Submitted to Energy, The International Journal -for publication
EXPOSURE TO AIR POLLUTANTS FROM COMBUSTION OF
COOKING FUELS: A CASE STUDY OF RURAL TAMIL NADU, INDIA
Jyoti Parikh, Kalpana Balakrishnan*, Vijay Laxmi and Haimanti Biswas
Indira Gandhi Institute of Development Research, Gen. Vaidya Marg,
Santosh Nagar, Goregoan (E), Mumbai –400 065
*Sri Ramchandra Medical College and Research Institute, Chennai
Abstract -- In this paper linkage between pollution exposures and the
type of kitchen and fuel types are established for rural houses by
monitoring the indoor air quality of 500 households. The results show
that exposure to chief cook is very high when cooking is performed
using biofuels in any type or location of kitchen. The individuals who
stay inside the house during the cooking activity using biofuels also
face high exposures.
1. INTRODUCTION
About half the worlds population relies on bio-mass - wood, agricultural residues and
charcoal as the primary source of domestic energy burning nearly 2 billion kg of biomass everyday in developing countries [1,2]. Combustion of bio-fuels in poorly
vented kitchens using poorly functioning stoves leads to the release of very high
concentrations of suspended particulate matter and noxious gases [3,4,5]. Exposure to
these pollutants has also been shown in several recent studies to be causally linked to
several health effects especially in women who cook with these fuels and young
children. Strong associations between bio-fuel exposure and increased incidences of
chronic bronchitis in women and acute respiratory infections in children have been
documented [6,7,8,9].
1
In rural India, 90% of the primary energy use is bio-mass, of which wood
accounts for 56%, crop residues for 16%, and dung-cakes for 21 % [10]. A disturbing
trend in rural India has been the increasing use of lower quality fuels like crop
residues and dung cakes. The free access to natural and sub-managed forest lands and
the wide gap between the demand and sustainable supply of wood fuel has led to
illicit exploitation of forests. Gathering of wood imposes high opportunity costs for
the women folk who have to travel long distances to gather fuel [11]. The emissions
from the bio-mass fuels vary with the type fuel used. The most polluting fuels are
also often the most energy inefficient [13].
Several studies concerning bio-mass combustion, air pollution and health have
been conducted in rural Indian villages [12,13,14]. Form information collected during
the recent National family Health Survey of India [15], prevalence of acute
respiratory infections in children [ARI] has been found to be as high as 6.6% in
houses using bio-fuels. One of the main concerns raised in some of these studies has
been the paucity of quantitative exposure data for these pollutants. Further, many
studies have been conducted with small sample sizes that do not adequately capture
the influence of exposure variables such as the type and location of kitchen, type of
stove, type fuel etc. on actual exposures. Lack of quantitative exposure information
has prevented drawing definitive conclusions and the development of accurate doseresponse relationships.
The present study has aimed at assessing exposures to respirable dusts in rural
households across four districts of Tamil Nadu. Through a combination of personal
2
monitoring and area measurements, 24 hour average exposures of both cooks and
other members of the households has determined in over 400 households that cover
all major sociocultural zones across the state.
Influence of multiple exposure
variables on dispersion and exposures has also been extensively examined. This has
been the largest exposure assessment exercise undertaken in Southern India thus far.
2. EMPIRICAL STUDIES
Raiyani [16] discusses the indoor concentration of TSP during the cooking
hours across the houses belonging to low socio-economic group in eastern
Ahmedabad. He concludes that houses using cattle dung, wood and coal emit large
amounts of TSP and the levels in houses using kerosene and LPG is mainly
contribution of outside environment rather than indoor sources. Ramakrishna [17]
tried to establish quantitative estimates of the influence of several environmental and
cultural characteristics like stove type, kitchen location, fuel type, etc. on TSP
exposures. The variable location of kitchen was found to be statistically significant.
