Determining Particulate Matter and Black Carbon Exfiltration Estimates

Subscriber access provided by SUNY DOWNSTATE

Article

Determining Particulate Matter and Black Carbon Exfiltration Estimates For Traditional Cookstove Use In Rural Nepalese Village Households Sutyajeet Inderjeet Soneja, James M. Tielsch, Frank C. Curriero, Benjamin Zaitchik, Subarna K. Khatry, Beizhan Yan, Steven N Chillrud, and Patrick N Breysse Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es505565d • Publication Date (Web): 06 Apr 2015 Downloaded from http://pubs.acs.org on April 8, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 26

Environmental Science & Technology

1

Determining Particulate Matter and Black Carbon

2

Exfiltration Estimates For Traditional Cookstove

3

Use In Rural Nepalese Village Households

4

AUTHORS: Sutyajeet I. SonejaŦ, James M. Tielsch§, Frank C. CurrieroΤ, Benjamin ZaitchikŤ,

5

Subarna K. KhatryŢ, Beizhan YanĦ, Steven N. ChillrudĦ, Patrick N. BreysseŦ*

6 7

Ŧ

8 9

§

Department of Environmental Health Sciences, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA

Department of Global Health, Milken School of Public Health and Health Services, George Washington University, Washington, DC 20037, USA Τ

10 11

Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA

12 13

Ť

Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218, USA

14

Ţ

15 16

Ħ

Nepal Nutrition Intervention Project Sarlahi, Kathmandu, Nepal

Division of Geochemistry, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA

17 18

KEYWORDS:

19 20 21

Exfiltration fraction; air exchange rate; mixing factor; particulate matter; black carbon; cookstove; low resource environment; biomass burning; ventilation; village; household; Indo Gangetic Plain

ACS Paragon Plus Environment

1


Page 2 of 26

22

ABSTRACT

23

A majority of Black Carbon (BC) emitted to the atmosphere in the Indo-Gangetic Plain (IGP)

24

region is from burning biomass fuel used in traditional, open-design cookstoves. However, BC

25

and particulate matter (PM) household emissions are not well characterized. Household emission

26

information is needed to develop emission profiles to validate regional climate change models

27

and serve as a baseline for assessing the impact of adopting improved stove technology. This

28

paper presents field-based household PM and BC exfiltration (amount exiting) estimates from

29

village homes in rural Nepal that utilize traditional, open-design cookstoves. Use of these stoves

30

resulted in a 26% mean PM exfiltration, ranging 6% to 58%. This is a significant departure from

31

an 80% estimate cited in previous literature. Furthermore, having a window/door resulted in an

32

11% increase in exfiltration when an opening was present, while fuel type had marginally

33

significant impact on emission. Air exchange rates (AER) were determined, with average (95%

34

CI) AER of 12 (10-14) per hour, consistent with previous studies. Also, BC to PM2.5 mass-ratio

35

composition during cooking was ascertained, with an average (95% CI) of 31% (24-39),

36

agreeing with previous biomass fuel emission composition literature.

37

ABSTRACT ART:

38 39 40 41 42 43


2

Page 3 of 26


44

Introduction

45

The Himalayan glaciers provide fresh water via glacial melt to more than a billion people

46

across South and East Asia1. These glaciers have begun melting at an alarming rate over the past

47

decade, threatening the fresh water supply for this region1. Furthermore, alterations in the overall

48

hydrological cycle in South Asia is becoming more apparent2. Monsoons are deviating from

49

typical seasonal patterns, impacting a number of groups within this region including farmers who

50

are responsible for feeding a significant portion of the world’s population3,4. While it is believed

51

that glacial retreat is driven by global warming, the rapidity of the retreat indicate other factors

52

may be at play1. Black carbon emitted into the atmosphere and subsequent deposition on glaciers

53

and snow pack is thought to be a major contributor to the changing climate and accelerated

54

glacial retreat in this region5.

55

Black carbon (BC), a component of particulate matter (PM), is produced by incomplete

56

combustion of fossil fuels, burning of biomass from forest fires, and from households and

57

factories that use wood, dried animal manure, or crop residue for cooking or other energy needs6.

58

Worldwide, more than three-quarters of the black carbon produced is thought to come from

59

developing countries, generated from cookstoves, open burning, and older diesel engines4. It is

60

estimated that around 2.7 billion people worldwide cook using biomass fuel, most of which are

61

poor and in developing countries7. Twenty percent of the total global BC emissions has been

62

attributed to emissions of BC from biomass burning in cookstoves8, and biomass cooking is

63

believed to contribute about two-thirds of BC emissions within South Asia9.

64

Within the Himalayas, BC may be responsible for as much as 50% of the total glacial retreat

65

seen9. Furthermore, recent studies have estimated BC as a major contributor to global warming,

66

surpassed only by CO2 and possibly methane10. Since BC has an average residence time in the


3


Page 4 of 26

67

atmosphere of a few days to weeks relative to greenhouse gases which range from years to

68

centuries, mitigating BC emissions to combat climate change can produce almost immediate

69

effects in terms of reduced radiative forcing, subsequently producing direct benefits for public

70

health10,11. Regional scale BC emission estimates, however, have a significant, 2-5 fold,

71

uncertainty5. The Indo-Gangetic Plain region (IGP) has been identified as a regional hotspot for

72

BC emissions10. The IGP is one of the most densely populated regions in the world and large

73

uncertainties exist for published BC emissions12,13.

