Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana (REACCTING): study rationale and protocol
Dickinson et al. BMC Public Health
Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana (REACCTING): study rationale and protocol
Katherine L Dickinson 0 1
Ricardo Piedrahita 0
Evan Coffey 0
Isaac J Rivera 1
Didier Muvandimwe 0
Vanja Dukic 0
Mary H Hayden 1
Adoctor Victor Abisiba
Yolanda Hagar 0
Nicholas Masson 0
Andrew Monaghan 1
Daniel F Steinhoff 1
Yueh-Ya Hsu 0
Rachael Kaspar 0
Bre'Anna Brooks 0
Michael Hannigan 0
Abraham Rexford Oduro
Christine Wiedinmyer 1
0 University of Colorado - Boulder , Boulder, CO 80309-0427 , USA
1 National Center for Atmospheric Research , PO Box 3000, Boulder, CO 80307 , USA
Background: Cooking over open fires using solid fuels is both common practice throughout much of the world and widely recognized to contribute to human health, environmental, and social problems. The public health burden of household air pollution includes an estimated four million premature deaths each year. To be effective and generate useful insight into potential solutions, cookstove intervention studies must select cooking technologies that are appropriate for local socioeconomic conditions and cooking culture, and include interdisciplinary measurement strategies along a continuum of outcomes. Methods/Design: REACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) is an ongoing interdisciplinary randomized cookstove intervention study in the Kassena-Nankana District of Northern Ghana. The study tests two types of biomass burning stoves that have the potential to meet local cooking needs and represent different rungs in the cookstove technology ladder: a locally-made low-tech rocket stove and the imported, highly efficient Philips gasifier stove. Intervention households were randomized into four different groups, three of which received different combinations of two improved stoves, while the fourth group serves as a control for the duration of the study. Diverse measurements assess different points along the causal chain linking the intervention to final outcomes of interest. We assess stove use and cooking behavior, cooking emissions, household air pollution and personal exposure, health burden, and local to regional air quality. Integrated analysis and modeling will tackle a range of interdisciplinary science questions, including examining ambient exposures among the regional population, assessing how those exposures might change with different technologies and behaviors, and estimating the comparative impact of local behavior and technological changes versus regional climate variability and change on local air quality and health outcomes. Discussion: REACCTING is well-poised to generate useful data on the impact of a cookstove intervention on a wide range of outcomes. By comparing different technologies side by side and employing an interdisciplinary approach to study this issue from multiple perspectives, this study may help to inform future efforts to improve health and quality of life for populations currently relying on open fires for their cooking needs.
Cookstoves; Household air pollution; Global health; Study protocol; Randomized intervention study
Biomass-burning cookstoves are widely recognized as a
significant source of pollutants impacting human health,
local and regional air quality, and global climate change.
Worldwide, it is estimated that three billion people use
biomass- and coal-burning fires to cook and heat their
homes . This widespread practice contributes to
several health, environmental, and social problems. Nearly
four million people are estimated to die prematurely
each year due to household air pollution from biomass
burning . Exposure to household air pollution from
burning biomass fuels has been linked to significant
morbidity and mortality from acute lower respiratory
infections in children , as well as chronic obstructive
pulmonary disease  and increased rates of
cardiovascular disease [5,6] among women, along with other
health issues . Local environmental impacts include
deforestation and land cover change associated with
fuelwood collection . Gathering fuel is also a time
consuming activity, particular in arid regions such as our
study area in Northern Ghana. This burden falls largely
on women and children, contributing to time poverty
 that, along with the health burden that falls
disproportionately on women (and children particularly female
children), limits opportunities for female empowerment
and development. In addition, cookstove emissions of
greenhouse gases, particulate black carbon, and other air
pollutants contribute to degraded air quality and global
climate change (e.g., [10-12]).
Despite growing attention to the wide-ranging negative
impacts of cooking with biomass, efforts to better
understand and find solutions to this problem have faced a
number of common challenges. These challenges include
matching stove technologies to local socioeconomic
conditions and cooking culture, and designing
comprehensive measurement strategies to effectively diagnose
reasons for the success or failure of a given intervention
along a continuum of steps in the causal chain from a
stove intervention to outcomes of interest. Together,
these challenges call for integrated, interdisciplinary
approaches to the design of cookstove studies and policies.
Challenges in stove selection and adoption
Technologically, the problem of open fire cooking using
solid fuels seems relatively straightforward to address: a
wide variety of improved cookstoves and cleaner fuel
sources exist that are more efficient and can reduce air
pollutant emissions. Yet efforts to make these
technologies available in areas of need throughout the world have
often failed to achieve their intended results [2,13].
Human behaviors specifically, acceptance and use of
improved stoves are key to the success of any cookstove
Two key and related challenges are locally appropriate
stove selection and promotion by those introducing new
technologies, and sustained stove adoption and use
among target populations . By stove selection, we
are referring to the processes of selecting the right
technology (or mix of technologies) that is most likely to
meet the needs of the target population while achieving
meaningful reductions in negative health and
environmental impacts. Some argue that only the cleanest, most
advanced, and usually imported cooking technologies
should be promoted, since these have the highest
probability of having meaningful impacts on health and
environmental outcomes. Others contend that introducing
affordable, feasible, locally-produced cookstoves that are
more efficient than open fires and more aligned with the
unique cooking practices and needs of a given context
can be an effective first step toward moving households
up the technology ladder in the long run [20,21].
Conceptually, the stove or energy ladder model is
rooted in a neo-classical understanding of energy use
that implies cleaner fuel usage with rising socioeconomic
status . Typically, this model also implicitly assumes
that households rely on a single source of cooking
energy at any given time. Empirically, however, studies
have found that rather than moving linearly up this
energy ladder in a step-by-step fashion, households often
rely simultaneously on multiple types of fuel and
cooking technologies to meet their cooking needs [15,18].
This energy or technology stacking allows households
greater flexibility: they can use different types of stoves
for different purposes, or alternate among different fuels
(essentially moving both up and down the ladder)
depending on availability and cost [15,18,22-24]. Of course,
these two models may both be correct in some respects;
while households may continue to use a mix of
technologies, it is possible that the technologies that
comprise the cooking stack may become cleaner over time.
In light of this view of how energy transitions occur, it
is perhaps not surprising that many stove intervention
studies have observed that households continue to use
their traditional stoves alongside improved stoves
[14,17,18,22,24]. Furthermore, the extent to which new
stoves are folded into the technology stack and can
ultimately displace traditional cooking methods (leading to
cleaner kitchens overall) depends heavily on how well
suited these new technologies are to local culture and
cooking practices . For example, a study of cooking
practices in Guatemala showed that more affluent
households (receiving remittances from migrant family
members) had liquid petroleum gas (LPG) stoves but
continued to rely on wood-burning stoves for most of
their cooking needs because these stoves were better
suited to the preparation of staple food items (beans, corn,
and tortillas) . Ultimately, without incorporating
traditional cooking practices into the design process, even
low-cost stoves are unlikely to be used .
Assessing stove intervention success
Stove intervention studies are motivated by the large
potential impacts of improved stove use on several final
outcomes, from public health to environmental quality.
However, there are a number of intermediate steps
linking the introduction of a new technology to these final
outcomes. The causal chain connecting a stove
intervention to three key endpoints, health burden, local to
regional air quality, and climate change, is shown in
Figure 1. Understanding this causal chain, and where it
may break down, is essential to learning about what
makes a particular intervention (in)effective, and how
future endeavors can improve upon existing efforts.
The first step in the causal chain involves cooking
behavior, specifically stove adoption and use among
households. The previous section detailed several possible
barriers to stove adoption; use of new stoves is not
guaranteed (even when they are distributed for free), and
thus concerted efforts to measure stove use are a key
component of an integrated measurement strategy. Use
can be measured through surveys, which ask
respondents about their cooking practices, as well as by
instruments such as stove use monitors (SUMs) . Each of
these measurement approaches has its strengths and
weaknesses. Surveys are subject to recall and social
desirability bias (i.e., respondents may be reluctant to
admit that they have not used new stoves provided by
researchers), but allow collection of detailed, qualitative
information on why stoves may or may not be used .
Meanwhile, SUMs allow cooking events to be estimated
from a time series of stove temperature measurements,
but require substantial effort in characterization of the
temperature monitor/stove system. A combination of
methods may thus be optimal to fully characterize stove
use, yet few studies to date have paired comprehensive
survey-based measurements with SUMs data collection.
One notable exception is a study that examined a
combined water filter and improved cookstove intervention in
Rwanda using surveys and electronic sensors to measure
use of both technologies . In this case, households
survey responses overreported the number of weekly
cookstove uses measured by sensors by about 40%.
The next step in the chain involves the quantification
of cooking emissions from the improved and traditional
cooking methods. Many studies have measured real-time
biofuel cooking emissions in laboratory settings using
Water Boiling Tests or WBTs (e.g., [29,30]), but fewer
have done field-based measurements [31-35]. Emission
measurements in the field are essential since many key
factors may vary between the lab and field setting (e.g.,
type and amount of fuels used).
Household air quality and personal exposure
measurements shed light on the next steps in the causal chain.
These measurements characterize the impact of changes
in cooking technologies on pollutant concentrations in
and around the home, and assess whether meaningful
reductions in peoples exposure to these pollutants have
Figure 1 Causal pathways linking introduction of clean cookstoves to outcomes of interest.
occurred. To measure these impacts, studies have most
commonly monitored concentrations and personal
exposures to carbon monoxide (CO) and particulate matter
less than 2.5 micrometers in diameter (PM2.5).
