Emission measurements from traditional biomass cookstoves in South

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Energy and the Environment

Emission measurements from traditional biomass cookstoves in South Asia and Tibet Cheryl L. Weyant, Pengfei Chen, Ashma Vaidya, Chaoliu Li, Qianggong ZHANG, Ryan Thompson, Justin Ellis, Yanju Chen, Shichang Kang, Ganesh Ram Shrestha, Mahesh Yagnaraman, Joseph Arineitwe, Rufus Edwards, and Tami C. Bond Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05199 • Publication Date (Web): 25 Feb 2019 Downloaded from http://pubs.acs.org on February 25, 2019

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Environmental Science & Technology

Emission measurements from traditional biomass cookstoves in South Asia and Tibet Cheryl L. Weyant,† Pengfei Chen,‡,¶ Ashma Vaidya,§ Chaoliu Li,‡ Qianggong Zhang,‡ Ryan Thompson,†,k Justin Ellis,†,⊥ Yanju Chen,†,# Shichang Kang,¶,‡,@ Ganesh Ram Shrestha,§ Mahesh Yagnaraman,4 Joseph Arineitwe,∇ Rufus Edwards,†† and Tami C. Bond∗,† †Environmental Engineering, University of Illinois Urbana-Champaign, IL ‡Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China 1

¶State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China §Center for Rural Technology - Nepal kMountain Air Engineering, Cottage Grove, Oregon ⊥National Oceanic and Atmospheric Administration (NOAA) #California Air Resources Board, Sacramento, California @University of Chinese Academy of Sciences, Beijing 100039, China 4First Energy Pvt. Ltd. Pune, Maharashtra, India ∇Center for Integrated Research and Community Development (CIRCODU), Kampala, Uganda ††Department of Epidemiology, School of Medicine, University of California Irvine E-mail: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Traditional biomass stoves are a major global contributor to emissions that impact

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climate change and health. This paper reports emission factors of particulate matter

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(PM2.5 ), carbon monoxide (CO), organic carbon (OC), black carbon (EC), optical ab-

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sorption, and scattering from 46 South Asian, 48 Tibetan, and 4 Ugandan stoves. These

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measurements plus a literature review provide insight into the robustness of emission

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factors used in emission inventories. Tibetan dung stoves produced high average PM2.5

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emission factors (23 and 43 gkg−1 for chimney and open stoves) with low average EC

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(0.3 and 0.7 gkg−1 , respectively). Comparatively, PM2.5 from South Asian stoves (7

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gkg−1 ) was in the range of previous measurements and near values used in inventories.

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EC emission factors varied between stoves and fuels (p < 0.001), without corresponding

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differences in absorption; stoves that produced little EC, produced enough brown car-

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bon to have about the same absorption as stoves with high EC emissions. In Tibetan

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dung stoves, for example, OC contributed over 20% of the absorption. Overall, EC

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emission factors were not correlated with PM2.5 and were constrained to low values,

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relative to PM2.5 , over a wide range of combustion conditions. The average measured

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EC emission factor (1 gkg−1 ), was near current inventory estimates.

TOC Art

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Introduction

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In populations without sufficient access to gas or electricity, household energy demand is

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often met with biomass fuels that are burned for heating, cooking, lighting, and other social

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and cultural purposes. About three billion people use solid fuels for household energy and

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the emissions negatively impact health, air quality, and climate on a global scale.

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Household biomass combustion contributes a large, but uncertain, fraction of total par-

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ticulate matter (PM), black carbon (BC), and organic carbon (OC) emissions. Zhang et al.

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(2009) estimated that about half of the total anthropogenic PM, 60% of BC and 85% of OC

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emissions are from household solid fuel combustion in China (in 2006), 1 while Ohara et al.

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(2007) 2 attributed 81% of the BC and 96% of OC emissions to household solid fuel combus-

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tion in Asia (in 2000). Globally, BC from household solid fuel combustion contributes an

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estimated 57% of total anthropogenic emissions (in 2010), 32% is from biomass (2100 Tg). 3

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These emission estimates are uncertain, in part, because they rely on few measurements that

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do not cover the range of solid fuel types, stove designs, and operation observed in real-world

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cooking. 3

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Previous measurements of emissions from traditional biomass stoves may poorly represent

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real-world cooking emissions. Approaches to measuring emissions have included (1) open

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burning of cooking fuels without a stove, 4,5 (2) laboratory stove testing with controlled tasks

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and fuel feeding, usually using a water boiling test (WBT), 6–10 (3) field tests in homes with

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uncontrolled fuel type and feeding rate but a controlled task (field WBT), 11–13 and (4) field

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tests in homes with uncontrolled fuel, feeding rate, and task (uncontrolled test). 9,14–22 Fuel

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type, feeding rates, and cooking tasks influence emissions; laboratory studies and field tests

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with the WBT have both been shown to have lower emissions and less variability compared

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to uncontrolled field tests. 15,23 Field studies with uncontrolled operation have typically had

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small sample sizes for each stove-fuel combination and high variability. 9,14–16 Given the

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global variability in fuels, stoves, cooking practices, and observed emissions, these studies

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may not reflect emissions outside their region of measurement. Tests in other regions would 3



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provide more confidence in global applicability of household biomass cooking emission factors.