74 75

Background. Cookstove emissions create indoor exposures and contribute to ambient air

76

pollution through passive exchanges between indoor and outdoor air (indirect venting) and

77

direct, active venting (i.e., chimney) to the outdoors. Open-design cookstoves, where combustion

78

byproducts are expelled directly into the indoor environment, result in high concentrations of

79

pollutants inside the home14–18. BC and PM emission factors for different stove designs have

80

been determined in laboratory settings19–21. These emission factors can vary by 5-6 fold

81

depending on stove design and fuel type19. In addition, the BC mass-ratio component of PM

82

under 2.5 micrometers in diameter (PM2.5) has been shown to vary from 5-52% using laboratory-

83

based test burns19. While laboratory-based studies are valuable for estimating stove emissions,

84

climate modeling requires estimates of household emissions. Characterization of cookstove

85

emissions emanating from a home requires an assessment of the PM exfiltrating from the house.

86

A fraction of PM emitted indoors during combustion will settle and deposit on indoor surfaces,

87

while the remaining will exfiltrate to the outdoor environment. Studies translating laboratory-

88

based cookstove emissions to ambient emissions from a house often use hypothetical air

89

exchange and particle deposition rates resulting in fractional exfiltration estimates. For example,


4

Page 5 of 26


90

an 80% exfiltration estimate of PM emitted by biomass combustion19, a published value with no

91

documentation based upon hypothetical particle deposition rates has been cited without any field

92

validation. Furthermore, measurement of air exchange rates from housing in developing

93

countries is extremely limited22–24 compounding our poor understanding of how much PM may

94

be exfiltrating to the outdoor environment. Estimates of PM and BC exfiltration from homes are

95

needed to reduce uncertainty in emission inventories. Exfiltration estimates can be used to

96

evaluate the impact of cookstove interventions (i.e., use of a chimney), designed to improve

97

indoor air quality, on outdoor air quality. These estimates can further inform studies assessing

98

indoor air pollution in other homes, where exfiltration of biomass air pollution has been

99

identified as a significant contributor to indoor PM pollution in non-biomass using homes25.

100

This paper presents air exchange rates and PM exfiltration estimates from homes in rural Nepal

101

that utilize traditional, open-design cookstoves. Estimates of variability are provided for

102

exfiltration as a function of housing and fuel characteristics in a real-world setting. Furthermore,

103

this paper assesses the BC to PM2.5 mass-ratio produced by biomass cooking in order to estimate

104

BC exfiltration from homes. In combination with an assessment of indoor PM concentrations,

105

PM and BC exfiltration fractions can be used to estimate house emissions to ambient air in order

106

to better assess regional air quality and climate change impacts.

107 108

Methods

109

Study Overview. Located in Sarlahi District Nepal, this exfiltration assessment study is nested

110

under a broader parent cookstove intervention trial known as the Nepal Nutrition Intervention

111

Project - Sarlahi (NNIPS) setup by the Johns Hopkins Bloomberg School of Public Health

112

(JHSPH). This community-based, cluster randomized, step-wedge trial is characterizing indoor


5


Page 6 of 26

113

PM exposure in ~3,000 homes from traditional and alternative cookstoves, assessing primary

114

health outcomes that include acute lower respiratory illness in children 1-36 months of age

115

(ALRI), as well as birth weight and preterm birth among newborn infants26. This cookstove

116

intervention trial is underway in 4 areas, referred to as Village Development Committees

117

(VDC’s), within the NNIPS site. Sarlahi is a rural area located in the Terai region of southern

118

Nepal (bordering Bihar State in India) and is representative of southern Nepal and most of

119

northern India with elevation approximately 200 meters above sea level26. Cooking with

120

traditional stoves, comprised of clay, mud, bricks, rice husk, and cow dung, are common in this

121

area26. The typical cooking style involves the burning of biomass fuels (wood, dried animal

122

manure, and crop residue) in traditional, open-design mud cookstoves without direct ventilation

123

to the exterior26.

124 125

Mock and Occupied Home Sampling. In order to assess exfiltration estimates of PM, a mock

126

house was built at the research site in Sarlahi District, Nepal. Based on parent trial study data, the

127

house (Figure 1) was representative of a typical household kitchen in this area with respect to

128

size, material, and number of openings. Homes in this region typically consist of mud, wood,

129

brick or cement walls with thatch roofs26. The mock house consisted of a 1-room floor plan with

Figure 1. Mock house representative of standard kitchen built in study area


6

Page 7 of 26


130

the ability to close and open 1-window and door. Housing material consisted of bamboo with

131

mud, logs, and tree branches, while roofing was half tile and thatch/grass. House dimensions

132

were: length 3.85m, width 4.65m, ground to the lowest roof point 1.8m, ground to apex of the

133

roof 2.7m, window 0.6m by 0.6m (located on the back wall), and door frame 1.28m width by

134

1.64m height (located on the front wall). Both the window and door had a hinged, wood-framed

135

metal panel that allowed for opening/closing. In addition, a traditional mud-based cookstove with

136

two openings was built inside according to typical practices for stove construction, placed on the

137

floor of the back wall.

138

Air quality data in the mock house included PM concentration collected passively with a

139

DataRAM pDR-1000AN (Thermo Scientific, Franklin, MA), carbon monoxide concentration

140

with an EasyLog USB CO Monitor (Lascar Electronics, Eerie, PA), and relative

141

humidity/temperature using the HOBO Data Logger (Onset Corp., Bourne, MA). All devices

142

were co-located at the center of the house before, during, and after simulated cooking events

143

(discussed later), and recorded data in 10-second intervals. The pDR-1000 was zeroed in the

144

field prior to each deployment using procedures recommended by the manufacturer. The CO

145

monitor was calibrated before deployment at JHSPH using standard procedures27.

