Updated Emission Factors from Diffuse Combustion Sources in Sub

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Environmental Measurements Methods

Updated emission factors from diffuse combustion sources in subSaharan Africa and their effect on regional emission estimates David Pfotenhauer, Evan R. Coffey, Ricardo Piedrahita, Desmond Agao, Rex Alirigia, Didier Muvandimwe, Forrest Lacey, Christine Wiedinmyer, Katherine L. Dickinson, Maxwell Dalaba, Ernest Kanyomse, Abraham Oduro, and Michael Hannigan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06155 • Publication Date (Web): 09 May 2019 Downloaded from http://pubs.acs.org on May 9, 2019

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Environmental Science & Technology

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Updated emission factors from diffuse combustion sources in

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sub-Saharan Africa and their effect on regional emission

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estimates

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KEYWORDS Emission factors, combustion, Africa, carbonaceous pollution, trash burning, kerosene lighting, cooking emissions

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ABSTRACT

David J. Pfotenhauer,*† Evan R. Coffey,† Ricardo Piedrahita,¥ Desmond Agao,§ Rex Alirigia,§ Didier Muvandimwe,† Forrest Lacey,ǁ Christine Wiedinmyerǁ‡, Katherine L. Dickinson,⁋ Maxwell Dalaba,§ Ernest Kanyomse,§ Abraham Oduro,§ Michael P. Hannigan† † University of Colorado Boulder, Mechanical Engineering, 1111 Engineering Dr. Boulder, CO 80309, United States ¥ Berkeley Air, 1900 Addison Street Suite 350 Berkeley, California 94704, United States § Navrongo Health Research Centre, Navrongo Upper East, Ghana ǁ National Center for Atmospheric Research, 3450 Mitchell Ln. Boulder, CO 80301 ⁋Colorado School of Public Health, 13001 E. 17th Place Aurora, CO 80045

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Diffuse emission sources outside of kitchen areas

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are poorly understood and measurements of their emission

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factors (EFs) are sparse for regions of sub-Saharan Africa.

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Thirty-one in-field emission measurements were taken in

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northern Ghana from combustion sources common to

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rural regions worldwide. Sources sampled included

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commercial cooking, trash burning, kerosene lanterns, and diesel generators. EFs were calculated for carbon monoxide

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(CO), carbon dioxide (CO2), as well as carbonaceous particulate matter, specifically elemental carbon (EC) and

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organic carbon (OC). EC and OC emissions were measured from kerosene lighting events (EFEC = 25.1 g/kg-fuel SD

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= 25.7, EFOC = 9.5 g/kg-fuel SD = 10.0). OC emissions from trash burning events were large and highly variable (EFOC

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= 38.9g/kg-fuel SD = 30.5). Combining our results with other recent in-field emission factors for rural Ghana, we

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explored updated emissions estimates for Ghana using a region specific emissions inventory. Large differences are

ACS Paragon Plus Environment

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calculated for all updated source emissions, showing a 95.9% increase in OC and 78% decrease in EC compared to

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prior estimates for Ghana’s emissions. Differences for carbon monoxide were small when averaged across all updated

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source types (-1%), though the household wood use and trash burning categories individually show large differences.

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Introduction

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Ambient air pollution poses a major threat to human health and well-being around the world, increasing risk

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for pulmonary and cardiovascular illness and contributing to an estimated loss of 4.2 million lives yearly.1 In

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particular, ambient contributions of emissions from African combustion sources are of growing concern as populations

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continue to increase. The African population is predicted to be 40% of the world’s population by 21002 and domestic

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and commercial combustion source emissions will scale upwards accordingly. If emissions are left unabated, sources

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of African particulate matter (PM) emissions are expected to contribute 50% to the total global output by 2030.3 These

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emissions of PM from combustion, often containing appreciable amounts of organic carbon (OC) and elemental

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carbon (EC), have been linked to adverse health symptoms, shortening lifespans, as well as climate driving

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mechanisms.4-6

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In many developing areas of the world, including Asia, Central America, and South America, there has been

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an increase in emissions research with the goal to better quantify and characterize emissions from small scale scattered

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point sources, or what we classify as diffuse anthropogenic combustion sources.7-12 In West Africa, though emissions

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from residential cooking environments have been studied11-13, in-field emissions of sources outside of kitchen areas

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remain poorly understood. Incomplete emission inventories for non-household and regional specific sources have led

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to large uncertainties in country-wide emissions predictions, climate models, and health effects estimates.8

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Combustion sources beyond residential cooking are known to contribute a significant portion to ambient pollution11;

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prominent sources include commercial cooking operations, backup diesel generators, gasoline and diesel vehicles,

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trash burning, kerosene lanterns, and charcoal production. Many, if not all, of these combustion activities are expected

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to persist and even increase with projected population growth throughout the African continent.