Shortterm CO exposure is associated with respiratory and
cardio-vascular morbidity, as well as mortality, while
long-term CO exposure has been associated with
negative birth outcomes, developmental effects, and central
nervous system effects, among others [7,36]. Personal
CO exposure has been measured in the field with
relatively cheap passive diffusion tubes for integrated
concentrations, which require refrigeration, have moderately
high uncertainty, exhibit batch-to-batch variability, lack
the ability to capture peak exposures (in real-time)
during cooking events, and have limits on length of
deployment as maximum deployment measurement periods
fall between one and two days [37-40]. Field studies
monitoring PM2.5 exposure have faced logistical
difficulties of obtaining subjects time-activity information and
sampling continuously for longer than 24 hours.
Cumulative PM2.5 filter sampling misses diurnal variations,
and shorter sample durations lead to higher
withinhome variability  contributing to increased
uncertainty in exposure estimates and intervention effects.
Recent advances in monitoring and battery technology, as
well cost reductions, have made it possible to measure
PM2.5 at longer durations with smaller and quieter
equipment. However, sometimes study participants are unable
or unwilling to wear the air monitoring equipment as
intended. Such breaches in protocol can lead to erroneous
conclusions about personal exposure; therefore, it is
essential to measure compliance.
This set of intermediate impacts is potentially linked to at
least three key outcomes: health burden, local to regional
air quality, and climate change. Given the large burden of
disease that is linked to household air pollution, many
cookstove studies conducted to date have focused on
assessing health burdens using a large number of different health
indicators. These include self-reported symptoms such as
eye irritation and headaches , pulmonary function and
respiratory symptoms , blood pressure and
cardiovascular health , biomarkers of exposure to
smokerelated compounds from urine , and biomarkers of
systemic inflammation linked to smoke exposure from
blood samples .
Emissions from cooking activities are not only
important as household air pollutants and immediate personal
exposures, but can also have detrimental impacts on air
quality and climate at regional and global scales.
Measurements of air pollutants, such as concentrations of
PM and trace gases, can enable the quantification of the
impact of cooking and biomass burning on regional air
pollutant concentrations. For example, daily PM2.5 filter
samples have been previously collected in Navrongo,
Ghana from 20092010 . Using source
apportionment techniques, observed particulate elemental carbon
(EC) and organic carbon (OC) and speciated elements
were used to identify six sources of PM2.5, namely
twostroke engine combustion, diesel combustion, gasoline
combustion, soil, biomass combustion, and road dust.
After dust, biomass combustion was found to be second
largest contributor to ambient PM concentrations in
Navrongo . Regional PM2.5 monitoring has also been
performed in Accra [47,48], Nigeria , Ouagadougou,
Burkina-Faso , Kenya , and Cairo .
To quantify larger-scale climate and air quality
impacts, there have been several efforts to measure [33,53]
and model the emissions from cooking activities around
the globe (e.g., [35,54,55]). The contribution of emissions
from cooking to the overall emissions burden of many
important air pollutants, including particulate black
carbon, is very large in many areas of the world. Further,
these emissions can impact regional air quality and
influence regional and global climate (e.g., ).
Individual steps in the described causal chain (Figure 1)
have received varying amounts of attention in
observational and intervention studies completed to date.
Table 1 summarizes the types of measurements and
analysis methods that have been included in some of
the larger published stove intervention studies. One
observation from this review is that some areas (e.g.,
exposure; health outcomes) have received considerably
more attention than others (e.g., field-based emissions
measurements, larger-scale climate and air quality).
Two studies, the Patsari stove interventions in Michoacan,
Mexico, and the Surya study in India (see Table 1 for
citations), did incorporate measurements across all of the
categories included in Table 1. However, there are important
limitations in some of the measurements in both studies.
For example, the Patsari study did not include any
electronic stove use monitors, thus limiting the ability
to quantify actual stove usage. Further, to date, the
focus of the Surya study has been on black carbon
emissions and this group has yet to include personal
exposure to other pollutants or objectively measured
The REACCTING (Research on Emissions, Air
quality, Climate, and Cooking Technologies in Northern
Ghana) study was specifically designed to include
indepth measurements along each step in the causal chain
depicted in Figure 1. REACCTING is an ongoing
interdisciplinary randomized controlled cookstove
intervention study in the Kassena-Nankana District of Northern
Ghana. The remainder of the paper details the study
protocol and methodology. Results of this study are
expected to generate novel insights regarding pathways
towards improving public health and environmental quality
in this region and beyond.
Table 1 Summary of measurements included in prior randomized cookstove intervention studies
Types of Measurements Included
Collection of studies involved
interventions with 500+
households using plancha
improved stoves, gas stoves,
and traditional (open fire)
Collection of studies involved
interventions with 600
households using Pastari (ICS)
and traditional (open fires)
Two Intervention Programs; Juntos
National (A), Barrick Gold Corp. (B)
with 57+ households using
improved custom brick stoves and
traditional (open fire) group for
Quarterly stove use
reporting stove use
Questionnaire & time
use diaries at enrollment
and 3 weeks after stove
566 households in three villages; Surveys measuring
EcoZoom Dura stove vs traditional. acceptability and stove
Intervention also included water use conducted monthly
filters for five months; SUMs on
subset of stoves
Collection of studies involved
interventions with 480+
households using a variety
of improved biomass stoves,
and traditional (mud/open fire)
Intervention of 500+ households Surveyed participants on
using constructed mud/brick stove cooking activity and fuel
and traditional (open fire) control wood gathering, SUMs
Price experiment that tested 2
nontraditional cookstoves over
Surveys used to access
perceptions of stoves,
Cookstove Sensing System
(WiCS) (in development)
Not measured in field
Field cooking tests (KPTs,
WBTs and CCTs) and lab
testing (WBT) in addition
to GHG emissions
No field measurements in Not done
Rwanda intervention study, in this
but field-based emissions study, but
testing using same stove planned
conducted in Uganda  for
BC (Concentrations only)
Blood pressure, acute
Table 1 Summary of measurements included in prior randomized cookstove intervention studies (Continued)
Not Measured CO
2,651 household intervention study Three surveys in four years
subsidizing construction of used to gauge stove usage,
inexpensive, locally-made mud cooking activity, fuel
stoves. Households responsible for expenditures, and perceptions
providing mud, labor, about their efficacy
and small payment for masonry
and maintenance. Public lottery
randomly assigned order of
construction and distribution.
200 household intervention
study. Two types of biomass
Methods and study design
The REACCTING study is located in the
KassenaNankana (K-N) District in Northern Ghana (Figure 2).
This area has been described in detail by Oduro et al.
. Briefly, the district has a population of about
156,000 and an area of 1,657 km2. The climate in this
region is generally hot and arid. A single rainy season
lasts from approximately May to October, with more
consistent rains occurring between June and September
(Figure 3). The Harmattan, which typically occurs from
late November through January, brings steady winds
from the north with Saharan dust. This begins a dry
season that continues until May. The K-N District is
located in the northern savanna vegetation zone of Ghana
dominated by woody shrubs and grassland. Much of the
land is used for subsistence agriculture, with the
dominant crop being millet.
Figure 2 Map of study area with cluster and health clinic locations.
Figure 3 Monthly rainfall and temperature in Navrongo.
The population of the K-N District is fairly homogeneous
culturally. According to data from a district-wide Health
and Demographic Surveillance Survey (HDSS) , about
80% of households in the district are located in rural areas,
while 20% live in areas classified as urban. Among rural
households, 88% report using biomass (wood or agricultural
waste) as their main cooking fuel, while another 9% rely
primarily on charcoal, and only about 3% of households cook
primarily with gas or electricity. The traditional cooking
method in this area is a three-stone open fire, and cooking
is done in both indoor and outdoor areas. Ghana has one
of the highest deforestation rates in Africa with the
countrys forest an estimated quarter of its original size .
REACCTING builds upon a successful project done in
2010 involving a collaboration between the Navrongo
Health Research Centre (NHRC), the National Center
for Atmospheric Research (NCAR), and the University
of Colorado-Boulder (CU). In that project, 222
households in Northern Ghana were surveyed to assess
knowledge, attitudes, and practices as well as cost of illness
associated with meningitis [70,71]. Motivated by studies
suggesting a possible link between indoor cooking and
meningitis [72,73], a follow-on pilot was conducted in
2011 to introduce efficient Envirofit G-3300 cookstoves
to five families to explore acceptability and barriers to
use in northern Ghana. Results from this pilot provided
initial evidence of local acceptance of improved cooking
technologies. Households that received the stoves were
satisfied with their performance, finding that they were
able to cook food faster and with less fuel than with
their traditional open-fire stoves. Some problems were
also reported with the stoves, mainly involving their
stability when cooking a viscous porridge that is a local
staple called Tuo Zaafi (TZ). The research team also
observed that many households cooked with multiple
stoves, including multiple three stone stoves (e.g., an
indoor and an outdoor stove) as well as charcoal stoves.
Building on this pilot, three additional types of
woodstoves (EZY rocket stove, Philips gasifier stove, Gyapa
rocket stove) were distributed to a total of 12 rural
households in the K-N District between November of
2012 and March of 2013. Feedback provided by these
households informed the subsequent design of the
cookstove intervention and assessment that are described
The REACCTING study includes 200 households for the
stove intervention. This study sample was randomly
selected from the population of the K-N District that met
our study criteria using data (described below) from the
district-wide Health and Demographic Surveillance Survey
(HDSS)  and a cluster random sampling methodology.
The social structure in this region is such that groups of
related households live in connected compounds. Each
compound is given a unique HDSS ID code, and this code
is painted onto the wall of the compound and acts as a
compound address. These codes consist of three letters
(the cluster ID), the first of which denotes the clusters
geographical region within the district (North, South, East,
West, and Central), and a two-digit compound number.
Household IDs are then assigned within each compound.