However, the difference in exposure between the traditional and improved stove was
not found to be significant. At the same time there was no significant seasonal
variations on TSP exposures. Smith [3] reveals that ventilation parameters and stove
type influence exposures to women. In addition to this outdoor concentrations can
also influence indoor exposures. Smith [18] goes on further to saying that population
exposure depends on pollution concentration as well as on the number of people
involved.
Since a large number of variables are involved in studies of air pollution and
human health, it is difficult to prove that air pollution has a clearly demonstrable
3
effect on human health at normal concentration. Bart et al [19] had tried to estimate
dose response functions for respiratory diseases among children based on data from
public clinics in Santiago, Chile. They found that prevalence of respiratory disease
among Santiago’s children is significantly affected by air pollution, measured as
PM10. Another study conducted in Colombia reported occurrence of obstructive
airways diseases among women cooking in open fires [20]. However in this study the
confounding effect of passive smoking was not considered. The survey conducted by
National Family Health Survey (NFHS) of India 1992-93 reveals that three fourth of
the households surveyed used wood or animal dung for cooking leading to 6.6%
prevalence rate of ARI among children under three years. The prevalence rate was
higher in case of children living in kuchcha houses [15]. The survey carried out by
NFHS (92-93) also reveals that women above 30 years of age, attributes 20% of
complete blindness and 17% of partial blindness attributable to cooking smoke from
biofuels. Women who have higher age, less education and those who are
economically and culturally less endowed have relatively higher prevalence of
blindness. Similarly the prevalence was higher for women staying in kuchcha houses
and without a separate kitchen [21].
3. DATA AND VARIABLES
This article is based on a comprehensive survey conducted in the rural areas of
Tamil Nadu covering 5028 households from 30 villages and 4 districts. The
objectives of the study were to ascertain the following
4
a) Socio-economic and infrastructure characteristics of the villages
b) Socio-economic characteristics of the households
c) Fuel consumption pattern in rural Tamil Nadu
d) Cooking behavior of the households in rural Tamil Nadu
e) Symptoms, disease and lung function test for respiratory symptoms
f) Willingness to pay for clean fuels and improved stoves
g) Environmental priorities in general and the reasons for not using clean fuels
3.1 Survey Design
The selections of the villages were made using multi stage sampling design.
At first stage stratification of state was done on the basis of its socio-cultural regions
(SCRs). Tamil Nadu (TN) was divided into 4 SCRs. Selection of one district per SCR
was done based on population median class. At stage 3 allocation of households to
each district based on the universe distribution of rural households were made. Then
stratification of districts was done by village population sizes. The villages were
divided into 4 stratum on the basis of population. In stratum 1 villages with
population less than 1000 were included, stratum 2 had villages with population 10003000, whereas in stratum 3 villages with population between 3000-5000 were
included. The 4th stratum was made for the villages having population more than
5000. Stratum 4 was excluded from the sample. The selection of the villages from
each stratum was done using population proportion sampling. Selection of households
within the selected village was performed using systematic random sampling.
5
The survey of 5028 households was a comprehensive one having following
features;

Face to face interview

Symptoms and health assessment

Diagnostic approach

Pollution monitoring and exposure.
For this particular exercise we are dealing with the fourth aspect which has
been stated above. Smaller sub- sample of 10 per cent of households was selected
randomly from larger sample of 5028 households for IAQ (indoor air quality)
monitoring and physicians’ diagnosis so as to get a complete picture of the larger
sample.
3.2 Methodology
A Random samples of 500 households were selected as described above for
IAQ measurement (valid measurements were however obtained from 418 households
due to technical and administrative problems). These households were distributed in
30 villages across four districts of Tamil Nadu.
IAQ assessments were conducted by determining respirable dust concentration
using personal samplers. Area/personal samples for respirable dusts were collected
and analyses according to NIOSH protocol 0600. Pumps and accessories were
supplied by SKC Inc and Dupont. For ambient respirable dust measurements, a high
volume sampler was used (Envirotech PM 460)
6
Although multiple measurements were taken during cooking as well as in the
absence of cooking activity, 3 variables were chosen as indices of exposure viz. RD1
(personal exposure to respirable dust while cooking) RD2 ( area measurement of
respirable dust while cooking is going on as a surrogate for personal exposure to
others) and RD3 (ambient concentration of respirable dust).
