Sources of variation for indoor nitrogen dioxide in rural residences of Ethiopia
Sources of variation for indoor nitrogen dioxide in rural residences of Ethiopia
Abera Kumie 2
Anders Emmelin 1
Sonny Wahlberg 1
Yemane Berhane 0
Ahmed Ali 2
Eyassu Mekonen 3
Alemayehu Worku 2
Doris Brandstrom 1
0 Addis Continental Institute of Public Health , Addis Ababa , Ethiopia
1 Umea International School of Public Health, Umea University , Umea , Sweden
2 School of Public Health, College of Health Sciences, Addis Ababa University , Addis Ababa , Ethiopia
3 Department of Pharmacology, Medical Faculty, Addis Ababa University , Addis Ababa , Ethiopia
Background: Unprocessed biomass fuel is the primary source of indoor air pollution (IAP) in developing countries. The use of biomass fuel has been linked with acute respiratory infections. This study assesses sources of variations associated with the level of indoor nitrogen dioxide (NO2). Materials and methods: This study examines household factors affecting the level of indoor pollution by measuring NO2. Repeated measurements of NO2 were made using a passive diffusive sampler. A Saltzman colorimetric method using a spectrometer calibrated at 540 nm was employed to analyze the mass of NO2 on the collection filter that was then subjected to a mass transfer equation to calculate the level of NO2 for the 24 hours of sampling duration. Structured questionnaire was used to collect data on fuel use characteristics. Data entry and cleaning was done in EPI INFO version 6.04, while data was analyzed using SPSS version 15.0. Analysis of variance, multiple linear regression and linear mixed model were used to isolate determining factors contributing to the variation of NO2 concentration. Results: A total of 17,215 air samples were fully analyzed during the study period. Wood and crop were principal source of household energy. Biomass fuel characteristics were strongly related to indoor NO2 concentration in one-way analysis of variance. There was variation in repeated measurements of indoor NO2 over time. In a linear mixed model regression analysis, highland setting, wet season, cooking, use of fire events at least twice a day, frequency of cooked food items, and interaction between ecology and season were predictors of indoor NO2 concentration. The volume of the housing unit and the presence of kitchen showed little relevance in the level of NO2 concentration. Conclusion: Agro-ecology, season, purpose of fire events, frequency of fire activities, frequency of cooking and physical conditions of housing are predictors of NO2 concentration. Improved kitchen conditions and ventilation are highly recommended.
Biomass fuel is the primary source of household energy in
developing countries. Fifty two percent of the global
population and more than 90% of rural homes in developing
countries use solid biomass fuels for cooking, heating,
and lighting purposes . Biomass fuel, also designated as
unprocessed or dirty solid biofuel, mainly includes
firewood, animal dung, agricultural residues and plant
Indoor air pollution is considered as one of the risk factors
causing high burden of diseases and of premature deaths
in developing countries [2-5]. IAP is recognized as a silent
and unprotected killer among rural women and children
who spend much of their time in the kitchen . The
burden of disease due to IAP is high in developing nations
that use biomass fuels. Smith estimated that IAP in India
accounted for 4.2-6.1% of the total national burden of
disease, which is considered as a major public health
concern. This proportion is tantamount to about half million
premature deaths annually .
The health risk of direct exposure to biomass combustion
is evident in part due to cooking practices in poorly
ventilated and crowded single roomed houses in developing
countries [4,8,9]. Studies have shown that proxy factors
related to socio-economic status like income education,
and use of biomass fuel to be highly associated with the
level of IAP [7,4,10,11]. Despite the prevailing knowledge
that exposure to biomass combustion products is high
[4,9,12-19], quantitative studies on factors affecting the
level of IAP are limited in developing countries .
Data on the measurement of indoor NO2 is limited in
developed countries. Research has shown that the mean
concentration of NO2 in kitchens with a gas stove varied
between 26 parts per billion (ppb) and 112 ppb [20,21],
while this was 18 ppb in kitchens equipped with electric
stoves . Median NO2 concentrations in living rooms
were 5.8 ppb in Ashford (United Kingdom), 6.1 ppb in
Minorca (Spain), 23.8 in Barcelona (Spain)  and 11
g/m3 (5.8 ppb) in Umea (Sweden) . In low income
homes of USA, mean concentrations of NO2 were found
to be 43 ppb and 36 ppb in kitchens and living rooms,
respectively  and 30 ppb in children's bedroom .