The target population for this intervention was rural
households in the K-N District that use biofuels (wood,
animal waste, and crop residue) as their main cooking
fuel sources. Within these rural households, we focused
on those individuals in closest proximity to cooking
activities: women and young children. Thus, we used a set
of cluster- and household-level criteria to generate a
subpopulation of eligible households from which to
randomly draw our study sample. To generate this
subpopulation, we first eliminated all clusters in the primarily
urban Central cluster, as well as other clusters in which
more than 25% of households were classified as urban in
the HDSS. For logistical reasons, we also eliminated a
small set of clusters that were deemed to be difficult for
interviewers to access. Since the intervention was rolled
out at the cluster level, as described in more detail below,
we also dropped all clusters that had less than 10 eligible
households after all of the household-level eligibility
criteria were applied. At the household level, to ensure a
relatively uniform, rural sample of households, households
that did not list biofuels as their main cooking fuel and
households that did not use boreholes as their main water
source were eliminated. Finally, we included only
households with at least one child under five and one woman
between the ages of 18 and 55.
Using this subpopulation, sample selection proceeded
in two phases. First, we randomly selected 25 clusters
using population weighting to determine the number of
clusters selected per region: five clusters were randomly
selected from the East, six from the North, eight from
the South, and six from the West (Figure 2). Next, ten
households (eight primary households and two alternates
to be used if the primary households could not be
enrolled) were randomly selected from the population of
eligible households in each of these clusters. Since cooking
duties may be shared within compounds and emissions
from one households cooking could affect exposure and
health outcomes of other households within the
compound, we included a maximum of one household per
compound. In cases where there were multiple eligible
households in a compound, we randomly selected only
one for inclusion in the sample. Given this sampling
methodology, our study sample can be said to be representative
of the subpopulation of the K-N District that meets our
eligibility criteria: rural, uses biofuels as their main
cooking source, and has women and young children in the
household. Overall, this subpopulation from which our
sample was selected includes 59% of all clusters in the
district (194 out of 331) and about 20% of all households in
the district (5,918 out of 29,403).
The study protocol was reviewed and approved by the
Human Subjects Committee at the National Center for
Atmospheric Research and the Institutional Review Board
of the Navrongo Health Research Centre. Informed
consent was obtained from all study participants prior to any
data collection. Oral consent was obtained for the
household survey, personal exposure monitoring, and household
environmental monitoring, and written consent was
obtained for the subclinical health measures
(anthropometrics and blood spots). (See Assessment of intervention
impacts subsection for full description of these
measurement techniques). For the measurements conducted with
children (personal exposure and subclinical health
measures), consent was obtained from each childs parent or
A series of community entry activities were undertaken by
the research team in order to inform community members
about the research project and to obtain local leaders
permission to carry out the proposed research activities.
NHRC investigators with extensive knowledge of the local
context and norms led this effort, which involved meetings
in all of the 25 clusters selected for inclusion in the study.
These meetings involved local chiefs, community elders,
opinion leaders, and womens groups. These meetings also
served to address any concerns participants may have had
and to foster trust in the studys objectives and fairness.
The selection of cookstove technologies for this study
was guided by a number of considerations. Based on
extensive feedback from households in the K-N district
who tested several stove models during the pilot phase
(20122013), the Philips Smokeless Woodstove and the
Gyapa Wood Stove (Figure 4) were deemed to be
potentially promising technologies for this population. The
former is a gasifier stove produced in Lesotho. This stove
is visually perceived as high-tech, requires power to
perform properly, and has been observed to be a low emitting
technology, Tier 4 stove, during lab testing . The latter
Figure 4 Traditional and improved stove technologies being
compared in the REACCTING study, shown with Stove Use
Monitors (SUMs) attached. Top left: traditional three-stone stove.
Top right: traditional charcoal stove. Bottom left: Philips Smokeless
Stove, Made in Lesotho (Southern Africa), Cost: ~US$125. Bottom right:
Gyapa Wood-Burning Stove. Made in Accra. Cost: ~US$15-25.
was designed and locally manufactured specifically to fit
the cooking needs of the study population; this process is
described below. These two stoves also represented two
distinct rungs in the stove ladder. On the lower rung,
the Gyapa stove is locally produced, affordable, and more
fuel efficient than three stone fires, though not expected
to drastically reduce cooking emissions. The Philips stove
represents a higher-rung stove: it is widely believed to be
among the cleanest biomass-burning stoves available and
has been used in other intervention studies (e.g., http://
www.projectsurya.org/). The Philips stove is also
substantially more expensive than the Gyapa stove and must be
imported into Ghana. Comparing these two stoves side by
side in the same population thus presents an opportunity
to generate novel data to inform the international debate
between those advocating incremental versus
transformative approaches to tackling the cookstove challenge.
The Gyapa Wood Stove was specifically designed for
use by populations in the Northern Regions of Ghana by
Relief International/Gyapa Enterprises (RI/Gyapa). RI is a
Los Angeles-based global humanitarian organization that
employs a team of 2,000 relief and development
professionals working to bridge the gap between immediate
emergency relief and long-term community development.
As part of its Social Enterprise program in Ghana, RI
supported production of the Gyapa Fuel Efficient Cookstove,
a locally produced and distributed improved cookstove
sold primarily for charcoal burning households across
Ghana. The Gyapa charcoal stove is the most popular
improved cookstove in Ghana and is comprised of the largest
improved cookstove production industry on the continent.
Since Gyapas inception in 2002, over 600,000 stoves have
been locally produced and sold in Ghana.
RI/Gyapa joined the REACCTING study team after
the project was funded, at the stage of the project when
stove technologies were being evaluated for inclusion in
the study design. They subsequently designed and
produced the Gyapa Wood Stove to fit the needs of rural
populations in the north of Ghana, and also provided
input and training on the stove distribution and education
components of the project. To develop the Gyapa Wood
Stove, RI/Gyapa developed and tested several prototypes
in Accra as well as in Navrongo to determine user
preference, applicability, required durability and suitability
for the study. A similar model was used in a past
intervention study in Accra, and saw significant decreases in
kitchen CO and PM2.5 levels . Multiple iterations of
test stove designs were produced and tested with wood
burning communities in Accra. Tests included
respondent likes/dislikes about the models, perceived fuel usage
and smoke emissions as well as eye and throat irritation,
cooking time, and comfort while cooking. Stove tests
also used similar sized pots for cooking as are used in
regions of Northern Ghana. Respondents perceived the
air quality in the cooking areas as better and reported
less smoke emission and less exposure to heat associated
with the use of the improved wood stove prototypes as
compared to traditional wood stoves. Stove manufacturers
used this feedback along with knowledge of combustion
efficiency and local supplies and skills to generate a final
The final prototype of the Gyapa Wood Stove included
a combustion chamber, often called a rocket-stove design,
with a ceramic liner on the inside and an outer liner of
insulation and saw dust to increase heat retention. The
additional insulation also creates a heat barrier that
reduces heat on the external parts of the stove to prevent
burns when handling the stove. The Gyapa Wood Stove
was produced by contracted ceramist and metal artisans
who are a part of the Gyapa network. The producers of
the woodstove model, as beneficiaries of improved stove
models themselves, also brought intimate knowledge of
Ghanaian cooking habits and cultures, which supported
the design process.
In addition to designing and manufacturing the Gyapa
Wood Stove, RI/Gyapa worked with the study team to
design and produce a pot support structure for the Philips
stoves (Figure 4). These stands were made of rebar and fit
around the Philips without modifying its design or
function. The stands provide more stability and enable the
accommodation of larger pots in order to make it more
culturally appropriate for local cooking practices.
Stove intervention design
The stove intervention of the REACCTING study
includes four different intervention arms: Group A
received two Gyapa stoves, Group B received two Philips
stoves, Group C received one of each type of stove, and
Group D serves as the control for the duration of the
study, but will receive their choice of stove at the
conclusion of the study. Stove stacking (i.e., households
using new cookstoves alongside traditional cooking
methods) had been observed in prior studies and we had
earlier observed use of multiple stoves among
households in the study area. Thus, two stoves were provided
to each intervention household to increase the
probability that households would begin to substitute away from
traditional stoves rather than simply adding a new stove
to their cooking technology mix. Because households
may prefer using different types of stoves for different
purposes (e.g., cooking TZ with the Gyapa stove but rice
or soup with the Philips), one study group (Group C)
has been provided one of each stove.
Small meetings involving study participants from one
or two clusters (816 study participants) were used to
educate participants about the two new stove
technologies, inform households about the study design and
objectives, randomize households into different treatment
groups, and distribute the stoves. These meetings were
held in November-December, 2013, between one and
five days after the study households were initially
contacted for the studys baseline survey (described in detail
in the next section). Meetings were held at a central point
within the cluster such as a school or a market. A
representative from each household in the cluster attended the
meeting; usually, this was the survey respondent (primary
cook). However, if the survey respondent was not
available, another household member attended in her place.
Education and outreach are essential to ensuring
takeup and appropriate use of any new technology. As such,
several steps were taken to ensure that participants were
given accurate information about the different stoves,
including how and why to use them. Retired female nurses
from the K-N District, who spoke the local languages
and were known and trusted by community members,
were enlisted as stove ambassadors. These ambassadors
and other members of the stove distribution teams were
trained in stove use, including the best way to feed the
stoves with fuel wood in order to increase the stoves
thermal performance and reduce smoke, as well as
effective outreach by our partners from RI/Gyapa, who
have extensive experience with stove promotion. In
other cookstove work, RI has found that marketing
stoves as an aspirational product has been more
successful than focusing on health impacts alone. That is,
uptake of stoves may be greater when they are promoted
as status symbols, or when other benefits such as
convenience, faster cooking times, and fuel/cost savings are
emphasized. While this message was delivered as part of
the training RI provided to the intervention team, the
particular make-up of the study team in this case (via
the Navrongo Health Research Centre) resulted in a
more health-focused message. We acknowledge this as a
potential limitation of our approach.
During the stove distribution meetings, the
ambassadors and stove distribution team members led a
demonstration of both types of stoves and gave participants the
opportunity to inspect the stoves and ask questions.
During the meeting, team members also explained the
study design to participants, including the fact that
different households would receive different types of stoves
so that the research team could assess which stove or
stoves worked best, and that some households would
not receive new stoves until the conclusion of the study
so that researchers could compare what happened in
households using new stoves with those using traditional
stoves. Participants were told that these households would
have their choice of stoves at the end of the study.