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In addition, few measurements are available from the Himalayas and Tibet, where biomass

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burning emissions have an important influence on glacial melting. 24

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This study reports emission measurements from 94 biomass combustion events in village

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homes in Tibet, Nepal, India, and Uganda. Fuels include wood, dung, agricultural waste,

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and combinations of these fuels. Emission factors and real-time variability of stove-fuel

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combinations were computed and compared with other biomass stoves in this and previous

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studies.

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Methods

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Emissions were measured from household cooking events from commonly used biomass stoves

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in households in Tibet, India, and Nepal. These measurements were part of a campaign to

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characterize BC emissions that deposit and accelerate glacial melting in the Himalayas. 25,26

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Tests in Uganda were measurements of opportunity, not published previously. Twelve previ-

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ously published tests in Honduras, from a study using the same methods, are also reported

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here for comparison. 14,23 All tests were of traditional stoves in each region.

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Stoves and fuels

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Table 1 provides summary results for each stove group and Table S1 summarizes the stove

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groups characterized.

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Traditional clay stoves called “chulhas” are widespread in South Asia. They are typically

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homemade, clay structures that are built into the floor of a kitchen with side ports for fuel

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feeding. There were regional variations in chulha construction and fuels, though all chulhas

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used a mix of biomass fuels. Six chulhas in Maharashtra, India were primarily fueled with

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wood and dung. Dung in this region was usually mixed with rice husks and packed around

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a stick, so the long pieces could be easily fed into the stove. Four traditional chulhas and

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seven improved chulhas (ICS) were measured in Nepal Midhills. The ICSs were similar to

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the traditional stoves in outward appearance, but had an internal baffle that was intended

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to improve the combustion. Dung in this region was made into patties that were broken and

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fed into the stove. Fuel consumption for chulhas in India and Nepal Midhills are described

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in Johnson et. al (2013). 27 In the Nepal Terai, 29 chulhas were measured with dung, wood,

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and agricultural waste fuels. Multiple fuels were almost always used, sometimes in series but

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often concurrently. The fuel was categorized as a particular fuel when it made up more than

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60% of the fuel mass, and otherwise was categorized as mixed. These categories were used

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to explore whether emission differences could be attributed to fuel type.

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In Tibetan homes, two stove types were found in mobile tent homes: open combustion

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stoves without chimneys (termed “Open dung”) and metal stoves with chimneys (termed

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“Chimney dung”). Measurements were taken in the Nam Co region of the Tibetan Plateau

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(4730 m above sea level ). Yak dung is the primary fuel at high elevations where woody plants

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cannot survive. Similar stoves are described in a study reporting indoor air concentrations by

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Xiao et. al (2015). 28 At lower elevations in the Tibetan Plateau, metal stoves with chimneys

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(termed “Chimney wood”) burned wood in permanent houses.

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Biomass fuel stoves can be used for cooking and heating. Stoves in Uganda, India,

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and Nepal Midhills were measured in warm climates or in summer months and were used

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primarily for cooking. Chulhas in the Nepal Terai were measured in winter and partially

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provided heating energy. In the winter, cooks discontinued fuel feeding when the meal was

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complete, but the heat from the chulha replaced other forms of household heating, such as

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straw burning. Stoves in Tibet were used for both cooking and heating; fuel was fed for

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heating even when food was not being prepared. Cooking and heating activities were not

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monitored during individual tests nor distinguished in data processing.

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Sampling methods

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All measurements were uncontrolled cooking tests where household cooks performed normal

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daily cooking activities using the stoves and fuels of their choice.

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Emissions were sampled using partial plume capture, as described previously. 14,29 Briefly,

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emissions were drawn from the plume at 1-3 ft above the cooking vessel using multiple inlet

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sampling probes described in Roden et al. (2006). 14 The height was determined by selecting

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a location as near to the stove as possible, while allowing the cook clear and free access to

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cook. When a chimney was present, a single inlet sample was drawn from the chimney.

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The sampling system has been described in Weyant et. al (2014) and is summarized

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here. 29 Particles were measured downstream of a 2.5 µm cut cyclone (URG 2000-30ED).