Increased level of NO2 was related with type of fuel and
ventilation efficiency . In addition, these studies
consistently demonstrated that the type of fuel, purpose of
fuel use, and location of indoor activities are associated
with increased NO2. The use of gas stoves and heaters
were often accompanied with exceeding the current
World Health organization (WHO) Air Quality Guideline
(AQG) of 21 ppb for 8 hours .
A particulate matter (PM) of various aerodynamic sizes is
also used to measure the level of IAP. The mean
concentration of respirable suspended particles (RSP) in homes
that use biomass fuels had variation depending on the
type of kitchen and fuel used: 500-2000 g/m3 in kitchens
of India , 1200 g/m3 in Mozambique , 850-1560
g/m3 in Guatemala , and 1400 g/m3 in Kenya .
In Bangladesh, the average concentration of particulate
matter (PM) of aerodynamic diameter of 10 microns
(PM10) in kitchens that use biomass fuels ranged between
237 and 291 g/m3 . Smith showed that the
concentration of PM10 commonly varied between 200-5000 g/
m3 . In Ghana and Nicaragua, the average
concentrations of PM2.5 varied between 320-650 g/m3 in Ghana and
Nicaragua [31,32]. These studies indicated that the type of
kitchen (whether inside or outside), the type of stove
(traditional or improved), ventilation condition, place of
measurement (kitchen or sleeping room), and type of
biomass fuel (wood, dung, or residues) were major factors
affecting the concentrations of indoor air pollutants. The
above cited aerial concentrations related to RSP, PM10 and
PM2.5 exceeded by a factor of 2 to 40 times of the
standards set by US Environmental Protection Agency,
(USEPA) for 24-hr and annual standards  and by a factor
of 10 to 80 times of the present WHO 8 hr of air quality
guideline (AQG) .
Biomass fuel in the form of firewood, agricultural residues
and animal dung is the primary source of household
energy in Ethiopia [34,35]. The majority of rural homes
have only 1 room serving all types of household activities
. Cooking takes place in the same room using
traditional unvented stoves. Such rooms do not have
functional ventilation outlets [3,37]. Pocket studies in
Ethiopia have shown high level of exposure to indoor air
pollution that exceeded the WHO 1-hour and 8-hours
AQG [27,38-40]. However, the results of these studies
could not be generalized to a larger population due to
their methodological limitations.
A recent study conducted by the authors revealed
increased indoor NO2 concentration levels . It also
has shown that ecology and season were factors that affect
NO2 concentrations. The present study was undertaken
with an effort to further explore other factors associated
with indoor air pollution in the rural Ethiopia. Given the
high level of exposure to IAP in poorly ventilated housing
units, increased morbidity and mortality due to acute
respiratory infection (ARI) in the general population,
especially among children under-five years old, are expected
[3,42]. Therefore, this study will have great operational
relevance in achieving the Millennium Development Goal
7 (MDG 7, target 9, indicators 27 & 29) by generating
important information on feasible interventions for the
reduction of IAP in the Ethiopian rural homes.
Materials and methods
Study setting, indoor air sampling and analysis for nitrogen
A longitudinal study was conducted to assess the level of
indoor air pollution, by measuring NO2 level, in rural
households over a period of 2 years (March 2000-April
2002) in a rural district (Meskan and Mareko) in
midsouthern Ethiopia. The presence of a Demographic
Surveillance System (DSS) that was instituted in the District
since 1986 was an opportunity for the assessment of
indoor NO2 concentrations. Indoor air samples for
nitrogen dioxide were taken in approximately 3,300 homes
with under five children.
NO2 was detected using a modified colorimetric Saltzman
method. A 24 hours indoor air sampling was done by
trained local enumerators at about 3 months interval to
collect data on the date, start and finishing time (hh:mm)
of each sample. A digital watch was used to record the
time. NO2 concentration was measured using Willems
Badge that was developed at the University of Wageningen
in the Netherlands [43-45]. The polyethylene passive
sampler consists of a small cylindrical cup equipped with
2 rings, chemically impregnated fibreglass placed at the
bottom of the cup and Teflon to serve as a wind barrier.