Following the stove demonstration and explanation of
the study, each participant drew a slip of paper with a
letter (A, B, C, or D) representing their intervention group.
For each cluster of eight households, two households were
assigned to each of the four intervention arms.
Participants in Groups A, B, and C received their stoves at the
meetings, and all participants were given matches and a
pair of iron bars for bracing pots while cooking. While we
considered conducting the randomization electronically
prior to the meetings, we ultimately decided that having
the participants draw their groups themselves during the
meeting increased the transparency of the randomization
process. Indeed, following the meeting, interviewers
reported that households that selected into the D group
expressed disappointment, but that they saw the process
as fair and legitimate and remained committed to being a
part of the study.
Within a week following the initial stove distribution,
stove ambassadors and other team members visited
households to provide additional training opportunities
on appropriate usage as well as to answer questions; the
objective was to ensure that participants felt as
comfortable as possible with the use of their new stoves.
Assessment of intervention impacts
Several assessment methods are being used to measure
indicators at multiple points along the causal chain shown
in Figure 1.
Cooking behavior and cooking technologies are closely
linked; we cannot understand the real world impact of a
cooking technology if we do not understand how that
technology alters behavior. In all 200 households, a series
of detailed household surveys are being conducted to
assess cooking behaviors, among other important social,
economic, and behavioral outcomes. Surveys are
administered in the local languages of the district (Kasem or
Nankam) by native speakers of each language. A baseline
survey was conducted in all households prior to stove
distribution (Nov-Dec 13). This survey took approximately
one hour to complete, and measured household
composition and demographics, attitudes and priorities, cooking
behaviors, knowledge and perceptions of health and
environmental issues related to cooking practices, demand for
new stoves, and self-reported health symptoms. To assess
cooking behavior, the respondents gave detailed
information about the number and type(s) of stoves used, type(s)
of fuel used, types of food cooked, as well as who cooked
within their household. The baseline survey also included
a detailed economic choice experiment exercise aimed at
measuring demand for new cooking technologies and
willingness to pay for specific stove attributes such as smoke
reduction, reduced fuel use, and shorter cooking times.
Follow-up surveys were completed in March, May/June,
and August of 2014. The follow-up surveys are
substantially shorter than the baseline (approximately 20 minutes)
and focus primarily on cooking behaviors as well as
selfreported heath symptoms. Additional surveys are scheduled
for December of 2014 and April and November/December
of 2015. These longitudinal surveys will track use of the
different stoves (both old and new stoves) over time,
including differences across seasons. For example, we expect
to observe more indoor cooking during the rainy season.
Stove preference and willingness to pay responses will also
be compared over time to assess how these measures
change as a function of a households experience with the
Survey-based measurements allow respondents to
provide detailed information about a range of factors,
including rich qualitative information about why stoves
have been used or not. However, self-reported stove use
data are also subject to measurement error due to recall
bias as well as social acceptability bias (i.e., respondents
may not want to offend researchers by telling them they
have not used their new stoves). Thus, in addition to
reported stove use information gathered through the
household surveys for all 200 households, stove usage is
being monitored electronically in a randomly selected
subset of 35 study households from the four different
intervention groups. In this subset, Stove Use Monitors
(SUMs, Labjack Digit-TL) are attached to stoves and
continuously measure temperature, such that stove use
can be assessed by observing an increase in temperature
in excess of ambient. Placement of the SUMs was tested
in the lab prior to the study with the two intervention
stoves. In the intervention groups (Groups A, B, C), both
new stoves and the most-used traditional stove are
monitored. In the households of the control arm (Group D),
the two most-used traditional stoves are monitored.
One-minute data for each SUMs deployed in the field is
being collected every 34 months.
SUMs have been used previously to assess stove
adoption [26,75]. As others have noted , monitoring
cookstoves can be challenging due to variability in usage
behaviors and varying stove thermal mass, leading to
different temperature profiles. This is especially true of the
three-stone fires, as they often have slower heating and
cooling times, and the stone arrangements can vary
substantially . Cooking event detection and cooking time
estimates will be calculated using methods described by
Ruiz-Mercado et al.  and Simons et al. , as well as
methods developed using our own observational data.
Real-time cooking emissions are measured in-field using
a modified controlled cooking test (CCT) . We are
measuring 1520 samples from each of the three main
stove types (Gyapa, Philips, three-stone). To measure
the emissions, we developed a monitor (E-Pod, Figure 5)
similar to the portable emission measurement system
(PEMS) designed by Aprovecho Research Center .
Figure 5 E-Pod setup for measuring in-field stove emissions.
The E-Pod uses low-cost sensors to measure real-time
carbon monoxide (CO), carbon dioxide (CO2), nitrogen
oxide (NO), nitrogen dioxide (NO2), total volatile
organic compounds (TVOCs), temperature and relative
humidity. CO, NO, and NO2 are measured with
electrochemical sensors (Alphasense B4), with CO also
measured with a metal oxide (MOx) semiconductor sensor.
CO2 is measured using a non-dispersive infrared
(NDIR) sensor, and TVOCs are measured with a
photoionization detector (PID Silver, Baseline-Mocon).
Total particulate matter (PM) is collected on a quartz
fiber filter for subsequent analysis of elemental and
organic carbon (EC/OC) as well as organic molecular
markers. These analyses allow us to further understand
the chemical nature of the exposures as well as derive
the origin of the particles. For each household
selected, emissions are measured during the entire
cooking process, starting from 15 minutes prior to lighting
the fire and ending 15 minutes after the fire is out. A
typical emissions observational period takes 2 to
4 hours, depending on the type of meal that is being
cooked. One of the primary meals is the thick millet
flour porridge called TZ, which is prepared by
boiling water, adding flour, then simmering and stirring
vigorously until there is a dense smooth porridge.
This process typically takes 30 to 45 minutes. This
starchy staple is usually eaten with a vegetable soup.
Woody biomass is the most common fuel used in
the study area, with charcoal (charred woody
biomass) occasionally entering the mix. The biomass is
from different trees found in the area such as neem
(Azadirachta indica), sheanut (Vitellaria paradoxa),
and mango (Mangifera indica).
Personal exposure and household air quality
Personal exposure and household air quality are
measured in the participating households throughout the
study period. These specific measurements are made
approximately once per year in the participating
households, and for the 35 households outfitted with SUMs,
these measurements are made approximately four times
per year. The additional measurements coordinated with
the SUMs enable the characterization of the SUMs
relationship with exposure, as well as the within-household
To assess personal exposure to pollution from biomass
combustion during a households monitoring period,
realtime CO monitors (EL-USB-CO300, Lascar Electronics)
with a one-minute resolution are worn by the survey
respondent (primary cook), children under five, and as many
other household members as are willing. CO has
previously been explored as a surrogate for PM2.5 exposure
from biomass combustion [40,57,79]. CO is relatively
straightforward to quantify continuously and requires
fewer resources compared to PM2.5. Past work often used
adsorption tubes for integrated CO exposure assessment,
but recent advances in sensor technology have made it
possible to use real-time wearable electrochemical CO
monitors [40,80-82]. Such monitors are simple to operate
and have a long battery life. Electrochemical CO sensors
in general demonstrate low inter-sensor variability, and
moderately good zero and span stability. However, the
sensor dynamics, specifically response times to changes in
concentrations, have not been well quantified.
Adults wear the CO monitors around their necks
using a lanyard, and children below age eight are given
specially designed t-shirts with pockets sewn on the
lapel. Forty-eight hour monitoring periods are employed
to account for day-to-day variability. The monitoring
periods typically begin on Monday, with distribution of
CO monitors to four households of a given cluster, one
from each study arm. The monitors are then collected
on Wednesday and redistributed to a set of four
households from a different cluster, until Friday when they are
picked up for calibration over the weekend.
During each sampling period, one of the four
households is selected for supplementary measurements:
personal PM2.5 (only for participants over the age of four),
step counting with pedometers, and microenvironmental
monitoring in the cooking area. The personal PM2.5
monitors are worn in small backpacks or fanny packs.
These monitors collect particles on quartz filters using
an impactor (25 F-2-2.5, URG Inc.) and pump (Airlite,
SKC Inc.) for EC/OC and organic molecular marker
analysis. Resulting PM2.5 samples are integrated over the
48-hour monitoring period. In these focus households, a
microenvironment monitor called a G-Pod
(mobilesensingtechnology.com) is also used to measure CO, CO2,
and PM2.5 in the cooking area during the monitoring
period. The G-Pod CO measurement uses the same
electrochemical sensing principal as the Lascar CO
monitors. CO2 is measured with a low-cost NDIR sensor
(S200, ELT Corp.), and cumulative PM2.5 is collected
using a quartz fiber filter with a 2 liter per minute flow rate.
The G-Pod is placed one meter off the ground, and one
meter away from the most-used cookstove. In a subset of
these households, near-continuous PM and temperature
sensors (University of California at Berkeley Particle and
Temperature Sensors or UCB-PATS, ) are deployed as
well, to supplement the integrated PM2.5 data. Each
household sampling visit concludes with a short stove usage
survey, intended to identify those in the household who have
cooked, the stoves that were used, the meals that were
cooked, and the fuel used. Participants are also asked to
provide an estimate of the fuel that will be used the
following day. If an estimate is provided, the fuel is weighed and
moisture content is measured.
The relationship between microenvironmental air quality
and personal exposure is highly dependent on individuals
time-activity patterns, namely when and how often
participants are in close proximity to emission sources. Thus, in a
subset of at least 10 households from each study arm,
proximity measurements are being collected using a
method similar to Allen-Piccolo et al. . Bluetooth LE
beacons are placed in the G-Pods (placed near stoves),
and individuals carry a mobile phone with a custom
Android application during personal exposure
measurements. The mobile phone receives the Bluetooth signal
emitted by the beacon, and the strength of that signal is
then roughly translated to a distance measure from the
person to the source. In addition to measuring proximity
to the G-Pods at the homes, the mobile phones in the
packs use GPS to record global position. This information
can further be applied to estimate distances to other
emission sources that are identified in the region.