For regression analysis the dependent variables chosen were RD1 and RD2.
The explanatory variables chosen were type of fuel, location of kitchen, number of
meals cooked per day, type of stove and respirable dusts in ambient air (RD3).
The values RD1 to RD3 were used to deduce the individual exposure during
one cooking period. Simple regression analysis was done to estimate the effect of
various factors on the level of pollution i.e, the level of respirable dust.
The basic equation estimated is:
RDx = constant +  Lockit1 +  Lockit2 +  Lockit3 +  Meals/Day
+  Stove +  Fuel +  RD3 + x
Where,
x = 1,2
RD1
RD2
= Personal exposures for respirable dust while cooking in g/m3
= Area measurement for respirable dusts inside the house while cooking in
g/m3
7
RD3
= respirable dust in the ambient air in g/m3
Lockit1
=1
if cooking is done inside kitchen without partition
=0
otherwise
=1
if cooking is done in separate kitchen inside house
=0
otherwise
=1
if cooking is done in separate kitchen outside house
=0
otherwise
Lockit2
Lockit3
Lockit4
= When cooking is done in open air
Stove
=1
if cooking is done in traditional chulha
=0
otherwise
=1
if biofuel is used for cooking
=0
otherwise
Fuel
Meals/day = Number of meals cooked by the household each day.
x
= error
term
The least personal exposure for respirable dust occurs when cooking is done in open
air using clean fuel and efficient stove and this is given by:
RD1 = constant +  Meals/Day +  RD3 + 
And the maximum personal exposure occurs when cooking is done inside kitchen
without partition using traditional stove and biofuels and this is given by:
RD1 = constant +  Lockit1 + Meals/Day +  Stove +  Fuel +  RD3+ 
8
The floor layout of different kitchen types and placement of samplers are
shown below.
Indoor Kitchen without partitions
Separate kitchen inside the house
Stove
Stove
2
Living area
1
2
1 Kitchen
Living area
Kitchen
Kitchen Type I
Kitchen Type II
Separate Kitchen outside the house
Living area
2
Outdoor Kitchen
stove
Living area
stove
1
1
2
Kitchen
Kitchen type III
Kitchen type-4
1 – Personal exposure for the cook – RD1
2 – Area measurement inside the house while cooking – RD2
Ambient environment high volume sampler – RD3 not shown
9
Kitchen
4. BRIEF PROFILE OF THE SAMPLE UNDER SURVEY
The households selected were distributed in 30 villages across 4 districts of
Tamil Nadu. The gender ratio in rural Tamil Nadu is 980 females per thousand males.
Across the sample the literacy rate for males was 68% and among females was 51%. 1.
The education profile is important because the socio-economic characteristics of the
households as well as its health are influenced by the education of the women class.
The average monthly per capita consumption expenditure on fuel and light in rural
Tamil Nadu is Rs. 11.20, which is comparatively less than what is spent by the
individuals in the urban areas (Rs. 15.37) (CMIE, 1999). Lower expenditure on fuels
means usage of fuels that are cheap and hence less efficient. In the study area about
96% of the household (See Fig-1) used only biofuels (firewood, wood-chips and
agricultural waste). Firewood was most common (75% of the households) followed
by agricultural waste (12% households) and wood-chips (4% of the households)2. Of
the biofuel users 36% used it in indoor kitchens without partitions, 30% in separate
kitchens inside the house, 19% used them in separate kitchen outside the house and
16% used them in open air cooking. Less than 10% of the households used clean fuels
(kerosene, LPG and biogas). Most of the households use traditional stoves, which
have high emission rates. (Figure given below). About 81% of the households under
survey cook two meals per day.