146

In addition, gravimetric PM2.5 was collected with a PM2.5 cyclone inlet (BGI, Waltham, MA)

147

on Teflon filters (37mm 2.0µm pore PTFE Membrane Filter w/ PMP ring Pel Life Sciences, Ann

148

Arbor, MI). A personal sampling pump (5400 BGI Inc., Waltham, MA) was utilized at a flow

149

rate of 4 L/min. Flow rates were recorded before and after sampling using a Drycal Flowmeter

150

(DC Light BIOS Intl., Butler, NJ). Filters were pre and post-weighed in a temperature and

151

humidity controlled room using an XP2U Microbalance (Mettler Toledo, Columbus, OH)

152

located at JHSPH. Pre-weighed Teflon filters were loaded into polypropylene filter cassettes


7


Page 8 of 26

153

(SKC Inc., Eighty Four, PA), along with filter pads (Pall Life Sciences, Ann Arbor, MI) and

154

37mm drain discs (Model #230800 Air Diagnostics and Engineering, Harrison, ME) at the field-

155

site in a field-developed clean box to minimize contamination during assembly while in Nepal.

156

After sampling, filter cassettes were sealed in plastic bags until returned to the United States,

157

where they were disassembled and post-weighed. Ten percent duplicate samples were collected

158

and all filter weights were blank corrected.

159

Fuels utilized during simulated cooking events in the mock house were derived from survey

160

data from the parent cookstove trial. The most common types of fuel were wood and a mixture of

161

wood/dung/crop waste28. Fuel sources were obtained from the same area, by the same person,

162

and subsequently sun dried to a consistent moisture content. The same person performed all of

163

the simulated cooking events in the mock house. Each fuel combination was tested three times.

164

In addition, two boundary conditions were tested, where both the window and door were either

165

open or closed during mock house testing.

166

The same test protocol was performed in 50 occupied homes randomly selected from the 2,854

167

households in the parent cookstove trial. Homes chosen reflected typical housing based on data

168

from the NNIPS cookstove intervention trial study (i.e., have 1 window/door, similar in kitchen

169

size and composition to the mock house). Eligible homes met the criteria of only using

170

cookstoves for personal food production, agreeing to not have other sources of combustion

171

(including tobacco) ongoing in the house during measurement. Testing was performed midday to

172

minimize interference to participants’ daily routines as well as smoke infiltration from

173

surrounding homes’ cooking activities. Fuel type utilized and window/door status were observed

174

in order to allow cooking preferences of the home to remain intact. The individual in the home

175

that did the most cooking performed the simulated cook session and was supervised by local


8

Page 9 of 26


176

staff for protocol compliance. Kitchen volume and number of windows/doors were also

177

recorded.

178 179

Exfiltration Fraction Determination. The air exchange rate (AER) and subsequent exfiltration 180

fraction (EF), or the amount of PM exiting a home

181

via natural ventilation, were determined via the

182

concentration decay method29. This method involves

183

measuring pollutant decay over time once the source

184

of the contaminant is terminated or removed in order

185 to determine passive air exchange rates between Figure 2. Hypothetical decay curves for indoor to outdoor29,30. Hypothetical decay curves for 186 PM and a non-reactive gas. 187

a non-reactive gas and PM illustrating the concentration decay method are displayed in Figure 2.

188

Assuming that the house is a simple one-room structure (i.e., no tight internal partitions), it can

189

be treated as a single zone and a mass-balance continuity equation can be used to estimate the

190

exchange of air between indoors and outdoors. For non-reactive gases (i.e., no other mechanism

191

of removal) this model assumes that the air exchange carries the pollutant from inside to out, or

192

visa versa if one is interested in infiltration. The air exchange rate (AER) is calculated assuming

193

perfect mixing conditions29 using Equation (1):

194

∗

(1)

195

Where Q is ventilation rate in m3/minutes, V is room volume (m3), C2 and C1 are concentrations

196

at various points in time, t2 and t1 are the corresponding time points (minutes), and k is a factor

197

used to account for imperfect mixing conditions. The AER, , is determined by regression of


9


198

Page 10 of 26

Ln( ) of the non-reactive gas against ∆ time. Equation (2) illustrates the simple linear regression

199

model used to obtain this value. Ln(C) = β0 + β1×t + ε, where ε ~ N(0,σ2)

200

(2)

201

Where C is the concentration of the tracer gas, t represents time (minutes), and β1 represents the

202

AER. Ventilation studies typically utilize a tracer gas not commonly found in the environment,

203

such as sulfur hexafluoride (SF6), to measure AER’s31. Given logistical limitations regarding

204

acquisition and transportation of SF6 to the study site, carbon monoxide (CO), deemed a viable

205

substitute32, was utilized as the tracer gas. CO was suitable because the concentrations produced

206

inside the home during cooking were more than two orders of magnitude higher than

207

background.

