on
11 April of 2015 but were cut short by the Gorkha earthquake on 25 April. The
early termination prevented the sampling of on-road mobile sources including
heavy-duty diesel trucks, which is now planned for phase two. Additional
measurements of cooking fires and other sources planned in the Makwanpur
District in the foothills south of Kathmandu were also canceled, but many
valuable data on similar sources had already been gathered. In this paper we
present a brief summary of the source sampling campaign and the details of
the trace gas measurements of fresh emissions obtained by Fourier transform
infrared (FTIR) spectroscopy and whole-air sampling (WAS). We also present
photoacoustic extinctiometer (PAX) data co-collected at 405 and 870 nm to
measure the optical properties and estimate the mass of the fresh BC and BrC
emissions. Substantial additional source characterization data based on
sampling with Teflon and quartz filters and a suite of other real-time
aerosol instruments will be presented separately (Jayarathne et al., 2016;
Goetz et al., 2016). Several weeks of high-quality filter, WAS, aerosol mass
spectrometer, and other real-time data from the supersite at Bode will also
be presented and discussed separately. Taken together, the NAMaSTE efforts begin
to reduce the information gap for these important undersampled sources.
Experimental details
Source types and site descriptions
Nepal has variable terrain ranging from high mountains to the low-elevation
plains in the Tarai. Our team was based out of the major population center
of Kathmandu and we traveled by truck to various locations in and around the
Kathmandu Valley while also traveling south to the Tarai region. The Tarai
sits on the southern edge of Nepal in the IGP with intensive agriculture,
terrain, and other similarities to the heavily populated region of northern
India. The emissions data we present were obtained from many sources
including two-wheeled vehicles (motorcycles and scooters), diesel- and
gasoline-powered generators, agricultural pumps, garbage fires, cooking
fires, crop residue burning, and brick kilns. This section briefly
summarizes the significance of each source and how they were sampled in our
study.
Motorcycles and scooters
Mobile emissions are extremely important in urban areas as they contribute
significantly to the degradation of air quality on local to regional scales
(Molina and Molina, 2002, 2004; Molina et al., 2007; Dunmore et al., 2015).
In the Kathmandu Valley, approximately 80 % of registered vehicles are
motorcycles or scooters and this is the fastest growing portion of the
transport sector in Kathmandu and nationally (MOPIT, 2014). Motorcycles are
generally larger with larger engines than scooters and in Nepal both now
burn unleaded Euro-3 gasoline. Together, nationally, these two-wheeled
vehicles consume about one third of the gasoline and ∼ 10 %
of total fuel used for on-road transport (WECS, 2014), with total sales of
diesel and gasoline approaching 1 Tg in 2015 (Nepal Oil Corporation Limited,
2015). Vehicle EFs are commonly obtained from bulk exhaust measurements
(USEPA, 2015) and the International Vehicle Emissions (IVEs) model
specifically generates EF for mobile sources in the developing world
(Shrestha et al., 2013). However, the detailed source chemistry (e.g.,
specific air toxics) is poorly known, especially for the developing world,
as most studies focus only on CO, NOx, PM2.5, and a few
hydrocarbons or total VOC in developed countries (e.g., Zhang et al., 1993;
Pang et al., 2014).
There are a number of approaches to measure vehicular emissions that include
in-use sampling while driving as well as more controlled dynamometer studies
(Yanowitz et al., 1999; Pelkmans and Debal, 2006). Franco et al. (2013)
outline the advantages and drawbacks to these various sampling techniques,
though we will not discuss them further here. We were able to measure the
emissions exhaust of five motorcycles and one scooter during start-up and
idling, which are considered common traffic situations in the Kathmandu
Valley. On 13 April 2015, we set up the NAMaSTE emissions measuring
equipment next to a motorcycle repair shop, and to limit sampling bias, we
deliberately tested every motorcycle or scooter that entered the shop for
servicing that day. Each motorcycle and scooter was sampled (start-up and
idling) pre- and post-servicing (one motorcycle was not sampled
post-service). The motorcycle or scooter brand, model, etc., are shown in Table
S1. The maintenance routine included an oil change, cleaning the air filters
and spark plugs, and adjusting the carburetor.
Generators
Nepal has no significant fossil fuel resources and insufficient hydropower.
As a result, load shedding for many hours per day is common nationally and
diesel- or gasoline-powered generators (a.k.a. gensets) are critical
infrastructure for industrial, commercial, institutional, and household use,
consuming about 57 000 Mg of fuel per year (World Bank, 2014). Based on fuel
use, the emissions from generators could be about 6 % of those from
the transport sector. A large variety of generators are deployed to meet
various size, power, and load capacity needs. In this study we sampled
exhaust emissions from one small diesel generator with 5 kVA capacity
(Chanqta, CED6500s) and a much larger diesel generator, located on the
ICIMOD campus, with 100 kVA capacity running at 1518 rpm (85 % of full
load). In addition to the two diesel generators, we sampled the exhaust
emissions from one gasoline-fueled generator (Yeeda, Y-113(1133106)) that
had a similar capacity (4 kVA) to the smaller diesel generator. Most
pollutants from these engines are emitted through the exhaust, though some
fraction likely escapes from fuel evaporation.
Agricultural water pumps
The use of diesel-powered agricultural pumps to extract groundwater for
irrigation is rapidly rising in rural regions of Nepal and India with few to
no operational regulations (Barker and Molle, 2004). The dependence on
diesel-operated pumps is likely to rise in South Asia as crop production
rises with population demands. Although massive groundwater extraction has
aided agricultural productivity in the region, the environmental impacts are
seldom investigated (Shah et al., 2000). The pumps are estimated to consume
∼ 1.3 Tg yr-1 of diesel fuel, over the entire IGP.
Diesel-powered engine emissions can cause adverse health effects and
unfavorable impacts on air quality, climate, crops, and soils (Lloyd and
Cackette, 2001). We sampled the exhaust from two smaller diesel pumps
(Kirloskar, 4.6 kVA, and Field Marshall R170a, 5 kVA) in the Tarai. We also
sampled the exhaust opportunistically from a much larger irrigation pump
(Shineray) in suburban Kathmandu. We were unable to confirm fuel type but
suspect it was gasoline based on the emissions chemistry.
Garbage burning
Open burning of garbage is poorly characterized even in the most
“developed” countries where it occurs with minimal oversight mostly in
rural areas (USEPA, 2006). In developing countries open burning of garbage
is much more prevalent, poorly characterized, and much less regulated if at
all. In Nepal, as throughout the developing world, open burning of garbage
is ubiquitous on a range of scales. Small, meter-scale piles of burning
trash are seen along roads and in uncultivated fields. Approximately 10–20 times larger areas of burning trash are also common at landfills, along
roadsides and riverbanks, and basically many accessible, uncultivated open
spaces, with these areas evidently serving as an informal public resource.
Given the large amount of refuse generated and the lack of economically
viable alternatives to burning (Pokhrel and Viraraghavan, 2005), garbage
burning is estimated to consume about 644 000 Mg of municipal solid waste
(MSW) annually in Nepal (Wiedinmyer et al., 2014) and have a major impact on
air quality, health, and atmospheric chemistry. The few available previous
measurements of garbage burning suggest it is particularly important as a
source of BC, hydrogen chloride, particulate chloride, several ozone
precursors, and air toxics such as dioxins (Costner, 2005; Christian et al.,
2010; Li et al., 2012; Lei et al., 2013; Wiedinmyer et al., 2014; Stockwell
et al., 2014, 2015). To our knowledge only one study reports reasonably
comprehensive EFs for authentic open burning of garbage in the developing
world, namely the landfill fire sampling in Mexico of Christian et al. (2010). Several lab studies have measured the emissions from garbage burning
under controlled conditions in great chemical detail (Yokelson et al., 2013;
Stockwell et al., 2014, 2015), but the relevance of these lab experiments
needs further evaluation against a better picture of real-world garbage
burning. More real-world data are also needed to evaluate and update the
garbage burning global inventory mentioned above (Wiedinmyer et al., 2014).
During NAMaSTE, we were able to contribute a modest but important expansion
of the real-world garbage burning sampling. We sampled mixed-garbage burning
on six occasions, and we conducted three experiments burning segregated trash since
some processing of garbage before combustion is common. The segregated trash
experiments isolated plastics and foil-lined bags in separate individual
burns. The components in each garbage burn are summarized in
Table S2 in the Supplement. The overall carbon fraction for mixed waste was calculated in
Stockwell et al. (2014) by estimating the carbon content of each component
in the mixture, and the value of overall carbon content calculated therein
is assumed in our mixed-garbage EF calculations (0.50). Polyethylene
terephthalate (PET) is the most common plastic used in metallized packaging, as is the case for chip and other foil-lined bags, and has a carbon
fraction of 0.63 (USEPA, 2010). Most plastic bags are composed of high- and
low-density polyethylene (HDPE, LDPE) mixed with PET, and thus we estimate a carbon
content of 0.745 in this study (USEPA, 2010).
Cooking stoves
Most global estimates of domestic biofuel consumption (∼ 3000 Tg yr-1) designate domestic biofuel burning as the second-largest BB source
behind savanna fires (Akagi et al., 2011). In the developing world, it is
estimated that the majority of biomass fuel is burned in Asia
(∼ 66 %; Yevich and Logan, 2003). The solid fuels regularly
burned include wood-derived fuels (e.g., hardwood, twigs, sawdust, charcoal)
and agricultural residues (e.g., crop waste, livestock dung) though the
fuel choice depends on availability, local customs, and the season. Yevich
and Logan (2003) estimate residential wood-fuel use for Nepal in 1985 as 9.8 Tg yr-1. They do not estimate dung-fuel use in Nepal, but the data they
provide for Indian states with populations similar to Nepal suggests that
about 1–2 Tg yr-1 of dung is combusted residentially in Nepal.
The cooking-fire measurements in this study were conducted in two phases.
First, measurements were conducted by simulating field cooking in a
laboratory to capture emissions from a wide range of stove and fuel types.
Fuels for the lab tests included wood, dung, mixed wood and dung,
biobriquettes, and biogas. Stove types included traditional single-pot
mud stove, open three-stone, bhuse chulo (insulated vertical combustion chamber),
rocket stove, chimney stove, and forced draft stove. In the second phase,
cooking emissions were sampled from authentic cooking fires in the kitchens
of several rural Nepali homes and one restaurant operated out of a personal
kitchen. The two kitchens that utilized the traditional one-pot clay stove
were separated from the main dwelling by a mud wall. The ventilation for all
cases was by passive draft through the door, open windows, and gaps between
the walls and roof. Smoke samples were taken from the upper corner of the
kitchen where the inflow and outflow of emissions were somewhat balanced and
we were able to grab representative samples of accumulated emissions not
needing weighting by the fire-driven flow. Several biofuels are available to
the home and restaurant owners including twigs and larger pieces of hardwood
(Shorea robusta and Melia azedarach (Bakaino)) and dung
shaped into logs or cakes sometimes containing minor amounts of straw.
Different fuels or a combination of fuels were consumed depending on cooking
preference. Our study was designed to bring more comprehensive trace gas and
aerosol field sampling to the effort to understand cooking fires. We note
that the women tending to the cookstoves went in and out of the kitchens
with their children during food preparation, so exposure is also a concern.
While our concentration data could be used directly for indoor exposure
estimates, a better approach for estimating exposure to the air toxics we
report is via our ratios to commonly measured species in the available
studies more focused on representative exposure.
Crop residue
Crop residue burning is ubiquitous during the dry season in the Kathmandu
Valley and rural Nepal. Globally, burning crop residue post-harvest is
widely practiced to enable faster crop rotation; reduce weeds, disease, and
pests; and return nutrients to the soil. Alternatives to crop residue
burning such as plowing residue into the soil or use as livestock feed have
drawbacks including increased risk of wind erosion of topsoil and poor
“feed” nutritional quality (Owen and Jayasuriya, 1989). Thus, open burning
of crop residue is a prevalent activity in both developing and developed
countries and it has important atmospheric impacts, but the emissions are
not well characterized (Yevich and Logan, 2003; Streets et al., 2013; Sinha
et al., 2014). Data for Indian states with a similar population to Nepal
suggest that total annual crop residue burning in Nepal is on the order of
6–7 Tg yr-1 (Yevich and Logan, 2003).
The land use in southern Nepal is representative of the much larger
Indo-Gangetic Plain, which are inhabited by nearly a billion people. Crop
residue types may impact emissions significantly; thus, mostly in the Tarai,
we characterized emissions from two regionally important crop types
separately: rice straw and wheat. Additionally, we sampled the emissions
from the burning of other crop residue types important in this region
including mustard residue, grass, and a mixture of these residues. The
carbon fractions assumed in this study were taken from previous analyses of
similar fuels compiled in Table 1 of Stockwell et al. (2014).
Brick kilns
Brick production is an important industry in South Asia and the number of
brick kilns in Nepal and India combined likely exceeds 100 000 (Maithel et
al., 2012) with perhaps ∼ 1000 kilns in Nepal that would
likely require ∼ 1–2 Tg of fuel per year. However, the
industry is neither unambiguously inventoried nor strongly regulated. The
previous trace gas and particulate emissions data available on brick kilns
are very limited (Christian et al., 2010; Weyant et al., 2014). We were not
able to sample a large number of kilns in Nepal, but we were able to greatly
expand the number of important trace gas and aerosol species and properties
quantified.
During NAMaSTE, we sampled two brick kilns just outside the Kathmandu Valley
that employed different common and regionally important technologies. The
first kiln sampled was a zigzag kiln, which is considered relatively
advanced due to an airflow system that efficiently transfers heat to
multiple brick chambers. We note that most zigzag kilns in the Kathmandu
Valley have a chimney upwards of 18 m high to minimize impacts on
immediate neighbors. The tall stacks have been sampled from a port on the
side, which is useful but raises uncertainties due to possible condensation
after sampling hot and moist exhaust or losses on stack walls past the sampling
point. Therefore, we elected to sample the zigzag emissions from a kiln
outside the valley with a shorter chimney and where our inlet could be
within several meters of the chimney where emissions had cooled to near-ambient temperature. This approach was followed to reliably sample the
“real” emissions. The zigzag chimney emissions were sampled for 5 h (09:00–14:00), which captured several firing–feeding cycles lasting
about 1 h each. By cycles we refer to the periodic addition of a
primarily coal–bagasse mix during the day through multiple feeding orifices
(a.k.a. stoke holes) above the firing chamber that were moved as the firing
progressed. We also occasionally diverted the sampling to capture the
emissions from these stoke holes. The smoke emitted from both the chimney
and stoke holes mostly appeared white with occasional puffs of brown smoke
when coal was added through the stoke holes.
The second kiln was a common batch-type clamp kiln. In clamp kilns, green
unfired bricks are stacked and brick walls are built up to surround the
unfired bricks. Each batch is stacked, fired, cooled and must be unloaded
before firing the next batch. There is no chimney to vent emissions as the
kiln ventilates freely through the sides and roof. The naturally escaping
emissions were sampled at or near-ambient temperature about 1 m from the
roof throughout the day. The clamp kiln smoke always appeared white with no
apparent periods of black smoke.
Generally the cheapest type of coal available is used in South Asian kilns.
Bricks are typically fired to 700–1100 ∘C, consuming significant
amounts of coal and biomass as detailed elsewhere (Maithel et al., 2012).
The practice of biomass co-firing to reduce the use of coal is common as it
reduces expense, but co-firing in general is also known to reduce
fossil-CO2 emissions and some criteria pollutants such as NOx and
SO2 (e.g., Al-Naiema et al., 2015). We expect that the emissions change
depending on the biomass-to-coal blending ratios in South Asia and that the
blend likely varies considerably between kilns. In the two kilns we measured
the primary fuel was coal; however, the clamp kiln was more substantially
co-fired with biomass. The coal piles next to the clamp kiln were adjacent
to large piles of cut hardwood; thus, the coal was likely co-fired with a
substantial amount of hardwood and the emissions data confirms that. We note
that we were not on site long enough to measure the emissions from the
entire kiln lifetime. Thus, we cannot probe seasonal variation in brick kiln
emissions. However, we did capture four to five entire firing cycles from each kiln
that should represent the emissions near the end of the dry season
production period. Some kiln operators suspect that these emissions may
reflect more efficient combustion (and more bricks per kilogram fuel) than when
the kilns are first started up in January under conditions of lower ambient
temperature.
Instrument details
Land-based Fourier transform infrared (LA-FTIR) spectrometer
A rugged, cart-based, mobile FTIR (MIDAC, Inc.) designed to access remote
sampling locations (Christian et al., 2007) was used for trace gas
measurements. The system can run on battery or generator power. The
vibration-isolated optical bench consists of a MIDAC spectrometer with a
Stirling cycle cooled mercury–cadmium–telluride (MCT) detector (Ricor, Inc.)
interfaced with a closed multipass White cell (Infrared Analysis, Inc.) that
is coated with a halocarbon wax (1500 Grade, Halocarbon Products Corp.) to
minimize surface losses (Yokelson et al., 2003). In the grab sampling mode
used for the FTIR trace gas data reported in this paper, air samples are
drawn into the cell by a downstream pump through several meters of 0.635 cm
o.d. corrugated Teflon tubing. The air samples are then trapped in the
closed cell by Teflon valves and held for 2–3 min for signal
averaging to increase sensitivity. Once the infrared (IR) spectra of a grab sample are
logged on the system computer a new grab sample can be obtained. This
facilitates collecting many grab samples. Cell temperature and pressure are
also logged on the system computer (Minco TT176 RTD, MKS Baratron 722A).
Spectra were collected at a resolution of 0.50 cm-1 covering a
frequency range of 600–4200 cm-1. Since the last report of the use of
this system (Akagi et al., 2013), several upgrades and changes were made: (1) the addition of a retroreflector to the White cell mirrors increased the optical
pathlength from 11 to 17.2 m, lowering previous instrument detection
limits; (2) renewing the Teflon cell coating with halocarbon wax to maintain
good measurements of ammonia (NH3), hydrogen chloride (HCl), hydrogen
fluoride (HF), and other species prone to absorption on surfaces; (3) mounting the mirrors to a stable carriage rather than the previous method of
gluing them to the cell walls; (4) the abovementioned Stirling cycle
detector, which gave the same performance as a liquid-nitrogen-cooled
detector without the need for cryogens; (5) the addition of two logged flow
meters (APEX, Inc.) and filter holders to enable the system to collect
particulate matter on Teflon and quartz filters for subsequent laboratory
analyses. The new lower detection limits vary by gas from less than 1 to
∼ 100 ppb and are more than sufficient for near-source
ground-based sampling as concentrations are much higher (e.g., ppm range)
than in lofted smoke (Burling et al., 2011). Gas-phase species including
carbon dioxide (CO2), carbon monoxide (CO), methane (CH4),
acetylene (C2H2), ethylene (C2H4), propylene
(C3H6), formaldehyde (HCHO), formic acid (HCOOH), methanol
(CH3OH), acetic acid (CH3COOH), furan (C4H4O),
hydroxyacetone (C3H6O2), phenol (C6H5OH),
1,3-butadiene (C4H6), nitric oxide (NO), nitrogen dioxide
(NO2), nitrous acid (HONO), NH3, hydrogen cyanide (HCN), HCl,
sulfur dioxide (SO2), and HF were quantified by fitting selected
regions of the mid-IR transmission spectra with a synthetic calibration
nonlinear least-squares method (Griffith, 1996; Yokelson et al., 2007). HF
and HCl were the only gases observed to decay during the several minutes of
sample storage in the multipass cell. Thus, for these species, the results
are based on retrievals applied separately to the first 10 s of data
in the cell (Yokelson et al., 2003). An upper limit 1σ uncertainty
for most mixing ratios is ±10 %. Post-mission calibrations with
NIST-traceable standards indicated that CO, CO2, and CH4 had an
uncertainty between 1 and 2 %, suggesting an upper limit on the field
measurement uncertainties for CO, CO2, and CH4 of 3–5 %. The
NOx species have the highest interference from water lines under the
humid conditions in Nepal and the uncertainty for NOx species is
∼ 25 % with the detection limits being near the upper end of
the reported range.