The sampler was set in a central wooden post of the rural
housing (locally called "tukul") after ensuring the room
was used for sleeping. The samplers in a batch were sent
to the field after proper assembly. Sampling in the house
was started by opening up the lid of the diffusive sampler
(Willems Badge) and finished by closing it back after 24
hours of sampling.
Samplers from the field sites were transported to Addis
Ababa where a centrally located laboratory was used for
analysis. The absorption filter from the sampler was
extracted with an acid solution of sulphanilamide and
N(1-naphtyl) ethylenediammonium dichloride (NEDA)
which converts the absorbed nitrogen dioxide into
NO2ions. The presence of these ions develops a red coloured
solution whose absorbance was measured at 540 nm
(Beckman DUR .64 Spectrophotometer). A standard curve
NO2solution was developed to calculate the NO2
concentrations using the following equation:
C = ((10 ^ 6 * mass) /(exp min* 40),
'C' is indoor air concentration of NO2 in micrograms per
cubic meter, 'mass' is mass of NO2 on the glass fibre used
for air sampling after subtracting mass of NO2 of the blank
glass fibre, 'expmin' is indoor air sampling duration in
minutes, and '40' is a constant of air sampling rate for the
diffusive sampler, 40 mlmn-1 .
The laboratory data quality was maintained using various
methods. Laboratory technicians were properly trained
and supervised on each day of laboratory analysis.
Laboratory protocols were structured and monitored by
standard practices. Internal validity were checked by the
analysis of standard solution, blank absorbance, and
control chart for NO2. The inter-laboratory variation was
controlled by comparing the variations of NO2
concentrations of duplicate samples and exposed samples
that were taken at different conditions.
Detailed description of the study area, sampling
procedures, air sample location, and the analytical method is
available elsewhere .
Assessment of determinants of indoor nitrogen dioxide
A structured questionnaire was administered at
household level immediately after the completion of the air
sampling to collect fuel use related data and events that
occurred at the time of NO2 sampling. Type of household
fuel, purpose of having fire, type of cooked food and its
timing were main variables collected during data
collection. Ecological and seasonal factors were also considered
due to their importance to affect indoor NO2 . The
physical dimensions (radius, axis, and wall height), the
presence of window and separate kitchen in the study
homes were extracted from the 1999 census data of the
Data management and analysis
Data were entered and cleaned using the EPI INFO
(version 6.04; Center for Diseases Control and Prevention,
Atlanta, GA, USA and World Health Organization,
Geneva, Switzerland). Consistency and completeness of
each questionnaire was checked during data collection,
entry and analysis. The data set was then exported first to
data base file (DBF) and then to Statistical Package for
Social Sciences (SPSS) (version 15.0; SPSS Inc., IL, USA)
for advanced statistical analysis.
After data exploration, the original data set of indoor air
NO2 concentration was transformed into logarithmic base
10 (log10) to meet the assumptions of normality for the
analysis of variance (ANOVA) and linear mixed model
regression analysis. In addition, box plots and stem plots
were extensively used to observe the existence of outliers
when comparing mean values of NO2 by categorical
variables. Variables describing firing events were categorized
in such a way to avoid multiple responses. These variables
were type of fuel, purpose and time of fire events, and type
of cooked food. One way ANOVA was employed for the
detection of any differences and changes in the dependent
variable represented by average indoor NO2concentration
in the presence of categorical biomass fuel variables.
A mixed linear model was used to find out the relative
importance of household characteristics on NO2 after
ensuring assumption of normality in the dependent
variable, linearity between dependent and independent
variables, and collinearity between variables. A unique
identifier for each household and the time variable
attached to each NO2 measurement were created for this
analysis. Ecology, season, type of biomass fuel, purpose
and the time of the fire vents, and the frequency of food
items were used for a fixed effect, while the quarterly
measurements of NO2 were considered for the repeated
effect. The intercept model was only used for the random
effect as all households with under-five children were
involved in the study. The use of unstructured covariance
structure was found to be the best fit for the linear mixed
One way repeated measures of ANOVA was employed to
analyze the presence of any difference in NO2 across time
periods after structuring the data lay out into a long data
format. The effect of housing characteristics (calculated
indoor volume of each home, presence of window and
kitchen) on indoor NO2 level was assessed using
hierarchical model for a multiple linear regression analysis.