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Commercial cooking often involves practices that can differ from residential scale cooking in fuel use and

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combustion environments, potentially causing variable emissions. Emissions from open commercial cooking

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operations have been overlooked and are understudied, with emissions from residential scale cooking used as proxy

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for emission inventories and estimates.8 Trash burning has also emerged as a complex problem in air quality


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assessments because of its inherent heterogeneous fuel composition and inconsistent combustion conditions, which

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can lead to large variances in emissions. Open domestic trash burning is common in many regions of the world lacking

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municipal infrastructure, resulting in large ambient air pollution contributions.14,15 Several studies have begun to report

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emission factors for this source as well as its impacts.7, 9,10, 16-18 Another common emission source in Africa, kerosene

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lanterns are known to emit large amounts of carbonaceous pollutants, causing deterioration of indoor air quality and

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affecting local and global climates.19-21 Lastly, diesel backup generator emissions have been well documented for

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properly operated and well-maintained engines in the US;22-26 however, only a few studies have characterized emission

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factors from in-field diesel generators operating at sub-optimal conditions.9-10

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Over the span of three years from 2014 to 2017, we performed multiple sampling campaigns in and around

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the Northern Ghanaian town of Navrongo to collect emissions from the major source categories outlined above.

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Measurements for each source were made in-field, and emission factors (EF) were calculated on a gram of pollutant

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per kilogram of fuel basis using the carbon mass balance method. We present emission factors for gas phase pollutants

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carbon dioxide (CO2) and carbon monoxide (CO), as well as for carbonaceous particulate matter (elemental carbon,

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EC, and organic carbon, OC). The samples collected for this study, in combination with extensive residential cooking

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emissions evaluations, performed as part of the Research of Emissions, Air Quality, Climate, and Cooking

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Technologies in Northern Ghana (REACCTING) study in the same region,11,13,27,28 serve as valuable additions to the

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growing knowledge of anthropogenic combustion sources in northern Ghana and similar sub-Saharan regions.

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Additional samples from kerosene lanterns were collected in Kigali, Rwanda and are also included in the dataset to

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explore interregional variation.

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In addition to adding new, in-field, and region-specific emission factors for use in emissions estimate

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modeling, the data presented in this study provide insight into variances and uncertainties that are introduced by

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collecting and analyzing samples from uncontrolled emissions sources in real world settings and environments.

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Applying the updated factors from this study to the Diffuse and Inefficient Combustion Emissions in Africa (DICE-

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Africa) inventory,8 we perform a brief analysis of Ghana’s emissions and highlight the differences in estimates that

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emerge when these locally sourced measurements and emission factors are used.

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Materials and Methods

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We collected 31 source emissions samples over the course of our field visits. Conversations with our collaborators in Navrongo allowed us to identify prominent and common combustion sources in the town and its


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surrounding region. We took 27 total samples from commercial cooking operations, trash burning, kerosene

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lanterns, and diesel generator sources in and around Navrongo. The remaining four samples were taken from

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kerosene lanterns in Kigali, Rwanda. Most sources were sampled opportunistically upon finding an active site, while

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others, specifically kerosene lighting events, were scheduled along with residential cookstove sampling as part of the

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REACCTING study. 11,13 Three additional source samples from charcoal cooking and bush burning are not

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considered in the bulk analysis or discussion due to fuel type mismatch or lack of a distinct source category, but are

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included within the supporting information (Section S4).

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Gas and particle emission samples were collected using a compact, battery-operated emission pod (EPOD,

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Hannigan Lab, Boulder CO), equipped with an array of sensors for gaseous measurements and a filter holder to

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collect particulate emissions. The EPOD inlet was a stainless steel cross probe attached to a tripod, which was

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positioned roughly 1 meter above each combustion source to sample source emission plumes, similar to one used for

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in-field cookstove measurements by Roden et al (2006).29 Multiple laboratory calibrations were performed on the

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gas phase sensors between 2014 and 2016 both before and after each sampling campaign. These calibrations account

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for baseline drift in sensor responses over time as well as sensitivity changes due to humidity and temperature. More

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detailed accounts of the EPOD, sensor capabilities, calibrations, and uncertainty can be found in the supporting

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information sections S1 and S2. An analogous sampling setup is also described by Coffey et al (2017).13

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Each source sample test began by running the EPOD for approximately 30 minutes in a background

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environment away from any emission source. This time interval allows the sensors to warm up and stabilize as well

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as determine a clear background. The sample starting time was then recorded directly before the fuel was lit, or in

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the case that the fuel was already combusting it was recorded when the probe was moved into the emission plume.