208

Figure

3

summarizes

passive

209

exfiltration pathways for PM from a

210

house. PM exfiltrates through open

211

windows/doors as well as through

212

cracks in the roof and walls (PME in

213

Figure 3). Exfiltration for PM is

214

complicated by the fact that the decay

215 216

over time will be governed by the Figure 3. Pathways for particulate matter exfiltration from a house losses due to settling, surface

217

deposition, as well as exfiltration. Calculating the AER based on the PM decay curve will

218

therefore overestimate the real passive air exchange. To address this problem, two decay curves

219

were measured as indicated in Figure 2. The slope of the PM decay curve was calculated in the

220

same manner as the CO decay curve, utilizing Equations (1) and (2). The fraction of PM decay


10

Page 11 of 26


221

associated with exfiltration was calculated by comparing the slopes of the decay curves (i.e., the

222

AERs) for CO and PM. Since CO is a non-reactive gas, its AER will not be affected by surface

223

deposition. As shown in Figure 2, the decay rate for PM will be faster than that for a gas. The

224

ratio of the AERs, Equation (3), provides an estimate of the fraction of PM that exfiltrates to the

225

ambient air:

EF

226

(3)

227

where SCO and SPM are the slopes of the respective decay curves, or the AERs, when calculated

228

according to Equation (2). Conceptually, if both agents had the same pathways for exfiltration

229

then the decay curves would overlap resulting in unity. Deviations from unity are accounted for

230

by the faster decay of PM due to particle losses on surfaces.

231 232

Exfiltration Fraction Test Protocol. A simulated cooking session was performed to create

233

elevated concentrations of PM and CO within the home. Prior to ignition, air-sampling

234

equipment was deployed in the mock house at a height of 1m in front from the edge of the stove

235

and 1.8m off the floor to maintain consistency between readings. Placement of sampling

236

equipment in occupied homes relative to the mock house was slightly altered due to logistical

237

limitations.

238

By generating PM and CO concentrations that were orders of magnitude higher than outdoors,

239

PM and CO infiltration from outdoor air into the home (assumed to be low relative to the indoor

240

generated contaminants) when using Equation (2) could be ignored. To determine PM and CO

241

decay curves, a modified version of the Water Boiling Test (WBT) 3.0 was used33. A cooking

242

session was simulated by bringing 2 pots of water (5 Liters each) to a rolling boil from ambient

243

temperature. The fire was then extinguished and remnant fuel removed from the house to halt


11


Page 12 of 26

244

further emissions. Passive PM, CO, and RH sampling were initiated 30 minutes prior to the

245

cooking session and continued for an additional two hours post-fire extinguishment. Gravimetric

246

sampling for PM2.5 took place only during the active flame period. Passive PM and CO

247

concentration decays were examined only after the flame was extinguished. Equations (2) and

248

(3) were applied to the decay curves to determine the AERs for CO and associated exfiltration

249

fraction. All tests were performed during the Spring of 2012, considered the dry season as the

250

outdoor temperature is moderate to high and precipitation is minimal.

251 252

Determining the Black Carbon Fraction of PM2.5. PM2.5 filter samples were collected during

253

the active flame period of the simulated cooking session in both the mock house and occupied

254

homes in order to assess BC content of the PM using a validated optical method34. It is assumed

255

that BC is mostly contained on smaller particles (i.e., PM< 2.5 m) and it is these particles that

256

drive the impacts to climate35, therefore measuring PM over a broader size distribution is not

257

necessary.

258 259

Mixing Factor Assessment. An additional analysis examined if further adjustments were

260

necessary when determining air exchange rates. Methods, data analysis, and results are presented

261

in the Supporting Information section.

262 263

Data Analysis. Exploratory analyses included generating summary statistics for AERs, PM and

264

CO ventilation rates, exfiltration fraction by fuel type and window/door status, and BC to PM2.5

265

mass-ratios. A linear mixed effects model with a random intercept and slope was utilized to


12

Page 13 of 26


266

examine if household factors are predictive of PM exfiltration. This regression model took the

267

form

268

EFijk = (β0+ b0k) + β1×Fi + (β2+ b2k)WDj + εijk

(4)

269

Where i, j, k index fuel type, window/door status, and house type respectively, and {b0k} are

270

independent Normal (0,σ02), {b2k} are independent Normal (0,σ22), errors {εijk} are independent

271

Normal (0,σe2), and {b0k}, {b2k}, and {εijk} are mutually independent.

272 273 274 275

The effect of fuel type (Fi) is fixed while house type is random. The effect of status of window/door during cooking (WDj) varies by house type to attain the random slope model. All statistical analyses were performed in the R Statistical Computing Environment (version 3.0.2; R Project for Statistical Computing, Vienna, Austria).

276 Results and Discussion

278

Example of decay curves for PM and CO

279

post-fire extinguishment determined in an

280

occupied house is presented in Figure 4. Both

281

curves

282

presented

283

relationship is seen for all tests performed.

284

The more rapid decay of PM indicates that

285

surface losses are significant.

286 287

mimic in

the Figure

hypothetical 2

and

curves

a similar

Figure 4. Example of PM (red) and CO (blue) decay curves post-fire extinguishment in an occupied house

277


13


Page 14 of 26

Table 1. Summary Statistics for CO Air Exchange and Ventilation Rates, and Particulate Matter Exfiltration Fraction (%)

Carbon Monoxide Air Exchange Rate (per hour)

Carbon Monoxide Ventilation Rate (m3/min)

Particulate Matter Exfiltration Fraction (%)

Data Type

N

All Data

50

Mock House

Mean (95% CI)

Range

10th%

25th%

Median

75th%

90th%

11.9 (9.7, 14.1)

2.3-41.8

4.2

6.8

10.6

15.5

20.5

20

9.6 (5.4, 13.8)

2.3-41.8

3.8

4.7

6.8

9.7

17.9

Occupied Homes

30

13.4 (11, 15.6)

4.2-35.2

7.5

9.1

11.7

15.8

21.7

All Data

50

6 (4, 7.9)

0.6-31.5

1

2.3

4

5.6

13.8

Mock House

20

6.4 (3.6, 9.3)