Three methods are used to measure human health
outcomes as part of the REACCTING study. The household
survey includes questions about self-reported health
symptoms, including respiratory symptoms, for the respondent
(primary cook) and all children under five in the
household. Subclinical health measures are also collected twice
a year to provide data on more continuous indicators of
these individuals health status. The two types of measures
include anthropometrics and biomarkers of inflammation.
Anthropometric measurements of height, weight, and
mid-upper arm circumference serve as indicators of an
individuals nutritional status, which in turn can be affected
by acute and chronic illnesses . While we are not
aware of any studies directly linking cookstove exposure
to these child growth measures, we hypothesize that
lowered exposure to cooking emissions over time may result
in better growth outcomes. Hanna et al.  made similar
measurements in their cookstove intervention study in
India. (Results were not significant in this study, which is
not surprising since use of the improved stoves was low).
Meanwhile, biomarkers are measured from blood spots
taken from study participants at the times of the major
surveys (Nov-Dec 13, May-June 14, Nov-Dec 14, and
May-June 15). The markers targeted in the analysis
include: C-reactive protein (CRP), Serum Amyloid A,
soluble cell adhesion molecules (sCAMs), including sICAM
and sVCAM, interleukins (IL-1B, IL-6, IL-8), and tumor
necrosis factor alpha (TNF-a). These markers are chosen
as they indicate the presence of systemic inflammation or
vascular injury, and perturbation in their regulation may
be a risk factor for cardiovascular disease. Previous
epidemiology and clinical studies have shown associations of
nearly all of these markers with exposure to particulate
matter [45,86,87]. The biomarker data will thus enable
assessment of potential changes in systemic inflammation
over time for each individual as well as across individuals
as a function of changing levels of smoke exposure.
Regional air quality
To understand the spatial and temporal variability of air
pollution and to help identify pollutant sources, G-Pods
are deployed throughout the study region. Such regional
monitoring has been undertaken in developed countries,
but very rarely in developing countries (for example, see
Mead et al. ). The G-Pods are configured to measure
O3, CO, NO, and NO2 using Alphasense B4
electrochemical sensors. Ozone, CO, and NO2 are also
measured using MOx sensors from SGX Technologies.
CO2 is measured using the previously mentioned NDIR
sensor. Some of the G-Pods also measure TVOCs using
photoionization detectors, as well as wind speed and
direction. The G-Pods are mounted three to four meters
above ground at the five Ghana Health Service clinics in
the K-N district: Paga, Kandiga, Kologo, Chiana, and the
Navrongo Health Centre (NHRC) (Figure 2). The NHRC
also serves as the study core monitoring site and, in
addition to the low-cost monitors, reference quality
instruments are operated there. At the NHRC, CO is
measured with a Thermo Model 48 CO analyzer set to
rezero every hour using a heated Pt-Al catalyst. Ozone is
measured with a 2B Technologies Model 202, while NO
and NOx are measured with 2B Technologies Models
401 and 410. CO2 is measured with a LI-COR 840a.
Weekly PM2.5 filter samples are also collected on 90 mm
quartz fiber filters using a cyclone (30E, URG Inc.) and
filter holder, employing a flow rate of 5.5 liters per
minute maintained with low-power vacuum pumps audited
monthly with a rotameter and checked daily with a flow
totalizer and timer. The PM2.5 sampling will expand
upon the work of Ofosu et al. , and filters are
analyzed for EC/OC and organic compounds.
Meteorological data are collected using a Climatronics sonic
anemometer and temperature and humidity sensor.
Additionally, pollution source sampling is conducted for
a variety of common emission sources in the region
including trash burning, different types of commercial
cooking, and vehicle emissions. The locations of major
sources will also be identified in order to inform analysis
of individuals proximity to these sources.
Analysis and integration
The REACCTING study was designed to provide robust
and integrated measurements at each stage in the causal
chain linking an improved cookstove intervention to key
outcomes of interest (Figure 1). Our methods are
informed by prior studies and assessment strategies,
drawing lessons from the strengths and weaknesses of those
experiences. To assess each intermediate and final
outcome, we typically employ multiple measurement
strategies rather than relying on a single source of data. This
redundancy in data sources enables our research team to
analyze and integrate across data streams to provide
more detailed and nuanced answers to the key research
questions. Three examples of these integrated analyses
are presented below.
Integrated stove use analysis to understand cooking
Understanding how, why, and which types of stoves are
used by the study households is a crucial first step in
analyzing and interpreting subsequent outcomes (e.g.,
emissions, air quality, health) in the causal chain. Data
from household surveys and electronic SUMs are
analyzed jointly to provide comprehensive information
about stove usage, activity, and preferences. Survey data
are available at approximately three month intervals
throughout the first year of the study, and six month
intervals during the second year. The survey data contain
information about every stove in all 200 study
households, including traditional and improved stoves.
Reported use of each stove in the week prior to the survey
(number of days on which each stove was used), on the
day prior to the survey, and at the time of the survey are
collected. Types of fuel used and dishes cooked on each
stove are also recorded. Meanwhile, SUMs measure
stove temperature every five minutes for a subset of
households in each intervention arm. Within these
households, measurements are available for improved stoves as
well as the most-used traditional stove. The integration of
the survey and the SUMs data streams provides robust
information about any reductions in three-stone stove usage
among the different intervention arms. Comparing results
between the surveys and the SUMs will also determine
whether households tend to over-report use of new stoves
in our study, as was found in a similar comparison in the
context of a stove intervention in Rwanda . Finally,
quantifying behaviors, such as the dishes cooked and the
perceptions of stove quality and performance, in addition
to the amount of stove use will help to point the way
forward towards interventions and scale-up efforts that can
be piloted in future studies to further increase stove
acceptability and use.
Integrated measurements to assess the contribution of
multiple emissions sources to personal exposure
The REACCTING study, like other cookstove
interventions, directly targets a key source of pollutants to which
individuals are exposed: cooking emissions within the
home. However, other sources of emissions, such as
vehicle emissions and trash burning, also contribute to
local and regional air quality, and thus personal
exposures to air pollutants. Integrating data across emissions,
personal exposure, microenvironmental, and regional air
quality measurements will allow us to better understand
the personal exposure contribution of household
cooking, along with these other emissions sources. One way
in which this will be accomplished is with the use of
source apportionment of the personal and regional
PM2.5 organic molecular markers. This technique uses
the covariance of chemical tracer species to apportion a
set of measurements into matrices of chemical
compositions and contributions, termed factor profiles and
contributions, respectively. Thus, we will identify organic
PM2.5 factor profiles in both the personal and ambient
samples and learn the impact of each on exposures and
ambient air quality. The source emissions
measurements will help validate the personal exposure source
apportionment results. In addition, comparison of the
profiles generated using the ambient and personal filter
samples will shed light on the validity of using ambient
measurements to understand personal exposure in the
Since the number of personal PM2.5 exposure samples
are constrained due to resource limitations, we will also
develop models to predict PM2.5 exposures based on the
easier to collect microenvironmental and regional
samples. Household microenvironment air quality
monitoring has been performed as a proxy for personal exposure
with mixed success [6,39,41,57,89]. This approach can
help predict personal exposure and assess local impacts,
but can require time-activity logging to reliably estimate
exposure with increasing numbers of pollution sources
(e.g., [39-41]). To predict time-integrated PM2.5
information from different data streams, we will use real-time
time-activity data to apportion users exposure time to
the microenvironment and ambient PM2.5 samples. In
the subset of samples with UCB-PATS monitors, the
microenvironment PM2.5 sample will be weighted by the
real-time PM measurement from the collocated PATS to
provide a better estimate of the contribution by taking
the microenvironment PM dynamics into account. We
will also predict personal CO exposure following a
similar procedure, but using the higher time resolution CO
at the personal, microenvironment, and regional scales.
Models have been developed to relate personal and
microenvironmental measurements in past cookstove studies
[6,41], but not with high-resolution time-activity data, nor
in this region. As done in other works [39,40,90], we will
also investigate the PM2.5 vs. CO relationship at the
personal and microenvironment levels.
Integration of field measurements to develop regional
emissions scenarios for air quality modeling
To assess the impact of cooking on air quality and climate,
emissions from this particular source must be quantified.
Emissions of PM (including organic and black carbon),
CO, and other reactive and greenhouse gases from
cooking are a function of activity and emission factors (or the
mass of emitted pollutant per time cooked or fuel used).
Emission factors are based on the stove and fuel used.
Activity is the information that describes cooking
practices, such as the timing and duration of cooking and
the types of dishes prepared.
The information collected as part of the REACCTING
surveys and the emissions, SUMS, and microenvironment
measurements will provide the basis for the development
of an emissions inventory for current and potential
cooking activities in the region. Emission factors are developed
from the emissions measurement experiments during
which emissions are directly measured and normalized to
the amount of fuel burned. These in-field measurements
will be compared to published laboratory measurements
with the goal of improving our ability to predict emissions
from alternative cooking technologies and fuels. As
previously discussed, the activity information collected via the
SUMs will provide data to constrain the typical timing and
duration of cooking events, and can be combined with the
survey information to assess typical cooking practices,
such as the frequency of specific dishes and meals. These
data enable the determination of when the emissions
occur, for how long, and under what conditions. Together,
the emission factors and activity information will be used
to produce hourly estimates of emissions from traditional
cooking practices, as well as from cooking activities that
use the technologies introduced in the REACCTING
study. The estimated household emissions inventories will
then be scaled to the greater regional population using the
The scaled-up emissions estimates will be used as
inputs into chemical transport models, such as the Weather
Research Forecasting model with Chemistry (WRF-chem)
, which simulate the coupled interactions between
regional weather and emissions from cooking activities and
other sources. WRF-chem will thus serve as a tool to scale
up our field results by simulating the regional air quality
(O3, CO, PM) across a broader region of western Africa
that will encompass the study area in northern Ghana.