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Sample durations ranged from 11 to 112 minutes and on average lasted 47 (±26, standard deviation) minutes, with

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individual sample times reported in the supporting information section S3. For open burning sources, measurements

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captured the full scope of combustion phases (lighting, flaming, and smoldering), but it was rare for any one sample

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to extend throughout all three phases due to how the emission sources were located. Several studies have observed

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large emissions from both ignition and fuel addition30-31, but for our samples of market cooking, trash burning, and

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diesel generator startups, it was unlikely for us to find and establish a sampling site prior to these events. By missing

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these early emissions in our sampling window, we acknowledge a possible lower bias in our measurements and

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emission factors.


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PM was collected on a 47mm quartz fiber filter (QFF, Pall TissuQuartz 2500-QAT) for each source sample

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to measure EC and OC averages across each sampling period. Prior to field deployment, the QFFs were conditioned

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at 500°C for 18h to remove possible contamination. The filters were then stored in pre-baked sterile amber glass jars

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separated by sterile aluminum foil discs. After PM collection, those filter samples were again stored in designated

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amber glass jars with sterile aluminum foil disks to separate filters from each measured source. Transportation and

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storage procedures followed those outlined by Piedrahita et al. (2017).11 EC and OC sample filter area densities

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were measured at the University of Colorado in Boulder following the NIOSH 5040 protocol.35 A 1.5cm2 filter

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punch from each sample was analyzed with a Sunset Laboratory OC/EC analyzer using the thermal-optical-

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transmittance method (TOT). QFF field-blanks were placed in each sample jar after every 3-4 samples. EC and OC

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loadings on the field-blanks were used to assess contamination from transportation, as well as possible volatilization

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and cross contamination between filters within a single jar. Further details on blank mass loadings can be found in

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the supporting information section S7. Our sampling system did not allow for multiple filter holders and thus we do

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not correct for gas-phase organic artefacts on the quartz filters in this analysis.

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Due to the nature of the apparatus probe, a 2.5μm cutoff was difficult to implement. Several studies,

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however, have noted that particle size distributions from biomass burning, meat cooking, diesel fuel, and kerosene

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combustion are primarily unimodal with peaks between 0.01-1.0μm.14,19,32,33 Thus, the PM collected for these source

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samples is assumed to have aerodynamic diameters below 2.5μm (PM2.5). It has been demonstrated, however, that

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certain plastics (Polystyrene, Polyvinyl Chloride) burning under smoldering conditions (1300-1500K) emit particles

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with size distributions ranging well above 2.5μm.34 We apply the same assumption of a unimodal particle size

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distribution below 2.5μm for our trash burning samples, but acknowledge that trash burning samples taken from

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garbage piles with certain plastics in abundance may have size distributions outside this assumption.

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In a highly polluted environment, the background subtraction of gaseous and particulate pollutants can be

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substantial and thus a vital requirement for using the chemical mass balance technique. We measured ambient

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conditions briefly before and after each sampling event to establish a reliable background period for both CO and

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CO2. Including a fraction of the background measurements in the time series of gaseous concentrations, we then

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background corrected by subtracting the 3rd percentile from the corresponding CO and CO2 concentrations. Limited

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time and resources prevented us from determining OC and EC background concentrations at each sampling site.

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Instead, to account for ambient carbonaceous PM we utilized averaged ambient particulate concentrations measured


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during the REACCTING study in the same area. The ambient carbonaceous PM concentrations in Navrongo were

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small compared to our source measurements (0.6% of averaged source OC and 3% of averaged source EC) but were

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still subtracted from each source concentration.11

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We used the partial capture carbon mass balance method to calculate emission factors (EFi; where i is the

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pollutant species), the details of which have been outlined elsewhere.13,29,36,37 This approach makes the assumption

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that all carbon mass emitted from a combustion source will be present as carbonaceous PM, CO, and CO2,39 with the

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omission of methane and larger hydrocarbons from the mass balance causing a minimal bias of 1 to 4%.36,37 Using

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prior knowledge of a fuel’s carbon content along with the measured gaseous concentrations of carbon emissions above

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background levels, we can calculate a given pollutant’s emission on a gram per kilogram-fuel basis. We estimated the

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fuel carbon content for each source using the most recent literature values for each fuel type. Although we note that a

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portion of non-carbonaceous emissions is left unaccounted for through our analysis, EC and OC typically comprise a

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majority of PM2.5 mass from source emissions, maintaining valuable insight into total PM emissions.10 Organic mass

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to OC (OM:OC) ratios have been used in other studies to provide better characterization of total PM emission estimates

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for these sources types. We provide OM emission values and explanation of our estimates for these sources in the

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supporting information; OM:OC conversion ratios ranged from 1.329 to 1.409 depending on the source type, and are

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based on previous estimates by Goetz et al. (2018).46 Lastly, modified combustion efficiencies (MCE = ΔCO2/(ΔCO2

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+ ΔCO) molar basis) were calculated using average CO and CO2 concentrations from across each sampling period. A

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further discussion of our MCE calculations is provided in the supporting information (S2).