1.6-28.1

2.5

3.2

4.6

6.5

12

Occupied Homes

30

5.7 (2.9, 8.4)

0.6-31.5

0.9

1.7

3.2

5.1

15.3

All Data

50

26.4 (22.4, 30.5)

5.8-57.6

10.3

14.8

22.1

37.4

45.4

Mock House

20

23 (15.9, 30.1)

6.3-57.6

8.9

13.6

19

28.2

45.3

Occupied Homes

30

28.7 (23.7, 33.6)

5.8-53.6

10.7

16.6

30.9

38.7

45.4

288 289 290 291 292 293 294


14

Page 15 of 26


295

Air Exchange, Ventilation Rate, and Exfiltration Fraction. Summary statistics of AERs,

296

ventilation rates, and exfiltration fraction, with AER and ventilation rates calculated from the

297

decay curves, are provided in Table 1.

298

Air exchange rates based on the CO decay curves are reported in Table 1, with a mean (95%

299

CI) for all tests equivalent to 11.9 h-1 (9.7, 14.1). Examining CO AER by window/door status

300

yielded an average of 5.9 h-1 (4.4, 7.3) for the closed category (n=10) and 13.4 h-1 (10.9, 16) for

301

having an opening present (n=40). This trend follows expectations for window/door status, with

302

an opening present allowing for increased airflow and hence a higher average AER. Limited

303

field studies examining AER in other village housing have been conducted. Measured AER for

304

Indian village homes have been recorded between 7 to 30 per hour22. A more recent study in

305

village homes located in Peru reported a range of AER’s between 0.5 h-1 to 20 h-1 depending

306

upon partition presence23. These ranges of values are in agreement with results found in rural

307

Nepal village housing, which has similar housing style to much of the Indo-Gangetic Plain

308

region. In addition to demonstrating similarities across village housing, these findings contribute

309

to the expansion of field-based residential AER data. Accurate estimation of AERs is critical to

310

understanding cookstove contributions to ambient air pollution as well as the broader climate

311

change impacts that may be associated with traditional vs. alternative stoves. Ventilation rates,

312

which are a function of the home volume and air exchange rate, are also presented for all tests in

313

Table 1. The ventilation rates calculated from CO decay for the mock house under varying

314

conditions averaged 6.4 m3/min and ranged from 1.6 to 28.1 m3/min. Occupied homes’ CO

315

ventilation rates similarly averaged 5.7 m3/min and ranged from 0.6 to 31.5 m3/min. As

316

expected, mock house ventilation rates were similar to the occupied homes.


15


Page 16 of 26

317

The average (95% CI) PM exfiltration fraction for all tests (n=50) was 26.4% (22.4, 30.5),

318

with values ranging from 5.8% to 57.6%. Mock and occupied home EF averages (95% CI) were

319

23% (15.9, 30.1) and 28.7% (23.7, 33.6), respectively. EFs for the mock and occupied houses

320

were not statistically different, thus conforming to the expectation that the designed mock house

321

kitchen is representative of a typical kitchen in this area. The average (95% CI) EF for closed

322

window/door status (n=10 tests) was 18.2% (13.1, 23.2), while having an opening present (n=40

323

tests) resulted in an increase to 28.5% (23.8, 33.2). Furthermore, the range of EF data is less

324

variable when the window/door are both closed relative to having an opening present.

325

Comparing open vs. closed conditions yielded statistically different results. When partitions are

326

closed, airflow through the kitchen is reduced, resulting in less exfiltration and a range of values

327

with a relatively smaller spread in contrast to having openings.

328

EF distributions broken down by fuel type are presented in Figure 5. A wide range of fuel

329

types were tested in the occupied homes because these were the fuel types used by the families

330

during the testing period. They included wood, crop residue, mixtures of wood/dung/crop

331

residue, dung/wood, and crop residue/wood. Only wood and mixture of wood/dung/crop residue

332

were evaluated in the mock house because these were the two fuel types most prevalent in the

333

parent cookstove trial. Fuel categories consisting of a mixture still had wood as the primary fuel

334

source. EF for fuel types of wood and crop residue by themselves had similar averages (95% CI)

335

of 30.2% (25.6, 34.8) and 28.1% (13.9, 42.4), respectively.


16

Page 17 of 26


Figure 5. Particulate matter exfiltration fraction presented by fuel type 336

Crop residue is a mixture of materials with the potential to produce a wider range of particle

337

sizes when burned, hence the potential for particle size distribution to be broader relative to

338

wood. Subsequently, one expects that EF variability for crop residue should be larger than that

339

seen for wood, which is shown in Figure 5.

340

Figure 5 has a line indicating where 80% exfiltration is located, which is a hypothetical value

341

with no field validation that has been previously cited but not independently published19. The

342

80% exfiltration is significantly greater than the averages and upper limits of the EF data,

343

presented in the overall EF data (Table 1) and in Figure 5. It is difficult to account for the

344

difference between the EF presented in this paper and the 80% estimate previously used19

345

because the assumptions made in the derivation of the theoretical 80% EF value have not been

346

published. One possible reason why the hypothetical estimate is greater than our measured

347

estimates may be that the actual mass median size of particulate matter generated from cooking

348

is larger than previously assumed, which can impact the assumed deposition rate. It should be

349

noted that temperature for all tests were measured, with no significant difference found and thus

350

considered not a factor.


17


Page 18 of 26

351

Utilizing a linear mixed effects model, factors that may impact EFs were evaluated. Pooling of

352

categories for fuel type was necessary due to the small sample sizes of certain fuel categories.