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PM2.5 mass was collected on 47 mm PTFE (polytetrafluoroethylene) fiber filters (Fluoro-

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pore Membrane Filters, FALP04700, Millipore) and measured gravimetrically using a mi-

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crobalance (Cahn C-31, Thermo Electron Corp) in a temperature and humidity controlled

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environment (20-25 deg C and 45-50% RH). EC and OC were collected on 47 mm quartz

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fiber filters (TISSUQUARTZ 2500QAT-UP, Pall). An additional quartz filter behind the

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Teflon filter collected adsorbed gas-phase carbon and the measured OC mass was subtracted

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from the primary quartz filter OC mass to correct for gas-phase adsorption. 30 Filters were

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sealed and stored in ice-packed insulated containers after collection and maintained at -4 ◦ C

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prior to analysis to prevent loss of volatile organic carbon. 31

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Particle optical properties were measured in real-time (1 Hz). Particle scattering was

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measured with a narrow-angle red-wavelength light sensor (635 nm, Aprovecho Research

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Laboratory). 75% percent of open dung and chimney dung stove tests in Tibet had an

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overranging scattering signal and reports of scattering were omitted for these tests. Absorp-

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tion was measured using a 3-wavelength (467, 530, and 660 nm) Particle Soot Absorption

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Photometer (PSAP, Radiance Research). The filter spot size correction was applied as in

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Bond et al. (1999). 32 Downstream of particle measurements and filtration, CO and CO2

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concentrations were measured in real-time (1 Hz) using an electrochemical sensor (SS1128, 6


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Senko) and a non-dispersive infrared sensor (Telaire T6615, GE), respectively.

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Flows were measured in-line at six sampling points in the system using a thin film flow

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calibrator (mini-BUCK calibrator APB-805000) before and after each measurement. In addi-

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tion, flows through each filter were measured in real-time with mass flow meters (Honeywell

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AWM3300V). The system was checked for leaks by confirming that the system could sus-

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tain negative pressure to the entire closed system. The type, mass, and moisture content

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(conductance meter) of the fuel was recorded for some events and is included in the SI

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dataset.

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Data analysis methods

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Emission factors in grams of pollutant per kilogram fuel (gkg−1 ) were calculated using the

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carbon-balance method, which relies on conservation of carbon mass before and after com-

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bustion. The method is described in the SI section 3 and in previous literature. 14,33 A carbon

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mass fraction of 0.5 was used for wood and agricultural waste and 0.3 was used for dung

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fuel, 34 with uncertainties of 2% and 10%, respectively. Emission factors are reported for

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CO (EFCO ), PM2.5 (EFPM ), EC (EFEC ), and OC (EFPM ). Optical emission factors were

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calculated similarly for scattering (EFscattering ) and absorption (EFabsorption ). The average

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emission factor uncertainty was ±22% for wood burning stoves and ±24% for dung stoves.

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The uncertainty was driven by calibration drift during measurement campaign.

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Several common metrics were computed to characterize the emissions. (1) The Absorp-

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tion ˚ Angström Exponent (AAE) quantifies the wavelength dependence of absorption. An

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AAE of 1 indicates particles with a constant refractive index over a range of wavelengths 35

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and is associated with black carbon, and higher values indicate brown or yellow material. (2)

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The mass scattering cross-section (MSC) is the scattering observed for a given aerosol mass,

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and is useful to translate real-time scattering measurements to approximate mass measure-

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ments. (3) The mass absorption cross-section (MACEC ) is an indication of the absorptivity

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of EC. Metric calculations are described in the SI section 3. 7



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All quartz filters were analyzed using a Sunset Laboratory OC/EC analyzer equipped

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for analysis using the thermal optical transmittance (TOT) method 36 with the temperature

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procedure in the SI section 4. A reflectance detector was added to the Sunset analyzer mid-

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project and 19 of 29 tests from the Nepal Terai and all tests in Tibet were also analyzed using

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thermal-optical reflectance (TOR). Neither TOT nor TOR analysis have been conclusively

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determined to be more accurate, but Chow et al. (2004) suggested that reflectance is more

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consistent across different temperature profiles and agrees better with EC determined by

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filter absorption. 37 Reflectance derived EC was used when available due to previous research

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and the observation that EC concentrations were better correlated with measured absorption

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(discussed further in Results and SI section 7).

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For real-time analyses, each minute of data was averaged and metrics and emission factors

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were calculated. These are termed “one-minute” averages, while the average of an entire

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cooking event is termed an “event average”. Statistical tests of comparison of means were

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done with the Welch test for unequal variances and unequal sample sizes and the Games-

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Howell post-hoc comparison test. 38,39 Significance was reported at the 0.05 level. Dispersion

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of the data was reported as the mean ± standard deviation.

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Results and discussion

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Average CO and PM2.5 emission factors

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Table 1 shows emission factors and particle optical metrics for each stove group in the

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measurement dataset and select metrics are presented in Figure 1. Previous studies are also

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summarized in Figure 1, tabulated in Table S6, and discussed in a later section.