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Results and Discussion

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Commercial Cooking

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We collected emissions samples from 10 commercial cooking events, capturing common dishes and cooking

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environments in this region; often these commercial cooking operations occur along roadsides or in markets. All

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events we sampled had locally sourced wood as the primary fuel. We classified the cooking operation by cooking

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method: frying where oil is a primary cooking ingredient (n=4), fish smoking (n=1), meat charbroiling (n=2), and

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pito brewing (n=3), which is regional fermentation process that involves rapid boiling of large vats of a starch-water

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mixture. Cooking areas were characterized as open three stone fires similar or larger in size compared to typical


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residential cooking environments, partially enclosed chambers surrounded on two to three sides by bricks or clay, or

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mostly enclosed clay oven type stoves into which fuel was fed through a small opening.

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Figure 1 shows cooking emission factors reported alongside recent literature values from in-field residential

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cooking tests. Across all cooking types, the mean OC emission factor was EFOC = 7.9g/kg-fuel with individual

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values ranging from 1.7 to 24.7 g/kg-fuel, and the mean EC emission factor was EFEC = 0.63 g/kg-fuel with

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individual values ranging from 0.23 to 1.52 g/kg-fuel. CO emission factors ranged from 1 to 148 g/kg-fuel with a

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mean of EFCO = 69 g/kg-fuel. MCE values ranged from 0.86 to 1.00 (mean MCE = 0.95). Because emission factors

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from larger scale cooking operations in developing nations have not yet been thoroughly investigated, we compare

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our data values with in-field residential cooking operations that have similar characteristics to the cooking events

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measured in this study.

Figure 1. Boxplots of commercial cooking emission factors from the cooking styles for both OC and CO. Emission factors for each species from this study(left) are plotted next to literature values for residential cooking events (right). Literature values were selected from studies that examined traditional stove cooking using wood fuels.

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Maximum EFOC values shown here exceed those reported in other studies that investigated open burning cooking

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emissions.7,13,29,40 The meat cooking operations EFOC values, with an average of 2.48 g/kg-fuel, most closely

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resembled those found from in-home open cooking studies, which had a range of 0.1 to 5.2 g/kg-fuel across


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presented studies. We observed high variability in emissions from the four fry cooking samples, with EFOC values

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ranging from 2.2 g/kg-fuel to 24.7 g/kg-fuel.

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The process of brewing pito involves boiling large cauldrons of a water and millet mixture, and because the

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brewers seek to boil the mixture as quickly as possible, large amounts of fuel are burned very rapidly to achieve

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maximum fire power. Recent studies have shown that increased fuel feed rates for biomass combustion can result in

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higher particulate matter and CO emission factors.13,40 The relatively high EFOC and EFEC values for pito brewing

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may either be a result of the increased fuel feed rate or a possible combustion cycle bias in which our sampling

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window captured multiple fuel addition events; elevated EC to OC ratios may also result from the continued flaming

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nature of these fires. Relative to literature values for open cooking the sample from fish smoking produced slightly

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elevated OC emissions (EFOC = 5.0 g/kg-fuel) and characteristic EC emissions (EFEC = 0.3 g/kg-fuel), but the

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measured CO emissions were low (EFCO = 1 g/kg-fuel). The fish smoking process involved lightly spraying the

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cooking flame with water at irregular intervals to generate increased amounts of smoke and steam to cook the fish

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resting on a rack above the combustion plume. No literature has reported CO emissions this low for similar cooking

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events, and no signs point to bias or equipment malfunction. We thus report the value, but with skepticism towards

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its validity.

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Current emission inventories for African regions have sparse reporting for commercial cooking emission

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factors, and residential cooking emission factors are used as proxy in the absence of measured values.3,8 Because

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large scale cooking operations can utilize different fire tending practices and fuel consumption rates, future efforts

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should be made to better understand and characterize these events and determine whether their emissions differ

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significantly from residential cooking events. Though fuel use for commercial activity is estimated by the UN to be

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roughly 37% of household cooking,8 the possibility of increased emissions from these different commercial

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practices compared to residential cooking, along with the activity generally occurring in densely populated areas,

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makes their characterization an important factor for health and atmospheric modeling studies.