353

With wood being the dominant fuel source for the categories of dung/wood, wood/dung/crop

354

residue, and crop residue/wood, these three sources were combined into one category and called

355

wood/other. The regression model examined window/door status (everything closed vs. opening

356

present) and fuel type (wood only and wood/other), with crop residue excluded due to the small

357

sample size. Reference levels for the covariates were set as wood for fuel type and everything

358

closed for window/door status. Regression results indicate that having an opening present

359

resulted in an 11.37% (SE=4.51) increase in exfiltration (p=0.022), while switching fuel type

360

from wood to wood/other resulted in a 7.64% (SE=3.83) decrease in exfiltration (p=0.0621).

361

Thus, window/door status was deemed a significant covariate while pooled fuel type was

362

marginally significant.

363 364

Ratio of Black Carbon to PM2.5 Analysis. Of the 50 PM2.5 filters collected during exfiltration

365

fraction tests, 43 were overloaded with PM thus too saturated to estimate BC using the optical

366

analysis method. The excluded filters exceeded the optical method’s upper threshold detection

367

limit which results in under prediction of BC values34,36. Fuel categories for the 7 samples with

368

acceptable loadings for BC analysis were broken down into wood/other (n=6) and crop residue

369

(n=1).

370 371 372 373


18

Page 19 of 26


Table 2. Summary Statistics for Black Carbon to PM2.5 Mass-Ratio N

Mean (95% CI)

Median

Range

PM2.5 Concentration (ug/m3)

7

4206.6 (2342.1, 6071)

3427

2341.2-8382.3

Black Carbon Concentration (ug/m3)

7

1317.5 (671.9, 1963.2)

1197.5

630.8-2470.7

Black Carbon to PM2.5 Ratio (%)

7

31.2 (23.8, 38.6)

29.5

17.6-41.3

374 375

Concentration summary statistics of BC and PM2.5, along with ratio estimates are provided in

376

Table 2. Measured only over the active cooking period of the simulated cooking test, the mean

377

(95% CI) ratio was 31.2% (23.8, 38.6), ranging from 17.6% to 41.3%. For the oversaturated

378

filters that were excluded, the most that can be reported is that all the mean BC to PM2.5 ratios

379

were larger than 9%.

380

Multiple studies have reported BC to PM ratios. For example, Rau (2007) reported elemental

381

carbon values from wood smoke particles ranging from 3 to 38%37, while other studies of wood

382

smoke have reported ratios that range from 8 to 53%38. Our estimates agree well with these

383

published data, further validating estimates determined in laboratory and field settings.

384

This research has major implications with regards to epidemiological and climate change

385

models that utilize emission inventories of BC generated by cooking activities. Specifically, this

386

research contributes to developing more robust estimates of the amount of BC that exits the

387

home and into the atmosphere as a result of cooking indoors with biomass fuels in traditional

388

stoves. To better understand the black carbon exfiltration from a cooking event, simple

389

calculations can be conducted. To present upper and lower bounds, the range of average PM2.5

390

concentration values during cooking (1.8 mg/m3 to 17.3 mg/m3) in the mock house were


19


Page 20 of 26

391

extracted. These values were multiplied by the volume of the mock house (40 m3) and BC/PM2.5

392

ratio (31.2%), producing a range of 23 to 216 milligrams of black carbon generated indoors over

393

the cooking event (mg of BC/cooking event). Subsequently, the mass of black carbon generated

394

indoors over the cooking event was then multiplied by the average exfiltration fraction (26.4%),

395

yielding the mass of black carbon that exfiltrates to the atmosphere over the course of a cooking

396

event (6 to 57 mg/cooking event). This range illustrates a rough estimate of the amount of black

397

carbon that can be emitted into the atmosphere from a household per cooking event after

398

accounting for deposition indoors.

399 400

Limitations. There are a number of limitations for this work. While we tried to keep combustion

401

characteristics constant in the mock house, burn rate, air-fuel ratio, flame turbulence, and

402

combustion temperature were not measured. These factors can influence particle size

403

distribution, which was also not measured. Differences in particle size will impact the settling

404

and fraction of PM that may deposit in cracks and on other surfaces. Furthermore, BC particles

405

may deposit at higher or lower rates than PM2.5 overall, but this issue was not measured. Future

406

work should also seek to address the incorporation of activity patterns in real-world homes to

407

assess if multiple family members within a home create changes in air exchange rate estimates.

408

Seasonality may also play a role with certain fuel types being more prevalent and is an area

409

requiring further exploration as sampling took place during one season (Spring). Due to

410

extremely high PM concentrations, oversaturation for the BC analysis occurred, thus future

411

studies should consider utilizing methods with pumps set to lower flow rates (i.e., 1.5 LPM). In

412

addition, all data collected for BC analysis was conducted utilizing a simulated cooking event

413

(water boil test) and further assessment over the course of actual cooking events is needed to


20

Page 21 of 26


414

assess the generalizability of the mock house testing. A limitation of using CO as the tracer gas is

415

that the fire had to be extinguished so that impact of the cooking fire on AER could not be

416

determined. Finally, our testing was limited to one area and one stove type, thus results may not

417

extrapolate to other areas and stoves. Data are needed on different housing stock, stoves

418

(traditional and improved), and analysis is also needed to assess the impact chimneys may have

419

on exfiltration.