In addition to the primary grab sample mode, the FTIR system was also used in
a real-time mode to support filter sampling when grab samples were not being
obtained. Side-by-side Teflon and quartz fiber filters preceded by cyclones
to reject particles with an aerodynamic diameter > 2.5 microns
were followed by logged flow meters. The flow meter output was then combined
and directed to the multipass cell where IR spectra were recorded at
∼ 1.1 s time resolution. In real-time or filter mode we did not employ
signal averaging of multiple scans and the signal-to-noise ratio is lower at high
time resolution. In addition, there could be sampling losses of sticky
species such as NH3on the filters. However, the data quality is still
excellent for CO2, CO, and CH4. This allowed the time-integrated
mass of particle species to be compared to the simultaneously sampled
time-integrated mass of CO and other gases and provided additional
measurements of the emissions for these three gases as described in detail in
the filter sampling companion paper (Jayarathne et al., 2016).
Whole-air sampling (WAS) in canisters
Whole-air samples were collected in evacuated 2 L stainless steel canisters
equipped with a bellows valve and preconditioned by pump-and-flush
procedures (Simpson et al., 2006). The canisters were filled to ambient
pressure directly in plumes (alternately from the FTIR cell for the zigzag
kiln) to enable subsequent measurement and analysis of a large number of
gases at UCI (Simpson et al., 2006). Species quantified included CO2,
CO, CH4, and 93 non-methane organic compounds (NMOCs) by gas
chromatography coupled with flame ionization detection, electron capture
detection, and quadrupole mass spectrometer detection as discussed in
greater detail by Simpson et al. (2011). Peaks of interest in the
chromatograms were individually inspected and manually integrated. The limit
of detection for most NMOCs that were sampled was 20 pptv, which was well
below the observed levels. Typically ∼ 60 WAS NMOCs were
enhanced in the source plumes, and we do not report the results for most
multiply halogenated species and the higher alkyl nitrates, which are mostly
secondary photochemical products. The species we do not report were not
correlated with CO and are generally not emitted directly by combustion
(Simpson et al., 2011). Styrene is known to decay in canisters and the
styrene data may be lower limits. In total, 96 WAS canisters were sent to Nepal to
support the source characterization and ambient monitoring site. Because we
anticipated needing canisters for a longer campaign, typically only one
emissions sample and one background sample were collected for each source on
each day. In total, 48 WAS canisters were filled in all, mostly in April, along with
FTIR and other instruments, but some additional source and background
measurements were conducted by WAS alone in June after the main campaign.
The trace gas measurement techniques used for the reported EFs are indicated
in the “method” row near the top of the Supplement and main tables.
Photoacoustic extinctiometers (PAX) at 405 and 870 nm
Particle absorption and scattering coefficients (Babs,
Bscat 1/Mm) at 405 and 870 nm were measured directly at 1 s
time resolution using two photoacoustic extinctiometers (PAX, Droplet
Measurement Technologies, Inc., CO) and single scattering albedo (SSA), and
absorption Ångström exponent (AAE) were calculated from these
measurements. This monitored the real-time absorption and scattering
resulting from BC and (indirectly) BrC. The two units were mounted with AC/DC
power options, a common inlet, desiccator (Silica Gel), and gas scrubber
(Purafil) in rugged, shock-mounted, Pelican military-style hard cases. Air
samples were drawn in through conductive tubing followed by 1.0 µm
size-cutoff cyclones (URG) at 1 L min-1. The continuously sampled air
is split between a nephelometer and photoacoustic resonator enabling
simultaneous measurements of scattering and absorption at high time
resolution. Once drawn into the acoustic section, modulated laser radiation
is passed through the aerosol stream and absorbed by particles in the sample
of air. The energy of the absorbed radiation is transferred to the
surrounding air as heat and the resulting pressure changes are detected by a
sensitive microphone. Scattering coefficients at each wavelength were
measured by a wide-angle integrating reciprocal nephelometer, using
photodiodes to detect the scattering of the laser light. The estimated
uncertainty in absorption and scattering measurements is ∼ 4–11 %
(Nakayama et al., 2015). Additional details on the PAX instrument can be
found elsewhere (Arnott et al., 2006; Nakayama et al., 2015). Due to damage
during shipping the PAXs were not available until repaired partway through the
campaign and PAX data are therefore not available for a few sources.
Calibrations of the two PAXs were performed frequently during the deployment
using the manufacturer recommended scattering and absorption calibration
procedures utilizing ammonium sulfate particles and a kerosene lamp to
generate pure scattering and strongly absorbing aerosols, respectively. The
calibrations of the scattering and absorption of light were directly compared to
measured extinction by applying the Beer–Lambert law to laser intensity
attenuation in the optical cavity (Arnott et al., 2000). As a quality
control measure, we frequently compared the measured total light extinction
(Babs+Bscat) to the independently measured laser
attenuation. For nearly all the 1 s data checked, the agreement was within
10 % with no statistically significant bias; consistent with (though not
proof of) the error estimates in Nakayama et al. (2015). The 405 nm laser in
the PAX has a common nominal wavelength that is usually not measured
precisely. After the mission a factory absorption calibration was performed
with NO2 gas that was within 1 % of the expected result (Nakayama et
al., 2015). As part of this calibration, the laser wavelength was precisely
measured as 401 nm. This difference from the nominal 405 nm wavelength adds
1 % or less uncertainty to the AAE and absorption attribution (Sect. 2.3).
We have continued to refer to the wavelength as 405 nm since this is a
standard nominal wavelength for aerosol optical measurements.
Other measurements
Two instruments provided CO2 data that were used in the analysis of the
PAX data. An ICIMOD Picarro (G2401) cavity ring-down spectrometer measured
CO2, CO, CH4, and H2O in real time. A Drexel LI-COR (LI-820)
that was factory calibrated immediately before the campaign also measured
CO2 in real time. The sampling inlet of the Picarro and/or LI-COR was
colocated with the PAX inlets so that the time-integrated PAX particle data
were easily ratioed to time-integrated CO2, allowing straightforward,
accurate synthesis of the PAX data with the mobile FTIR and WAS grab sample
measurements as described below. A suite of other instruments (mini aerosol
mass spectrometer (mAMS); seven wavelength, dual spot aethalometer (model AE33) from Drexel) and the filters employed during the source sampling for
subsequent analysis at UI will be described in more detail in companion
papers (Jayarathne et al., 2016; Goetz et al., 2016).
Emission ratio and emission factor determination
The excess mixing ratios above the background level (denoted ΔX for
each gas-phase species “X”) were calculated for all gas-phase species.
The molar emission ratio (ER) for each gaseous species X relative to CO or
CO2 was calculated for the FTIR and WAS species. For the single WAS
sample of any source the ER was simply ΔX divided by ΔCO or
ΔCO2. The source-average ER for each FTIR species, typically
measured in multiple grab samples, was estimated from the slope of the linear
least-squares line (with the intercept forced to zero) when plotting ΔX vs. ΔCO or ΔCO2 for all samples of the source
(Yokelson et al., 2009; Christian et al., 2010). Forcing the intercept
effectively weights the points obtained at higher concentrations that reflect
more emissions and have greater signal-to-noise ratios so that error is dominated by
calibration uncertainty. Alternate data reduction methods usually have little
effect on the results as discussed elsewhere (Yokelson et al., 1999). For a
handful of species measured by both FTIR and WAS, it is possible to average
the ERs from each instrument for a source together, as in Yokelson et
al. (2009). However, in this study, due to the large number of FTIR samples
(∼ 5–30) and small number of WAS samples (typically one) of each
source, we simply used the FTIR ER for “overlap species” (primarily
CH3OH, C2H4, C2H2, and CH4).
From the ERs, emission factors (EFs) were derived in units of grams of
species X emitted per kilogram of dry biomass burned by the carbon mass
balance method, which assumes that all of the major carbon-containing emissions
have been measured (Ward and Radke, 1993; Yokelson et al., 1996, 1999):
EFXgkg-1=FC×1000×MMxAMC×ΔXΔCO∑j=1nNCj×ΔCjΔCO,
where FC is the measured carbon mass fraction of the fuel; MMx is
the molar mass of species X; AMC is the atomic mass of carbon
(12 g mol-1); NCj is the number of carbon atoms in species j; n is the
total number of measured species; ΔCj or ΔX referenced
to ΔCO are the source-average molar emission ratios for the
respective species. The carbon fraction was either measured directly (ALS
Analytics, Tucson, Table S3) or assumed based on measurements of similar
fuel types (Stockwell et al., 2014). The denominator of the last term in Eq. (1) estimates total carbon. Based on many combustion sources measured in the
past, the species CO2, CO, and CH4 usually comprise 97–99 % of
the total carbon emissions (Akagi et al., 2011; Stockwell et al., 2015). Our
total carbon estimate includes all the gases measured by both FTIR and WAS
in grab samples of a source, and we include the carbon in elemental and
organic carbon (ratioed to CO) measured during filter sampling. Ignoring the
carbon emissions not measurable by our suite of instrumentation (typically
higher molecular-weight oxygenated organic gases) likely inflates the EF
estimates by less than ∼ 1–2 % (Andreae and Merlet, 2001;
Yokelson et al., 2013; Stockwell et al., 2015).
Biomass fire emissions vary naturally as the mix of combustion processes
varies. The relative amount of smoldering and flaming combustion during a
fire can be roughly estimated from the modified combustion efficiency (MCE).
MCE is defined as the ratio ΔCO2/ (ΔCO2+ΔCO) and is mathematically equivalent to (1 / (1+ΔCO/ΔCO2) (Yokelson et al., 1996). Flaming and smoldering combustion often
occur simultaneously during biomass fires, but a very high MCE
(∼ 0.99) designates nearly pure flaming (more complete
oxidation), while a lower MCE (∼ 0.75–0.84 for biomass fuels)
designates pure smoldering. Source-averaged MCE was computed for all sources
using the source average ΔCO /ΔCO2 ratio as above. In
the context of biomass or other solid fuels, smoldering refers to a mix of
solid-fuel pyrolysis and gasification (Yokelson et al., 1997) that does not
occur in the liquid fuel sources we sampled (e.g., motorcycles, generators,
pumps). However, given the large difference in the heat of formation for
CO2 and CO (283 kJ mol-1) and CO being the most abundant
carbon-containing emission from incomplete combustion, MCE and ΔCO /ΔCO2 were useful qualitative probes of their general
operating efficiency.
The time-integrated excess Babs and Bscat from the PAXs were used
to directly calculate the source average single scattering albedo (SSA,
defined as Bscat/(Bscat+Babs)) at both 870 and 405 nm
for each source. The PAX time-integrated excess Babs values at 870 and 405 nm
were used directly to calculate each source-average AAE.
AAE=-logBabs,1Babs,2logλ1λ2
Emission factors for BC and BrC were calculated from the light absorption
measurements made by PAXs at 870 and 405 nm (described in Sect. 2.2.3).
Aerosol absorption is a key parameter in climate models; however, inferring
absorption from total attenuation of light by particles trapped on a filter or from the assumed optical properties of a mass measured by thermal or optical
processing, incandescence, etc., can sometimes suffer from artifacts
(Subramanian et al., 2007). In the PAX, the 870 nm laser is absorbed in situ
by black carbon containing particles only without filter or filter-loading
effects that can be difficult to correct. We directly measured aerosol
absorption (Babs, Mm-1) and used the
manufacturer-recommended mass absorption coefficient (MAC)
(4.74 m2 g-1 at 870 nm) to calculate the BC concentration
(µg m-3). Our BC mass values are easily scaled if a user feels
a different MAC is preferable for one or more sources. The total uncertainty
in the MAC is not well known, but the coefficient of variation recommended by
the review article our BC MAC is based on is 16 % at 550 nm for fresh, uncoated
combustion aerosol (Bond and Bergstrom, 2006). However, some fresh BC may
have some coating and the assumption of an AAE of 1 to calculate the MAC at
870 nm is not exact. Overall, ∼ 25–30 % is probably a reasonable
“typical” uncertainty, with the error being asymmetric in that we are more
likely to overestimate BC mass due to coating-induced MAC increases. To a
good approximation, sp2-hybridized carbon has an AAE of 1.0 ± 0.2 and
absorbs light proportional to frequency. Thus, Babs due only to
BC at 405 nm would be expected to equal 2.148×Babs at
870 nm. This assumes that any coating effects are similar at both wavelengths,
and it has other assumptions considered reasonably valid, especially in biomass
burning plumes by Lack and Langridge (2013). Following these authors, we
assumed that excess absorption at 405 nm, above the projected amount, is
associated with BrC absorption, and the BrC (µg m-3)
concentration was calculated using a literature-recommended brown carbon MAC
of 0.98 ± 0.45 m2 g-1 at 404 nm (Lack and Langridge,
2013). The BrC mass calculated this way is considered roughly equivalent to
the total organic aerosol (OA) mass, which as a whole weakly absorbs UV
light, and not the mass of the actual chromophores. The MAC of bulk OA varies
substantially and the BrC mass we calculate with the single average MAC that we
used is only qualitatively similar to bulk OA mass for “average” aerosol
and even less similar to bulk OA for non-average aerosol (Saleh et al.,
2014). The BrC mass estimated by PAX in this way was independently sampled
and worth reporting, but the filters and mAMS provide additional samples
of the mass of organic aerosol emissions that have lower per-sample
uncertainty for mass. Most importantly, the optical properties from the PAX
(SSA, AAE, and absorption EFs calculated as detailed below) are not impacted
by MAC variability or filter artifacts. In the case where only a mass
emission is reported, a user has to calculate the absorption and scattering
with uncertain MAC or mass scattering coefficient values while also retaining any systematic error in
the mass measurement, though we note that mass measured by a PAX can always
be converted back to absorption (using the same MAC) without adding error. As
mentioned above, the PAXs were run in series or in parallel with a CO2
monitor. The mass ratio of BC and BrC to the simultaneous colocated
CO2, measured by either the Picarro or LI-COR, was multiplied by the
FTIR-WAS grab sample EF for CO2 to determine mass EFs for BC and BrC in
g kg-1. From the measured ratios of Babs and
Bscat to CO2, the EFs for scattering and absorption at 870
and 405 nm (EF Babs, EF Bscat) were calculated and
reported in units of square meters emitted per kilogram of dry fuel burned. We reiterate
that the absorption and scattering EFs do not depend on assumptions about the
AAE of BC or MAC values. Both the CO2 and PAX sample were often diluted
by using a Dekati Ltd. Axial Diluter (DAD-100), which was factory calibrated
to deliver 15.87 SLPM (standard liter per minute) of dilution air at an atmospheric pressure of
1004.6 mbar. Since both instruments samples were diluted by the same amount
the dilution factor does not impact the calculation of PAX / CO2
ratios. On the other hand, the dilution could have some impact on
gas–particle partitioning and the mass of BrC measured. More on the dilution
system (and additional aerosol measurements) will be in a forthcoming
companion paper (Goetz et al., 2016). Related measurements of elemental and
organic carbon on the filters will be discussed by Jayarathne et al. (2016).
Emission factors for sources with mixed fuels
Several of the cooking fires burned a mix of wood and dung, mixed garbage
was burned, and the brick kilns co-fired some biomass with the dominant coal
fuel. It is not possible to quantify the exact contribution of each fuel to
the overall fuel consumption during a specific measurement period or even in
total. Thus, for the mixed-fuel cooking fires, we simply assumed an equal
amount of wood (0.45 C) and dung (0.35 C) burned and used the average carbon
fraction for the two fuels (0.40) (Stockwell et al., 2014; Table S3). For
mixed garbage we used a rigorous laboratory carbon content determination
(0.50; Stockwell et al., 2014) as opposed to a field determination that
relied in part on visual estimates of the amount of components (0.40; Christian et al., 2010). For the zigzag kiln, we used the measured carbon
content of the coal (0.722). For the clamp kiln, which likely had more
co-fired biomass, we used a weighted carbon content assuming 10 % biomass
(at a generic 0.50 carbon content) and 90 % coal (measured carbon content
0.660). The weighted average carbon content for the clamp kiln is about
2.5 % lower than for the pure coal. The correction is speculative but in
the appropriate direction. The assumed carbon fractions are indicated in
each table and the new fuel analyses performed for NAMaSTE for several fuel
types are compiled in Table S3. For mixtures differing from those we used,
the EFs scale with the assumed carbon fraction.
There are a few unavoidable additional uncertainties in assigning EFs to
specific fuels for the brick kilns due to the possibility of emissions from
the clay during firing. An estimate of the impact can be made from
literature data. Clay typically contains well under 1 % organic
material, and some can be lost during firing though residual C can increase
the strength of the fired product and limited permeability makes complete
combustion of the C in the clay difficult to achieve (Wattel-Koekkoek et
al., 2001; Organic Matter in Clay, 2015). For a generous exploratory
estimate, we can assume the green bricks are 1 % by mass organic matter
that is all C. The brick / coal mass ratio reported by Weyant et al. (2014) is
6–26, and we take 15 as an average. Overall, 15 kg of clay at 1 % C would have 150 g
of C and 1 kg of coal at 70 % C would have 700 g C. Thus, if all of the
C in the clay was emitted, it would cause about 18 % of the total C
emissions from the production process as an upper limit. The impact on the
EF per kilogram coal fuel that we calculated by the carbon mass balance (CMB)
method depends on the species-specific ER to CO2 in the emissions from
the clay C. If the ER for a species due to heating clay C is the same as
burning coal C, then there is no effect on the EF computed by the CMB per
kilogram coal even though some of the species is actually coming from the clay. If
the ER for “heating” clay C is much higher or lower than the ER for
burning coal C (e.g., a factor 10), then for some non-CO2 species, we
would calculate increases or decreases in the CMB-calculated EFs relative to
what actually is produced from the coal fuel. These are only large if a
species is emitted mostly from clay combustion (vide infra).
The absorption Ångström exponent (AAE) calculated at 405 and
870 nm as a function of single scattering albedo (SSA) at 405 nm for fuel
types measured during the NAMaSTE campaign. The error bars represent ±1
standard deviation of the AAE measured for different burns (or different
samples in the case of brick kilns). Note: “hw” indicates hardwood fuels.
The AAE for agricultural pumps was not measured but is assumed to be 1 because the SSA at 405 nm was indicative of pure BC. AAE was only measured
on one garbage burning fire (value of 0.971) though the SSA at 405 nm on
another garbage burning fire indicates that its AAE was larger than 1.