Descriptive statistics, tables and inferential statistics were
mainly used to present the findings. Further detailed
management and data quality control are presented elsewhere
Characteristics of fuel use
The study was conducted for 2 years period involving
17215 indoor air samples in 3300 households with no
refusal of participation. About 98% of air samples were
taken at times of fire events in households. Biomass fuel
in a form of wood, crop residues, and cow dung were
largely used in 71%, 65%, and 32% of samples,
respectively. In 2.6% of households, other type of fuels that were
seasonally used including eucalyptus dry leaves, corncobs,
and leaves of false banana. The use of mixed type of fuel
was a common pattern (Figure 1). All firing events,
whether for cooking or not, took place mainly indoors.
Cooking, lighting, heating, and insect repellent were
reported as main reasons for having fire events in
households in the last 24 hours during the time of air sampling
(Table 1). Cooking foods and heating the space, (in 98%
and 34% of the samples, respectively), were the major
activities for fuel use. The use of biomass smoke for insect
repellent was observed in approximately 13% of samples.
Cooking and heating activities simultaneously took place
in one third of the samples, while other activities in
combination were rarely practiced representing less than 2%
of the samples. Households had the practice of fire use
three times a day. There were fewer activities at night that
required the use of biomass fuel. Respondents in 73% of
the samples perceived that firing at home took place
relatively longer in the evenings than other times.
With regard to cooked food items, kale cooking,
traditional coffee ceremony, bread and local staple diet
(locally called "kocho") baking were the usual type of
traditional foods that were prepared during the 24 hours of
indoor NO2 sampling. Traditional flat bread (locally
called "injera") and its accompanying sausage (locally
called "wat") were rarely cooked, and only observed in
less than 10% of the samples. Over 90% of cooking
activities took place in the mornings and evenings, and rarely
1TF6iyg8pu9er9oe)f 1fuel and its use pattern that was observed during 24 hours of indoor NO2 sampling, Butajira, Ethiopia, 2000-2002 (n =
Type of fuel and its use pattern that was observed during 24 hours of indoor NO2 sampling, Butajira, Ethiopia,
2000-2002 (n = 16899). For each type of fuel, the number in brackets indicates the proportion of type fuel used singly and in
combination with others in reference to respondent's judgment comparing with the usual days.
Table 1: Distribution of purpose of having fire and its timing that were observed during the 24 hours indoor NO2 sampling, Butajira,
Ethiopia, 2000-2002 (n = 16899)
Total use of fire
*Percentages did not add up 100% due to multiple responses
nights. Commonly cooked food items were kale
("gomen"), traditional coffee ceremony, and bread in
82%, 81% and 68% of samples, respectively. Other
cooking activities were reported in 7.5% of the samples. These
include roasting and boiling of peas and beans (locally
called "kolo" and "nifro"), boiling of milk, cooking of
cucumber and boiling of maize.
The association between fuel characteristics and
ecological setting is presented in Table 2. Overall, there was a
difference in the type of biomass fuel and its purpose of use.
The use of wood predominated in the highland, while
crop residues prevailed in lowlands. Heating of the
housing space was more frequent in highlands (40%) than
lowlands (28%). Cooking of any three food items and
having three fire events per indoor air sampling day were
commonly practiced in 73% and 80% of samples,
respectively. Ecology was strongly related to all fuel use
characteristics (p < 0.05).
Consistency of fire use events
There was not any difference in the time and frequency of
fire use, type of fuel and type of cooked food items that
occurred between the sampling time and 1 week recall
period prior to that. It was only possible to identify that a
religious holiday related to "Romodan" (the Moslem
fast*Percentages did not add up 100% due to multiple responses
P value for X2
ing month) was implicated to be a factor for additional
cooking food items such as vegetable and meat soup,
which took place relatively longer than the usual days of
The level of NO2 by the characteristics of fuel use
The relative difference in NO2 concentration by proxy fuel
factors is indicated in Table 3. The concentration of NO2
was found to significantly differ by type of fuel. On the
average, households that used wood had geometric mean
(GM) and geometric standard deviation (GSD) of 71.2
(2.8) g/m3. These values for cow dung and crop residues
were 67.5 (2.9) g/m3 and 56.1 (2.7) g/m3, respectively.