420 421

Author Contributions

422

The manuscript was written through contributions of all authors. All authors have given approval

423

to the final version of the manuscript. Specific contributions were as follows: SonejaŦ designed

424

the study, conducted primary data collection and sample processing, performed data analysis,

425

and was the main author of the paper; KhatryŢ assisted with primary data collection; YanĦ and

426

ChillrudĦ assisted with black carbon sample processing and analysis; CurrieroΤ and ZaitchikŤ

427

assisted with data analysis; BreysseŦ and Tielsch§ assisted in the design of the study, provided

428

oversight to data collection, and assisted with data analysis. All coauthorsŢĦΤŤŦ§ assisted with

429

writing of this paper.

430 431

Corresponding Author

432

Patrick N. Breysse, Ph.D.

433

Department of Environmental Health Sciences

434

Bloomberg School of Public Health

435

Johns Hopkins University


21


436

615 North Wolfe Street, Room E6628

437

Baltimore, MD 21205

438

Phone: 1-410-955-3608

439

Fax: 1-410-955-9334

440

[email protected]

Page 22 of 26

441 442

Funding Sources

443

Funding sources included a grant from The Environment, Energy, and Sustainability Institute at

444

Johns Hopkins, grant support from the National Institute of Environmental Health Sciences

445

(ES015558; ES003819), and the Thrasher Research Fund (02830-4).

446 447

Acknowledgements

448

We would like to thank NNIPS field research supervisors and staff, Steven LeClerq, Pramod

449

Sah, Samjhana Magar, Ana Rule, D’ann Williams, Jana Mihalic, Lance Wallace, Andrew

450

Persilly, and Chen Chen. This manuscript is dedicated to Dr. Alison Geyh, a mentor and

451

inspiration.

452

Supporting Information

453

An additional analysis examining mixing factors assessed if further adjustment was necessary

454

when determining air exchange rates. This information is available free of charge via the Internet

455

at http://pubs.acs.org/.


22

Page 23 of 26


456

Abbreviations

457

BC, black carbon; PM, particulate matter; PM2.5, particulate matter with aerodynamic diameter

458

of 2.5 micrometers; EF, exfiltration fraction; AER, air exchange rate; IGP, Indo Gangetic Plain

459 460

References

461 462

1.

Xu B, Cao J, Hansen J, Yao T, Joswia DR, Wang N, Wu G, Wang M, Zhao H, Yang W. Black soot and the survival of Tibetan glaciers. Proc. Natl. Acad. Sci. 2009;106(52):22114.

463 464

2.

Ramanathan V. Aerosols, Climate, and the Hydrological Cycle. Science 2001;294:21192124. doi:10.1126/science.1064034.

465 466

3.

Ramanathan V, et al. Atmospheric Brown Clouds Regional Assessment Report with Focus on Asia. 2008. Available at: http://tinyurl.com/68r7mpd.

467 468

4.

Schmidt CW. Black Carbon: The Dark Horse of Climate Change Drivers. Environ. Health Perspect. 2011;119(4):A172.

469 470

5.

Ramanathan V, Carmichael G. Global and regional climate changes due to black carbon. Nat. Geosci. 2008;1(4):221–227.

471 472

6.

Wallack JS, Ramanathan V. The Other Climate Changers: Why Black Carbon and Ozone Also Matter. Foreign Aff. 2009;88:105.

473 474

7.

WHO. WHO | Indoor air pollution and health. 2011. Available http://www.who.int/mediacentre/factsheets/fs292/en/. Accessed October 14, 2011.

475 476 477

8.

Bond TC, Streets DG, Yarber KF, Nelson SM, Woo JH, Klimont Z. A technology-based global inventory of black and organic carbon emissions from combustion. J Geophys Res 2004;109(D14):D14203.

478 479

9.

Adler T. Better Burning, Better Breathing: Improving Health with Cleaner Cook Stoves. Environ Health Perspect 2010;118(3):A124-A129.

480 481 482 483

10. USAID/ECO-Asia CDCP. Black Carbon Emissions in Asia: Sources, Impacts, and Abatement Opportunities. 2010. Available at: http://www.cleanenergyasia.net/library/black-carbon-emissions-asia-sources-impacts-andabatement-opportunities.

484 485 486 487

11. Anenberg SC, Schwartz J, Shindell D, Amann M, Faluvegi G, Klimont Z, JanssensMaenhout G, Pozzoli L, Van Dingenen R, Vignati E, Emberson L, Muller NZ, West JJ, Williams M, Demkine V, Hicks WK, Kuylenstierna J, Raes F, Ramanathan V. Global Air Quality and Health Co-benefits of Mitigating Near-Term Climate Change through Methane

at:


23


Page 24 of 26

488 489

and Black Carbon Emission Controls. Environ. Health Perspect. 2012;120(6):831-839. doi:10.1289/ehp.1104301.

490 491 492

12. Sasser E, et al. US EPA Report to Congress on Black Carbon: Department of the Interior, Environment, and Related Agencies Appropriations Act, 2010. US EPA; 2012. Available at: http://www.epa.gov/blackcarbon/2012report/fullreport.pdf.

493 494

13. Rehman IH, Ahmed T, Praveen PS, Kar A, Ramanathan V. Black carbon emissions from biomass and fossil fuels in rural India. Atmos Chem Phys Discuss 2011;11:10845–10874.

495 496 497 498

14. Balakrishnan K, Sankar S, Parikh J, Padmavathi R, Srividya K, Venugopal V, Prasad S, Pandey VL. Daily average exposures to respirable particulate matter from combustion of biomass fuels in rural households of southern India. Environ. Health Perspect. 2002;110(11):1069.

499 500 501

15. Bruce N, Perez-Padilla R, Albalak R. Indoor air pollution in developing countries: a major environmental and public health challenge. Bull. World Health Organ. 2000;78(9):1078– 1092.