Results and discussion
Overview of aerosol optical properties
As mentioned above, we measured absorption and scattering coefficients
directly and calculated single scattering albedo at 405 and 870 nm. One
wavelength-independent SSA value is often assumed for BB aerosol, but we
find, as seen previously, that the SSA varies by wavelength for each source
(Liu et al., 2014; McMeeking et al., 2014). The AAE is related to the
wavelength dependence of the absorption cross section. The AAE for pure BC is
assumed to be ∼ 1, while higher values of AAE indicate relatively more
UV absorption and the presence of BrC. Figure 1 plots the source-average AAE
vs. the source-average SSA at 405 nm showing that high AAE is associated
with high SSA. In Fig. 1 we show source-averaged AAEs ranging from
∼ 1–5 and source-averaged SSA values at 405 nm ranging from
0.37–0.95 for the sources tested in this study. The error bars are 1 standard deviation of the average for each source type sampled more than
once. The “high-AAE” sources appearing toward the upper right-hand corner
(e.g., dung and open wood cooking, clamp kiln) are associated with significant
light absorption that would be overlooked by a consideration of BC alone. We
note that both PAXs were not operational during the generator and motorcycle
sampling days, and the PAX 870 was not operational during the irrigation pump
sampling and for several garbage burns. We assumed that the pumps emitted
only BC (this assumption is supported by the very low SSA) and used the MAC
of BC at 405 nm (10.19 m2 g-1) to calculate BC for this one
source (Bond and Bergstrom, 2006). Both PAXs were operational for only one
garbage burn, which had a low AAE near 1. Additional data from the
aethalometer and filters, including for tests where one or both PAXs were not
operational, will be presented in companion papers (Jayarathne et al., 2016;
Goetz et al., 2016).
It is important to consider the differences in optical properties for the
aerosol emitted by the various biofuel–stove combinations used in this
understudied region with high levels of biofuel use. Dung-fired cooking had
a significantly higher AAE (4.63 ± 0.68) than cooking with hardwood
(3.01 ± 0.10). The AAE is also generally lower for improved stove
types (1.68 ± 0.47) when compared to traditional open cooking (i.e.,
without an insulated combustion chamber) (Fig. 1). In general, the optical
properties vary significantly by fuel type and the mix of combustion
processes. As established in previous studies (e.g., Christian et al., 2003;
Liu et al., 2014), BC is emitted by flaming combustion and BrC is emitted
primarily during smoldering combustion, and both can contribute strongly to
the total overall absorption. Thus, the fuels that burned at a higher
average MCE usually produced relatively more BC, which is also reflected in
lower AAE and SSA values. These trends are similar to those observed during
the third and fourth Fire Lab at Missoula Experiment (FLAME-3, -4) (Lewis et
al., 2008; McMeeking et al., 2014; Liu et al., 2014). Additional PAX results
will be discussed by fuel type along with the trace gas results in the
following sections.
Fleet average emission factors (g kg-1) and standard deviation
for two-wheeled vehicle measurements.
Compound (formula)
EF pre-service
EF post-service
fleet avg (SD)
fleet avg (SD)
Method
FTIR
FTIR
MCE
0.619
0.601
Carbon dioxide (CO2)
1846 (690)
1816 (562)
Carbon monoxide (CO)
710 (389)
761 (327)
Methane (CH4)
7.60 (7.24)
6.74 (4.54)
Acetylene (C2H2)
11.7 (11.1)
7.89 (5.83)
Ethylene (C2H4)
13.2 (3.9)
11.4 (4.2)
Propylene (C3H6)
3.32 (0.75)
2.58 (1.03)
Formaldehyde (HCHO)
0.548
0.535
Methanol (CH3OH)
bdl
bdl
Formic acid (HCOOH)
9.57 ×10-2
5.95 ×10-2
(3.57 ×10-2)
(1.84 ×10-2)
Acetic acid (CH3COOH)
bdl
bdl
Glycolaldehyde (C2H4O2)
bdl
bdl
Furan (C4H4O)
bdl
bdl
Hydroxyacetone (C3H6O2)
2.10 (3.18)
2.41 (0.99)
Phenol (C6H5OH)
4.84 (3.55)
3.02 (2.29)
1,3-Butadiene (C4H6)
1.30 (0.51)
1.19 (0.56)
Isoprene (C5H8)
bdl
bdl
Ammonia (NH3)
0.113 (0.034)
0.032 (0.023)
Hydrogen cyanide (HCN)
0.841 (0.428)
0.678 (0.174)
Nitrous acid (HONO)
bdl
bdl
Sulfur dioxide (SO2)
bdl
bdl
Hydrogen fluoride (HF)
bdl
bdl
Hydrogen chloride (HCl)
bdl
bdl
Nitric oxide (NO)
2.94 (2.39)
1.89 (0.81)
Nitrogen dioxide (NO2)
bdl
bdl
Note: “bdl” indicates below the detection
limit; C fraction: 0.85 – source is Kirchstetter et al. (1999).
Motorcycle emissions
The average EFs (g kg-1) based on FTIR and WAS for the pre- and post-service
fleet are shown in Table 1, and bike-specific pre and post results are included
in Supplement Table S4. As a fleet, we found that after servicing, MCE,
NOx, and most NMOCs were slightly reduced and CO slightly increased; however, these fleet-average changes are not statistically significant given
the high variability in EF. Interestingly, for individual motorbike-specific
comparisons (Table S4), in four out of five bikes, the MCE actually
decreased after servicing, indicating less efficient (though not necessarily
less “clean”) combustion, but this result is not statistically
significant. To ensure that effects such as background drift did not cause
this result, we verified that the same results occur when obtaining slopes
from plots using absolute (i.e., not background-corrected) mixing ratios. A
similar lack of reduction in gas-phase pollutants has been reported in the
literature following repair and maintenance (Chiang et al., 2008) and has
been attributed to the complexity in adjusting carburetors to optimal
combustion conditions (Escalambre, 1995). Our high CO emissions did not
always correlate with high hydrocarbon emissions. While we do not know the
exact cause of this, this effect has been seen in other vehicle studies with
a variety of explanations (Beaton et al., 1992; Zhang et al., 1995). While the
gaseous pollutants were not significantly reduced post-service, the fleet's
total particulate emissions did decrease significantly, and we refer to
Jayarathne et al. (2016) for a detailed comparison.
CO had the highest emissions of any gas after CO2, and the FTIR-measured
average EFs pre- and post-service over 700 g kg-1 are about 10 times the
typical EF for CO observed in BB. The FTIR-measured average MCE for the post-service motorcycles was ∼ 0.60, equivalent to a CO / CO2
molar ER of ∼ 0.66, dramatically highlighting the poor
efficiency of the engines. We were initially surprised by this result, but
it is confirmed by WAS in that the one WAS sample of start-up and idling
emissions returned a CO / CO2 ER (0.789) that is within the FTIR-sample
range. In fact, even higher CO / CO2 ERs (3.2–4.2) are generated
for the start-up of motorcycles in the IVE model, which is based on sampling in
developing countries (Oanh et al., 2012; Shrestha et al., 2013). Of 11 227 vehicles of all types tested by remote sensing during on-road use in
Kathmandu in 1993, about 2000 had a CO / CO2 ER higher than 0.66 (fleet
average 0.39, range 0–3.8, Zhang et al., 1995).
The next most abundant emissions after CO were C2 hydrocarbons
(∼ 24 g kg-1), BTEX (benzene, toluene, ethylbenzene, and
xylenes) compounds (∼ 15 g kg-1) and then the sum of measured
oxygenated volatile organic compounds (OVOCs) and CH4 each at
∼ 7 g kg-1. The OVOC from this source were mostly phenol,
hydroxyacetone, and acetone (Tables 1 and S4). The BTEX and acetone data are
from the one motorcycle that was analyzed by WAS pre-service. The WAS
provided several overlap species with the FTIR and many additional
non-methane hydrocarbons (NMHCs) not measured by FTIR. First, we note, in
agreement with the FTIR, that ethylene and acetylene were the most abundant WAS
NMHC species and that they accounted for ∼ 38 % of the total WAS NMHC
emissions. The acetylene-to-ethylene ratio in this sample was 0.45, which is
similar to previous roadside studies of all traffic (Tsai et al., 2006; Ho et
al., 2009). Significantly, the WAS sample showed high concentrations of BTEX
compounds, some of which are important carcinogens and all of which can lead
to significant secondary organic aerosol (SOA) production (Platt et al.,
2014). Toluene is a common gasoline additive and is sometimes used as a
tracer for gasoline evaporation (Tsai et al., 2006). However, in our
motorcycle data, aromatics account for ∼ 31 % of the NMHC in the
exhaust emissions, with toluene being the most abundant aromatic. Platt et
al. (2014) measured BTEX emission factors from about 10 to 100 g kg-1 (a
range for driving to idling) for two-stroke motor scooter exhaust, also
finding that toluene was the most abundant aromatic and with the BTEX
accounting for ∼ 40 % of VOC. The combustion process in motorcycle
engines is generally less efficient than in automobile engines (Platt et al.,
2014), and the incomplete combustion can lead to emissions of many NMHC
components in the gasoline. For instance, the exhaust emissions of branched
C5-C6 alkanes, including 2-methylpentane and i-pentane
(sometimes a tracer for gasoline evaporation; Morikawa et al., 1998; Guo et
al., 2006) were also significant in the
motorcycle exhaust. Previous studies also found that the VOC emission profile
from motorcycle exhaust was similar to gasoline headspace analysis (Liu et
al., 2008). In summary, inefficient motorcycle engines produce exhaust
containing a suite of NMHCs that overlaps with those produced by fuel
evaporation. However, there may be significant variability in headspace and
exhaust measurements as observed by Lyu et al. (2016).
The air toxic and common BB tracer HCN was emitted by the motorcycles at
about 1/10 the ER to CO typically measured for BB. However, because of
the very high motorcycle CO emissions, the EF for HCN for motorcycles was
actually similar to that for BB. This is of importance for health effects
and the use of HCN as a BB tracer in urban areas (Moussa et al., 2016), especially in developing countries where motorcycles are prevalent (Yokelson
et al., 2007; Crounse et al., 2009). A few other emissions stood out in the
dataset, including high emissions of 1,3-butadiene (∼ 1.3 g kg-1). While 1,3-butadiene is not a component of gasoline, it is a known
component of vehicle exhaust (e.g., Duffy and Nelson, 1996) and is believed
to originate from the combustion of olefins (Perry and Gee, 1995). The EPA
has highlighted 1,3-butadiene as having the highest cancer risk of air
toxics emitted by US motor vehicles (USEPA, 1993), and exposure in densely
populated urban centers can have significant negative health impacts.
One scooter was sampled by FTIR during this campaign and the CO emissions of
the smaller scooter engine were only one fourth to one half those of the
motorcycles (Table S4). The scooter exhaust emissions were also
significantly lower for most other species captured by FTIR. The scooter,
however, was the only motorbike sampled that produced detectable
formaldehyde, a known carcinogen, irritant, and important radical precursor
in urban atmospheres (Vaughan et al., 1986; Volkamer et al., 2010).
It is important to note that the average EFs from this study are not
intended to represent the entire Kathmandu fleet of vehicles (or even all
motorcycle use) as there is significant emissions variability between
vehicles depending on running conditions (road conditions, driving patterns,
maintenance, emissions control technology; Holmén and Niemeier, 1998;
Popp et al., 1999) and engine specifics (model, size, age, power, fuel
composition, combustion temperature and pressure, etc.; Zachariadis et al.,
2001; Zavala et al., 2006). Larger studies similar to Zhang et al. (1995)
are needed to get fleet averages. However, motorcycles and motor scooters
have been identified as major contributors to transport sector pollution in
Kathmandu (Shrestha et al., 2013) and elsewhere (Oanh et al., 2012; Platt et
al., 2014), and we provide chemically detailed real-world EFs for motorcycles
under some common operating conditions that were previously unmeasured in
Kathmandu.
Because of the diversity in fleet characteristics and how operating
conditions are subdivided, it is difficult to compare our results to other studies, but
some of the species we measured are explicitly provided in other vehicle
emissions estimates (Oanh et al., 2012; Shrestha et al., 2013; Platt et al.,
2014). Probably the most direct comparison is with Oanh et al. (2012), who
reported EFs (in g km-1) specifically for motorcycles for both start-up and
running for the Hanoi 2008 average fleet based on the IVE that included some
overlap species with our study (NOx, CH4, acetaldehyde,
formaldehyde, benzene, and 1,3-butadiene). Except for 1,3-butadiene our
average ratios to CO for these species for start-up and idling are only
3–26 % of theirs for start-up or running. Zhang et al. (1995) noted
that partially functional catalytic converters convert VOC to CO (rather
than CO2) lowering the VOC / CO ratio and also that these devices were
becoming more common in the overall Kathmandu fleet, which points to
emission control technology as a source of variability. The motorcycles we
tested were all four-stroke and built by some of the world's largest
manufacturers in India, where catalytic converters are required on two-stroke
vehicles but were not required for four-stroke bikes until 2015. The Indian
motorcycle emissions standards are based on an idling test and become
increasingly stringent every 5 years (factor of 14.25 reduction for CO
from 1991 to 2010). In response, a variety of emission control measures are
incorporated in the motorcycle engines to reduce “engine out” emissions as
opposed to “after treatment”. Some of these measures are described in
detail by Iyer (2012), while others are proprietary. The durability of many
of these measures is very low (Ntziachristos et al., 2006, 2009), meaning
they deteriorate with age despite minor service. Fuel quality (adulteration)
is also noted as a widespread issue for emissions control (Iyer, 2012). In
summary, it is quite possible that our VOC / CO ratios are lower than Oanh et
al. (2012), mostly because of increased prevalence of emissions control
technology (although poorly maintained) in Kathmandu in 2015 compared to
Hanoi in 2008.
In general, our emission ratios can be used with, e.g., CO EFs from other
studies to roughly estimate additional chemical details for operating
conditions we did not sample. It is also interesting that we observed that
the emitted gases did not change significantly after servicing. It is
possible that gas-phase pollutants would have decreased post-service under
“cruising” conditions, but we were limited to testing start-up and idling
emissions. A study in Hong Kong found that replacing old catalytic converters
had a large impact on emissions, but minor servicing did not (Lyu et al.,
2016). Thus, major servicing might be required to mitigate gas-phase
pollutants in general. Finally, our filter results suggested that the
particulate matter (PM) emissions were reduced post-service (Jayarathne et
al., 2016). Therefore, it is likely that minor servicing of motorcycles is
beneficial if it reduces the PM without making the vast majority of the gases
significantly worse. The EFs (in g kg-1) here could theoretically be
converted to fuel-based EFs (g km-1) using a conversion factor based on
motorcycle fuel economy. However, this is a complex process in practice
(Clairotte et al., 2012), and it would probably be more meaningful to combine
our ER to CO with fuel-based CO emission factors measured under the
appropriate conditions.
Generator emissions
Three generators (two diesel and one gasoline) were sampled about 1 m downstream of the exhaust manifold and the EFs are shown in Table S5. The
larger diesel generator located on the ICIMOD campus is professionally
maintained and had a much smaller EF CO (4.10 g kg-1) and a higher MCE (0.998)
than the smaller (rented) diesel generator (MCE 0.962; EF CO 76.1 g kg-1). The
smaller rented diesel generator had 18–150 times higher emissions for the
five non-CO2 gases measured from both sources. The one gasoline
generator we sampled had much higher CO emissions (> 1000 g kg-1)
and was much less efficient (MCE 0.437) than both diesel generators. This is
similar to the gasoline-powered motorcycles discussed in Sect. 3.2 that also
had high EFs for CO (> 700 g kg-1) with generally low MCEs.
Not surprisingly, the one diesel generator sampled by FTIR (the small
rental) did emit high concentrations of NOx (∼ 24 g kg-1), while NOx emissions remained below the detection limit for the
gasoline-powered generator sampled by FTIR (Vestreng et al., 2009). NO is
the main form of fresh combustion NOx, but it is converted to NO2
within minutes and peroxyacetyl nitrate and nitrate within a few hours (affecting aerosol and
O3 levels) as discussed elsewhere (Akagi et al., 2013; Liu et al.,
2016). The gasoline-powered generator emitted more NMHCs than both diesel
generators and likely produces high secondary aerosol that has been observed
in gasoline vehicle emission studies (Platt et al., 2013). We measured
gasoline generator BTEX emissions that were ∼ 20 times greater
than those from the large diesel generator and note that the SOA yields from the photooxidation of m-xylene, toluene, and benzene are significant
(Ng et al., 2007). We were able to measure HCHO emissions by FTIR from the
small diesel generator (2.75 g kg-1) and the gasoline generator (0.61 g kg-1).
Even though the diesel generator ran much more cleanly overall (for gas-phase
pollutants), it produced significantly more HCHO than the gasoline generator
and we recall that HCHO was below the detection limit for the gasoline-powered
motorcycles we measured. This suggests that diesel may tend to produce higher
HCHO emissions than gasoline. As mentioned in Sect. 3.2, HCHO is an air
toxic and is important in atmospheric chemistry. Overall, OVOCs were not
clearly associated with either fuel, with the gasoline generator having
higher EFs for acetaldehyde, acetone, phenol, and furan but lower EFs for
HCHO and organic acids.
Other evident differences between the generators were potentially based on
fuel. The large well-maintained diesel generator emitted more of the heavier
NMHCs including heptane, octane, nonane, decane, and methylcyclohexane than
the lesser-maintained gasoline generator. The gasoline generator had much
higher EFs for the smaller-chain NMHCs (C2H2, C2H4,
C2H6, C3H6, etc.). While the diesel fuel generators we
sampled burned more cleanly overall in terms of gas-phase pollutants, diesel is
normally considered a much dirtier fuel in terms of soot production. The two
PAX instruments were not operational for sampling generators, but filters
were collected and demonstrated a higher EF PM for the small diesel
generator than the gasoline generator, as will be highlighted by Jayarathne
et al. (2016).
We were able to sample both the smaller diesel generator and the gasoline
generator during both start-up and free-running conditions. The diesel
generator produced concentrations about twice as high for most measured
species during start-up as opposed to free-running conditions, while the
gasoline-fueled generator did not show these start-up concentration spikes.
Sharp emission spikes peaking during both cold and hot start-ups of diesel
engines have been observed previously (Gullet et al., 2006). This is often
attributed to periods of incomplete combustion during ignition and could
have significant impacts on air quality as power cuts are a frequent,
intermittent occurrence throughout the valley.
In summary, the well-maintained diesel generator had much lower EFs for most
overlapping gases measured (except large alkanes, which were a minor overall
component), but gasoline could have advantages in terms of NOx and PM
emissions at the cost of increases in most other pollutants unless they
could be reduced by better maintenance. Although vehicular emissions are
most commonly reported, emissions from gasoline- and diesel-powered
generators can also have large impacts in urban regions subject to
significant load shedding, which is relevant throughout Nepal and especially
in the Kathmandu Valley (World Bank, 2014).
Emission factors (g kg-1) for agricultural diesel irrigation
pumps including EFs weighting only start-up emissions.