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Trash Burning

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Navrongo has several locations in and near town designated for general collection and burning of trash.

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Piles of collected trash in this area are commonly lit in the morning, and continue burning and releasing emissions

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throughout the day until natural extinguishing occurs. We collected emissions from seven different trash burning

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events varying in size and composition. Measured EFOC values were high (mean EFOC = 38.9g/kg-fuel with a range


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of 2.6 – 95.6 g/kg-fuel), while EFEC values were significantly less prominent (mean EFEC = 0.59 g/kg-fuel with a

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range of 0.03 – 1.96 g/kg-fuel). EFCO values (mean EFCO = 96 g/kg-fuel with a range of 36 – 136 g/kg-fuel) were

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higher than literature values, though still maintaining some overlap7,9,16. The sampling periods for each fire spanned

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across the flaming and smoldering stages of combustion and resulted in a mean MCE of 0.90 with values ranging

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from 0.84 to 0.97.

Figure 2. OC and CO boxplots for trash burning samples. Emission factors from this study (left) are plotted next to literature values for trash and waste burning EFs (right).

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We observed large variances in emission factors across the individual events sampled, which was expected

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for emissions of this type since combustion environments, trash composition, and fuel moisture can all contribute to

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alterations in emission rates.9,10,15,17 Coefficients of variation (COV = (σ/μ)*100%) across the samples are 79% for

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EFOC, 128% for EFEC, and 39% for EFCO. Although no estimates for carbon content were made during our sampling,

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visual inspection of the trash contents found large varying amounts of plastics and polymer waste (carbon content

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>80%), suggesting that using a single carbon content (45% for this study)15 for all trash burning events may lead to

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misrepresentation when used to estimate emission factors.

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Notable differences in emission factors were specifically observed between samples taken during different seasons. The three samples taken during Ghana’s rainy season, each after a night with a heavy rain storm, had higher


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OC emissions (EFOC = 64.4± 27.3 g/kg-fuel) than the remaining samples that were taken later in the year during the

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drier seasons (EFOC = 19.8± 15.3 g/kg-fuel). Large OC emissions from damp or moist mixed waste burning events

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have recently been reported by Jayarathne et al. (2018) (mean EFOC = 61.1 g/kg-fuel), and are of similar magnitude

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to those reported in our measurements. These seasonal effects were also observed in the CO emissions: EFCO = 127

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±8.8g/kg-fuel for the damp trash samples and EFCO = 73 ± 32.3 g/kg-fuel for the dry. Increased emissions have been

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closely correlated with fuel moisture content for several biomass fuels.41-43 Since water must be evaporated from the

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fuel before combustion can occur, fuel moisture reduces available energy from the combustion area, causing

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decreased temperatures, lower combustion efficiencies, and higher emissions.44,45

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EC to OC ratios were small (EC/OC < 0.02) across all trash burning samples except for one (EC/OC =

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0.66), which had fuel composed primarily of paper and cardboard products. Combustion efficiency has been

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correlated with combustion phase, and consequently whether carbonaceous particulate matter emissions are

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predominantly organic or elemental.13,14,45Although we observed high combustion efficiencies even in the trash

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burning tests that resulted in the low EFEC, EFOC and MCE values were correlated (R2 = 0.90). High OC emissions

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corresponding to lower MCE measurements in trash burning are also noted in other studies.7,46 While inherent

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characteristics of a trash pile may affect the emissions, such as composition and the physical distribution of the trash

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within the pile itself, our emissions measurements may also be biased depending on the phase of combustion

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sampled and whether the sampling window included emissions from the lighting through the smoldering phase.

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In-field samples from trash burning that have large emission factors, as reported in this study as well as

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Jayarathne (2018), signal a need for better understanding of trash burning, as well as how certain factors, such as

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unbalanced combustion phase sampling, poor carbon content estimates, or unaccounted fuel moisture may lead to

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variance in these types of emissions measurements. Failing to understand the exact cause of these high-emitting

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trash burning events could result in improper representation of emissions factors, leading to inaccurate emissions

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estimates when scaled up with activity data.

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Kerosene Lanterns

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Nine in-home samples were taken during residential kerosene lighting events from Rwanda (n = 4) and Ghana

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(n = 5). For the sampling process, residents were asked to light and maintain the lamps in a manner characteristic of

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standard operation. Locally sourced kerosene fuel was used for each lamp. We sampled from eight hurricane-style

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lanterns and one simple wick type lamp.