1
The rural literacy rate for the entire state is 62.66%. The male literacy rate is considerably higher at
73.75% and the female literacy rate is much lower at 51.33%.
2
In rural India, 91% of the households use biofuel for cooking of which the share of wood alone is
71.7%.
10
Figure –1:Fuel Consumption Pattern
Only Biofuels
8%
Biofuels & Clean
fuels
Only Clean fuels
31%
61%
Figure2: Distribution of stoves
13%
Figure3: Kitchen Types
Polluting
Stove
Clean
Stove
16%
36%
19%
87%
30%
Figure4: Distribution of Meals cooked/Day
13%
6%
One meal/day
Two meals/day
Three
meals/day
81%
11
Inside kitchen
without
partition
Separate
kitchen inside
house
Separate
kitchen outside
house
Open air
cooking
5. RESULTS AND ANALYSIS
5.1 Results of respirable dust measurements
The results of field measurements of respirable dust (RD) show that the
concentration of RD was the highest during cooking with biofuels. Personal exposures
ranged from around 70 g/m3 for houses using clean fuel to around 2000 g/m3 in
houses using biofuels. The concentration at various locations during cooking with
biofuels depended on the type of kitchen. Agricultural waste resulted in the highest
personal exposure. The results are shown in the bar charts given below. Another
important feature that is worth mentioning is that Tamil Nadu does not appear to have
the practise of cooking with animal dung.
12
Fig 5
Mean Concentration of RD
1 for Different Kitchen Location and Fuel Types
Me
an
co
nce
ntr
ati
on
in
ug/
m3
2500
Indoor kitchen with
no partition
2000
1500
Separate kitchen
inside the house
1000
500
0
Firewood
Kerosene
Woodchips
Agricultural waste
LPG
Separate kitchen
outside the house
Outdoor cooking
Fig 6
Mean Concentration of RD
2 for Different Kitchen Location and Fuel Types
Inside
kitchen
without
partition
Separate
kitchen
inside house
2000
Me1800
an1600
co 1400
1200
nc.1000
in 800
ug/ 600
400
m3 200
0
Kerosene
Firewood Woodchips
Agricultural waste
13
LPG
Separate
kitchen
outside
house
Open air
cooking
5.2 Analysis of Regression Results
RD1, RD2 and RD3 were estimated and the results are summarised in the tables
given below. RD1 was used as a dependent variable. For subsequent regressions
Natural log of RD1 was used as an explained variable because the variations in RD1
were considerably higher and with this transformation the adjusted R2 increases from
0.35 to 0.67. The location of kitchen is an important explanatory variable given the
fact that most of them used biofuels. The exposure of chief cook increases as one shift
from location4 (cooking in the open air) to location1 (cooking is done inside kitchen
which has no partition). This can be explained on the grounds that in open air
concentration of respirable dust is lower because it gets readily diffused into the
atmosphere. Surprisingly the variable RD3 was found to be significant at 5%
confidence level3 which, can be explained in terms of diffusion from outside air to
indoor air through the doors, windows and ventilators. The variables meals cooked
per day and fuel was found to be insignificant. Insignificance of ‘fuel’ can be
explained on the ground that there is a strong multi-collinearity between the fuel and
the type of stove4 used for cooking. This is because each major fuel requires a unique
type of stove. Stove variations were not significant among biofuels and the number of
households using improved chulha was none in the sample. Moreover the sample
being a random representative did not contain adequate number of clean fuels (More
than 90% of the households used biofuels), thereby the variable ‘fuel’ turned out to be
3
4
The t-value was found to be 6 approximately.
The regression between type of fuel and type of stove gave R2 value above 70%.
14
insignificant. To avoid multi-collinearity trap one of the variable i.e, fuel was
dropped. As a result the t-value of stove increased sharply.
15
Table – 1: Estimation of RD1
Dep. Var
Exp Var
Coeff.