Compound (formula)
EF Ag pump 1
EF Ag pump 1
EF Ag pump 2
EF Ag pump 2
EF Ag pumps
emphasize start-up
emphasize start-up
avg (SD)
Method
FTIR
FTIR
FTIR
FTIR
–
MCE
0.987
0.974
0.996
0.990
0.992
Carbon dioxide (CO2)
3103
3038
3161
3133
3132 (41)
Carbon monoxide (CO)
26.0
51.3
7.36
20.2
16.7 (13.2)
Methane (CH4)
3.80
6.14
1.41
2.85
2.61 (1.69)
Acetylene (C2H2)
0.413
2.18
0.08
0.748
0.246 (0.237)
Ethylene (C2H4)
5.37
9.15
1.47
3.04
3.42 (2.75)
Propylene (C3H6)
1.85
3.26
0.424
0.894
1.14 (1.01)
Formaldehyde (HCHO)
0.506
1.23
5.29 ×10-2
0.175
0.280 (0.320)
Methanol (CH3OH)
3.59 ×10-2
0.119
5.77 ×10-3
1.33 ×10-2
2.08 ×10-2
(2.13 ×10-2)
Formic acid (HCOOH)
bdl
bdl
bdl
bdl
bdl
Acetic acid (CH3COOH)
bdl
bdl
bdl
bdl
bdl
Glycolaldehyde (C2H4O2)
bdl
bdl
bdl
bdl
bdl
Furan (C4H4O)
bdl
bdl
bdl
bdl
bdl
Hydroxyacetone (C3H6O2)
bdl
bdl
bdl
bdl
bdl
Phenol (C6H5OH)
0.449
0.583
0.117
0.258
0.283 (0.235)
1,3-Butadiene (C4H6)
0.809
1.47
0.194
0.399
0.501 (0.435)
Isoprene (C5H8)
1.55 ×10-2
7.20 ×10-2
1.93 ×10-2
2.30 ×10-2
1.74 ×10-2
(2.69 ×10-3)
Ammonia (NH3)
9.27 ×10-3
6.42 ×10-2
1.32 ×10-3
1.32 ×10-3
5.29 ×10-3
(5.62 ×10-3)
Hydrogen cyanide (HCN)
0.188
0.458
4.77 ×10-2
0.282
0.118 (0.099)
Nitrous acid (HONO)
0.348
0.307
0.346
0.373
0.347 (0.001)
Sulfur dioxide (SO2)
bdl
bdl
bdl
bdl
bdl
Hydrogen fluoride (HF)
bdl
bdl
bdl
bdl
bdl
Hydrogen chloride (HCl)
bdl
bdl
bdl
bdl
bdl
Nitric oxide (NO)
5.31
5.09
15.9
15.7
10.6 (7.5)
Nitrogen dioxide (NO2)
2.19
1.86
1.20
1.15
1.69 (0.70)
EF black carbon (BC)
6.13
–
5.31
–
5.72 (0.58)
EF Babs 405 nm (m2 kg-1)
62.4
–
54.1
–
58.3 (5.9)
EF Bscat 405 nm (m2 kg-1)
62.9
–
24.0
–
43.4 (27.5)
SSA 405 nm
0.502
–
0.307
–
0.405 (0.137)
Note: “bdl” indicates below the detection limit; C fraction:
0.85 – source is Kirchstetter et al. (1999).
Agricultural diesel pump emissions
In this study, two groundwater irrigation diesel pumps were sampled by FTIR
and the EFs are reported in Table 2. In addition, a surface-water irrigation
pump was sampled by WAS canisters only and showed massively higher CO
emissions than the two other pumps in our study indicating that it was probably
gasoline-powered. The WAS data may be mainly of interest to characterize old
or poorly maintained pumps and the EFs are included in Supplement Table S6.
For the two diesel pumps sampled by FTIR, the grab samples during cold
start-up differed from the samples during regular continuous operation by a
much larger degree than the variability in grab samples for the other
sources, so we computed EF by two methods. Method one is our standard
approach based on the ER plot using all the samples. The start-up emissions
can be outliers in this approach and get lower weight accordingly. Thus, we
also computed ERs from the sum of the individual ERs and used those to
generate a second set of EF that weights the start-up emissions more. Our
standard approach yields the EFs shown in Table 2, columns 2 and 4, with an
average of those two columns in column 6. We have included columns 3 and 5
with EFs calculated from the sum of excess emissions that emphasizes start-up
more. The alternate EF calculation reflects the increased emission of
hydrocarbon species during ignition. CO also increases substantially, while
NOx decreases slightly. We believe the most representative EFs for
model input are taken from the standard approach that does not add weight to
the start-up conditions, as most pumps are likely operated over longer
periods of time. However, all the data are provided, should a user prefer a
different approach.
Although the 870 nm PAX was not operational on this day, the EFs
(m2 kg-1) of Babs and Bscat for aerosols measured at 405 nm
and the SSA are reported in Table 2 for the complete sampling cycle. The SSA
at 405 nm (0.405 ± 0.137) indicates that the diesel pump emissions
were dominated by strongly absorbing aerosols, and if we assume there are no
BrC emissions from this source, a reasonable assumption supported by the
AE33 data, the absorption at 405 nm can be used to get a rough estimate for
EF BC. The average EF BC (5.72 ± 0.58 g kg-1) is very high compared to
typical values closer to 1 g kg-1 for most sources.
From the average emissions in Table 2, we see that the two pumps sampled by
FTIR were not as prolific emitters for most pollutants as many other sources
sampled in this study. However, the emissions of NOx and absorbing
aerosol were comparatively high. Especially taken together, the emissions
from diesel-powered generators and agricultural water pumps are likely
significant in both urban and rural regions of Nepal and should be
included in updated emissions inventories.
Garbage burning emissions
For an overview of our Nepal garbage burning (GB) data that also allows us
to compare to authentic field- and lab-measured GB, we tabulated (Table S7)
our study-average Nepal mixed GB EFs along with mixed GB EFs from two lab
studies (Yokelson et al., 2013; Stockwell et al., 2015), field measurements
of open GB in Mexican landfills (Christian et al., 2010), and a single
airborne sample of a Mexican dump fire (Yokelson et al., 2011). Figure 2
displays the major emissions from these studies in order of their abundance
in the NAMaSTE data. We observe an interesting mix of compounds usually
associated with burning biomass (OVOCs) and fossil fuels (NMHC and BTEX) as
well as nitrogen and chlorine compounds. Even though the methodology and
locales varied considerably, the EFs reported in each study show reasonable
agreement for most overlap compounds (Fig. 2). The average EFs of smoldering
compounds for mixed-garbage burns in Nepal were generally slightly higher
than the other studies and the average MCE was lower (0.923, range in MCE
0.864–0.980). This is consistent with observations by several coauthors
that flaming-dominated GB is more common in winter months in Nepal when GB
also provides heat. The comparison also suggests that the lab results for
compounds not measured in the field (e.g., Yokelson et al., 2013; Stockwell
et al., 2015) could be used if scaled with caution. The NAMaSTE-specific EFs
for garbage burning are reported for each fire in Table 3 along with our
study average for mixed GB EFs, and we discuss some emissions next.
Garbage burning emission factors (g kg-1) compiled for
laboratory measurements (Yokelson et al., 2013; Stockwell et al., 2015)
(green, black), field measurements of open burning in Mexican landfills
(Christian et al., 2010) (blue), a single airborne measurement from a Mexican
dump fire (Yokelson et al., 2011) (purple), and our current study of mixed
garbage (red). Error bars indicate 1 standard deviation of the EF for each
study where available.
The laboratory mixed-garbage burning experiments during FLAME-4 were the
first to yield a glycolaldehyde EF (0.658 g kg-1) for trash burning.
Our 14 April fire burning “mostly plastics” in Nepal produced a very high
glycolaldehyde EF (4.56 g kg-1). In both cases, the actual
glycolaldehyde source is probably paper products, since glycolaldehyde is a
product of cellulose pyrolysis (Richards, 1987). Glycolaldehyde in our first Nepal segregated plastics burn
likely resulted from newspaper used as kindling for ignition. This burn also
had high EFs for a few other OVOCs, especially formic and acetic acid and
formaldehyde (5.30, 2.22, and 5.23 g kg-1). The high EFs in this study
indicate that garbage burning may be an important source of these aldehydes
and acids. Co-firing paper with plastics is also the likely reason our
14 April mostly plastics simulation burned at a significantly lower MCE
than the pure plastic shopping bags that were burned during the FLAME-4
campaign. Most garbage is a more complex mixture than just paper and plastic, so our average EFs for garbage burning in Nepal in Table 3 are based on only
the results from sampling mixed-garbage burns.
Emission factors (g kg-1) for individual garbage burns sampled
during NAMaSTE and average EFs and 1 standard deviation for mixed-garbage
burning.
Compound (formula)
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF plastics
EF plastics
EF mixed garbage
garbage 1
garbage 2
garbage 3
garbage 4
garbage 5
garbage 6
chip bags
burn 1
burn 2
avg (SD)
Method
FTIR + WAS
FTIR + WAS
WAS
WAS
WAS
WAS
FTIR
FTIR
WAS
–
MCE
0.937
0.980
0.926
0.863
0.864
0.967
0.989
0.962
0.990
0.923 (0.050)
Carbon dioxide (CO2)
1446
1773
1641
1498
1498
1756
2249
2473
2695
1602 (142)
Carbon monoxide (CO)
61.5
22.8
84
152
151
38.0
15.9
62.2
16.6
84.7 (55.5)
Methane (CH4)
2.22
0.531
4.15
12.5
3.82
0.542
0.279
2.04
0.684
3.97 (4.47)
Acetylene (C2H2)
1.49
0.261
0.269
0.101
0.674
1.18
0.434
2.23
0.298
0.662 (0.562)
Ethylene (C2H4)
9.33
0.768
2.05
1.72
3.725
0.578
1.85
9.36
0.477
3.03 (3.29)
Propylene (C3H6)
1.98
0.426
1.940
1.999
3.884
0.167
0.520
3.53
0.150
1.73 (1.34)
Formaldehyde (HCHO)
4.15
0.507
nm
nm
nm
nm
0.475
5.23
nm
2.33 (2.57)
Methanol (CH3OH)
1.23
0.146
0.271
2.429
0.590
3.38 ×10-2
3.43 ×10-2
0.98
bdl
0.783 (0.914)
Formic acid (HCOOH)
0.585
0.323
nm
nm
nm
nm
0.126
5.30
nm
0.454 (0.185)
Acetic acid (CH3COOH)
1.63
0.118
nm
nm
nm
nm
4.42 ×10-2
2.22
nm
0.872 (1.066)
Glycolaldehyde (C2H4O2)
2.41
bdl
nm
nm
nm
nm
2.44 ×10-2
4.56
nm
2.41 (–)
Furan (C4H4O)
0.349
7.77 ×10-2
nm
nm
nm
nm
bdl
0.234
nm
0.213 (0.192)
Hydroxyacetone (C3H6O2)
2.70
0.664
nm
nm
nm
nm
bdl
2.59
nm
1.68 (1.44)
Phenol (C6H5OH)
0.776
5.09 ×10-2
nm
nm
nm
nm
0.127
1.42
nm
0.414 (0.513)
1,3-Butadiene (C4H6)
0.930
0.127
0.205
0.177
0.116
4.86 ×10-2
0.192
1.07
3.41 ×10-4
0.267 (0.329)
Isoprene (C5H8)
0.145
bdl
1.84 ×10-2
0.103
bdl
6.80 ×10-4
9.59 ×10-2
0.226
bdl
6.67 ×10-2 (6.86 ×10-2)
Ammonia (NH3)
bdl
0.761
nm
nm
nm
nm
bdl
5.66 ×10-2
nm
0.761 (–)
Hydrogen cyanide (HCN)
0.551
0.312
nm
nm
nm
nm
0.374
0.955
nm
0.432 (0.169)
Nitrous acid (HONO)
0.564
0.422
nm
nm
nm
nm
0.164
2.50
nm
0.493 (0.100)
Sulfur dioxide (SO2)
bdl
bdl
nm
nm
nm
nm
bdl
bdl
nm
bdl
Hydrogen fluoride (HF)
bdl
bdl
nm
nm
nm
nm
bdl
bdl
nm
bdl
Hydrogen chloride (HCl)
3.03
1.61
nm
nm
nm
nm
bdl
77.9
nm
2.32 (1.01)
Nitric oxide (NO)
1.43
1.61
nm
nm
nm
nm
2.02
2.36
nm
1.52 (0.12)
Nitrogen dioxide (NO2)
1.14
0.983
nm
nm
nm
nm
1.20
1.69
nm
1.06 (0.11)
Carbonyl sulfide (OCS)
0.133
2.71 ×10-2
8.62 ×10-2
8.03 ×10-2
0.106
1.33 ×10-2
nm
nm
2.03 ×10-2
7.43 ×10-2 (4.60 ×10-2)
DMS (C2H6S)
–
1.27 ×10-3
1.89 ×10-3
2.70 ×10-2
6.74 ×10-3
4.71 ×10-5
nm
nm
1.19 ×10-2
7.39 ×10-3 (1.13 ×10-2)
Chloromethane (CH3Cl)
0.895
5.05 ×10-2
0.343
1.59
1.26
6.55 ×10-2
nm
nm
5.72 ×10-2
0.702 (0.648)
Bromomethane (CH3Br)
6.71 ×10-3
5.47 ×10-4
2.93 ×10-3
1.16 ×10-3
1.41 ×10-3
3.96 ×10-4
nm
nm
5.53 ×10-5
2.19 ×10-3 (2.39 ×10-3)
Methyl iodide (CH3I)
3.26 ×10-4
–
4.41 ×10-4
4.81 ×10-4
2.55 ×10-4
1.21 ×10-4
nm
nm
1.54 ×10-5
3.25 ×10-4 (1.45 ×10-4)
1,2-Dichloroethene (C2H2Cl2)
0.260
1.44 ×10-2
4.75 ×10-3
2.70 ×10-3
1.02 ×10-2
4.92 ×10-3
nm
nm
5.94 ×10-4
4.96 ×10-2 (1.03 ×10-1)
Methyl nitrate (CH3NO3)
0.185
6.45 ×10-2
2.21 ×10-2
1.02 ×10-2
7.61 ×10-2
8.44 ×10-4
nm
nm
7.99 ×10-2
5.98 ×10-2 (6.84 ×10-2)
Ethane (C2H6)
5.64
6.09 ×10-2
0.830
2.11
1.42
7.19 ×10-2
nm
nm
3.04 ×10-2
1.69 (2.09)
Propane (C3H8)
3.15
2.52 ×10-2
0.388
0.913
0.920
3.01 ×10-2
nm
nm
1.68 ×10-2
0.904 (1.169)
i-Butane (C4H10)
0.445
1.25 ×10-3
3.81 ×10-2
5.79 ×10-2
6.52 ×10-2
–
nm
nm
0.002
0.122 (0.183)
n-Butane (C4H10)
1.87
1.41 ×10-2
0.190
0.341
0.650
1.19 ×10-2
nm
nm
1.86 ×10-2
0.513 (0.707)
1-Butene (C4H8)
3.89
8.36 ×10-2
0.569
0.502
1.23
5.51 ×10-2
nm
nm
6.45 ×10-2
1.05 (1.45)
i-Butene (C4H8)
1.93
5.80 ×10-2
0.508
0.400
0.829
2.62 ×10-2
nm
nm
7.90 ×10-3
0.625 (0.705)
trans-2-Butene (C4H8)
0.630
7.09 ×10-3
9.55 ×10-2
0.135
0.160
6.89 ×10-3
nm
nm
1.28 ×10-2
0.172 (0.233)
cis-2-Butene (C4H8)
0.580
6.04 ×10-3
7.27 ×10-2
9.72 ×10-2
0.102
4.91 ×10-3
nm
nm
9.46 ×10-3
0.144 (0.218)
i-Pentane (C5H12)
1.13
–
2.00 ×10-2
–
2.43 ×10-2
–
nm
nm
3.00 ×10-2
0.391 (0.639)
Continued.