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Table 1: Average and standard deviation of emission factors and emission metrics separated by stove group. Stove group

N

EFCO gkg−1

EFPM gkg−1

EFEC gkg−1

EFOC gkg−1

AAE -

MSC m2 g

MACEC m2 g

South Asian chulha India chulha Nepal Hills chulha Nepal Terai chulha Nepal ICS chulha Average chulha

6 4 29 7 46

69.0 (21.9) 56.2 (37.4) 117.7 (35.9) 70.3 (15.2) 98.8 (40.2)

11.9 (7.6) 4.3 (0.8) 6.7 (2.6) 5.8 (2.6) 7.0 (4.0)

0.9 (0.3) 1.1 (0.3) 1.7 (0.8) 0.8 (0.6) 1.4 (0.8)

5.6 (5.2) 3.4 (2.1) 2.3 (1.8) 3.3 (1.7) 3.0 (2.7)

2.3 (0.6) 1.8 (0.1) 1.6 (0.2) 1.7 (0.2) 1.7 (0.3)

1.8 (0.6) 2.1 (0.9) 1.7 (0.6) 3.0 (1.9) 1.9 (1.1)

21.6 (4.7) 25.7 (20.7) 10.9 (2.3) 25.4 (7.8) 14.7 (8.6)

Tibetan stoves Open dung Chimney dung Chimney wood Average Tibetan

14 14 20 48

130.4 (43.1) 107.4 (29.9) 121.4 (31.4) 120.0 (35.2)

42.7 (26.0) 23.2 (12.6) 12.7 (10.2) 24.5 (20.8)

0.7 (0.8)c 0.3 (0.1)c 0.9 (0.8)c 0.7 (0.7)c

31.6 (19.8) 16.2 (10.2) 8.5 (7.3) 17.5 (15.9)

3.8 (1.0) 3.2 (0.5) 2.3 (0.6) 3.0 (0.9)

6.2 (2.2)b 2.0 (2.4)b 4.1 (2.2) 4.4 (2.3)b

41.3 (13.8) 69.2 (27.5) 27.3 (6.1) 43.8 (24.8)

Other traditional Uganda 3-stone fire Hondurasa

4 12

67.2 (14.5) 116 (55)

14.6 (11.8) 8.5 (1.6)

0.8 (0.6) 1.5 (0.3)

4.2 (3.0) 4.0 (0.9)

1.6 (0.3) 1.5 (0.3)

1.4 (0.9) 2.2 (0.6)

29.2 (9.9) 17.0 (6.3)

a Results

were previously published in Roden et al. (2006), and are included here for comparison. 14 with over-ranging scattering sensor were omitted (discussed below). c Thermal-optical reflectance was used for the charring correction instead of transmittance. b Tests

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CO emission factors varied significantly by stove group. They ranged from an average

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of 56±37 gkg−1 for chulhas measured in the Nepal Midhills to 130±43 gkg−1 for Tibetan

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open dung stoves. The average for all South Asian chulhas (99±40 gkg−1 ) was lower than

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for traditional stoves measured in Honduras and Tibet (116±55 gkg−1 and 120±35 gkg−1 , p

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= 0.001, respectively). 14

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PM2.5 and CO emission factors followed a similar pattern; stove groups with a lower CO

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emission factor also had a lower PM2.5 emission factor. The PM2.5 emission factor for the

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South Asian chulhas, (7.0±4.0 gkg−1 ) was lower than for Tibetan chimney dung (23±13

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gkg−1 , p = 0.003) and for open dung stoves (43±26 gkg−1 , p = 0.003). The CO and PM2.5

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emission factors for event averages across all regions were weakly correlated (r = 0.3, p
0.05). The

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highest values were from Tibetan open dung stoves (6.2 m2 g−1 ). Most average MSC values

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were in the range of previous estimates; Roden et. al (2006) reported an average of 2.2 m2 g−1

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(530 nm) from field tests in Honduras 14 and values of 2.5 – 4.2 m2 g−1 (550 nm), have been

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reported from biomass burning. 40–42 In the Tibetan open and chimney dung stoves, tests

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were omitted when overranging occurred in the scattering sensor. When partial scattering

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data were included (overranging seconds treated as missing and no events omitted), the MSC

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tended to be slightly higher; 6.6 m2 g−1 for open dung stoves and 3.5 m2 g−1 for chimney dung

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stoves. Yet, these averages are likely to be lower bounds because high scattering events were

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omitted while particle mass was collected.

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Scattering and PM2.5 emission factors for event averages were correlated across all stove

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groups (r = 0.77, p < 0.001), yet tended to have lower levels of correlation within groups (SI

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section 6). Low correlation coefficients (r = 0.0 – 0.6) occurred in all groups except for chulhas

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(r = 0.77). A combination of high variability and relatively few events contribute to these

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low correlation coefficients. Low correlation coefficients highlight the inherent uncertainties 10


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when using scattering as a proxy for particle mass as is done with many low cost continuous

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particle monitors.