Any combination of biomass fuel use did not significantly
affect NO2 concentration.
The GM (GSD) concentration of NO2 representing a
single purpose of having a fire in a household was 69.2 (2.7)
g/m3, any two purposes was 57.1 (2.9) g/m3 and any
three or more purposes was 55.6 (2.8) g/m3. Multiple
comparisons indicated that high level of NO2 was related
to only single purpose compared to combination of them
(p < 0.05). The concentration of NO2 had a declining
linear trend from a single activity to combined activities
(pvalue for linear trend (p < 0.05).
Multiple food cooking was strongly related to NO2 indoor
level compared to any single food preparation (p < 0.05).
An increasing linear trend with the number of cooked
food was also observed (p-value for linear trend (p <
0.05). Coffee drinking as well as bread and "Kocho"
baking were the usual types of food that were frequently
Increased level of NO2 was significant among households
that frequently used firing (p < 0.05). One time of fire use
per day was related to GM (GSD) NO2 of 30.8 (3.34) g/
m3, while this was 64.3 (2.79) g/m3 for any
combination of timing of fire in reference to the morning, daytime,
evening or nighttime. NO2 had an increasing trend with
Table 3: 24 hr indoor NO2 concentrations related to household characteristics in a bivariate analysis, Butajira, Ethiopia, 2000-2002 (n =
X2 linear trend*
*Linear trend was using EPI INFO version 6.04 STATCALC calculator Chi for linear trend. Those who used and not used the respective fuel type
were considered as cases and controls.
In a mixed model linear regression, type of ecology,
season, type of fuel, frequency of fire events and number of
foods cooked per day were able to explain overall
variations in NO2 concentrations (Table 4). A household being
in a highland, wet season, use of crop residues, any time
of having a fire event, frequency of food items, and
interaction between ecology and season emerged as predictors
of indoor NO2 concentration. The purpose of fire events
did not make any effect.
Level of NO2 by time of measurement and housing
The time for NO2 measurement from our database and
variables on housing (calculated volume, window, and
kitchen) from Butajira DSS database were extracted for the
analysis. The mean (SD) NO2 measurements during our
study period was 4.37 (1.90) per household, while the
median was 5. Nearly 70% and 56% of the households
had at least 4 and 5 measurements of NO2, respectively.
There was a significant difference in the repeated
measurements of NO2 concentrations over time [Wilk's Lambda =
0.993, (F(4,2047) = 3.46, p < 0.05 with multivariate eta
squared = 0.007 and observed power of 89%]. The five
NO2 measurements used for comparison in ascending
order of time 1 to time 5 (n = 2186) had GM (GSD) as
follows: 68.6 (2.58), 70.3 (2.54), 69.1 (2.47), 70.1 (2.49),
and 65.2 (2.62) g/m3 (n = 2051) Post hoc pair-wise
comparison showed the overall difference was accounted
between time 2 and time 5, and time 4 and time 5. The
repeated NO2 measurements also differed by ecology,
(F(1,2049) = 260.5, p < 0.05) and by location of
households (locally called 'peasant associations'), [F(8,2042) =
48.9, p < 0.05)].
The calculated volume of "tukul" was linearly related to
NO2 concentration [ (95% CI): 0.104 (0.055, 0.153].
The addition of window in the 2nd model did not show
any association, while kitchen in the 3rd model showed a
significant relationship with indoor NO2. The indoor
volume showed positive relationship, while kitchen was
negatively related to indoor NO2. Indoor volume alone
Table 4: Estimates of fixed effects of fire use characteristics in households having fire events during last 24 hours, Butajira, Ethiopia,
2000-2002 (n = 3849)
95% Confidence Interval of slope
Indoor Fire use characteristics
Dependent Variable: indoor concentration in linear scale transformed to log 10; : Reference group
indicated NO2 to vary by about 1.0 g/m3 for every 10 m3.
Both volume and kitchen were able to explain less than
1% variations in NO2 [adjusted R2 = 0.008] (Table 5).