Compound (formula)
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF plastics
EF plastics
EF mixed garbage
garbage 1
garbage 2
garbage 3
garbage 4
garbage 5
garbage 6
chip bags
burn 1
burn 2
avg (SD)
n-Pentane (C5H12)
4.09
3.90 ×10-2
0.435
0.698
1.21
1.69 ×10-2
nm
nm
1.85 ×10-2
1.08 (1.54)
1-Pentene (C5H10)
2.53
4.19 ×10-2
0.341
0.374
1.07
2.86 ×10-2
nm
nm
3.75 ×10-2
0.731 (0.960)
trans-2-Pentene (C5H10)
0.700
1.67 ×10-2
0.108
0.126
0.270
6.63 ×10-3
nm
nm
9.65 ×10-3
0.205 (0.260)
cis-2-Pentene (C5H10)
0.320
7.43 ×10-3
5.14 ×10-2
5.73 ×10-2
0.118
2.83 ×10-3
nm
nm
4.29 ×10-3
9.29 ×10-2 (1.19 ×10-1)
3-Methyl-1-butene (C5H10)
0.129
3.80 ×10-3
3.99 ×10-2
2.63 ×10-2
4.66 ×10-2
1.98 ×10-3
nm
nm
2.79 ×10-3
4.12 ×10-2 (4.65 ×10-2)
1,2-Propadiene (C3H4)
0.198
1.74 ×10-2
2.84 ×10-2
3.92 ×10-3
6.76 ×10-2
1.25 ×10-2
nm
nm
5.38 ×10-3
5.47 ×10-2 (7.39 ×10-2)
Propyne (C3H4)
0.315
3.27 ×10-2
5.41 ×10-2
1.18 ×10-2
9.60 ×10-2
2.92 ×10-2
nm
nm
1.10 ×10-2
8.99 ×10-2 (1.14 ×10-1)
1-Butyne (C4H6)
3.61 ×10-2
2.08 ×10-3
–
1.84 ×10-3
1.12 ×10-2
1.17 ×10-3
nm
nm
8.69 ×10-4
1.05 ×10-2 (1.49 ×10-2)
2-Butyne (C4H6)
2.46 ×10-2
1.07 ×10-3
–
1.47 ×10-3
8.79 ×10-3
7.65 ×10-4
nm
nm
4.00 ×10-4
7.34 ×10-3 (1.02 ×10-2)
n-Hexane (C6H14)
0.761
–
0.101
0.126
0.417
5.05 ×10-3
nm
nm
1.54 ×10-2
0.282 (0.309)
n-Heptane (C7H16)
0.707
9.86 ×10-3
9.61 ×10-2
0.154
0.413
5.41 ×10-3
nm
nm
5.10 ×10-3
0.231 (0.277)
n-Octane (C8H18)
0.411
1.24 ×10-2
6.53 ×10-2
0.078
0.313
1.36 ×10-3
nm
nm
1.24 ×10-2
0.147 (0.172)
n-Nonane (C9H20)
0.134
3.81 ×10-3
5.94 ×10-2
0.076
0.158
3.68 ×10-3
nm
nm
2.77 ×10-2
7.24 ×10-2 (6.43 ×10-2)
n-Decane (C10H22)
0.266
1.00 ×10-2
7.99 ×10-2
0.153
0.224
2.36 ×10-2
nm
nm
bdl
0.126 (0.106)
2,3-Dimethylbutane (C6H14)
3.73 ×10-2
–
3.11 ×10-3
8.79 ×10-4
3.62 ×10-3
–
nm
nm
2.70 ×10-3
1.12 ×10-2 (1.74 ×10-2)
2-Methylpentane (C6H14)
0.342
3.36 ×10-3
4.32 ×10-2
6.59 ×10-2
9.48 ×10-2
–
nm
nm
4.35 ×10-3
0.110 (0.134)
3-Methylpentane (C6H14)
8.60 ×10-2
–
0.228
bdl
bdl
bdl
nm
nm
1.64 ×10-3
0.157 (0.100)
2,2,4-Trimethylpentane (C8H18)
bdl
bdl
bdl
bdl
bdl
bdl
nm
nm
bdl
bdl
Cyclopentane (C5H10)
5.84 ×10-2
1.63 ×10-4
1.00 ×10-2
4.35 ×10-3
1.41 ×10-2
3.05 ×10-4
nm
nm
7.39 ×10-4
1.46 ×10-2 (2.22 ×10-2)
Cyclohexane (C6H12)
bdl
9.80 ×10-3
bdl
bdl
bdl
bdl
nm
nm
2.85 ×10-3
9.80 ×10-3 (–)
Methylcyclohexane (C7H14)
0.100
1.71 ×10-3
3.61 ×10-3
–
5.23 ×10-3
bdl
nm
nm
–
2.76 ×10-2 (4.81 ×10-2)
Benzene (C6H6)
5.66
0.389
2.74
1.59
3.60
1.68
nm
nm
0.285
2.61 (1.85)
Toluene (C7H8)
2.68
5.74 ×10-2
0.574
0.645
0.802
0.139
nm
nm
3.23 ×10-2
0.817 (0.960)
Ethylbenzene (C8H10)
2.18
2.11 ×10-2
0.232
0.239
0.289
2.75 ×10-2
nm
nm
1.61 ×10-2
0.498 (0.831)
m/p-Xylene (C8H10)
1.14
3.42 ×10-2
0.279
0.329
0.228
3.55 ×10-2
nm
nm
1.41 ×10-2
0.342 (0.412)
o-Xylene (C8H10)
0.657
1.78 ×10-2
0.153
0.195
0.296
1.92 ×10-2
nm
nm
7.75 ×10-3
0.223 (0.238)
Styrene (C8H8)
0.347
3.33 ×10-3
0.493
0.811
0.349
0.199
nm
nm
2.00 ×10-3
0.367 (0.274)
i-Propylbenzene (C9H12)
6.80 ×10-2
bdl
–
9.97 ×10-3
5.58 ×10-3
1.20 ×10-3
nm
nm
1.19 ×10-3
2.12 ×10-2 (3.14 ×10-2)
n-Propylbenzene (C9H12)
7.19 ×10-2
3.29 ×10-3
2.45 ×10-2
2.43 ×10-2
5.79 ×10-2
3.73 ×10-3
nm
nm
2.35 ×10-3
3.09 ×10-2 (2.83 ×10-2)
3-Ethyltoluene (C9H12)
0.128
4.97 ×10-3
2.95 ×10-2
2.67 ×10-2
2.18 ×10-2
2.50 ×10-3
nm
nm
1.84 ×10-3
3.55 ×10-2 (4.65 ×10-2)
4-Ethyltoluene (C9H12)
4.28 ×10-2
2.59 ×10-3
2.03 ×10-2
2.26 ×10-2
1.69 ×10-2
1.22 ×10-3
nm
nm
9.27 ×10-4
1.77 ×10-2 (1.52 ×10-2)
2-Ethyltoluene (C9H12)
6.49 ×10-2
2.45 ×10-3
1.79 ×10-2
2.06 ×10-2
2.95 ×10-2
2.72 ×10-3
nm
nm
1.65 ×10-3
2.30 ×10-2 (2.31 ×10-2)
1,3,5-Trimethylbenzene (C9H12)
6.33 ×10-2
3.78 ×10-3
3.73 ×10-2
4.34 ×10-2
5.17 ×10-2
2.40 ×10-3
nm
nm
9.73 ×10-4
3.36 ×10-2 (2.52 ×10-2)
1,2,4-Trimethylbenzene (C9H12)
7.24 ×10-2
5.70 ×10-3
2.49 ×10-2
2.34 ×10-2
2.29 ×10-2
4.25 ×10-3
nm
nm
1.67 ×10-3
2.56 ×10-2 (2.47 ×10-2)
1,2,3-Trimethylbenzene (C9H12)
2.15 ×10-2
2.53 ×10-3
2.21 ×10-2
2.49 ×10-2
1.55 ×10-2
4.76 ×10-3
nm
nm
1.44 ×10-3
1.52 ×10-2 (9.49 ×10-3)
alpha-Pinene (C10H16)
1.66 ×10-2
bdl
0.135
2.40 ×10-2
2.48 ×10-2
bdl
nm
nm
7.81 ×10-3
5.00 ×10-2 (5.65 ×10-2)
beta-Pinene (C10H16)
bdl
bdl
–
bdl
8.27 ×10-2
3.10 ×10-4
nm
nm
–
4.15 ×10-2 (5.83 ×10-2)
Ethanol (C2H6O)
–
6.01 ×10-2
0.103
0.147
0.117
1.06 ×10-2
nm
nm
–
8.74 ×10-2 (5.31 ×10-2)
Acetaldehyde (C2H4O)
8.39
0.271
1.167
2.51
0.276
0.108
nm
nm
0.143
2.12 (3.20)
Continued.
Compound (formula)
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF mixed
EF plastics
EF plastics
EF mixed garbage
garbage 1
garbage 2
garbage 3
garbage 4
garbage 5
garbage 6
chip bags
burn 1
burn 2
avg (SD)
Acetone (C3H6O)
5.38
1.01
1.04
2.42
3.57
0.380
nm
nm
0.950
2.30 (1.90)
Butanal (C4H8O)
0.907
4.22 ×10-2
7.68 ×10-2
0.102
0.415
1.40 ×10-2
nm
nm
6.21 ×10-2
0.259 (0.349)
Butanone (C4H8O)
0.755
5.37 ×10-2
1.94 ×10-3
0.419
1.89 ×10-2
2.54 ×10-2
nm
nm
0.472
0.212 (0.310)
EF black carbon (BC)
0.561
6.04
nm
nm
nm
nm
1.58
1.69
nm
3.30 (3.88)
EF brown carbon (BrC)
–
–
nm
nm
nm
nm
–
–
nm
–
EF Babs 405 (m2 kg-1)
5.72
60.2
nm
nm
nm
nm
16.1
17.3
nm
–
EF Bscat 405 (m2 kg-1)
197
52
nm
nm
nm
nm
26.6
70.0
nm
–
EF Babs 870 (m2 kg-1)
nm
28.6
nm
nm
nm
nm
nm
nm
nm
–
EF Bscat 870 (m2 kg-1)
nm
14.1
nm
nm
nm
nm
nm
nm
nm
–
SSA 405 nm
0.972
0.463
nm
nm
nm
nm
0.623
0.802
nm
–
SSA 870 nm
–
0.329
nm
nm
nm
nm
–
–
nm
–
AAE
–
0.971
nm
nm
nm
nm
–
–
nm
–
Note: “bdl” indicates below the detection limit; “–”
indicates concentrations were not greater than background; “nm” indicates
not measured; see Table S1 for garbage compositions. C fractions: mixed
garbage (0.50) – source is Stockwell et al. (2014); plastics (0.74) and chip bags (0.63) – source is USEPA (2010) (see Sect. 2.1.4 for details).
NMHCs were major emissions with ethylene and acetylene always important for
both the mixed-garbage and the mostly plastic burns. Interestingly, benzene
(a carcinogen) was just below ethylene as the most abundant NMHC in mixed-garbage burning emissions overall (Fig. 2). Estimates of waste burning by
country for all countries are presented in Wiedinmyer et al. (2014). For
Nepal, the estimated amount of waste burned is 644 Gg per year. Based on our
average benzene EF for garbage burning (2.61 ± 1.85 g kg-1), we estimate
that trash burning in Nepal produces ∼ 1.68 Gg benzene (range
0.490–2.87 Gg) annually. The central estimate of Wiedinmyer et al. (2014)
is 0.580 Gg yr-1 of benzene emitted from Nepali garbage burning; at the lower
end of our range but only 34 % of our mean.
As observed in Fig. 2, EF HCl varies significantly between experiments and
within the same study. Yokelson et al. (2013) reported a lab-measured EF HCl
of 10.1 g kg-1, whereas Stockwell et al. (2014) reported their highest
lab-measured EF HCl at 1.52 g kg-1. These values are close to the upper and
lower end of EF HCl for authentic Mexican landfill fires (1.65–9.8 g kg-1)
(Christian et al., 2010). HCl fell below the detection limit in some FTIR
grab samples collected during NAMaSTE, indicating that GB emissions can
differ depending on which components are burning during a particular grab
sample. Our 14 April burn with fuels that were mostly plastics had extremely
high EF HCl (77.9 g kg-1), suggesting that many of the bags burned were made
from polyvinyl chloride (PVC). Our average EF for HCl for mixed GB was 2.32 ± 1.01, well within the range for Mexican GB. The other major
halogenated emission detected from mixed GB was chloromethane (by WAS) at an
EF up to 1.59 g kg-1 (average 0.702 ± 0.648 g kg-1).
HCN is considered useful as a biomass burning tracer (Li et al., 2000) but
was emitted by the mixed-garbage and mostly plastic burns with an EF HCN
that is similar to BB. We did not collect data in Nepal for acetonitrile,
which is also used as a BB tracer, but the high CH3CN / HCN ratios in
Stockwell et al. (2015) for laboratory garbage burning suggest a similar
issue may occur. This should be factored into any source apportionment based
on using these compounds as tracers in regions where the emission sources
include BB and either or both of garbage burning and motorcycles (e.g., Sect. 3.2).
Carbonyl sulfide (OCS) is emitted by natural (oceans, volcanoes, etc.), BB,
and anthropogenic (automobiles, fossil fuel combustion) sources (Kettle et
al., 2002). Two of our mixed-garbage burns had high EF OCS (> 0.1 g kg-1), and these are the first measurements reporting an EF
OCS for GB. Burns 1 and 5 (Table 3) both had high OCS and both had a higher percentage of food
waste. Because OCS is relatively inert in the troposphere, it freely
transports into the stratosphere where it photodissociates and oxidizes and
can ultimately contribute to particle mass. The other S species we could
measure remained low such as dimethyl sulfide (DMS) or below detection (SO2).
The global garbage burning inventory of Wiedinmyer et al. (2014) had to rely
on the EF BC (actually a filter-based EC (elemental carbon) measurement) from just one study
(0.65 g kg-1, Christian et al., 2010). Both PAXs were operational during one
mixed-garbage burn, and we measured an EF BC of 6.04 g kg-1 (with an AAE ∼ 1) almost 10 times larger than the previously measured EF
for BC, suggesting a strongly BC-dominated aerosol. In addition, we can
estimate an upper limit for EF BC for some of the other trash fires by
assuming all 405 nm absorption is due to BC while the 870 PAX was not
operational. This provides our 405-estimated values in Table 3 and they
range from ∼ 0.561 to 1.69 g kg-1. Thus, our EF of 6.04 g kg-1 is
likely a high-end value from a flaming-dominated garbage fire (MCE 0.980), while our lower values come from fires with more smoldering (MCE
∼ 0.96) that are probably more common. Overall our PAX data
suggests an upward revision for the literature-average garbage-burning EF BC
to something above 1 g kg-1. However, with only one robust PAX-based EF BC
determination, we will rely on the detailed EC and OC (organic carbon) particulate analysis from
NAMaSTE to better characterize this source in Jayarathne et al. (2016).
The modified combustion efficiency (MCE) shown in descending order
for each cookstove–fuel combination measured in this study. The stove type is
listed followed by the main fuel constituents and an indication whether the
source was a lab or field measurement. Note: “hw” indicates hardwood fuels;
“d” indicates dung; “cc” indicates charcoal; “t” indicates twigs; and
“sd” indicates sawdust.
Cooking-fire emissions
There were two main goals of our cooking-fire measurements. One was to
increase the amount of chemically and optically detailed trace gas and
aerosol information that has been quantified in the field to allow more
comprehensive assessments of the atmospheric and health impacts. The second
was to obtain this type of detailed information for cooking fires that
represent the most common global practice (open hardwood-fuel cooking
fires); a major undersampled regional cooking practice (dung-fueled cooking
fires); and, in exploratory fashion, a diverse range of stove–fuel combinations being considered as mitigation strategies.
First, we illustrate the range of cooking technologies that we sampled and
support some basic observations by plotting the MCE of all the stove–fuel combinations that we tested in decreasing order in Fig. 3. Several things
stand out. Firstly, the biogas, the bhuse chulo sawdust, and
biobriquette-fueled stoves had the highest MCE in our (limited) testing out
of the wide range of possibilities and generally had smaller gas-phase EFs.
The two measurements for biogas varied substantially and the differences
could be a gas leak through the supply line and/or lingering BB emissions
present in the laboratory room; thus, we favor the field values. Biogas has
proven to be a viable alternative to traditional wood sources especially in
rural Nepal, where agriculture and animal husbandry are the main sources of
income (Katuwal and Bohara, 2009); however, biogas stoves remain
unaffordable for poorer households. The higher MCEs in our emissions survey
study suggest that more extensive testing of biogas or the bhuse chulo could be
warranted. The complete individual emissions for all stoves and fuels measured
during NAMaSTE are included in Supplement Table S8. Another apparent feature
of Fig. 3 is the sharp drop off in MCE for the tests on the right side of
the figure, which were mostly field measurements as opposed to the generally
higher MCE in lab measurements. This suggests that “lower” MCE near 0.92
for wood and 0.90 for dung are apparently representative of real-world use.
More field tests were planned but were not completed due to the earthquake.
However, lower stove MCE in the field compared to lab testing has been
reported previously (Bertschi et al., 2003; Roden et al., 2008; Stockwell et
al., 2014), and the literature-average MCE for field use is close to 0.92
(Akagi et al., 2011). Thus, we are fairly confident in adjusting the lab
data for open cooking to reflect lower efficiency in order to use the lab tests to
augment the field data. The straightforward adjustment procedure is
described next.
A frequently measured smoldering compound (e.g., CO or CH4) can be used
as a reference for other smoldering compounds, and CO2 is a good
reference for other flaming compounds. Similar to previous work (e.g.,
Yokelson et al., 2008, 2013; Stockwell et al., 2014, 2015), we obtained field
representative values from the lab data by multiplying the lab ER to CH4
(measured by FTIR or WAS) for smoldering compounds and the lab ER to CO2
(measured by FTIR or WAS) for flaming compounds by the field EF for CH4
and CO2, respectively. Our full original NAMaSTE data are in Table S8,
and the adjusted laboratory data for gases for traditional open hardwood and
dung cooking fires were averaged together with our authentic field values to
estimate our NAMaSTE-average EF for open wood and dung cooking fires. Those
estimates along with values from a few other studies that reported a
reasonably large number of EFs for cooking fires burning wood and dung are
shown in Table 4 and form the basis for much of the ensuing discussion.
Compiled emission factors (g kg-1) and 1 standard deviation
for open traditional cooking fires using dung and wood fuels. The NAMaSTE
values include field measurements and adjusted laboratory measurements.