RD1
constant -1437.43
fuel
293.7
lockit1
682.15
lockit2
555.38
lockit3
304.11
meals/day 26.29
RD3
11.51
stove
1120.95
Ln RD1 constant
3.52
lockit1
0.43
lockit2
0.4
lockit3
0.23
meals/day -0.005
RD3
0.008
stove
2.61
Ln RD1 constant
3.5
lockit1
0.44
lockit2
0.4
lockit3
0.23
RD3
0.008
stove
2.62
Sd. error
239.86
438.32
99.42
103.25
130.51
74.44
1.6
425.76
0.19
0.08
0.08
0.1
0.06
0
0.09
0.15
0.08
0.08
0.12
0
0.09
T-Stat
-5.1
0.67
6.86
5.38
2.33
0.35
7.21
2.63
18.17
5.34
4.76
2.2
-0.1
5.98
27.82
23.32
5.37
4.76
2.2
6
28.2
2
Adj. R
0.35
0.67
0.67
Initially we started with the notion that there is a positive relationship between
RD1 and number of meals cooked per day but the variable was found to be
insignificant and relationship turned out to be negative. This can be explained on the
ground that personalised exposure was done for one meal cooked and not for all the
meals in a day or 24-hour period. Since the number of meals cooked is observed on a
24-hour basis, the relationship was found to be insignificant and the correlation
negative. Overall the variable ‘number of meals cooked per day’ is highly important
as this determines exposure to the chief cook. Hopefully when we look at the
symptoms and health aspects of pollution this variable would feature differently.
Inspite of this and for the reason stated above we had to drop the variable for
16
regression with RD1 and RD2. The mean exposure of the chief cook when the cooking
is done inside the house without partition using biofuels and traditional stoves is5:
Ln RD1 = 3.499+ 0.435 Lockit1 + 2.62 Stove + 0.008 RD3 + ,
which comes to 1312.91 g/m3. On the other hand if cooking is done in open air
using clean fuel and efficient stove, the concentration of respirable dust is 61.56
g/m3 which is roughly 21 times less. The recommendation to reduce exposure to the
chief cook appears to be provision of improved cookstove and clean fuels like LPG
and electricity.
5
The average RD3 value is 78.16 g/m3.
17
Table – 2: Estimation of RD2
Dep. Var
RD2
Ln RD2
Ln RD2
Ln RD2
Exp Var
Coeff.
constant -1407.14
fuel
231.54
lockit1
1200.72
lockit2
815.12
lockit3
208.91
meals/day -21.91
RD3
8.25
stove
849.18
constant
2.49
fuel
0.52
lockit1
1.96
lockit2
1.55
lockit3
0.64
meals/day
-0.08
RD3
0.005
stove
1.86
constant
2.54
lockit1
1.95
lockit2
1.53
lockit3
0.63
meals/day 0.078
RD3
0.005
stove
2.34
constant
2.4
lockit1
1.96
lockit2
1.53
lockit3
0.63
RD3
0.005
stove
2.34
Sd. error
T-Stat
214.78
354.05
89.53
97.21
126.98
66.7
1.63
342.29
0.26
0.43
0.11
0.12
0.15
0.08
0
0.41
0.26
0.11
0.11
0.15
0.08
0.001
0.13
0.21
0.11
0.12
0.15
0.002
0.12
-6.55
0.65
13.41
8.39
1.65
-0.33
5.05
2.48
9.6
1.21
18.13
13.17
4.16
-0.94
2.72
4.5
9.92
18.08
13.1
4.12
-0.97
2.72
18.75
11.38
18.13
13.07
4.11
2.6
18.76
2
Adj. R
0.49
0.67
0.67
0.67
Estimation of RD2 gives more or less the same result in terms of R2, which
implies that the variables that explain RD1 also explain RD2. But the coefficient
associated with location of kitchen as well as their associated t-value has increased
sharply. Each of the variable included except meals per day is insignificant at 5%
level of significance. So it was dropped in further analysis.