502 503

16. Reid H, Smith K. Indoor Smoke Exposures from Traditional and Improved Cookstoves: Comparisons among Rural Nepali Women. Mt. Res. Dev. 1986:293-303.

504 505 506

17. Begum BA, Paul SK, Dildar Hossain M, Biswas SK, Hopke PK. Indoor air pollution from particulate matter emissions in different households in rural areas of Bangladesh. Build. Environ. 2009;44(5):898-903. doi:10.1016/j.buildenv.2008.06.005.

507 508

18. Smith KR. Air Pollution and Rural Biomass Fuels in Developing Countries: a Pilot Village Study in India and Implications for Research and Policy.

509 510

19. Venkataraman C. Residential Biofuels in South Asia: Carbonaceous Aerosol Emissions and Climate Impacts. Science 2005;307(5714):1454-1456. doi:10.1126/science.1104359.

511 512 513

20. National Risk Management Research Laboratory RTP, NC. Greenhouse Gases From SmallScale Combustion Devices In Developing Countries: Phase IIA Household Stoves in India. 2000.

514 515 516

21. MacCarty N, Ogle D, Still D, Bond T, Roden C. A laboratory comparison of the global warming impact of five major types of biomass cooking stoves. Energy Sustain. Dev. 2008;12(2):56–65.

517 518

22. Smith KR. Biofuels, Air Pollution, and Health: A Global Review. New York: Plenum Press; 1987.

519 520

23. Williams PRD, Unice K. Field Study of Air Exchange Rates in Northern Highlands of Peru. Environ. Forensics 2013;14(3):215-229. doi:10.1080/15275922.2013.814182.

521 522

24. McCracken JP, Smith KR. Emissions and efficiency of improved woodburning cookstoves in Highland Guatemala. Environ. Int. 1998;24(7):739–747.


24

Page 25 of 26


523 524 525 526

25. Salje H, Gurley ES, Homaira N, Ram PK, Haque R, Petri W, Moss WJ, Luby SP, Breysse P, Azziz-Baumgartner E. Impact of neighborhood biomass cooking patterns on episodic high indoor particulate matter concentrations in clean fuel homes in Dhaka, Bangladesh. Indoor Air 2014;24(2):213-220. doi:10.1111/ina.12065.

527 528 529 530

26. Tielsch JM, Katz J, Zeger SL, Khatry SK, Shrestha L, Breysse P, Checkley W, Mullany LC, LeClerq SC. Designs of Two randomized, community-based trials to assess the impact of alternative cookstove installation on respiratory illness among young children and reproductive outcomes in rural Nepal. BMC Public Health 2014;14(1):1271.

531 532 533

27. Torrey CM, Moon KA, Williams DAL, Green T, Cohen JE, Navas-Acien A, Breysse PN. Waterpipe cafes in Baltimore, Maryland: Carbon monoxide, particulate matter, and nicotine exposure. J. Expo. Sci. Environ. Epidemiol. 2014. doi:10.1038/jes.2014.19.

534 535

28. Soneja S. Preliminary Data Analysis on Housing and Fuel Characteristics for Sarlahi District, Nepal. 2011.

536 537

29. Sherman MH. Tracer-gas techniques for measuring ventilation in a single zone. Build. Environ. 1990;25(4):365–374.

538 539

30. ACGIH. Industrial Ventilation: A Manual of Recommended Practice. 2010th ed. Cincinnati, OH: ACGIH; 2010.

540 541 542 543

31. Moore R. Tracer Gas Testing Applications for Industrial Hygiene Evaluations. EHS Today 2004. Available at: http://ehstoday.com/industrial_hygiene/instrumentation/ehs_imp_36992/. Accessed October 27, 2011.

544 545 546 547 548

32. Samfield M. Project Summary: Air Infiltration Measurements Using Tracer Gases: A Literature Review. Research Triangle Park, NC: United States Environmental Protection Agency; 1995. Available at: http://nepis.epa.gov/Exe/ZyPDF.cgi/30003X2I.PDF?Dockey=30003X2I.PDF. Accessed April 3, 2014.

549

33. Bailis R, Ogle D, MacCarty N, Still D. The Water Boiling Test (WBT) Version 3.0. 2007.

550 551 552 553

34. Yan B, Kennedy D, Miller RL, Cowin JP, Jung K, Perzanowski M, Balletta M, Perera FP, Kinney PL, Chillrud SN. Validating a nondestructive optical method for apportioning colored particulate matter into black carbon and additional components. Atmos. Environ. 2011;45(39):7478-7486. doi:10.1016/j.atmosenv.2011.01.044.

554 555 556

35. Shrestha G, Traina SJ, Swanston CW. Black Carbon’s Properties and Role in the Environment: A Comprehensive Review. Sustainability 2010;2(1):294-320. doi:10.3390/su2010294.

557

36. Yan B. Personal Communication: Validity of Optical Method. 2014.


25


Page 26 of 26

558 559

37. Rau JA. Composition and Size Distribution of Residential Wood Smoke Particles. Aerosol Sci. Technol. 1989;10(1):181-192. doi:10.1080/02786828908959233.

560 561 562

38. Just B, Rogak S, Kandlikar M. Characterization of Ultrafine Particulate Matter from Traditional and Improved Biomass Cookstoves. Environ. Sci. Technol. 2013:130319140434009. doi:10.1021/es304351p.

563


26

Determining Particulate Matter and Black Carbon Exfiltration Estimates

Recommend Documents