Selected household characteristics affecting the level of
indoor air pollution have their own role in changing the
level of NO2 in the context of our study area. Nearly all
households in the study area used biomass fuel in the
form of firewood, crop residues, and animal dung among
which the first two predominated. The population in the
study area is known for using wood most of the time
throughout the year, crop residues such as stocks of maize
and barley during harvest times, and animal dung during
summer . While biomass fuel sources are relatively
cheap and easily available locally, fuels of fossil origin
such as kerosene was only used to light local lamps for the
interior of housing units at night. Kerosene is less
affordable for rural residents to use it for cooking purposes
compared to residents of urban areas such as Addis Ababa
. The use of biomass fuel as a primary source of
household energy is consistent with findings of studies in other
developing countries [1,10,19,28,38,48].
Biomass fuel was extensively used for cooking traditional
foods compared to other purposes of having fire events.
Cooking was the most frequent household fuel use
activity reported during all times of the day. Heating was the
second most frequent activity; whereas only about a tenth
of the households used biomass fuel to repeal mosquitoes
at night. The use of heating indoor space predominated in
colder villages, mainly in the highlands, while repealing
mosquitoes prevailed in the lowlands. It is evident from
the data that home heating in the highlands might have
caused an additional fuel use burden, which possibly
contributed to the increased concentration of indoor NO2
compared to the relatively low fuel use burden required
for repelling insects in the lowlands. The extreme
temperature difference  might have contributed to the
variation in the use of fuel for heating between the two
ecological settings. Low temperature in early mornings
and nights is common in the highlands of Ethiopia.
Villages in the lowlands inherently possess a risk to malaria
caused by mosquito bites and, therefore, there is a cultural
practice in the study area for using indoor firing events to
repel mosquitoes [42,50].
Traditional foods that do not require a stock for more
than a day were routinely cooked. The cooking time was
equally important in all cases of cooking which involved
commonly the mornings, mid-days and evenings.
Traditional coffee and bread making are also common daily
practices in the study area. Coffee in each household was
served for a group of neighborhoods nearly on daily basis,
which is a cultural heritage of Ethiopia. The relative time
and cost of preparing these food items are a bit less than
that for "injera" and "wat" which are widely used in other
parts of Ethiopia, especially in the temperate and
highland areas. The practice of "injera" and "wat" is expensive
and the raw material, locally called "teff" is considered as
a cash crop for the rural residents in the study area. The
linear relationship between the number of food items
cooked and frequency of cooking with the level of IAP is
obvious given the increased respective amount of biomass
fuels and the corresponding higher emission of other
pollutants including nitrogen dioxide. This is in part a
reflection of "dose-response" relationship.
Cooking and heating are the main household activities
that lead to the excess NO2 concentration due to solid
biomass fuel use in general, and in the highland areas in
particular. In other studies, given the range of the purpose of
biomass fuel use, cooking has been implicated as the
main factor for the greater proportion of exposure to IAP
in developing countries [4,51]. Biomass fuel emits about
50 times more pollution during cooking compared to
cleaner fuels , while the exposure magnitude of
breathing in pollutants could be twice more for the same
population . Therefore, the magnitude of health risk due to
biomass combustion can reach as much as 2-3 times
greater than the risk among clean fuel users [4,9]. Indoor
smoke from biomass fuel is attributed to loss of healthy
p-value for beta
life in poor countries due to known health outcomes such
as ARI, acute lower respiratory infections, and chronic
obstructive lung diseases . It is possible to speculate
based on our findings that higher degree of exposure to
indoor air smoke goes to mothers and children who often
spend most of their time indoors. This implies that the
attainment of Child Health MDGs would be a challenge
in developing countries, like Ethiopia.
Assumption of the within-subjects (within a household
over time) variation in indoor NO2 concentration by time
was certainly important given the possible differences in
the exposure to various fuel characteristics within
households. This determined the presence of differences in
exposure factors. The repeated between subject difference
in NO2 demonstrated by ecological factors and
seasonality is an important effect that requires closer attention for
designing an appropriate intervention, as well. The study
revealed only an interaction between ecology and
seasonality affecting the indoor NO2 concentration by time. This
was supported by our data that indicated variations of
NO2 to be dependent on the type of fuel in the bivaraite
analysis, although this association remained significant
for a crop residue in a linear mixed model.