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Elemental and organic carbon

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In contrast to CO and PM2.5 emission factors, EC emission factors were highest for South

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Asian chulhas (1.4 gkg−1 ) and lowest in Tibetan chimney dung stoves (0.3 gkg−1 , p < 0.001).

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The chimney dung stoves also produced significantly lower EC emission factors compared

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to the chimney wood stoves (p = 0.009). The EC emission factor for the Tibetan open

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dung stoves was between the other Tibetan stove groups, without significant differences (p

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> 0.05), yet was significantly lower than for chulhas (p = 0.03).

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The EC emission factors were notably small in Tibetan stoves, considering the large

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PM2.5 emission factors. Figure 2 shows the emission factors of EC, OC, and PM2.5 for all

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events, ordered by the EC emission factor magnitude. EC ranged from near zero to 4 gkg−1

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while OC and PM2.5 ranged from about 4 - 100 gkg−1 and had little relationship with the

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EC emission factors (r = -0.03, p > 0.05). Thus, estimations of EC as a fraction of PM2.5

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would produce spurious results. Only in the Tibet open dung stoves is a relationship between

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EC and OC (or PM2.5 ) apparent, and this is largely driven by a single outlier. It appears

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that processes that produce high OC (or PM2.5 ) have little bearing on the amount of EC

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formed, and that EC may be constrained to a fairly low emission factor over a wide range

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of combustion conditions.

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100

PM emission factor

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OC emission factor EC emission factor

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60 40 20

4 3 2 1 0

3 stone fire

South Asian Chulha

Tibet chimney wood

Tibet chimney dung

Tibet open dung

−1

0

EC (gkg )

−1

Emission factor (gkg )


Events Figure 2: Particle emission factors for individual events. The events are sorted by the EC emission factor magnitude within each stove group. The top panel shows the emission factors of PM2.5 as a line segment, OC as a light colored bar, and EC as a dark colored bar. EC is low in comparison with OC and PM2.5 , and the scale is enhanced in the bottom panel to more clearly show the range of EC emission factors.

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Figures S4 - S7 show traces from the thermal-optical analysis used to determine OC and

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EC. Samples from Tibet underwent large amounts of OC charring during the analysis which

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confounded the accurate detection of EC. The estimated amount of EC varied depending on

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use of the transmittance or reflectance signal (TOT or TOR) as exemplified in Figure 3 and

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discussed in the SI section 7.

12



Tibet: Chimney dung

−1

Absorption emission factor (m kg )

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2

40 30 Transmittance > reflectance Transmittance < reflectance Transmittance EC Reflectance EC 2 −1 MAC transmittance 113.6 m g 2 −1 MAC reflectance 69.2 m g

20 10 0 0

.2 .4 −1 EC emission factor (gkg )

.6

Figure 3: Scatter plot showing the relationship between absorption and EC emission factors determined by TOT and TOR methods in Tibetan chimney dung stoves. For each event, two EC emission factors are shown; determined by optical transmittance (black circle) or reflectance (gray square). The dotted lines are average MACEC derived from each method.

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Tests in the Nepal Terai had similar OC and EC emission factors when either TOR

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or TOT methods were used (p>0.5). However, stove groups in Tibet had large differences

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depending on the optical analysis method (p 0.05). This is striking because it is in contrast to the order-of-magnitude differences in

243

the PM2.5 emission factors between groups (Figure 4 compared to Figure 1: center). This

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contrast is also apparent in the similar distributions of absorption emission factors within

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events compared to the range of scattering emission factors (Figure 5: left and center,

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discussed in next section).

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Most absorption is expected to be due to EC, yet stove groups with statistically lower

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EC emission factors did not have lower absorption. This is expressed by the range in the

249

amount of absorption per EC (MACEC ) between groups. The MACEC ranged from 10.9 –

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69.2 m2 g−1 and was highest in stoves with low EC emission factors (Tibetan dung stoves)

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and lower when EC emissions factors were high (chulhas).

252

The MACEC values observed were higher than typically found for other sources of black

253

carbon. A MACEC value recommended for pure BC in models is 7.5±1.2 m2 g−1 at 550 nm. 44

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South Asian chulhas had values that were a factor of two higher and MACEC of Tibetan

255

stoves was a factor of five higher than this value. Some absorption is likely attributable to

256

absorbing OC (brown carbon), 45 so the ratio of all particle absorption over EC mass would

257

overestimate the true value of MACEC . In cases where the EC:OC ratio is small, the relative

258

impact of OC absorption on MACEC can be high.

259

The impact of absorption by OC can be approximated by applying linear regression using

14


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260


Equation 1.