Compound (formula)
EF hardwood cooking
EF dung cooking
EF wood open cooking
EF wood open cooking
EF dung burning
NAMaSTE avg
NAMaSTE avg
Akagi et al. (2011)
Stockwell et al. (2015)
Akagi et al. (2011)
(SD)a
(SD)
avg (SD)
avg (SD)b
avg (SD)
MCE
0.923
0.898
0.927
0.927
0.839
PM
–
–
6.73 (1.61)
–
22.9
Carbon dioxide (CO2)
1462 (16)
1129 (80)
1548 (125)
1548 (125)
859 (15)
Carbon monoxide (CO)
77.2 (13.5)
80.9 (13.8)
77.4 (26.2)
77.4 (26)
105 (10)
Methane (CH4)
5.16 (1.39)
6.65 (0.46)
4.86 (2.73)
4.86 (0.20)
11.0 (3.3)
Acetylene (C2H2)
0.764 (0.363)
0.593 (0.443)
0.970 (0.503)
0.602 (0.361)
nm
Ethylene (C2H4)
2.70 (1.17)
4.23 (1.39)
1.53 (0.66)
2.21 (1.40)
1.12 (0.23)
Propylene (C3H6)
0.576 (0.195)
1.47 (0.58)
0.565 (0.338)
0.317 (0.145)
1.89 (0.42)
Formaldehyde (HCHO)
1.94 (0.75)
2.42 (1.40)
2.08 (0.86)
1.70 (0.74)
nm
Methanol (CH3OH)
1.92 (0.61)
2.38 (0.90)
2.26 (1.27)
2.05 (1.63)
4.14 (0.88)
Formic acid (HCOOH)
0.179 (0.071)
0.341 (0.308)
0.220 (0.168)
0.620 (0.533)
0.460 (0.308)
Acetic acid (CH3COOH)
3.14 (1.11)
7.32 (6.59)
4.97 (3.32)
8.90 (9.27)
11.7 (5.1)
Glycolaldehyde (C2H4O2)
0.238 (0.155)
0.499 (0.260)
1.42 (–)
0.455 (0.149)
nm
Furan (C4H4O)
0.241 (0.024)
0.534 (0.209)
0.400 (–)
0.228 (0.162)
0.950 (0.220)
Hydroxyacetone (C3H6O2)
1.26 (0.09)
3.19 (2.24)
nm
0.480 (0.367)
9.60 (2.38)
Phenol (C6H5OH)
0.496 (0.159)
1.008 (0.348)
3.32 (–)
0.264 (0.085)
2.16 (0.36)
1,3-Butadiene (C4H6)
0.204 (0.144)
0.409 (0.306)
nm
3.37 ×10-2 (9.67 ×10-3)
nm
Isoprene (C5H8)
4.16 ×10-2 (2.23 ×10-2)
0.325 (0.443)
nm
0.145 (0.077)
nm
Ammonia (NH3)
0.259 (0.253)
3.00 (1.33)
0.865 (0.404)
7.88 ×10-2 (6.90 ×10-2)
4.75 (1.00)
Hydrogen cyanide (HCN)
0.557 (0.247)
2.01 (1.25)
nm
0.221 (0.005)
0.530 (0.300)
Nitrous acid (HONO)
0.452 (0.068)
0.276 (0.101)
nm
0.291 (0.169)
nm
Sulfur dioxide (SO2)
bdl
bdl
nm
0.499
6.00 ×10-2 (–)
Hydrogen fluoride (HF)
bdl
bdl
nm
bdl
nm
Hydrogen chloride (HCl)
7.51 ×10-2 (7.99 ×10-2)
3.76 ×10-2 (3.59 ×10-2)
nm
bdl
nm
Nitric oxide (NO)
1.62 (1.30)
2.22 (1.02)
1.72 (0.75)
0.319 (0.089)
0.500
Nitrogen dioxide (NO2)
0.577 (0.348)
0.898 (0.444)
0.490 (0.330)
1.11 (0.28)
nm
Carbonyl sulfide (OCS)
1.87 ×10-2 (1.15 ×10-2)
0.148 (0.123)
nm
nm
nm
DMS (C2H6S)
0.255 (0.359)
2.37 ×10-2 (7.67 ×10-4)
nm
nm
nm
Chloromethane (CH3Cl)
2.36 ×10-2 (1.62 ×10-2)
1.60 (1.53)
nm
nm
nm
Bromomethane (CH3Br)
5.61 ×10-4(3.01 ×10-4)
5.34 ×10-3 (3.02 ×10-3)
nm
nm
nm
Methyl iodide (CH3I)
1.23 ×10-4(1.11 ×10-4)
4.39 ×10-4(1.78 ×10-4)
nm
nm
nm
1,2-Dichloroethene (C2H2Cl2)
1.24 ×10-4(3.00E-5)
4.97 ×10-3 (–)
nm
nm
nm
Methyl nitrate (CH3NO3)
6.96 ×10-3 (5.73 ×10-3)
1.46 ×10-2 (1.94 ×10-2)
nm
nm
nm
Ethane (C2H6)
0.160 (0.122)
1.075 (0.300)
1.50 (0.50)
nm
nm
Propane (C3H8)
0.202 (0.140)
0.457 (0.137)
nm
nm
nm
i-Butane (C4H10)
0.406 (0.478)
0.215 (0.126)
nm
nm
nm
n-Butane (C4H10)
1.11 (1.48)
0.29 (0.09)
nm
nm
nm
1-Butene (C4H8)
0.726 (0.904)
0.399 (0.331)
nm
0.245 (0.148)
nm
i-Butene (C4H8)
0.846 (1.113)
0.281 (0.091)
nm
nm
nm
trans-2-Butene (C4H8)
6.78 ×10-2 (5.98 ×10-2)
0.151 (0.010)
nm
nm
nm
cis-2-Butene (C4H8)
5.51 ×10-2 (4.76 ×10-2)
0.102 (0.016)
nm
nm
nm
i-Pentane (C5H12)
8.58 ×10-2 (1.58 ×10-2)
0.811 (0.387)
nm
nm
nm
n-Pentane (C5H12)
2.18 ×10-2 (1.73 ×10-2)
0.190 (0.254)
nm
nm
nm
1-Pentene (C5H10)
1.43 ×10-2 (9.36 ×10-3)
0.168(0.086)
nm
nm
nm
trans-2-Pentene (C5H10)
1.05 ×10-2 (8.30 ×10-3)
0.115 (0.035)
nm
nm
nm
cis-2-Pentene (C5H10)
8.69 ×10-3 (–)
5.14 ×10-2 (7.55 ×10-3)
nm
nm
nm
3-Methyl-1-butene (C5H10)
7.43 ×10-3 (5.79 ×10-3)
5.58 ×10-2 (3.50 ×10-2)
nm
nm
nm
1,2-Propadiene (C3H4)
2.33 ×10-2 (1.07 ×10-2)
7.15 ×10-2 (6.76 ×10-2)
nm
nm
nm
Propyne (C3H4)
6.39 ×10-2 (3.07 ×10-2)
0.172 (0.156)
nm
nm
nm
1-Butyne (C4H6)
1.28 ×10-2 (4.73 ×10-3)
2.29 ×10-2 (1.38 ×10-2)
nm
nm
nm
2-Butyne (C4H6)
1.02 ×10-2 (6.56 ×10-3)
1.86 ×10-2 (9.11 ×10-3)
nm
nm
nm
n-Hexane (C6H14)
1.85 ×10-2 (–)
0.291 (0.248)
nm
nm
nm
n-Heptane (C7H16)
1.01 ×10-2 (1.35 ×10-2)
0.114 (0.069)
nm
nm
nm
n-Octane (C8H18)
1.75 ×10-2 (–)
4.77 ×10-2 (9.85 ×10-3)
nm
nm
nm
n-Nonane (C9H20)
4.87 ×10-2 (6.40 ×10-2)
4.68 ×10-2 (2.55 ×10-2)
nm
nm
nm
n-Decane (C10H22)
6.90 ×10-2 (9.61 ×10-2)
4.71 ×10-2 (4.03 ×10-2)
nm
nm
nm
2,3-Dimethylbutane (C6H14)
1.57 ×10-2 (1.16 ×10-2)
0.112 (0.105)
nm
nm
nm
2-Methylpentane (C6H14)
9.93 ×10-3 (1.29 ×10-2)
0.231 (0.192)
nm
nm
nm
3-Methylpentane (C6H14)
6.79 ×10-3 (6.63 ×10-3)
0.155 (0.137)
nm
nm
nm
2,2,4-Trimethylpentane (C8H18)
– (–)
0.100 (0.080)
nm
nm
nm
Cyclopentane (C5H10)
4.06 ×10-3 (–)
0.146 (0.178)
nm
nm
nm
Cyclohexane (C6H12)
1.16 ×10-2 (–)
0.224 (0.255)
nm
nm
nm
Methylcyclohexane (C7H14)
1.62 ×10-2 (–)
4.76 ×10-2 (3.96 ×10-2)
nm
nm
nm
Benzene (C6H6)
1.05 (0.19)
1.96 (0.45)
nm
2.58 (2.68)
nm
Toluene (C7H8)
0.241 (0.160)
1.26 (0.05)
nm
0.290 (0.311)
nm
Ethylbenzene (C8H10)
4.19 ×10-2 (4.25 ×10-2)
0.366 (0.085)
nm
nm
nm
Continued.
Compound (formula)
EF hardwood cooking
EF dung cooking
EF wood open cooking
EF wood open cooking
EF dung burning
NAMaSTE avg
NAMaSTE avg
Akagi et al. (2011)
Stockwell et al. (2015)
Akagi et al. (2011)
(SD)a
(SD)
avg (SD)
avg (SD)b
avg (SD)
m/p-Xylene (C8H10)
9.57 ×10-2 (7.99 ×10-2)
0.601 (0.294)
nm
0.265 (0.380)
nm
o-Xylene (C8H10)
3.93 ×10-2 (4.31 ×10-2)
0.228 (0.083)
nm
nm
nm
Styrene (C8H8)
8.71 ×10-2 (6.69 ×10-2)
0.255 (0.091)
nm
0.234 (0.306)
nm
i-Propylbenzene (C9H12)
1.70 ×10-2 (1.67 ×10-2)
1.87 ×10-2 (1.40 ×10-2)
nm
nm
nm
n-Propylbenzene (C9H12)
1.78 ×10-2 (1.58 ×10-2)
3.10 ×10-2 (1.45 ×10-2)
nm
nm
nm
3-Ethyltoluene (C9H12)
2.62 ×10-2 (5.41 ×10-3)
5.61 ×10-2 (2.38 ×10-2)
nm
nm
nm
4-Ethyltoluene (C9H12)
2.07 ×10-2 (1.19 ×10-2)
3.57 ×10-2 (1.74 ×10-2)
nm
nm
nm
2-Ethyltoluene (C9H12)
2.10 ×10-2 (1.16 ×10-2)
3.39 ×10-2 (1.34 ×10-2)
nm
nm
nm
1,3,5-Trimethylbenzene (C9H12)
2.14 ×10-2 (–)
1.79 ×10-2 (8.32 ×10-3)
nm
7.01 ×10-2 (9.27 ×10-2)
nm
1,2,4-Trimethylbenzene (C9H12)
1.74 ×10-2 (2.35 ×10-2)
3.91 ×10-2 (1.65 ×10-2)
nm
nm
nm
1,2,3-Trimethylbenzene (C9H12)
2.16 ×10-2 (–)
2.34 ×10-2 (4.30 ×10-3)
nm
nm
nm
alpha-Pinene (C10H16)
2.02 ×10-2 (2.33 ×10-2)
0.348 (0.487)c
nm
0.197 (0.257)
nm
beta-Pinene (C10H16)
4.67 ×10-2 (–)
0.471 (–)c
nm
nm
nm
Ethanol (C2H6O)
0.128 (0.017)
0.563 (0.589)
nm
nm
nm
Acetaldehyde (C2H4O)
0.541 (0.362)
1.88 (1.63)
nm
0.792 (0.439)
nm
Acetone (C3H6O)
0.524 (0.256)
1.63 (0.38)
nm
nm
nm
Butanal (C4H8O)
8.28 ×10-3 (6.27 ×10-3)
5.40 ×10-2 (2.19 ×10-2)
nm
nm
nm
Butanone (C4H8O)
0.232 (0.286)
0.262 (0.109)
nm
8.04 ×10-2 (4.98 ×10-2)
nm
EF black carbon (BC)
0.221 (0.127)
4.15 ×10-2 (3.18 ×10-2)
0.833 (0.453)
nm
nm
EF brown carbon (BrC)
8.59 (5.62)
5.54 (1.66)
nm
nm
nm
EF Babs 405 (m2 kg-1)
10.6 (6.8)
5.85 (1.95)
nm
nm
nm
EF Bscat 405 (m2 kg-1)
40.4 (23.8)
49.5 (5.8)
nm
nm
nm
EF Babs 870 (m2 kg-1)
1.04 (0.60)
0.197 (0.151)
nm
nm
nm
EF Bscat 870 (m2 kg-1)
1.51 (0.52)
0.922 (0.324)
nm
nm
nm
EF Babs 405 just BrC (m2 kg-1)
8.40 (5.48)
5.43 (1.62)
nm
nm
nm
EF Babs 405 just BC (m2 kg-1)
2.24 (1.28)
0.423 (0.324)
nm
nm
nm
SSA 405 nm
0.605 (0.061)
0.811 (0.164)
nm
nm
nm
SSA 870 nm
0.794 (0.009)
0.893 (0.043)
nm
nm
nm
AAE
3.01 (0.10)
4.63 (0.68)
nm
nm
nm
Note: “bdl” indicates below the detection
limit; “–” indicates concentrations were not greater than background;
“nm” indicates not measured. a NAMaSTE gas-phase data include
adjusted laboratory and unadjusted field values. Aerosol values include field
measurements only (see Sect. 3.6). b This includes laboratory
adjusted values (see Stockwell et al., 2014, 2015); additional gas-phase
compounds are reported therein. c High monoterpene values likely
due to wood kindling.
We focus next on dung cooking fires, which are prevalent in South Asia. To
our knowledge, there are very few studies that report any EFs for dung
burning (Akagi et al., 2011), and this work significantly expands the
gas-phase emissions data. The NAMaSTE-derived dung cooking-fire average in
Table 4 includes four traditional dung cooking fires (one-pot mud stoves and
three-stone) and an open fire intended to represent an authentic open warming
fire outside a rural home. The open warming fire had a lower MCE (0.876)
than our two field dung cooking fires (0.910 ± 0.003) that was
slightly closer to the low MCE (0.839) average value reported in Akagi et
al. (2011) based on open pasture burning of dung in Brazil (Christian et
al., 2007) and laboratory burns of Indian dung (Keene et al., 2006).
As shown for dung-fuel cooking fires in Table 4, our EFs for CH4 (6.65 ± 0.46 g kg-1) are lower than the literature average reported in Akagi
et al. (2011) (11 g kg-1), although both are within the range (3–18 g kg-1)
reported by Smith et al. (2000) for simulated rural cooking in India. OVOCs
were major emissions, and we provide the first EFs for many OVOCs (e.g.,
formaldehyde, acetone, glycolaldehyde, acetaldehyde). Acetic acid and
hydroxyacetone were the most abundant OVOCs, though the Nepal EFs (7.32 and
3.19 g kg-1) are lower than the Brazil EFs (14.3 and 9.6 g kg-1) reported by
Christian et al. (2007) at a lower MCE. This work considerably expands our
knowledge of NMHCs from this source and reports a much higher EF for
C2H4 (4.23 g kg-1) and also many previously unobserved NMHCs at high
levels. In particular, our new NMHC data include high emissions for BTEX
compounds, especially benzene and toluene (1.96, 1.26 g kg-1). Other notable
compounds with high emissions that were previously unobserved include
chloromethane (1.60 g kg-1) and carbonyl sulfide (0.148 g kg-1). This is
consistent with the elevated Cl and S content in the dung sample from
Montana (0.19 % S, 0.05 % Cl; Table S3). Chloromethane is the main form of
organic chlorine in the atmosphere (Lobert et al., 1999) and is discussed
more below.
As expected, the high N content of dung (1.9 %, Table S3) led to high
emissions for N-containing gases including NH3 (3 g kg-1), NOx
(∼ 3 g kg-1), and HCN (∼ 2 g kg-1). Our NOx EF
is higher than previously reported and this is an EPA-regulated criteria pollutant that is an important precursor to ozone, acid rain, and nitrate
aerosols. The high NH3 (3.00 ± 1.33 g kg-1) and acetic acid (7.32 ± 6.59 g kg-1) emissions we observed, also previously observed in Brazil
dung-fire emissions, might lead to ammonium acetate in secondary aerosol.
Laboratory measurements during FLAME-4 were the first to report HCN from
wood cooking fires (Stockwell et al., 2014), though the ERs to CO were about
5 times lower than what is typically observed for other BB fuels. The
NAMaSTE real-world wood cooking fires had higher HCN EFs (0.557 ± 0.247 g kg-1) than in the lab (0.221 g kg-1); however, our HCN-to-CO ratio for
dung burning is 3.5 times higher than for wood. Despite the lower ER for
wood, its dominance as a fuel means both should be considered an important
source of HCN in the atmosphere. The cooking source continues during the
monsoon, when open burning is reduced, and likely contributes to the large
HCN anomaly observed by satellite in the anticyclone over the Asian monsoon
(Park et al., 2008; Randel et al., 2010; Glatthor et al., 2015). The NAMaSTE
ΔHCN /ΔCO ratios should be considered when using HCN in any
source apportionment of pollution sources in areas subject to biomass
burning and dung cooking along with the motorcycles and garbage burning
mentioned above.
Yevich and Logan (2003) estimated annual Asian use of dung as a biofuel in
1985 at 123 (±50 %) Tg, with India accounting for 93 Tg. The
NAMaSTE field measurements of dung burning were conducted in the Tarai
region, which makes up the southern part of Nepal and likely represents
similar cooking conditions to those in northern India. Fernandes et al. (2007) estimated that only 75 Tg yr-1 of dung is burned globally, while Yevich
and Logan (2003) estimated a slightly higher global value (136 Tg). If we
take the average of these two studies as an estimate of dung biofuel use
(106 Tg), then we estimate from our EFs that 0.78 Tg acetic acid, 0.21 Tg
HCN, and 0.17 Tg CH3Cl are emitted from dung burning each year. This
accounts for ∼ 33, 51, and over 100 % of the previously
estimated total biofuel burning emissions for these species in the late
1990s (Andreae and Merlet, 2001). Our estimate of HCN emitted solely from
burning dung accounts for ∼ 4–8 % of HCN thought to be
emitted by total BB annually in earlier work (Li et al., 2000). Our estimate
of CH3Cl emitted by dung burning alone is ∼ 18 % of the
total global CH3Cl emitted by BB in the inventory of Lobert et al. (1999). They also cited a high Cl content of dung (4360 mg kg-1) and concluded
that BB was the largest source of CH3Cl in the atmosphere. The contribution
of dung burning to acetic acid, HCN, CH3Cl, and other species should be
included in updated inventories of global BB and biofuel emissions.
We report the first BrC emissions data from dung burning (to our knowledge)
in Table 4 based on our NAMaSTE field-measured values only. Our EF BrC of
5.54 ± 1.66 g kg-1 is qualitative but substantial, and our more
rigorously measured AAE (4.63 ± 0.68) is higher than our NAMaSTE value
for wood cooking (3.01 ± 0.10). Expressed in terms of light
absorption, BrC accounted for ∼ 93 % of aerosol absorption
at 405 nm for dung burning and 79 % for wood burning. In addition, for
dung burning the BC absorption EF at 870 nm was only 3.5 % of the
“BrC-only” absorption EF at 405 nm. Even for wood burning, the BC
absorption EF at 870 nm was just 12 % of the BrC absorption EF at 405 nm.
From these values we see that dung cooking fires are an important BrC source
in South Asia and that BrC from cooking fires in general is of great
importance for understanding their climate impacts. Our EF BC (0.04 g kg-1)
for dung is lower than the suggested EF reported in Venkataraman et al. (2005) (0.12 g kg-1) for lab-burned cattle dung, though it is within the low
end of the range estimated by Xiao et al. (2015) (0.03–0.3 g kg-1) for dung
cooking fires. The sum of our BC and BrC emissions (∼ 5.5 g kg-1) is significantly lower than total carbon (EC + OC, 22 g kg-1) reported
for lab measurements of dung cooking fires in Keene et al. (2006), but the
methods used are difficult to compare. Both studies highlight the need for
more measurements of this source. The SSA for dung cooking fires is
statistically higher at both wavelengths than for wood cooking, but both
sources produced fresh smoke with SSA < 0.9, indicating it would
(initially) warm the atmosphere and cool the surface, impacting climate
(Praveen et al., 2012). Our values of EF Babs, EF Bscat, AAE, and
SSA at 405 and 870 nm shown in Table 4 for dung and wood burning are
independent of MAC estimates and can be used in models directly to estimate
the optical properties, forcing, etc.
Open cooking fires using hardwood fuel are the most common cooking
technology globally. Our NAMaSTE measurements significantly increase the
number of gases that have been measured in hardwood open cooking-fire
emissions in the field. We report a few new OVOCs with high EF such as
acetone (0.524 g kg-1) and many new EFs for NMHCs (Table 4). The NAMaSTE
results include lower emissions of total BTEX compounds from wood cooking
fires (∼ 1.5 g kg-1) than dung cooking fires (∼ 4.5 g kg-1) but confirm the high EF for these species previously reported in
lab studies (∼ 3.2 g kg-1, Stockwell et al., 2015). DMS
emissions have not been reported previously for open cooking with wood, and
our EF is relatively high (0.255 g kg-1) for a BB source (Simpson et al.,
2011). Rather than walk the reader through all the data in Table 4, we
reiterate the main result, which is that models can now use much improved
speciation of the trace gases emitted by cooking fires. This can be seen by
comparing columns 2 and 4 (the literature average) in Table 4. The agreement
is good for most species previously measured in the field. For example, the
NAMaSTE-average MCE (0.923) is very close to the Akagi et al. (2011) field
average MCE (0.927). In addition, NAMaSTE provides data in column 2 for
about 70 gases not previously measured in field work to our knowledge.
The numerous trace gas EFs we measured for open-hardwood cooking fires in
Nepal also present an important validation opportunity for cooking-fire
trace gas measurements made on simulated cooking fires in a lab study that
featured many advanced instruments mostly never deployed on field
cooking fires. In FLAME-4, the lab cooking-fire EFs for trace gases were
adjusted to the field average MCE (0.927) and reported in Table S3 of
Stockwell et al. (2015). In Table 4 we show the overlap species between
NAMaSTE and FLAME-4. There are a few noticeable deviations between the lab
and NAMaSTE EF for NMOC. The lab / field EF ratios are shown in parentheses
for acetic acid (2.8), hydroxyacetone (0.38), BTEX (2), and HCN (0.40).
However, comparing columns 2 and 5 shows agreement within 1 standard
deviation of the mean for more than 70 % of the ∼ 26 overlap
species. Fuel S and N content differences may explain the EF differences for
SO2 and NOx. In general the agreement suggests the FLAME-4 trace
gas EF are useful, especially for the > 100 species that the study
measured that were not measured in the field (Stockwell et al., 2015; Hatch
et al., 2015). The FLAME-4 and NAMaSTE data will be used to update the
tables in Akagi et al. (2011), creating a new literature average.