Ecology, season, type of biomass fuel, the purpose and
time of having fire events, and the frequency of cooked
food items were all found to affect the level of indoor air
in a bivariate analysis, while this association was
consistent in a linear mixed model except for the type of biomass
fuel for wood. In western countries, the type of fuel (gas
or electricity), occupancy density, the number of cooked
meals, frequency of cooking, season and income were
found to significantly impact indoor NO2 concentrations
[20,24-26,53]. Those studies have indicated the presence
of strong link among factors responsible for the increased
level of indoor NO2 both in the developed and developing
countries. The new finding in our study is the presence of
ecology as a factor that predominately affected the level of
NO2. The high level of NO2 in the highland areas can also
be explained by high proportion of wood fuel use. Wood
is at least better than crop residues and animal dung in the
energy ladder  and provides relatively better energy
efficiency. When wood is used, it oxidizes relatively more
indoor air nitrogen because of the relatively high
combustion temperature than others.
The effect of housing volume was found to show little
importance in affecting IAP as measured by NO2, which
was against our hypothesis. Together with the presence of
window and kitchen in the multiple linear models, there
was only very small proportion of explainable variance
(less than 1%) in NO2 concentration despite the statistical
significance. The computed model was not able to
indicate a practical relevance in explaining the direction and
strength of the association between the magnitudes of
indoor air pollution and the physical housing
characteristics in our study area. The significant difference, however,
could only be explained due to large sample size that
could have picked up small differences for calculating
pvalue. Rural housing units, due to their nature of
construction, allow the easily passage of indoor smoke through
their thatched roof, open eves and unplastered or partially
plastered wall, restricting the continued built up of indoor
air pollutants. It is quite common to observe visually the
penetration of intense smoke through such structures
during active cooking times in early mornings when there is
good visual contrast (personal observation). Windows in
the majority of housing units in the area are represented
by just small circular holes (usually < 5% of the floor area,
which is often closed due to the fear of wind drafts).
Furthermore, opening of windows is culturally believed to
affect resident's heath in our study settings.
Lack of assessment for additional air pollutants such as
PM and the absence of real time measurement for the
indoor air pollution were major limitations of this study.
Nevertheless, the present study has shown that ecology,
season, purpose of fire events, the frequency of cooked
food items and the frequency of fire events as being
predictors of indoor NO2 in a rural setting.
Agro-ecology, season of the year, fire use characteristics
and the physical structure of the housing were found to be
important determinants of indoor air pollution in this
Further study on personal exposure assessment using NO2
and PM is highly recommended. In addition, relating
indoor NO2 levels with commonly seen childhood
diseases, such as respiratory symptoms, is another area of
significant relevance for a research given the high level of IAP
in our study settings. Finally, the provision of separate
kitchen, improved stoves with hood, and presence of
window in kitchens are highly advised to manage low level of
List of abbreviations
AQG: ambient air quality; DSS: Demographic
Surveillance System; EPA: Environmental Protection Agency;
GM: geometric mean; GSD: geometric standard deviation;
g/m3: gram per cubic meter; IAP: indoor air pollution;
MDG: Millennium Development Goal; g/m3: micro
grams per cubic meter; mlmn-: millilitre per minute;
NEDA: N-(1-naphtyl) ethylenediammonium; NO2:
nitrogen dioxide; PM: particulate matter; PM10: aerodynamic
diameter of 10 microns PM; ppb: parts per billion; ppm:
parts per million; RSP: respirable suspended matter;
WHO: World Health Organization.
The authors declare that they have no competing interests.
AK and AE were involved in the study protocol design
development, data collection, data quality monitoring,
data analysis and preparation of the manuscript. YB and
AA were involved in analysis and editing draft
manuscripts. SW and DB designed, trained, and supervised NO2
data collection and laboratory analysis. EM was involved
in supervising and monitoring laboratory data analysis.
AW was involved mainly in data analysis and its data
quality management. All authors contributed to revising
the final manuscript.
The Authors thank Sida/Sarec and the Umea International School of Public
Health for funding this study. We are grateful for the Butajira Rural Health
Program, including all enumerators, field supervisors and laboratory
technicians for their contribution in data collection and data quality assurance.
The encouragement extended by the School of Public Health, the Medical
Faculty of Addis Ababa University is highly respected. We are also grateful
to all colleagues at the School of Public Health, Addis Ababa University for
their continued encouragement to complete the study. Ato Bizu Gelaye, Dr
Mesganaw Fantahun and Dr Damen H/Mariam deserve respect for their
encouragement in the analysis and write up of the manuscript.
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