EFabsorption = β1 EFEC + β2 EFOC + β3 (EFEC × EFOC )

(1)

261

β1 is an estimate of the absorption per mass of EC, but unlike MACEC , Equation 1 allows

262

apportioning some absorption to OC with the coefficient β2 and OC and EC interactions

263

2 −1 2 −2 with β3 . Over all events, β1 was 12.8 m2 g−1 EC , β2 was 0.7 m gOC , and β3 was -0.17 m gEC×OC

264

(R2 = 0.9, p < 0.001 for all coefficients). EC does explain most of the variability in the

265

2 absorption: β1 = 15.5 m2 g−1 EC , R = 0.7, p < 0.001), in the case where β2 and β3 terms are

266

omited. However, estimates of the absorption emission factors using purely EC regression

267

are prone to errors when the EC:OC ratio is either high or low; EFabsorption is underestimated

268

by 40% on average when the EC:OC ratio is less than 0.2 and is overestimated by about

269

40% when the EC:OC ratio is greater than 0.8. When Equation 1 is used instead, the error

270

(20%) is not impacted by different EC:OC ratios (Figure S11). The regression is explored

271

further in Figure S10 and Table S7.

272

When Equation 1 was applied to emission factors of EC and OC reported in the lit-

273

erature (Table S6), calculated absorption emission factors were in the same range as the

274

stove groups in this study. This suggests that the combination of EC and OC emitted from

275

traditional stoves tends to result in a fairly stable absorption emission factor (confidence

276

interval: ±15%, compared to ±25% for EC). The average value for the measurements re-

277

ported here was 20.6±11.5 m2 kg−1 . When regression-derived literature emission factors were

278

included, the average absorption emission factor was 14.84±6.6 m2 kg−1 . This suggests the

279

average absorption emission factors found here are fairly similar across most traditional stove

280

combustion conditions.

15



Page 16 of 30

Tibet: Open dung Tibet: Chimney dung Tibet: Chimney wood S. Asia: Clay chulha Uganda: 3 stone fire Literature: field Literature: lab 0

20

40

60 2

−1


Figure 4: Absorption emission factors of fresh particle emissions are similar across all stove groups. For the literature emission factors, the two red points are from Roden et al. (2006) and Grieshop et al. (2017) 14,17 and all others are determined using measured OC and EC values (Table S6) and a regression equation derived from this study.

281

In some stove groups, most absorption was attributed to OC, not EC. Using the Equation

282

1, for Tibetan open dung stoves, 70% of the absorption was attributed to OC. In comparison,

283

OC contributed to about 45% of the absorption in Tibetan chimney dung stoves, 20% in

284

Tibetan chimney wood stoves, and 10% in chulhas (Figure S9).

285

Values of AAE also indicate that cookstoves produce a significant amount of absorbing

286

organic brown carbon. AAE values ranged from 1.7 for chulhas to 3.8 for Tibet open dung,

287

where values higher than 1 suggest that absorbing OC is present. When AAE was used to

288

attribute absorption to OC, 46 using a delineating value of AAE of 1, 22% of the absorption

289

(530nm) was attributed to OC for Tibetan open dung stoves, 21% for Tibetan chimney dung

290

stoves, 12% for Tibetan chimney wood stoves, and 1% for chulhas. Although the attribution

291

of absorption to OC is uncertain, it remains significant for Tibetan dung stoves. Given the

292

proximity of Tibetan dung stoves to glaciers, ice melting in this region is likely accelerated

293

by absorbing OC in addition to EC.

294

Measurements of AAE in this study are in the same range as Roden et al. (2006) (1-5) 14

295

and higher than those observed by Grieshop et al. (2017) (1.2-1.3), 17 suggesting variable

296

brown carbon emissions in biomass cookstoves.

16


Page 17 of 30


297

Variability within events

298

One-minute average metrics were used to assess variability within events. Probability den-

299

sity functions (PDFs) in Figure 5, show the range and density of one-minute metric values

300

throughout all events in each stove group. The one-minute average scattering and absorption

301

emission factors and AAE all have wider ranges than observed in event averages. Particle

302

scattering emission factors ranged over five orders of magnitude, absorption ranged over

303

three orders of magnitude, and AAE ranged from 0 to 8. The convergence of the absorption

304

emission factor between stoves, discussed above, is even more apparent it the distribution of

305

real-time data; the EC emission factor from Tibetan open dung stoves was half that from

306

South Asian chulhas, yet the distributions in absorption were nearly identical. Those same

307

stove groups produced distributions of particle scattering that were the most divergent. In

308

Tibetan stoves the scattering emission factor for the chimney wood stove shows a bimodal

309

distribution and this feature is due both to biomodal events and differences between events

310

as shown in Figure S13 . .8

(a)

1.5

(b)

.6 Density

Density

1 .4

.5 .2 0 −1 10

10

0

10

1

10

2

10 2

3

10

4

0 −1 10

−1

0

10

1

10 2

Scattering emission factor (m kg ) S. Asia: Clay chulha Tibet: Chimney wood

10

2

10

3

−1


Tibet: Chimney dung Tibet: Open dung

S. Asia: Clay chulha Tibet: Chimney wood


Figure 5: Probability density plots of one-minute averages of emission parameters for all tests within each group. (a) Scattering emission factor. (b) Absorption emission factor.