Emissions sources in the simulations will be developed
from the field-based cooking and regional air quality
emissions measurements described above, as well as from
existing emissions inventories (e.g., ). Biomass burning
emissions will be estimated from the Fire Inventory from
NCAR (FINN) model . WRF-chem simulations will be
performed for a variety of cookstove adoption scenarios
(e.g., widespread adoption of Gyapa stoves, of Philips
stoves, of both types of stoves, no adoption of clean
cookstove technologies, etc.) based on the results from
the surveys and for both historical and future climate
change scenarios. The results will be used to more
thoroughly examine the ambient exposures to which the
regional population is exposed, and how that might
change with different technologies and behaviors.
Further, the models can be used to assess the comparative
impact of local behavior and technological changes
versus regional climate variability and change on local air
quality and health outcomes.
The practice of cooking with biomass over open fires is
widespread throughout much of the world. In many ways,
this is understandable: this cooking method is low-tech
and requires few resources beyond locally available
materials (biomass, stones) and the time of household
members (for collecting fuel and preparing food). While
there are many reasons to believe that shifting cooking
practices could have wide-reaching benefits for some of
the worlds most disadvantaged populations, achieving this
objective in practice requires well-designed interventions
that understand and integrate existing cultural practices.
The REACCTING study represents an attempt to
systematically address some of the challenges that previous
cookstove studies and interventions have faced in order
to generate multidisciplinary and detailed data that can
be used to inform broader efforts to change cooking
behaviors in this region and elsewhere. The cookstove
intervention we have implemented employs both
highand low-tech biomass stove options in order to inform
the debate between those advocating transformative
approaches and those arguing that incremental progress is
more feasible and will achieve more in the long run. In
addition, we distribute two stoves to each household
assigned to an intervention group in our study. This is
intended to address the potential for stove stacking.
Formative research in the area showed that prior to any
intervention, households were using multiple stoves and
a mix of technologies to meet their cooking needs. By
providing households with multiple new stoves, and in
some cases two types of new stoves (Group C), we
hypothesize that households will begin to substitute
away from traditional stoves and toward exclusive use
of improved stoves.
REACCTING is well-poised to generate useful data on
the impact of a cookstove intervention on a wide range of
outcomes, from cooking behavior to emissions, exposure,
human health, and feedbacks on air quality and regional
climate change. A comprehensive and coordinated
assessment strategy is being employed to generate consistent
and comparable data on all of these outcomes across the
four different stove intervention groups. By integrating across
these different data streams, REACCTING will allow us to
study the impacts of the newly introduced stove technologies
from a variety of angles, informing future efforts to combat
this pressing public health challenge.
BC: Black carbon; CCT: Controlled cooking test; CO: Carbon monoxide;
CRP: C-reactive protein; EC: Elemental carbon; FINN: Fire Inventory from
NCAR; HDSS: Health and Demographic Surveillance Survey; IL: Interleukin;
K-N: Kassena-Nankana; LPG: Liquid petroleum gas; MOx: Metal oxide;
NDIR: Non-dispersive infrared; NHRC: Navrongo Health Research Center;
NO: Nitrogen monoxide; NO2: Nitrogen dioxide; O3: Ozone; OC: Organic
carbon; PEMS: Portable emission measurement system; PM: Particulate matter;
PM2.5: Particulate matter less than 2.5 microns in diameter; Pt-Al: Platinum on
Alumina; REACCTING: Research on Emissions, Air quality, Climate, and Cooking
Technologies in Northern Ghana; RI: Relief International; sCAM: Soluble cell
adhesion molecule; SUM: Stove use monitor; TNF-a: Tumor necrosis factor
alpha; TSP: Total suspended particulates; TVOCs: Total volatile organic
compounds; TZ: Tuo Zaafi (millet-based porridge); UCB-PATS: University of
California at Berkeley Particle And Temperature Sensor; WBT: Water boiling test;
WRF-chem: Weather Research Forecasting model with Chemistry.
KD led the social science and health components of the study (design, data
collection and analysis), collaborated on the overall study design and
integration, wrote and edited the manuscript. EK managed all aspects of
project implementation in the field, participated in pretesting and project
development efforts, aided in writing and editing the manuscript. RP
designed, collected, and analyzed data on stove use, personal exposure,
emissions, and air quality, conducted review of literature, wrote substantial
portions of the manuscript. EC designed, collected, and analyzed data on
stove use, personal exposure, emissions, and air quality, conducted review of
literature, wrote substantial portions of the manuscript. IR collaborated on
social science data collection and analysis, reviewed literature, prepared map
figures, contributed to writing of manuscript. JA participated in survey
design and pretesting, conducted community entry activities, contributed to
writing and editing of manuscript. RA participated in survey design and
pretesting, conducted community entry activities, contributed to writing and
editing of manuscript. DM designed, collected, and analyzed data on stove
emissions, conducted review of literature, contributed to writing of
manuscript. MD led development of Gyapa stove model and stove
distribution methodology, contributed to writing and editing of manuscript.
VD conducted formative research on health issues in study area, collaborated
on overall study design and integration, edited manuscript. Ma.H conducted
formative research on health issues in study area, contributed to design of
social science and health measurement approaches, collaborated on overall
study design and integration, edited manuscript. DD-S collaborated on design
of health measurements, edited manuscript. VA participated in survey design
and pretesting, conducted community entry activities, contributed to writing
and editing of manuscript. DA participated in survey design and pretesting,
conducted community entry activities, contributed to writing and editing of
manuscript. YC-HS developed health modeling methods, edited manuscript.
NM designed, collected, and analyzed data on stove emissions, edited
manuscript. AM contributed to design of climate modeling component,
wrote and edited manuscript sections. AT contributed to development of
Gyapa stove model and stove distribution methodology, contributed to
writing and editing of manuscript. DS contributed to climate modeling
component of study, aided in survey data processing, edited manuscript. Y-YH
aided in survey data processing and analysis, edited manuscript. RK aided in
survey data processing and analysis, edited manuscript. BB aided in survey data
processing and analysis, edited manuscript. AH conducted formative research
on health issues in study area, facilitated collaboration between US and Ghana
institutions to enable project development and implementation, edited
manuscript. Mi.H served as principal investigator on EPA grant funding project,
led physical science components of project, wrote and edited manuscript. AO
collaborated on the overall study design, all aspects of project implementation
in the field, provided data from HDSS, edited manuscript. CW served as principal
investigator on NSF grant funding project, managed all aspects of project
development and implementation, coordinated collaboration between US
and Ghana institutions, led design of regional air quality and climate modeling
study components, wrote and edited manuscript. All authors read and approved
the final manuscript.
The authors would like to acknowledge funding sources for the project, the
National Science Foundation (Grant # GEO1211668) and the US
Environmental Protection Agency (Grant # RD 8354201). In addition, we
thank Jacob Moss, the Global Alliance for Clean Cookstoves, and Amy
Sticklor for their valuable input during the studys design phase. Shubhayu
Saha provided input and visual materials for the economic components of
the household survey.
1. World Health Organization. Fact Sheet No 292: Household Air Pollution and Health ; 2014 .
2. Smith KR , Bruce N , Balakrishnan K , Adair-Rohani H , Balmes J , Chafe Z , et al. Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution . Annu Rev Public Health . 2014 ; 35 : 185 - 206 .
3. Smith KR , Samet JM , Romieu I , Bruce N. Indoor air pollution in developing countries and acute lower respiratory infections in children . Thorax . 2000 ; 55 ( 6 ): 518 - 32 .
4. Kurmi OP , Semple S , Simkhada P , Smith WCS , Ayres JG . COPD and chronic bronchitis risk of indoor air pollution from solid fuel: a systematic review and meta-analysis . Thorax . 2010 ; 65 ( 3 ): 221 - 8 .
5. Smith KR . National burden of disease in India from indoor air pollution . Proc Natl Acad Sci . 2000 ; 97 ( 24 ): 13286 - 93 .
6. Baumgartner J , Schauer JJ , Ezzati M , Lu L , Cheng C , Patz JA , et al. Indoor air pollution and blood pressure in adult women living in rural China . Environ Health Perspect . 2011 ; 119 ( 10 ): 1390 .
7. 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 - 92 .
8. Geist HJ , Lambin EF . Proximate causes and underlying driving forces of tropical deforestation . Bioscience . 2002 ; 52 ( 2 ): 143 - 50 .
9. Blackden CM , Wodon Q, eds. Gender, time use, and poverty in sub-Saharan Africa . 2006 ; World Bank Working Paper #73
10. Streets DG , Bond TC , Carmichael GR , Fernandes SD , Fu Q , He D , et al. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 .. J Geophys Res-Atmos . 2003 ; 108 :D21.
11. Gustafsson O , Krusa M , Zencak Z , Sheesley RJ , Granat L , Engstrom E , et al. Brown clouds over south asia: biomass or fossil fuel combustion? Science . 2009 ; 323 ( 5913 ): 495 - 8 .
12. Christian TJ , Yokelson RJ , Cardenas B , Molina LT , Engling G , Hsu SC . Trace gas and particle emissions from domestic and industrial biofuel use and garbage burning in central Mexico . Atmos Chem Phys . 2010 ; 10 ( 2 ): 565 - 84 .
13. Hanna R , Duflo E , Greenstone M. Up in Smoke: The Influence of Household Behavior on the Long-run Impact of Improved Cooking Stoves . National Bureau of Economic: Research; 2012 .
14. Davis M. Rural household energy consumption: the effects of access to electricity-evidence from South Africa . Energ Policy . 1998 ; 26 ( 3 ): 207 - 17 .