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To our knowledge, there has only been one study, Lam et al. (2012),20 that has simultaneously measured

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gaseous and carbonaceous PM emission factors from kerosene lighting events. In their study, Lam et al. (2012)

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calculated EFs from both simple wick (9 lab samples; 7 in-field samples from southwest Uganda) and hurricane-style

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lanterns (3 lab samples using kerosene sourced in US). Figure 3 shows the EFs from our in-field measurements of

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hurricane style lanterns alongside the literature values for both wick and hurricane style kerosene lamps. We compare

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our in-field measurements of hurricane style lanterns to lab tests done on similar lamps, as well as to other in-field

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measurements taken from wick style lamps. EFOC varied across all our samples from 1.51 to 31.2 g/kg-fuel with a

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mean of EFOC = 9.5 g/kg-fuel, and EFEC ranged more widely from 0.04 to 65.7 g/kg-fuel with a mean of EFEC = 25.1

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g/kg fuel. EFCO values spanned 1.5 to 50.9 g/kg-fuel, with a mean CO value of EFCO = 20.4 g/kg-fuel

Figure 3. OC, EC, and CO boxplots for kerosene lighting samples. Emission factors from our in-field sampling of hurricane style lanterns are plotted next to the only literature values available for kerosene lamp field measurements, as well as hurricane lab measurements. Not plotted is EFCO = 0.52 ± 0.19 g/kg-fuel from Fan & Zhang (2001) due to axis scale, as well as the emission factors from a wick style lamp we sampled in Kigali.

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On average, the three hurricane samples taken in Kigali, Rwanda show elevated carbonaceous PM emissions

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compared to the samples taken in Navrongo: EFOC = 17.6 g/kg-fuel, EFEC = 39.2 g/kg-fuel for the samples taken in

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Kigali, compared to EFOC = 3.3 g/kg-fuel, EFEC = 8.7 g/kg-fuel from the samples taken in Navrongo. Differences

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between these samples may be attributable to regional differences in fuel refinement quality, but also may result from

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variations in operational practices. High flame operation of hurricane lanterns can result in significantly increased


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emissions,14,47,48 and because we established no standardized method for lamp operation, a wide variety of lighting

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conditions and emissions are thus represented.

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Comparing measurements exclusively between the Hurricane style kerosene lanterns, our measurements

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from Navrongo had similar EC emission factors and higher OC emission factors compared to the lab measurements

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from Lam et al. (2012), which reported EFOC = 0.5 ± 0.3 g/kg-fuel and EFEC = 9 ± 1 g/kg-fuel. The Kigali

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measurements, though having higher EC emissions than the lab measurements of hurricane lanterns from Lam et al.

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(2012), showed lower EFEC than the field samples of wick style lamps from Lam et al. (2012). Large EFOC values were

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also noted from the samples taken in Kigali.

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The simple wick style lamp we measured in Kigali, which was characterized as a kerosene filled glass bottle

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with a cloth wick, showed the second highest EFEC value (64.6g/kg-fuel) of our samples, which was closer in value to

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the simple wick style lanterns reported in Lam et al. (2012) than most hurricane style lanterns. The Kigali wick sample

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also showed elevated EFOC (16.0g/kg-fuel) compared to both the Navrongo measurements as well as literature values.

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To our knowledge, substantial OC emissions have not previously been reported for kerosene lighting sources. Previous

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measurements of kerosene lamp emissions have focused on the elemental or black carbon emissions for purpose of

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climate and radiative forcing values or soot generation for aerosol calibration.49-50 Depending on the extent of the

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uncertainties created by filter storage and gas-phase artefacts, our field measurements show that EFOC can be

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significant and should be further investigated.

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Kerosene lamps are most commonly used indoors and can generate particulate matter concentrations well

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above WHO guidelines.19,48 Future sampling with increased attention given to fuel quality, user preference, and

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operating parameters may offer valuable insight into how residential kerosene lantern use can cause sub-optimal

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emission factors that more accurately represent realistic use.

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Diesel Backup Generators

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We sampled the exhaust from two 4-stroke diesel backup generators of differing sizes and power outputs

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common of local generator usage. One generator, used to power a hospital and research center in Navrongo, Ghana,

288

had a large power output (John Deere J200K [160kW]), and the other, a generator used to power a telecommunications

289

tower, had a smaller rating. The telecom generator model could not be identified beyond its manufacturer (Cummins),

290

but is known to have a power rating characteristic of African telecommunications tower generators within the range

291

of 7.5-40kW.51 Very few studies have investigated in-field OC emission factors from backup generators, and there is


12

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292

a specific absence of information regarding backup generators in sub-Saharan African countries, where most

293

generators being used are older models and likely operating sub-optimally.