As noted earlier, aerosol emissions from wood cooking fires are a major
global issue. Our EF BC (0.221 ± 0.127 g kg-1) for hardwood
cooking fires is significantly lower than the Akagi et al. (2011) literature
average (0.833 ± 0.025 g kg-1) based on EC measurements but was
within the range reported in Christian et al. (2010)
(0.205–0.674 g kg-1). Our BC and BrC combine to
∼ 9 g kg-1, which is ∼ 40 % larger than the typical
value for PM2.5 from biofuel sources (∼ 7 g kg-1, Akagi et
al., 2011). To our knowledge we report the first field-measured EF
Babs and EF Bscat for wood cooking fires at 405 and
870 nm (Table 4), which can be used in models without MAC assumptions. We
also provide rare measurements of SSA and AAE for fresh cooking-fire aerosol
in Tables 4 and S8. Our AAE for hardwood cooking fires (3.01 ± 0.10) is
higher than that which Praveen et al. (2012) measured in hardwood cooking-fire smoke
(2.2) in the IGP in northern India. More work is required to examine how
methodological differences, aging, and sample size vs. real regional
variability affect measurements of regional averages. Our hardwood cooking
SSAs (0.794, 870 nm; 0.605, 405 nm) indicate an absorbing fresh aerosol,
but SSA has been seen to increase rapidly with aging in BB plumes (Abel et
al., 2003; Yokelson et al., 2009; Akagi et al., 2012). In summary, our PAX
data from Nepal increase the total amount of sampling and approaches used to
estimate regional average cooking-fire aerosol properties. Incorporating our
data would nudge the regional average for hardwood cooking fires towards
higher BrC / BC ratios, and we show that dung cooking fires are also an
important BrC source. Additional NAMaSTE aerosol data will be reported in
companion papers (Jayarathne et al., 2016; Goetz et al., 2016).
Health impacts of indoor cooking-fire emissions are a major global concern
(e.g., Davidson et al., 1986, and Fullerton et al., 2008). We did not target
exposure assessment in NAMaSTE, but our data can be used in a piggyback
approach with studies focused on longer-term exposure to a key indoor air
pollutant to estimate exposure to other air toxic gases not measured in
those exposure studies following Akagi et al. (2014). We give one example.
Based on our measurements it is possible to extrapolate concentrations of
trace species not measured in previous studies. For example, assuming
similar emission profiles, we can scale indoor CO measured by Davidson et
al. (1986) to estimate indoor benzene concentrations and exposures. In their
study indoor concentrations of CO were 21 ppm, which would equate to 183 ppb
benzene using the ER (benzene / CO) from our study for dung cooking. The
same approach can be extended to any of the gases we measured for any of the
stove and fuel types. Overall, we were able to survey a very large variety
of cooking technologies, practices, and fuel options representative of a
diverse region and to identify candidate technologies for further testing and
possible wider use. The large amount of new gas and aerosol data from
NAMaSTE as a whole should improve model representation and help to better
understand the local and regional climate, chemistry, and health impacts of
domestic and industrial biofuel use.
Crop residue fire emissions
We present the first detailed measurements of trace gas chemistry and
aerosol properties for burning authentic Nepali crop residues, and we also
significantly expand the field emissions characterization for global
agricultural residue fires. The EFs for each fire are compiled in
Supplement Table S9. We examine the representativeness of our trace gas
grab sampling, justify a small adjustment to the trace gas data, and then
discuss the implications of the trace gas and aerosol results.
A detailed suite of EFs for several crop residues commonly burned in the US
and globally that is based on continuous lab measurements over the course of
whole fires is reported in Stockwell et al. (2014, 2015). A few fuels they
measured overlap with our Nepal study, including wheat and rice straw. The
average MCE (0.954) for our Nepal grab samples burning wheat varieties is
very close to the lab-measured wheat straw burning MCE (0.956), though other
crop types do not compare as well. When we compare our Nepal-average MCE for
all our crop residue fire grab samples (0.952) to earlier field
measurements, we find that the MCEs reported in Mexico (0.925) by Yokelson et al. (2011)
and in the US (0.930) by Liu et al. (2016) are significantly lower. In
addition, the previous field studies obtained more grab samples of a larger
number of fires and sampled from the air, which is unlikely to return too
low an MCE. The MCE that we obtained from the real-time FTIR CO and CO2
measurements that supported filter collection was also lower (e.g.,
∼ 0.933) and closer to the abovementioned field MCE values.
Thus, we believe our Nepal-average MCE based on grab samples is likely
biased upwards. Thus, to make our Nepal EFs more representative of the
likely Nepal (and regional) average, we have adjusted it to the average
airborne-measured field MCE (0.925) observed for crop residue burning in
another developing country (Mexico) according to procedures similar to
Stockwell et al. (2014) and also described in Sect. 3.6 above. These
adjusted EFs for selected compounds are included in Table 5 along with
values from selected other previous studies. Additional compounds measured
in this study (both original and adjusted) are included in Supplement
Table S9.
Summary of emission factors (g kg-1) and 1 standard
deviation for crop residue burns from this study and others.
Compound (formula)
EF crop residue
EF crop residue (food fuels)
EF crop residue
Yokelson et al. (2011)
Stockwell et al. (2015)
NAMaSTE
avg (SD)a
avg (SD)
avg (SD)b,c
MCE
0.925
0.925
0.925
Carbon dioxide (CO2)
1398 (55)
1353 (80)
1401 (68)
Carbon monoxide (CO)
71.9 (28.4)
68.7 (25.2)
72.3 (23.9)
Methane (CH4)
4.21 (3.53)
3.49 (2.19)
2.79 (0.85)
Acetylene (C2H2)
0.193 (0.059)
0.331 (0.277)
0.216 (0.063)
Ethylene (C2H4)
0.974 (0.470)
1.34 (0.80)
0.890 (0.230)
Propylene (C3H6)
0.417 (0.224)
0.576 (0.415)
0.492 (0.094)
Formaldehyde (HCHO)
1.55 (0.78)
1.93 (1.32)
0.865 (0.298)
Methanol (CH3OH)
2.24 (1.33)
1.87 (1.53)
1.01 (0.37)
Formic acid (HCOOH)
0.840 (0.571)
0.633 (0.846)
0.119 (0.055)
Acetic acid (CH3COOH)
3.80 (2.35)
3.88 (3.64)
0.871 (0.719)
Glycolaldehyde (C2H4O2)
–
2.29 (3.04)
4.07 (4.03)
Furan (C4H4O)
–
0.355 (0.445)
0.116 (0.049)
Hydroxyacetone (C3H6O2)
–
1.69 (2.03)
1.48 (0.62)
Phenol (C6H5OH)
–
0.494 (0.480)
0.341 (0.170)
1,3-Butadiene (C4H6)
0.127 (0.060)
3.63 ×10-3 (4.51 ×10-3)
0.180 (0.068)
Isoprene (C5H8)
–
0.220 (0.170)
1.97 ×10-2 (1.57 ×10-2)
Ammonia (NH3)
1.48 (1.13)
1.10 (1.05)
1.32 (1.10)
Hydrogen cyanide (HCN)
0.134 (0.252)
0.381 (0.259)
0.630 (0.463)
Nitrous acid (HONO)
–
0.395 (0.221)
0.377 (0.084)
Sulfur dioxide (SO2)
–
1.06 (0.36)
2.54 (1.09)
Hydrogen fluoride (HF)
–
–
bdl
Hydrogen chloride (HCl)
–
0.472 (0.320)
2.65 ×10-2 (–)
Nitric oxide (NO)
1.73 (0.66)
1.44 (0.42)
1.72 (0.93)
Nitrogen dioxide (NO2)
2.92 (1.77)
1.65 (0.47)
0.630 (0.203)
Ethane (C2H6)
0.764 (0.414)
–
0.566 (–)
Propane (C3H8)
0.237 (0.126)
–
0.186 (–)
1-Butene (C4H8)
0.113 (0.050)
0.134 (0.100)
0.119 (0.007)
Benzene (C6H6)
–
0.301 (0.177)
0.379 (0.091)
Toluene (C7H8)
–
0.296 (0.228)
0.224 (0.041)
Ethylbenzene (C8H10)
–
–
6.24 ×10-2 (4.05 ×10-3)
m/p-Xylene (C8H10)
–
0.107 (0.088)
0.297 (0.319)
PM
5.26 (1.98)
–
–
EF black carbon (BC)
–
–
0.831 (0.497)
EF brown carbon (BrC)
–
–
10.9 (6.5)
EF Babs 405 (m2 kg-1)
–
–
19.2 (8.0)
EF Bscat 405 (m2 kg-1)
–
–
116 (80)
EF Babs 870 (m2 kg-1)
–
–
3.94 (2.36)
EF Bscat 870 (m2 kg-1)
–
–
33.1 (29.5)
EF Babs 405 just BrC (m2 kg-1)
–
–
10.7 (6.3)
EF Babs 405 just BC (m2 kg-1)
–
–
8.47 (5.06)
SSA 405 nm
–
–
0.818 (0.146)
SSA 870 nm
–
–
0.825 (0.082)
AAE
–
–
2.15 (0.79)
a Yokelson et al. (2011) data are adjusted to a lower
carbon fraction (0.42). b NAMaSTE gas-phase EF values are
adjusted to MCE 0.925 (see Sect. 3.7). c Additional gas-phase
compounds are in Table S9.
Figure 4 shows the top OVOCs, NMHCs, and S- or N-containing compounds
emitted and shows good agreement with literature values for overlap species.
As noted in Stockwell et al. (2014), glycolaldehyde (the simplest
“sugar-like” molecule) is a major emission from crop residue fires and
Fig. 4 shows that glycolaldehyde is the dominant NMOC by mass from the
NAMaSTE crop residue fires. When we compare them to other fuel types, the EFs of
glycolaldehyde from our study, smoldering Indonesian rice straw (Christian
et al., 2003), and an assortment of US crop residue fuels (Stockwell et
al., 2014) are significantly higher than from other BB sources (Burling et
al., 2011; Johnson et al., 2013; Akagi et al., 2013). Glycolaldehyde was
below the detection limit for one NAMaSTE crop type (mustard residue),
suggesting emissions variability by fuel type and/or fuel properties. Our
average glycolaldehyde EF (4.07 g kg-1 ± 4.03) is similar to typical EFs
for total PM from BB, and glycolaldehyde has also been shown to be an
efficient aqueous phase SOA precursor (Ortiz-Montalvo et al., 2012). Other
oxygenated species emitted in large amounts by the crop residues burned in
NAMaSTE include butanone (methyl ethyl ketone) (1.93 ± 2.41 g kg-1) and
hydroxyacetone (1.48 ± 0.62 g kg-1). The Nepal data are higher or
similar to previous data for many OVOCs but noticeably lower for methanol,
formaldehyde, and organic acids. As expected the emissions of OVOCs were
greater than NMHCs, though there are also large emissions of C2 NMHCs
and BTEX compounds.
The emission factors (g kg-1) and ± 1 standard deviation
for the most abundant OVOCs, NMHCs, and S- or N-containing compounds emitted
from crop residue burns. The crop residue fires from other studies (Yokelson
et al., 2011; Stockwell et al., 2015) are shown in red and green.
Figure 4 shows several major S- and N-containing compounds including
significant SO2 emissions (2.54 g kg-1). While the SO2 emissions are
large compared to most BB types, the emissions from other S-containing
compounds (OCS, DMS) are limited. SO2 is an important precursor of
sulfate aerosols and was also a significant emission from grasses and crop
residue in Stockwell et al. (2014). This update is important to include in
emissions inventories as many global and regional estimates rely on the much
smaller value (0.4 g kg-1) reported by Andreae and Merlet (2001) (Streets et
al., 2003). Yokelson et al. (2011) noted high emissions of NOx from
crop residue fires sampled near the beginning of the Mexican dry season when
plant N content may be higher. Our Nepal NOx (∼ 2.5 g kg-1)
emissions for this fire type were measured in April, 6 months after the dry
season started in October, and may reflect lower fuel N content. The higher
NOx emissions (4.65 g kg-1) in Mexico may have also reflected higher wind
speed as an important mechanism, but one that requires airborne sampling to
probe.
Unlike US crop residue fires (Stockwell et al., 2014), HCl remained below
the detection limit in nearly every crop residue burn. As a landlocked
country these crops are not as influenced by chlorine-rich maritime air.
Additionally, in comparison to US crops, most rural agriculture in Nepal
may be less augmented by chemical pesticides. There are, however, detectable
emissions of CH3Cl, which have not been measured previously in the
field for crop residue burning. This new information for CH3Cl should
be considered when assessing global emissions of reactive chlorine (Lobert
et al., 1999).
The absorption and scattering coefficients at 405 and 870 nm were measured
for five of the six crop residue fires. The fire-average SSA at 870 nm and AAE
for these crop residue fires span a wide range. SSA (870) ranges from
0.579–0.981 (average 0.82 for both 870 and 405 nm), and AAE ranges from
∼ 1.58–3.53 (average near 2). The AAE as a function of SSA
colored by MCE is shown in Fig. 5 for all the real-time 1 s data collected
during crop residue fires. The AAE increases sharply at high SSA, while the
MCE distinctly decreases at increasing SSA. These observations support
previous interpretations that BrC is produced primarily by smoldering
combustion at lower MCEs for most BB fuel types (Liu et al., 2014; McMeeking
et al., 2014). Similar trends were observed for all other fuel types except
for the zigzag brick kiln, which will be discussed in the next section. The
BC and OC literature average for crop residue fires reported by Akagi et al. (2011) was based on only two fires. Our average EF
BC (0.831 ± 0.497 g kg-1) from five crop residue fires is similar to the literature
value (0.75 g kg-1), while we report the EF for BrC for the first time (10.9 ± 6.5 g kg-1), which is considerably larger than the global average OC reported in
Akagi et al. (2011) but in good agreement with the NAMaSTE,
simultaneously measured filter organic mass (∼ 10 g kg-1)
(Jayarathne et al., 2016). More importantly from an absorption standpoint,
we report EFs for Babs and Bscat at both wavelengths for this
fuel type in column 4 of Table 5.
The AAE calculated at 405 and 870 nm vs. SSA at 870 nm for all
crop residue burn samples measured every second during emissions collection.
Each data point is colored by MCE. The AAE increases sharply at high SSA,
while the MCE distinctly decreases at increasing SSA. BC emissions are
associated mostly with high MCE flaming and BrC emissions are associated
mostly with low MCE smoldering. Most source types demonstrated a similar
trend.
Brick kiln emissions
Very little is known about the chemical composition of brick kiln emissions.
There are very few studies and most of what is reported focuses on a few key
pollutants including CO, PM, and BC (Weyant et al., 2014). A previous study
measured a larger suite of emissions from authentic brick kilns in Mexico
(Christian et al., 2010); however, the fuel burned in those kilns was
primarily biomass and the NMOC emissions were somewhat comparable to those
from biomass burns. Coal is the main fuel used in brick kilns globally and
to our knowledge NAMaSTE produced the first quantitative emissions data for
numerous atmospherically significant species from authentic coal-fired brick
kilns in a region heavily influenced by this source. The individual EFs for
both brick kilns sampled in this study are reported in Table 6. There are
large differences between the two kilns types that stand out in Table 6
despite our lack of opportunity to measure inherent kiln variability. We
will first discuss the kiln emissions individually and then follow with a
detailed kiln comparison.
Zigzag emissions
The zigzag kiln emissions had a very high average MCE (0.994), and the EFs
for most smoldering compounds (e.g., most NMOC) were much reduced. Not
surprisingly, the EFs for flaming compounds including HCl, HF, NOx, and
SO2 were high. High emissions of NOx and S-containing gases are
important as ozone and aerosol precursors and because they can enhance
deposition and O3 impacts on nearby crops and negatively impact crop
yield. The latter issue is especially relevant since brick kilns are
commonly seasonal and located on land leased from farmers, where the
depletion of the soil to collect clay for bricks is already another
agricultural productivity issue.
The zigzag kiln was the only source in our NAMaSTE study that emitted
detectable quantities of HF. It has been suspected that brick kilns are an
important source of atmospheric fluorides since fluorine is typically present
in raw brick materials (USEPA, 1997). We found that HF was a major emission
from the zigzag brick kiln with an average EF of 0.629 g kg-1 and a
peak concentration of ∼ 13 ppm. HF is a phytotoxic air pollutant, and
agricultural areas with visible foliar damage in Pakistan were suspected to
be impacted by HF emissions from nearby brick kilns (Ahmad et al., 2012).
While HF is rapidly transformed to particulate fluoride, much previous work
confirms adverse effects of HF or particulate fluoride from various sources
on crops (Haidouti et al., 1993; Ahmad et al., 2012). Since many brick kilns
are present in agricultural regions, this first confirmation of high HF
emissions is an important finding and should also be included in assessments
of kiln impacts on agriculture. HF emissions from brick kilns likely vary
considerably depending on the F content of the clay (and possibly the coal)
being fired (as discussed further below). HF is also very reactive, but
perhaps particle fluoride could serve as a regional indicator for brick kilns
with more work.
Emission factors (g kg-1) for a single clamp kiln, zigzag
kiln, and stoke holes on the zigzag kiln.
Compound (formula)
EF clamp kiln
EF zigzag kiln
EF coal stoke holes
at zigzag kiln
Method
FTIR + WAS
FTIR + WAS
FTIR
MCE
0.950
0.994
0.861
Carbon dioxide (CO2)
2102
2620
2234
Carbon monoxide (CO)
70.9
10.1
230
Methane (CH4)
19.5
8.73 ×10-2
4.59
Acetylene (C2H2)
5.58 ×10-2
1.65 ×10-2
1.87 ×10-2
Ethylene (C2H4)
1.27
4.32 ×10-2
0.445
Propylene (C3H6)
1.49
6.58 ×10-2
0.808
Formaldehyde (HCHO)
8.21 ×10-2
bdl
bdl
Methanol (CH3OH)
1.77
0.112
0.437
Formic acid (HCOOH)
0.241
5.84 ×10-2
0.180
Acetic acid (CH3COOH)
0.430
0.471
11.3
Glycolaldehyde (C2H4O2)
bdl
bdl
bdl
Furan (C4H4O)
0.383
bdl
bdl
Hydroxyacetone (C3H6O2)
1.81
bdl
1.61
Phenol (C6H5OH)
0.429
1.54 ×10-2
bdl
1,3-Butadiene (C4H6)
0.103
1.51 ×10-2
bdl
Isoprene (C5H8)
8.66 ×10-2
2.46 ×10-2
1.47
Ammonia (NH3)
0.317
bdl
bdl
Hydrogen cyanide (HCN)
1.39
0.446
2.28
Nitrous acid (HONO)
0.320
4.45 ×10-2
1.33
Sulfur dioxide (SO2)
13.0
12.7
28.5
Hydrogen fluoride (HF)
bdl
0.629
0.888
Hydrogen chloride (HCl)
bdl
1.24
1.86
Nitric oxide (NO)
bdl
1.28
10.4
Nitrogen dioxide (NO2)
0.297
8.21 ×10-2
1.36
Carbonyl sulfide (OCS)
–
3.42 ×10-3
nm
DMS (C2H6S)
–
3.68 ×10-5
nm
Chloromethane (CH3Cl)
–
2.22 ×10-2
nm
Bromomethane (CH3Br)
2.62 ×10-3
2.59 ×10-3
nm
Methyl iodide (CH3I)
bdl
2.01 ×10-3
nm
1,2-Dichloroethene (C2H2Cl2)
–
4.45 ×10-5
nm
Methyl nitrate (CH3NO3)
2.36 ×10-5
2.92 ×10-3
nm
Ethane (C2H6)
5.37
2.06 ×10-3
nm
Propane (C3H8)
3.00
1.97 ×10-3
nm
i-Butane (C4H10)
0.342
1.60 ×10-3
nm
n-Butane (C4H10)
1.16
1.92 ×10-3
nm
1-Butene (C4H8)
0.347
1.68 ×10-3
nm
i-Butene (C4H8)
0.428
1.47 ×10-3
nm
trans-2-Butene (C4H8)
0.346
1.44 ×10-3
nm
cis-2-Butene (C4H8)
0.214
9.65 ×10-4
nm
i-Pentane (C5H12)
0.349
3.70 ×10-2
nm
n-Pentane (C5H12)
0.811
3.26 ×10-2
nm
1-Pentene (C5H10)
0.233
1.60 ×10-3
nm
trans-2-Pentene (C5H10)
0.249
2.64 ×10-3
nm
cis-2-Pentene (C5H10)
0.093
9.01 ×10-4
nm
3-Methyl-1-butene (C5H10)
5.72 ×10-2
3.32 ×10-4
nm
1,2-Propadiene (C3H4)
4.97 ×10-4
2.15 ×10-5
nm
Propyne (C3H4)
1.80 ×10-3
bdl
nm
1-Butyne (C4H6)
bdl
bdl
nm
2-Butyne (C4H6)
bdl
bdl
nm
n-Hexane (C6H14)
0.670
2.16 ×10-2
nm
n-Heptane (C7H16)
0.617
3.04 ×10-3
nm
Continued.