15. Masera OR , Saatkamp BD , Kammen DM . From linear fuel switching to multiple cooking strategies: a critique and alternative to the energy ladder model . World Dev . 2000 ; 28 ( 12 ): 2083 - 103 .
16. Gupta G , Khlin G. Preferences for domestic fuel: analysis with socio-economic factors and rankings in Kolkata , India. Ecol Econ . 2006 ; 57 ( 1 ): 107 - 21 .
17. Taylor MJ , Moran-Taylor MJ , Castellanos EJ , Elas S. Burning for sustainability: biomass energy, international migration, and the move to cleaner fuels and cookstoves in Guatemala . Ann Assoc Am Geogr . 2011 ; 101 ( 4 ): 918 - 28 .
18. van der Kroon B , Brouwer R , van Beukering PJ . The energy ladder: theoretical myth or empirical truth? Results from a meta-analysis . Renew Sust Energ Rev . 2013 ; 20 : 504 - 13 .
19. Subramanian M. Global health: deadly dinners . Nature . 2014 ; 509 ( 7502 ): 548 - 51 .
20. Hiemstra-Van der Horst G , Hovorka AJ . Reassessing the energy ladder: household energy use in Maun , Botswana. Energ Policy . 2008 ; 36 ( 9 ): 3333 - 44 .
21. Simon GL , Bailis R , Baumgartner J , Hyman J , Laurent A. Current debates and future research needs in the clean cookstove sector . Energy Sustain Dev . 2014 ; 20 : 49 - 57 .
22. Campbell B , Vermeulen S , Mangono J , Mabugu R. The energy transition in action: urban domestic fuel choices in a changing Zimbabwe . Energ Policy . 2003 ; 31 ( 6 ): 553 - 62 .
23. Elias RJ , Victor DG . Energy Transitions in Developing Countries: A Review of Concepts and Literature . In: Program on Energy and Sustainable Development , Working Paper. Stanford: Stanford University ; 2005 .
24. Ruiz-Mercado I , Masera O , Zamora H , Smith KR. Adoption and sustained use of improved cookstoves . Energ Policy . 2011 ; 39 ( 12 ): 7557 - 66 .
25. Heltberg R. Factors determining household fuel choice in Guatemala . Environ Dev Econ . 2005 ; 10 ( 03 ): 337 - 61 .
26. Ruiz-Mercado I , Canuz E , Smith KR. Temperature dataloggers as stove use monitors (SUMs): field methods and signal analysis . Biomass Bioenergy . 2012 ; 47 : 459 - 68 .
27. Mobarak AM , Dwivedi P , Bailis R , Hildemann L , Miller G . Low demand for nontraditional cookstove technologies . Proc Natl Acad Sci . 2012 ; 109 ( 27 ): 10815 - 20 .
28. Thomas EA , Barstow CK , Rosa G , Majorin F , Clasen T. Use of remotely reporting electronic sensors for assessing use of water filters and cookstoves in rwanda . Environ Sci Technol . 2013 ; 47 ( 23 ): 13602 - 10 .
29. Jetter J , Zhao Y , Smith KR , Khan B , Yelverton T , DeCarlo P , et al. Pollutant emissions and energy efficiency under controlled conditions for household biomass cookstoves and implications for metrics useful in setting international test standards . Environ Sci Technol . 2012 ; 46 ( 19 ): 10827 - 34 .
30. Carter EM , Shan M , Yang X , Li J , Baumgartner J. Pollutant emissions and energy efficiency of chinese gasifier cooking stoves and implications for future intervention studies . Environ Sci Technol . 2014 ; 48 ( 11 ): 6461 - 7 .
31. Roden CA , Bond TC , Conway S , Pinel ABO. Emission factors and real-time optical properties of particles emitted from traditional wood burning cookstoves . Environ Sci Technol . 2006 ; 40 ( 21 ): 6750 - 7 .
32. Johnson M , Edwards R , Alatorre Frenk C , Masera O. In-field greenhouse gas emissions from cookstoves in rural Mexican households . Atmos Environ . 2008 ; 42 ( 6 ): 1206 - 22 .
33. Roden CA , Bond TC , Conway S , Osorto Pinel AB , MacCarty N , Still D. Laboratory and field investigations of particulate and carbon monoxide emissions from traditional and improved cookstoves . Atmos Environ . 2009 ; 43 ( 6 ): 1170 - 81 .
34. Johnson , M. , N. Lam , D. Pennise , D. Charron , T. C. Bond , V. Modi and J. A. Ndemere ( 2011 ). In-home emissions of greenhouse pollutants from rocket and traditional biomass cooking stoves in Uganda , USAID.
35. Rehman I , Ahmed T , Praveen P , Kar A , Ramanathan V. Black carbon emissions from biomass and fossil fuels in rural India . Atmos Chem Phys . 2011 ; 11 ( 14 ): 7289 - 99 .
36. U.S. EPA. Integrated Science Assessment for Carbon Monoxide (Final Report) . U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-09/019F, 2010 .
37. Smith KR , McCracken JP , Thompson L , Edwards R , Shields KN , Canuz E , et al. Personal child and mother carbon monoxide exposures and kitchen levels: methods and results from a randomized trial of woodfired chimney cookstoves in Guatemala (RESPIRE) . J Expo Sci EnvironEpidemiol . 2009 ; 20 ( 5 ): 406 - 16 .
38. Burwen J , Levine DI. A rapid assessment randomized-controlled trial of improved cookstoves in rural Ghana . Energy Sustain Dev . 2012 ; 16 ( 3 ): 328 - 38 .
39. Dionisio KL , Howie SRC , Dominici F , Fornace KM , Spengler JD , Donkor S , et al. The exposure of infants and children to carbon monoxide from biomass fuels in The Gambia: a measurement and modeling study . J Expo Sci EnvironEpidemiol . 2012 ; 22 ( 2 ): 173 - 81 .
40. McCracken JP , Schwartz J , Diaz A , Bruce N , Smith KR. Longitudinal relationship between personal CO and personal PM2. 5 among women cooking with woodfired cookstoves in Guatemala .. PLoS One . 2013 ; 8 ( 2 ): e55670 .
41. Cynthia AA , Edwards RD , Johnson M , Zuk M , Rojas L , Jimnez RD , et al. Reduction in personal exposures to particulate matter and carbon monoxide as a result of the installation of a Patsari improved cook stove in Michoacan Mexico . Indoor Air . 2008 ; 18 ( 2 ): 93 - 105 .
42. Diaz E , Smith-Sivertsen T , Pope D , Lie RT , Diaz A , McCracken J , et al. Eye discomfort, headache and back pain among Mayan Guatemalan women taking part in a randomised stove intervention trial . J Epidemiol Community Health . 2007 ; 61 ( 1 ): 74 - 9 .
43. Clark ML , Peel JL , Burch JB , Nelson TL , Robinson MM , Conway S , et al. Impact of improved cookstoves on indoor air pollution and adverse health effects among Honduran women . Int J Environ Health Res . 2009 ; 19 ( 5 ): 357 - 68 .
44. Li Z , Sjdin A , Romanoff LC , Horton K , Fitzgerald CL , Eppler A , et al. Evaluation of exposure reduction to indoor air pollution in stove intervention projects in Peru by urinary biomonitoring of polycyclic aromatic hydrocarbon metabolites . Environ Int . 2011 ; 37 ( 7 ): 1157 - 63 .
45. Banerjee A , Mondal NK , Das D , Ray MR . Neutrophilic inflammatory response and oxidative stress in premenopausal women chronically exposed to indoor air pollution from biomass burning . Inflammation . 2012 ; 35 ( 2 ): 671 - 83 .
46. Ofosu FG , Hopke PK , Aboh IJ , Bamford SA . Biomass burning contribution to ambient air particulate levels at Navrongo in the Savannah zone of Ghana . J Air Waste Manage Assoc . 2013 ; 63 ( 9 ): 1036 - 45 .
47. Aboh IJK , Henriksson D , Laursen J , Lundin M , Ofosu FG , Pind N , et al. Identification of aerosol particle sources in semirural area of Kwabenya , near Accra, Ghana, by EDXRF techniques. X-Ray Spectrom. 2009 ; 38 ( 4 ): 348 - 53 .
48. Rooney MS , Arku RE , Dionisio KL , Paciorek C , Friedman AB , Carmichael H , et al. Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana . Sci Total Environ . 2012 ; 435 - 436 : 107 - 14 .
49. Obioh IB , Ezeh GC , Abiye OE , Alpha A , Ojo EO , Ganiyu AK . Atmospheric particulate matter in Nigerian megacities . Toxicol Environ Chem . 2013 ; 95 ( 3 ): 379 - 85 .
50. Boman J , Lindn J , Thorsson S , Holmer B , Eliasson I. A tentative study of urban and suburban fine particles (PM2.5) collected in Ouagadougou , Burkina Faso. X-Ray Spectrom. 2009 ; 38 ( 4 ): 354 - 62 .
51. Gatari MJ , Boman J. Black carbon and total carbon measurements at urban and rural sites in Kenya, East Africa . Atmos Environ . 2003 ; 37 ( 8 ): 1149 - 54 .
52. Abu-Allaban M , Lowenthal DH , Gertler AW , Labib M. Sources of PM10 and PM2. 5 in Cairo's ambient air . Environ Monit Assess . 2007 ; 133 ( 1-3 ): 417 - 25 .
53. Rehman IH , Kar A , Arora A , Pal R , Singh L , Tiwari J , et al. Distribution of improved cook stoves: analysis of field experiments using strategic niche management theory . Sustain Sci . 2012 ; 7 ( 2 ): 227 - 35 .
54. 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 . Atmospheres ( 1984 - 2012 ). J Geophys Res . 2004 ; 109 :D14.
55. Zhang Q , Streets DG , Carmichael GR , He K , Huo H , Kannari A , et al. Asian emissions in 2006 for the NASA INTEX-B mission . Atmos Chem Phys . 2009 ; 9 ( 14 ): 5131 - 53 .