294

EFOC values were 2.86 (g/kg fuel) for the smaller generator and 0.71 (g/kg fuel) for the larger generator. EFEC

295

values were small for both generators (0.06 g/kg-fuel for the smaller generator and 0.51 g/kg-fuel for the larger).

296

Jayarathne et al. (2018) measured the exhaust from a residential 4-stroke 5kW diesel generator during idling conditions

297

and calculated an EFOC of 7.31 g/kg fuel.10 Although we note increased OC emissions from smaller sized generators,

298

extensive research on backup generators suggests that operating parameters such as power loading, age and

299

maintenance, sulfur content of fuel, and operating temperature all factor into a generators’ emission rate of total and

300

carbonaceous PM.24-26

301

Table 1 shows the EFs of EC, OC, and CO in comparison with other literature values. We find good

302

agreement between our measurements and those from similar in-field measurements of diesel generators. Lower CO

303

emissions were measured from the 160kW generator (5.3 g/kg-fuel) compared to the telecom generator (27.4 g/kg-

304

fuel). Similar trends were observed in Stockwell (2017),9 with a larger (100kW) better maintained generator emitting

305

less CO (EFCO = 4.1 g/kg-fuel) than a smaller (5kW) generator (EFCO = 76.1 g/kg-fuel). The larger of our two sampled

306

generators had a similar EC emission factor to the generator measured in Jayarathne (2018), though an order of

307

magnitude less OC.

308

Table 1. Diesel Backup Generator Emission Factors EFOC (g/kg-fuel) 0.71

EFEC (g/kg-fuel) 0.51

EFCO (g/kg-fuel) 5.3

Cummins 7.50-40kW

2.86

0.06

27.4

Jayarathne (2018)

5kW

7.31

0.58

-

Stockwell (2016)

5kW

-

-

76.1

Stockwell (2016)

100kW

-

-

4.1

Zhu (2009)

Average from 10-100kWa

-

-

17.0

Shah (2006)

Average from 60-2000kWb

-

-

5.98

EPA AP-42

11-156kWc

-

-

16.5

Reference This Study This Study

Generator Rating John Deere J200K 160kW

Average from

a Average

emission factors taken from 14 Diesel Backup Generators Average emission factors taken from 18 Diesel Backup Generators c Average emission factors taken from 8 Off-Road Diesel Engines Including Generators52 b


13


Page 14 of 21

309

The in-field emission factors presented here, in conjunction with the results from similar measurements made

310

by Stockwell (2016) and Jayarathne (2018), suggest that diesel generators used for small scale commercial operations

311

may have EFs greater than those measured from well maintained, larger industrial sized generators.9-10

312

Comparison and Discussion Across Emission Sources

313

Figure 4 displays the comparison across measured EF values. We observe prominent differences between

314

source types when compared on a per kg-fuel basis. Kerosene EFEC values are higher than the other source types

315

sampled in this study. An observation not yet reported in literature for kerosene lanterns, EFOC values are prominent

316

and similar in magnitude to other sources. However, we measured the highest OC emissions measurements from

317

kerosene lanterns in Kigali Rwanda, suggesting that either

318

regional fuel quality or lamp use technique are the cause.

319

Because kerosene lanterns have widespread use in areas of the

320

world with unreliable electricity or lack of solar powered

321

lanterns, further evidence of elevated emission factors is of

322

great concern, especially when users are within enclosed

323

spaces and in close proximity to the source and emissions.48

324

Under certain circumstances, trash burning EFOC

325

values were found to be higher than the other sources tested.

326

Burning trash for its disposal is a common practice in many

327

parts of the world and, given the wide variability noted

328

across our samples and potential for intense carbonaceous

329

PM emissions, it should be considered for further study. Our

330

in-field measurements showed large variations most likely

331

caused by many uncontrolled real-world factors: cooking

332 333 334 335 336

Figure 4. EC, OC, and CO boxplots for all discussed emission samples. Commercial cooking emission factors are disaggregated into meat cooking, frying, and brewing activities. The single fish smoking sample is excluded to ensure all boxes have sample size greater than one.

environments and practices (stove type, cooking practices, and fuel feed rates), combustion quality (diesel generator maintenance and age), fuel qualities (Rwanda vs. Ghana kerosene), and fuel composition and condition (trash

heterogeneity and/or moisture). Uncertainty was also introduced by our inability to capture the ignition and early


14

Page 15 of 21


337

combustion phases for every measured source. It should be considered for future studies to capture emissions from

338

the lighting, flaming, and smoldering phases in order to reduce uncertainties in EFs and avoid biasing from

339

potentially misrepresentative sampling.