Compound (formula)
EF clamp kiln
EF zigzag kiln
EF coal stoke holes
at zigzag kiln
n-Octane (C8H18)
0.549
1.58 ×10-3
nm
n-Nonane (C9H20)
0.434
2.42 ×10-3
nm
n-Decane (C10H22)
0.428
2.02 ×10-3
nm
2,3-Dimethylbutane (C6H14)
0.127
3.59 ×10-3
nm
2-Methylpentane (C6H14)
0.398
4.84 ×10-3
nm
3-Methylpentane (C6H14)
0.312
1.17 ×10-2
nm
2,2,4-Trimethylpentane (C8H18)
bdl
8.02 ×10-4
nm
Cyclopentane (C5H10)
0.134
8.53 ×10-4
nm
Cyclohexane (C6H12)
5.55 ×10-2
2.98 ×10-3
nm
Methylcyclohexane (C7H14)
5.84 ×10-2
bdl
nm
Benzene (C6H6)
1.68
8.25 ×10-3
nm
Toluene (C7H8)
1.05
2.80 ×10-2
nm
Ethylbenzene (C8H10)
0.279
1.35 ×10-2
nm
m/p-Xylene (C8H10)
1.06
5.74 ×10-2
nm
o-Xylene (C8H10)
0.377
2.18 ×10-2
nm
Styrene (C8H8)
2.62 ×10-3
4.56 ×10-3
nm
i-Propylbenzene (C9H12)
2.84 ×10-2
4.07 ×10-4
nm
n-Propylbenzene (C9H12)
3.82 ×10-2
1.82 ×10-3
nm
3-Ethyltoluene (C9H12)
0.091
6.93 ×10-3
nm
4-Ethyltoluene (C9H12)
3.55 ×10-2
3.69 ×10-3
nm
2-Ethyltoluene (C9H12)
2.76 ×10-2
2.30 ×10-3
nm
1,3,5-Trimethylbenzene (C9H12)
5.88 ×10-2
4.30 ×10-3
nm
1,2,4-Trimethylbenzene (C9H12)
8.46 ×10-2
5.59 ×10-3
nm
1,2,3-Trimethylbenzene (C9H12)
2.76 ×10-2
2.03 ×10-3
nm
alpha-Pinene (C10H16)
bdl
1.49 ×10-3
nm
beta-Pinene (C10H16)
bdl
1.31 ×10-3
nm
Ethanol (C2H6O)
–
4.84 ×10-3
nm
Acetaldehyde (C2H4O)
4.13 ×10-2
6.94 ×10-2
nm
Acetone (C3H6O)
–
1.46E-01
nm
Butanal (C4H8O)
bdl
2.19 ×10-3
nm
Butanone (C4H8O)
–
2.29 ×10-3
nm
EF black carbon (BC)
1.72 ×10-2 (7.50 ×10-3)
0.112(0.063)
nm
EF brown carbon (BrC)
1.74(0.34)
0.913(0.278)
nm
EF Babs 405 (m2 kg-1)
1.86 (0.24)
2.03 (0.70)
nm
EF Bscat 405 (m2 kg-1)
32.8 (2.1)
21.2 (12.8)
nm
EF Babs 870 (m2 kg-1)
8.16 ×10-2 (3.56 ×10-2)
0.530 (0.300)
nm
EF Bscat 870 (m2 kg-1)
0.670 (0.129)
1.75 (0.25)
nm
EF Babs 405 just BrC (m2 kg-1)
1.70 (0.33)
0.895 (0.273)
nm
EF Babs 405 just BC (m2 kg-1)
0.155 (0.102)
1.14 (0.64)
nm
SSA 405 nm
0.946 (0.007)
0.881 (0.098)
nm
SSA 870 nm
0.895 (0.029)
0.779 (0.103)
nm
AAE
4.19 (0.73)
1.92 (0.50)
nm
Note: “bdl” indicates below the detection limit; “–”
indicates concentrations were not greater than background; “nm” indicates
not measured; C fractions: zigzag kiln (0.722), clamp kiln (0.644) (see
Sect. 2.4).
Because of the large number of FTIR grab samples over the sampling day,
which lasted approximately 5 h, we can construct a rough time series of
the kiln emissions, with resolution averaging about 12 min. To emphasize
chemistry, normalize for fuel consumption rates, and account for somewhat
arbitrary grab sample dilution, in Fig. S1 we plot selected ERs to CO2.
The ERs of HCl and HF to CO2 rise first and track together over time.
The ERs of NO and SO2 rise next, and their observed peak is about 2 h after the halogens. This is consistent with the halogens being driven
from clay at 500–600 ∘C (USEPA, 1997). The halogen peaks
are then followed by a peak in the NOx and SO2 emissions likely
from the coal fuel.
The AAE calculated at 405 and 870 nm vs. SSA at 870 nm for the
zigzag kiln measured every second during emissions collection. Each data
point is colored by MCE. This deviates from the typical trend in that the
highest MCEs are not clustered at the lowest SSA or AAEs. Some BrC is emitted at
a variety of “higher” MCEs.
As noted in Sect. 2.1.7, in hopes of obtaining representative emissions from
this particular brick kiln, we sampled the smoke coming out of the top of
the chimney stack, but we also sampled the lesser amount of emissions
escaping the coal-feeder stoke holes located on the “roof” of the kiln.
Table 6 also includes the EFs specific to the emissions from the stoke
holes. The MCE is significantly reduced (0.861); consequently, the EFs of
smoldering compounds are much higher with, e.g., high EF CO (230 g kg-1). Oddly,
the stoke hole smoke also had higher EFs for HF, HCl, NOx, and
SO2, compounds normally emitted during flaming combustion. This is
probably because the stoke holes are much closer to the combustion zone, and
many internally generated species are scavenged in the kiln and stack walls
before being emitted from the stack. Some kilns have internal water
reservoirs below the stack to scavenge the smoke as rudimentary emissions
control. However, these stoke hole emissions do not need to be weighted much
if at all in an assessment of overall emissions as the vents are normally
closed.
Table 6 includes the EFs for BC and BrC, and the EFs for scattering and
absorption at 405 and 870 nm, calculated from all the real-time PAX (and
colocated CO2) data above background for separate plumes throughout
the sampling day, that we then averaged together. The SSA at 870 nm (0.779 ± 0.103) indicates that BC contributes to the absorption in the fresh
emissions, while the AAE (1.92 ± 0.50) implies that the emissions are
not pure BC. The PAX data suggest that a little under half the absorption at
405 nm is due to BrC. Weyant et al. (2014) reported a range of EFs for EC
for South Asian brick kilns (0.01–3.7 g kg-1) and our EF BC (0.112 g kg-1) falls
within the range they report. We note that for all the other sources sampled
in NAMaSTE and in the BB literature, high values of SSA and AAE are mostly
associated with a low MCE (smoldering) and low SSA and AAE is associated with
high MCE (flaming). This is illustrated for crop residues in Fig. 5. For the
zigzag kiln this pattern is less pronounced. In the zigzag kiln, the
highest MCE values are not clustered at the lowest SSA and AAE (Fig. 6). Nearly
all the real-time data from the zigzag kilns was at high MCE (> 0.95) but accompanied by some evidence for BrC emissions. Given the
plethora of possible UV-absorbing compounds in OA, characterizing the
variety of primary and secondary “BrC types” with different absorption
intensities, abundances, and lifetimes is an important area for future
research (Saleh et al., 2014).
Clamp kiln emissions
The clamp kiln emissions had a lower average MCE (0.950) than the zigzag
kiln (though still reflecting primarily efficient combustion), which is not
surprising since we estimate that the fuel had a larger component of biomass.
Consequently the EFs for most products of incomplete combustion are
∼ 5–3000 times higher than those from the zigzag kiln and
also higher than values reported for a clamp kiln in Mexico that burned
mostly sawdust at an average MCE of 0.968 (Christian et al., 2010). Even
though the MCE was lower, the clamp kiln EF SO2 (13.0 g kg-1) was almost
the same as the zigzag kiln. This is most likely rationalized at least in
part by the higher sulfate emission factors for the zigzag kiln (Jayarathne
et al., 2016). For all grab samples of the clamp kiln, the NO remained below
the detection limit, while NO2 only had detectable quantities for three
grab samples near the end of the day. HCl and HF probably remained below the
detection limit because of lower halogen content in the clay (vide infra and
Table S3).
If we convert and sum the NO2, NO, and HONO emissions to “NOx as
NO”, this quantity is more than 3.5 times higher from the zigzag kiln. The
coal from both kilns had similar N content, so the difference in NOx
emissions is most likely traced to the higher MCE in the zigzag kiln.
However, we cannot completely rule out a different contribution of “thermal
NOx” between the kilns. Co-firing coal with biomass is a common
practice in power plants as it has been shown to decrease combustion zone
temperature and thermally dependent NOx formation, thereby reducing
several criteria pollutants including NOx (USEPA, 2007; Al-Naiema et
al., 2015). Thus, the lower NOx EFs from the clamp kiln could be partly
due to co-firing with more biomass.
The differences in NMOC emissions for the two kiln types were dramatic. We
simply list some common pollutants and precursors of concern and include the
approximate clamp-kiln-to-zigzag-kiln EF ratio in parentheses after each:
CO (7), CH4 (223), ethane (2604), ethylene (30), benzene (203),
methanol (16), and phenol (28). In addition, many species were emitted at high
levels from the clamp kiln but were below the detection limit from the
zigzag kiln, including formaldehyde, furan, hydroxyacetone, and ammonia.
The main emissions overall from the clamp kiln in order of mass were
CO2, CO, CH4, SO2, ethane, propane, hydroxyacetone, BrC,
methanol, and benzene. Methane is an important short-lived climate pollutant, and the CH4 EF for the clamp kiln (19.5 g kg-1) is among the highest seen
for any combustion source. The other alkanes were also extremely enhanced
all the way through n-decane, which had an EF of 0.428 g kg-1. These
enormous EFs for alkanes are not typical for BB and might reflect burning
coal inefficiently. Another possible explanation is that used motor oil is
reportedly sometimes disposed of as fuel in brick kilns or added to the fuel
to impart color to bricks (USEPA, 1997; Christian et al., 2010). The
enhancement observed for the alkanes throughout the C1–C10 size
range that we could measure suggests that even larger alkanes are also
enhanced. Large alkanes have recently attracted attention as important SOA
precursors (Presto et al., 2010). In our clamp kiln data, the sum of the EFs
for NMOCs we measured that are known to have high yields for SOA (BTEX plus
phenol) is ∼ 5 g kg-1, which is already much larger than the
initial EF OA as crudely approximated from the EF BrC (∼ 2.0 ± 0.4 g kg-1).
The EF BC (0.02 g kg-1) for the clamp kiln was much lower than for the zigzag
kiln, and the co-collected filter data are consistent with this result.
Weyant et al. (2014) also noted similar “low” EFs for EC for several brick
kilns measured in that study. The EF BrC was greater for the clamp kiln than
the zigzag kiln, which is consistent with the filter OC and an expected
result given a more significant biomass contribution to overall fuel. The
AAE and SSA were much greater for the clamp kiln than the zigzag kiln
(Table 6).
We had only one sample of the coal from each kiln, and the elemental analysis
is shown in Table S3. The likely higher fuel variability for the non-C trace
substances limits us to a few general comments. The measured emissions of
the sulfur species from both kilns (including stoke holes) accounted for
about 60–111 % of the nominal S in the coal, which is a good match given
experimental uncertainty. The measured emissions of N-containing species
from both kilns were significantly lower than the nominal coal N. Much of
the missing N was likely emitted as N2, especially at high MCE
(Kuhlbusch et al., 1991; Burling et al., 2010). Finally, the zigzag kiln
emissions had significantly higher halogen content than the 0.3 g kg-1 upper
limit for the zigzag coal. This is consistent with our speculation above
that much of the halogen emissions come from the clay and that this is a
source of kiln-to-kiln variability.
This is by no means an exhaustive evaluation of South Asian brick kiln
emissions. However, because there are so few studies detailing the chemical
composition of brick kiln emissions, this is a valuable addition to the
current body of measurements. In terms of comparative pollution between the
two technologies, there are some trade-offs. The clamp kiln we sampled
produced far more BrC and a large suite of NMOC pollutants and precursors
typically associated with inefficient combustion of biomass (e.g., HCHO and
benzene) or (likely) inefficient combustion of motor oil or coal (e.g.,
alkanes). The zigzag kiln we sampled produced significantly more BC,
NOx, HCl, and HF, where the latter two could be larger partly because
of the clay and not only the kiln design. For SO2 the kilns were not
significantly different. Ultimately, since the zigzag kiln is thought to
produce significantly more bricks per unit fuel use than the clamp kiln
(e.g., Weyant et al., 2014), this ratio should be further investigated for
scaling emissions (on a per brick basis). The zigzag kiln is very likely
preferred from the standpoint of pollutants emitted per brick produced,
which is a major factor in selecting mitigation strategies. More measurement
and modeling studies will clearly be needed to fully assess the impact of
brick kiln emissions and subsequent atmospheric chemistry in the region.
Conclusions
We investigated the trace gas and aerosol emissions from a large suite of
major undersampled sources around Kathmandu and the Indo-Gangetic plain of
southern Nepal. Our source characterization included motorcycles, kilns,
wood and dung cooking fires, crop residue burning, diesel and gasoline
generators, agricultural pumps, and open garbage burning. We report the
emission factors (grams of compound emitted per kilogram of dry fuel burned)
for ∼ 80 important trace gases measured by FTIR and WAS,
including important NMHCs up to C10 and many oxygenated organic
compounds. We also measured aerosol mass and optical properties using two
PAX systems at 405 and 870 nm. We report important aerosol optical
properties that include emission factors (in m2 kg-1) for scattering and
absorption at 405 and 870 nm, single scattering albedo, and absorption
Ångström exponent. From the direct measurements of absorption we
estimated black and brown carbon emission factors (in g kg-1).
Although we were not able to sample the transport sector extensively due to
the Gorkha earthquake, we were able to measure several motorbikes pre- and
post-service. The minor maintenance led to minimal if any reduction in
gaseous pollutants, consistent with the idea that more major servicing is
needed to reduce gas-phase pollutants. Motorcycles were in general among the
least efficient sources sampled, and the CO EF was on the order of
∼ 700 g kg-1, about 10 times that of a typical biomass fire.
For most fossil fuel sources, including generators and agricultural pumps,
diesel burned more efficiently than gas but produced more NOx, HCHO,
and aerosol.
Numerous trace gas emissions (many for the first time in the field) were
quantified for open cooking fires and several improved cooking stoves with
several fuel variations. Authentic open dung cooking fires emitted high
levels of BrC (5.54 ± 1.66 g kg-1), NH3 (3.00 ± 1.33 g kg-1),
organic acids (7.66 ± 6.90 g kg-1), and HCN (2.01 ± 1.25 g kg-1),
where the latter could contribute to space-based observations of high levels
of HCN in the lower stratosphere above the Asian monsoon. HCN and some
alkynes > C2 (previously linked to BB) were also observed
from several non-biomass burning sources. BTEX compounds were major
emissions of both dung (∼ 4.5 g kg-1) and wood (∼ 1.5 g kg-1) cooking fires and a simple method to estimate indoor exposure to
the many important air toxics we measured in the emissions is described. Our
PAX data suggest relatively more absorption by BrC as opposed to BC from
cooking fires than may be currently recognized, especially for dung burning.
Biogas, as expected, emerged as the most efficient and least polluting
cooking technology out of approximately a dozen types subjected to limited
testing.
The first global garbage burning inventory relied on measurements from very
few studies and information for many compounds is often limited to
laboratory simulations (Wiedinmyer et al., 2014). Our authentic Nepali
garbage burning data shift the global average observed for this source to
lower MCE and significantly more BC and BTEX emissions than in previous
measurements, while supporting previous measurements of high HCl. Crop
residue burning produced EFs in good agreement with literature values with
relatively high emissions of oxygenated organic compounds (∼ 12 g kg-1) and SO2 (2.54 ± 1.09 g kg-1). We observed an EF for BrC of
∼ 11 g kg-1 or about 4 times higher than the previous organic
carbon literature average, which was based on fewer data. Our EF BrC is
qualitative but in agreement with our absorption data and SSA in showing
that BrC absorption is important for this major global BB type.
There are very few studies detailing the chemical emissions from brick
kilns. While we were only able to sample two brick kilns in this study, we
present a significant expansion in chemical speciation data. The two brick
kilns sampled had different designs and utilized different clay, coal, and
amounts of biomass for co-firing with the main coal fuel. Consequently the
two kilns produced very different emissions. A zigzag kiln burning
primarily coal at high efficiency produced larger amounts of BC, NOx,
HF, and HCl, (the halogen compounds likely mostly from the clay), while the
clamp kiln (with relatively more biomass fuel) produced dramatically more
organic gases, organic aerosol (BrC), and aerosol precursors including large
alkanes. Both kilns were significant SO2 sources, with their emission
factors averaging ∼ 13 g kg-1.
Overall, we report the first, or in any case rare, optically and chemically detailed
emissions data for many undersampled biomass burning sources and other
undersampled sources in developing countries. Companion papers will report
results from other co-deployed techniques, such as filter sampling and
mini-AMS, a source apportionment for a fixed supersite, and model
interpretation as guidance for mitigation strategies. In summary, we have
provided the first extensive suite of gases for most of the sources. For
cooking fires and crop residue burning, which are major South Asian and
global sources, we have shown that absorption by both BrC and BC is
significant, with BrC absorption even more pronounced for dung fuel compared
to wood fuel. On the other hand, though we have begun to address these
undersampled sources, given the diversity and abundance of the sources, much
more work is needed, especially for gensets, pumps, traffic, and brick
kilns. Future measurements and modeling are also needed to better understand
the evolution of the emissions we report here.