56. Anenberg SC , Balakrishnan K , Jetter J , Masera O , Mehta S , Moss J , et al. Cleaner cooking solutions to achieve health, climate, and economic cobenefits . Environ Sci Technol . 2013 ; 47 ( 9 ): 3944 - 52 .
57. Naeher L , Smith K , Leaderer B , Neufeld L , Mage D. Carbon monoxide as a tracer for assessing exposures to particulate matter in wood and gas cookstove households of highland Guatemala . Environ Sci Technol . 2001 ; 35 ( 3 ): 575 - 81 .
58. Zuk M , Rojas L , Blanco S , Serrano P , Cruz J , Angeles F , et al. The impact of improved wood-burning stoves on fine particulate matter concentrations in rural Mexican homes . J Expo Sci Environ Epidemiol . 2006 ; 17 ( 3 ): 224 - 32 .
59. Masera O , Edwards R , Arnez CA , Berrueta V , Johnson M , Bracho LR , et al. Impact of Patsari improved cookstoves on indoor air quality in Michoacn, Mexico . Energy Sustain Dev . 2007 ; 11 ( 2 ): 45 - 56 .
60. Berrueta VM , Edwards RD , Masera OR . Energy performance of wood-burning cookstoves in Michoacan , Mexico. Renew Energy . 2008 ; 33 ( 5 ): 859 - 70 .
61. Romieu I , Riojas-Rodrguez H , Marrn-Mares AT , Schilmann A , Perez-Padilla R , Masera O. Improved biomass stove intervention in rural mexico . Am J Respir Crit Care Med . 2009 ; 180 ( 7 ): 649 - 56 .
62. Armendriz-Arnez C , Edwards RD , Johnson M , Rosas IA , Espinosa F , Masera OR . Indoor particle size distributions in homes with open fires and improved Patsari cook stoves . Atmos Environ . 2010 ; 44 ( 24 ): 2881 - 6 .
63. Fitzgerald C , Aguilar-Villalobos M , Eppler AR , Dorner SC , Rathbun SL , Naeher LP . Testing the effectiveness of two improved cookstove interventions in the Santiago de Chuco Province of Peru . Sci Total Environ . 2012 ; 420 : 54 - 64 .
64. Rosa G , Majorin F , Boisson S , Barstow C , Johnson M , Kirby M , et al. Assessing the impact of water filters and improved cook stoves on drinking water quality and household air pollution: a randomised controlled trial in rwanda . PLoS One . 2014 ; 9 ( 3 ): e91011 .
65. Ramanathan N , Lukac M , Ahmed T , Kar A , Praveen P , Honles T , et al. A cellphone based system for large-scale monitoring of black carbon . Atmos Environ . 2011 ; 45 ( 26 ): 4481 - 7 .
66. Kar A , Rehman IH , Burney J , Puppala SP , Suresh R , Singh L , et al. Real-time assessment of black carbon pollution in Indian households due to traditional and improved biomass cookstoves . Environ Sci Technol . 2012 ; 46 ( 5 ): 2993 - 3000 .
67. Praveen P , Ahmed T , Kar A , Rehman I , Ramanathan V. Link between local scale BC emissions in the Indo-Gangetic Plains and large scale atmospheric solar absorption . Atmos Chem Phys . 2012 ; 12 : 1173 - 87 .
68. Oduro AR , Wak G , Azongo D , Debpuur C , Wontuo P , Kondayire F , et al. Profile of the navrongo health and demographic surveillance system . Int J Epidemiol . 2012 ; 41 ( 4 ): 968 - 76 .
69. Appiah-Gyapong J , Aguh N , Atta-Boakye G . Outcome Evaluation: UNDP Institutional Support to Integrate Climate Change and National Development Plans. United: Nations Development Program (UNDP) ; 2011 . Sustained Biomass Use and Promotion of Alternative Cooking Devices and Fuels: Evaluation Report.
70. Akweongo P , Dalaba MA , Hayden MH , Awine T , Nyaaba GN , Anaseba D , et al. The economic burden of meningitis to households in kassena-nankana district of northern ghana . PLoS One . 2013 ; 8 ( 11 ): e79880 .
71. Hayden MH , Dalaba M , Awine T , Akweongo P , Nyaaba G , Anaseba D , et al. Knowledge , attitudes, and practices related to meningitis in Northern Ghana . AmJTrop Med Hyg . 2013 ; 89 ( 2 ): 265 - 70 .
72. Hodgson A , Smith T , Gagneux S , Adjuik M , Pluschke G , Mensah NK , et al. Risk factors for meningococcal meningitis in northern Ghana . Trans R Soc Trop Med Hyg . 2001 ; 95 ( 5 ): 477 - 80 .
73. Duki V , Hayden M , Forgor AA , Hopson T , Akweongo P , Hodgson A , et al. The role of weather in meningitis outbreaks in Navrongo, Ghana: a generalized additive modeling approach . J Agric Biol Environ Stat . 2012 ; 17 ( 3 ): 442 - 60 .
74. Pennise D , Brant S , Agbeve SM , Quaye W , Mengesha F , Tadele W , et al. Indoor air quality impacts of an improved wood stove in Ghana and an ethanol stove in Ethiopia . Energy Sustain Dev . 2009 ; 13 ( 2 ): 71 - 6 .
75. Ruiz-Mercado I , Canuz E , Walker JL , Smith KR. Quantitative metrics of stove adoption using Stove Use Monitors (SUMs) . Biomass Bioenergy . 2013 ; 57 : 136 - 48 .
76. Simons AM , Beltramo T , Blalock G , Levine DI . Comparing methods for signal analysis of temperature readings from stove use monitors . Biomass Bioenergy . 2014 ; 70 : 476 - 88 .
77. Bailis , R. Controlled Cooking Test. Shell Foundation (Household Energy and Health Pro ; 2004 .
78. MacCarty N , Still D , Ogle D. Fuel use and emissions performance of fifty cooking stoves in the laboratory and related benchmarks of performance . Energy Sustain Dev . 2010 ; 14 ( 3 ): 161 - 71 .
79. Dionisio KL , Howie SRC , Dominici F , Fornace KM , Spengler JD , Adegbola RA , et al. Household concentrations and exposure of children to particulate matter from biomass fuels in the Gambia . Environ Sci Technol . 2012 ; 46 ( 6 ): 3519 - 27 .
80. Li C , Kang S , Chen P , Zhang Q , Guo J , Mi J , et al. Personal PM2 .5 and indoor CO in nomadic tents using open and chimney biomass stoves on the Tibetan Plateau . Atmos Environ . 2012 ; 59 : 207 - 13 .
81. Ochieng C , Vardoulakis S , Tonne C. Are rocket mud stoves associated with lower indoor carbon monoxide and personal exposure in rural Kenya? Indoor Air . 2013 ; 23 ( 1 ): 14 - 24 .
82. Oluwole O , Ana GR , Arinola GO , Wiskel T , Falusi AG , Huo D , et al. Effect of stove intervention on household air pollution and the respiratory health of women and children in rural Nigeria . Air Qual Atmos Health . 2013 ; 6 ( 3 ): 553 - 61 .
83. Edwards R , Smith KR , Kirby B , Allen T , Litton CD , Hering S. An inexpensive dual-chamber particle monitor: laboratory characterization . J Air Waste Manag Assoc . 2006 ; 56 : 789 - 99 .
84. Allen-Piccolo G , Rogers JV , Edwards R , Clark MC , Allen TT , Ruiz-Mercado I , et al. An ultrasound personal locator for time-activity assessment . Int J Occup Environ Health . 2009 ; 15 ( 2 ): 122 - 32 .
85. Alderman H. Anthropometry . In: Grosh M, Glewwe P , editors. Designing Household Survey Questionnaires for Developing Countries . Washington, DC: The World Bank; 2000 . p. 251 - 72 .
86. Chuang K-J , Chan C-C, Su T-C , Lee C-T , Tang C-S. The effect of urban air pollution on inflammation, oxidative stress, coagulation, and autonomic dysfunction in young adults . Am J Respir Crit Care Med . 2007 ; 176 ( 4 ): 370 - 6 .
87. Brook RD , Rajagopalan S , Pope CA , Brook JR , Bhatnagar A , Diez-Roux AV , et al. Particulate matter air pollution and cardiovascular disease an update to the scientific statement from the American heart association . Circulation . 2010 ; 121 ( 21 ): 2331 - 78 .
88. Mead MI , Popoola OAM , Stewart GB , Landshoff P , Calleja M , Hayes M , et al. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks . Atmos Environ . 2013 ; 70 : 186 - 203 . doi:10.1016/j. atmosenv. 2012 .11.060.
89. Bruce N , McCracken J , Albalak R , Schei M , Smith KR , Lopez V , et al. Impact of improved stoves, house construction and child location on levels of indoor air pollution exposure in young Guatemalan children . J Expo Sci Environ Epidemiol . 2004 ; 14 : S26 - 33 .
90. Northcross A , Chowdhury Z , McCracken J , Canuz E , Smith KR. Estimating personal PM2.5 exposures using CO measurements in Guatemalan households cooking with wood fuel . J Environ Monit . 2010 ; 12 ( 4 ): 873 . doi:10.1039/b916068j.
91. Grell GA , Peckham SE , Schmitz R , McKeen SA , Frost G , Skamarock WC , et al. Fully coupled online chemistry within the WRF model . Atmos Environ . 2005 ; 39 ( 37 ): 6957 - 75 .
92. Liousse C , Assamoi E , Criqui P , Granier C , Rosset R. Explosive growth in African combustion emissions from 2005 to 2030 . Environ Res Lett . 2014 ; 9 ( 3 ): 035003 .
93. Wiedinmyer C , Akagi S , Yokelson RJ , Emmons L , Al-Saadi J , Orlando J , et al. The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning . Geosci Model Dev . 2011 ; 4 : 625 .