17



Page 18 of 30

(b)

(a) .8

Density

.6 0

2

4

6

8

0

2

4

6

8

0

2

4

6

8

.4 .2 0

0

2

4

6

8

Absorption Angstrom exponent S. Asia: Clay chulha Tibet: Chimney wood


Figure 6: (a) Probability density functions of one-minute averages for the absorption Angstrom exponent for four stove groups. (b) Three figures show the overall PDF for each stove group in bold and the PDFs of each individual event measured within that stove group.

311

The variability within stove groups is apparent in the range of results in Figure 1, but

312

there are also variations in the shape of PDFs for events within stove groups. This is

313

shown in Figure 6 and Figures S12- S14. These figures indicate that individual events with

314

similar averages can have widely varying emission characteristics, such as wide or narrow

315

distributions or bimodality. Variation in emission distributions

316

that features other than stove, fuel, and region influence the combustion and emissions.

317

Subtle elements of stove construction and fuels likely affect overall emission factor averages,

318

but the variable features of the distributions within groups are likely caused by user cooking

319

practices. Probability distributions of real-time data may reflect variations in stove operation

320

better than event averages, and the use of distributions coupled with observations may aid

321

our ability to explain emission variability.

322

Effect of fuel choice on emission characteristics

323

The effect of fuel choice on emissions was not part of the study design. However, for two

324

stove groups, wood stoves in the Nepal Terai and chimney stoves in Tibet, stove design and 18


between events suggests

Page 19 of 30


325

cooking practice were relatively constant while fuel choice varied. Emissions within these

326

groups were examined to evaluate the influence of fuel choice.

327

In the Nepal Terai, CO and PM2.5 emission factors were lowest in wood stoves and

328

higher when dung or agricultural waste was predominant, but these differences were not

329

significant (p > 0.05). The EC emission factor for fuel mixtures that were predominantly

330

agricultural waste (2.2±0.9 gkg−1 ) was higher than other fuel groups (1.0±0.6 gkg−1 , p =

331

0.02). Similarly, EC/(EC+OC) was 0.5±0.1 for tests with agricultural waste fuel and an

332

average of 0.3±0.1 for other fuels in the region. However, the difference was not significant

333

(p > 0.05). Dry, low density, agricultural waste fuels may be more likely to undergo flaming

334

(i.e. EC producing) combustion compared higher density, higher moisture retaining fuels

335

such as wood and dung. Guofeng et al. (2012) similarly found higher EC emission factors

336

for shubs (70% higher) and agricultural waste (120% higher) compared to woody fuels, 47

337

but others have found the opposite. 19,48

338

The chimney stoves measured in Tibet were used with either pure dung or wood fuel.

339

The differences in the emissions between these stove groups have been discussed above, but

340

in summary: dung fuel produced higher PM2.5 , similar CO, and lower EC emission factors

341

compared to wood fuel. Dung fuel also produced higher AAE than wood fuel, suggesting

342

OC that is more absorbing when dung fuel is used. However, only differences in EC were

343

statistically significant (p = 0.009).

344

Comparison with previous measurements and emission inventories

345

In order to estimate emission fluxes to the atmosphere, inventory developers typically select

346

a single emission factor or range for a particular type of fuel and use by examining pub-

347

lished measurements. Figure 1 and Table S6 summarize the body of knowledge available to

348

inventory developers.

349

Emission factors from South Asian chulhas were similar to those previously reported in

350

field tests for PM2.5 and CO (Figure 1), and therefore, as expected, also similar to recommen19



351

dations for emission inventories. The PM2.5 emission factor (7.0±4.0 gkg−1 ) was within the

352

range proposed by Akagi et al. (2011) (6.6±1.7 gkg−1 ), 49 and similar to the GAINS (Green-

353

house gas - Air pollution Interactions and Synergies) inventory choice of about 8 gkg−1 (510

354

mgMJ−1 ) for biofuel-burning traditional stoves. 3 On the other hand, EC emission factors

355

(1.4±0.8 gkg−1 ) were higher than those used in the GAINS inventory (1.1 gkg−1 ) and

356

tended to be higher than those reported previously (0.9± 0.5 gkg−1 , p = 0.02).