340

Lastly, we use the DICE-Africa emissions

341

inventory to explore the implementation of recent in-field

342

emissions factors, and how they change prior estimates

343

for Ghana’s total yearly emission output.8 Since the

344

compilation of the DICE-Africa inventory, only one other

345

study has generated emission factors specific to Ghanaian

346

sources: Coffey et al. (2017). Using the values reported in

347

our study along with the household cooking emission

348

factors calculated by Coffey et al. (2017), we calculated

349

new total emissions estimates for each of the updated

350

sources.13 Figure 5 shows the differences observed

351

between original estimates (CO, EC, and OC) and those

352

made with the updated in-field emission factors.

353

Household cooking emissions decrease for CO, EC, and

354

OC, (ΔCO = -23.7 kt, ΔEC = -2.1 kt, ΔOC = -2.7 kt), representing a 9%, 76%, and 27% lower estimate,

355

respectively, from the original estimates. Emissions from commercial cooking show a 185% increase in OC. The

356

largest increase is observed in the trash burning CO and OC estimates, with increases of 154% and 725%

357

respectively (ΔCO = 21.3 kt, ΔOC = 12.6 kt). Total CO emissions differences across all updated sources nearly

358

balance out between the increase in trash burning and decrease in household fuelwood use, resulting in a 1%

359

decrease; total OC emission estimates across all updated sources nearly doubled with a 95.9% increase. Lastly,

360

although kerosene fuel use causes less total emissions compared to other sources, we see a 720% increase in

361

kerosene OC emissions from the previous estimate.

362

Figure 5. Differences in Ghanaian CO, EC, and OC emissions, given in kilotonnes (kt), between prior estimates and estimates made using the updated emission factors provided in this study. Emission factors for household cooking taken from Coffey et al (2017) as indicated; activity data for emissions estimates taken from DICE-Africa (Marais 2017). Tabulated emission estimates given in the supporting material (Section S6).

Our estimates show decreases in EC across all source types. Because of the noted differences between

363

emissions from our Ghana and Rwanda samples, estimates for kerosene emissions were calculated using the factors

364

exclusively from Ghana. Overall, across the updated emission sources, we observed a 78% decrease in total EC


15


Page 16 of 21

365

emissions, with kerosene and household wood use causing the largest reductions. The reduction in kerosene EC

366

emissions is most likely because the replacement emission factors contain only measurements of hurricane lanterns;

367

although no wick lanterns were measured during the sampling campaign, a larger study would be needed to assess

368

the true representation of lantern type and thus emissions from kerosene use.

369

In the future, larger sample sizes and improved sampling methods will help to ensure more complete in-

370

field emission factors, but the data presented in this study provide a useful step forward in improving emission

371

inventories for sub-Saharan African countries. The use of our in-country results to calculate the differences observed

372

in Figure 5 displays the sensitivity country-wide emission estimates can have when an alternate set of emission

373

factors are employed. Although the reliability between sets of emission factors is beyond the scope of this paper, by

374

providing in-field emission factors from sources in sub-Saharan Africa, we contribute to the efforts being made to

375

generate increasingly versatile and reliable emission inventories for this region.

376

Author Information

377

Corresponding Author

378

*E-mail: [email protected]

379

ORCID:

380

David Pfotenhauer: 0000-0001-7336-3900

381

Present Address

382

‡ University of Colorado, CIRES, 216 UCB Boulder, CO 80309.

383

Notes:

384

The authors declare no competing financial interest

385

Funding

386

This publication was developed under Assistance Agreement No. #R835424 awarded by the U.S. Environmental

387

Protection Agency to Michael P. Hannigan. It has not been formally reviewed by EPA. The views expressed in this

388

document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse

389

any products or commercial services mentioned in this publication. Funding also from the National Science

390

Foundation (Grant #1211668).

391

Acknowledgements


16

Page 17 of 21


392

We acknowledge the United States Environmental Protection Agency (Grant #R835424) and the National Science

393

Foundation (Grant #1211668) for funding. Special thanks to the Navrongo Health Research Center personnel, who

394

helped locate the emission sources and perform the measurements, (Rex Alirigia, Manies Achazanga, Rockson,

395

Desmond Agao, Felix and Jonathan) and the Hannigan research group for their valuable insights and support.

396

Supporting Information.

397

This information is available free of charge via the Internet at http://pubs.acs.org.

398



Word document containing EPOD sensor calibration and data quality, EF calculations, source pictures and

399

notes, EFs from undiscussed source types, ECOC analysis of frying samples, tabulated emission estimates

400

for Ghana.

401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424



Excel sheet containing source EFs, fuel types, and carbon contents.

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Updated Emission Factors from Diffuse Combustion Sources in Sub

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