357

Emission factors from combustion in Tibet have not been published previously, and the

358

values reported here diverge from inventory recommendations. PM2.5 emission factors in

359

Tibet were high (25 gkg−1 ±21 gkg−1 ) compared to the GAINS inventory and to emission

360

factors from field tests in the literature (10 gkg−1 ±6 gkg−1 , p = 0.03, Table S6). CO emission

361

factors from Tibetan stoves (120±35 gkg−1 ) were higher than a recommendation of Akagi

362

et al. (2011) of 77 gkg−1 for wood and 105 gkg−1 for dung. 49 EC emission factors in Tibet

363

(0.7±0.7 gkg−1 ) were lower than other published values, although this difference was not

364

significant, p > 0.05. 3,50

365

There are several reasons emissions from stoves in Tibet could differ from other stoves

366

in other areas, such as fuels used, use of stoves for heating, atmospheric conditions or a

367

combination of these factors. The dung used in Tibetan stoves was not mixed with biomass

368

as was typical in other places and yak dung was used, whereas cow or buffalo dung is more

369

typical in South Asia. In addition the stoves were used for both cooking and space heating

370

which may result in higher emission factors because heating stoves may have longer periods

371

of unattended smoldering, while cooking stoves are more likely to be tended to maintain

372

high temperatures and flaming conditions (smoldering discussed in SI section 13). Tissari

373

et al. (2008) found that PM2.5 emissions from smoldering combustion were about 10 times

374

higher than “normal combustion” at 11.1 gkg−1 PM1 , largely due to elevated organic carbon

375

emissions. 51

376

Tibetan stoves were partially heating stoves and previous measurements have also sug-

377

gested higher PM2.5 emission factors for heating stoves compared to cooking stoves. The

20


Page 20 of 30

Page 21 of 30


378

US EPA compilation of emission factors (AP-42) uses a PM10 emission factor (comparable

379

to PM2.5 because combustion particles are typically smaller than 2.5µm 22 ) of 15.3 gkg−1 for

380

conventional wood heating stoves and 17.3 gkg−1 for fireplaces. 52 The wood heating stove

381

in Tibet was in the range of the AP-42 PM emission factor for heating stoves and is about

382

double that of cooking stoves in this study (7±4 gkg−1 ). Broderick et al. (2005) reviewed

383

fireplace emission factors in the United States and observed a log-normal distribution with

384

a long tail, where about 8% of PM emission factors were above 20 gkg−1 . 53

385

Finally, high altitude combustion may result in lower combustion efficiencies as oxygen

386

concentrations are only about 60% of those at sea level. The atmospheric pressure in Tibet,

387

at the location of the dung stove measurements, was 567 hPa, and for the wood stoves was 687

388

hPa. In comparison, stoves measured in Nepal were measured at 989 hPa and in Honduras,

389

around 1000 hPa. 14 One other high elevation combustion test was reported previously where

390

a heating stove measured at 731 hPa produced high PM (28.3 gkg−1 ) and CO (219 gkg−1 )

391

emission factors. 54 However, the altitude effect was not replicated in a controlled test at

392

834 hPa. 54 Lower flame temperatures and slower burning rates have been observed in high

393

altitude combustion, 48 but effects on emission factors are not well documented.

394

Overall, the PM2.5 emission factor from Tibetan stoves was two times higher than emis-

395

sion inventory estimates. The average EC emission factor for Tibetan stoves was 25% lower

396

and for South Asian chulhas was 55% higher than the GAINS inventory estimate. High

397

PM2.5 emission factors from smoldering combustion may occur in many global cooking en-

398

vironments and are likely high emitting , low probability events (the tail of a log-normal

399

distribution). These events are a challenge for inventory estimates where few measurements

400

are available and many stoves and cooking conditions have not been measured. The same

401

situation does not appear to apply for EC, which did not have the same variability observed

402

for PM2.5 . EC may be constrained to fairly low emission factors over a wide range of com-

403

bustion conditions and inventory estimates may be less likely to need significant adjustments

404

for EC.

21



405

The EC emission factor is likely sufficiently constrained that a global average emission

406

factor for biomass stoves is appropriate. However, emissions of PM2.5 show regional variabil-

407

ity and a highly skewed log-normal distribution, such that a global value is prone to higher

408

uncertainty. For PM2.5 , regionally determined and applied emission factors in inventories is

409

likely more appropriate than a global average.

410

Acknowledgement

411

The authors thank Dr. Kirk Smith his contributions to original project design and site con-

412

nections. This research was supported by EPA STAR 83503601 Characterization of Emis-

413

sions from Small, Variable Solid Fuel Combustion Sources for Determining Global Emissions

414

and Climate Impact. The contents are solely the responsibility of the authors and do not

415

necessarily represent the official views of the US EPA. US EPA does not endorse the purchase

416

of any commercial products or services mentioned in the publication.

417

Supporting Information Available

418

Description of stoves and fuels, metric calculations, OCEC parameter file, details about

419

thermal-optical reflectance and transmittance, compilation of literature emission factors,

420

figures showing non-carbon components of PM2.5 , figure showing organic carbon aborption,

421

and figures showing additional probability density functions. Dataset of emission character-

422

istics for individual events.

423

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Emission measurements from traditional biomass cookstoves in South

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