18
The mean exposure of inmates when cooking is done inside the house in a
kitchen with no partition using biofuels and traditional stove is:
Ln RD2 = 2.399+ 1.957Lockit1 + 2.336 Stove + 0.005 RD3 + ,
which comes to 1187.97 g/m3. Thus we see the exposure of respirable dust is very
high for the individuals who are not involved in the cooking activity but stay indoors.
This includes the children, the sick as well as the aged people. These people are
vulnerable in case they are not mobile. The mean exposure when cooking is done in
the open air using clean fuels is 16.28 g/m3, which is 73 times less than when
cooking, is done in closed space using traditional stoves.
5.3
Discussion
The latest WHO air quality guidelines prescribe no thresholds for health
effects associated with particulate exposure. However, the national air quality
standards from Central Pollution Control Board (CPCB) on respirable dust for 24hours in rural residential areas is 100 g/m3 (TEDDY, 98-99). On studying the sample
closely we observe that for various kitchen types (whether lockit1 or lockit2 or lockit3
or lockit4) the pollution level is much above the specified standard during cooking
hours. Only when clean fuel is used for cooking in open air, we observe the pollution
level below the specified standard.
From the graph it is quite clear that the chief cook face greater exposure levels
in material of whether they cook inside the house or in the open air. The exposure
levels are largely reduced if cooking is done with the efficient stoves. Likewise the
19
concentration profile of respirable dust inside the house two metres from the cooking
zone face a similar trend. The maximum concentration of respirable dust is observed
when cooking is done inside kitchen with no partition using traditional stoves.
Figure 7: Personal exposure for chief cooks at various concentration during two
1400
(127)
(103)
1200
(48)
1000
(9)
800
600
400
200
(37)
The numbers in
paranthesis
indicate number
the number of
measurements for
each type of
stove/kitchen
1
le
an
st
ov
e/
Lo
ck
Tr
it4
ad
.S
to
ve
/L
oc
ki
Tr
t2
ad
.S
to
ve
/L
oc
ki
Tr
t1
ad
.S
to
ve
/L
oc
ki
Tr
t3
ad
.S
to
ve
/L
oc
ki
C
t4
le
an
st
ov
e/
Lo
ck
it2
0
C
Mean concentration in ug/m3
hours of cooking
NB:- Lockit1 - Inside kitchen without partition, Lockit-2 – Separate kitchen inside house,
Lockit3 – Separate kitchen outside house, Lockit4 – Open air cooking
20
Figure 8:Concentration profile for various kitchen and stove types at two metres
from cooking
1400
1200
1000
C Mean concentration in ug/m3
le
an
st
ov
e/
Lo
Tr
ck
ad
it4
.S
to
ve
/L
oc
Tr
ki
ad
t2
.S
to
ve
/L
oc
Tr
ki
ad
t1
.S
to
ve
/L
oc
Tr
ki
ad
t3
.S
to
ve
/L
oc
C
ki
le
t4
an
st
ov
e/
Lo
ck
it2
800
600
400
200
0
NB:-
Lockit1 - Inside kitchen without partition, Lockit-2 – Separate kitchen inside house,
Lockit3 – Separate kitchen outside house, Lockit4 – Open air cooking
One of the surprising aspects of the random sample selected was the extraordinary
low frequency of households using clean fuels or improved stoves. Larger samples
that purposively included houses with interventions and inclusion of parameters such
as ventilation, type of house and quantity of fuel would allow better judgements to be
made regarding the dispersion of pollutants.
6. SUMMARY AND CONCLUSIONS
The basic premise of this exercise is to determine the linkages between
exposures (of the chief cook and the residents of the households) and the kitchen and
21
fuel types. Across the random and representative sample we observed predominant
use of wood as cooking fuel.
The regression analysis reveals that there is a strong correlation between
exposure to pollutants and location of kitchen. The concentration of respirable dust is
maximum when cooking is done inside the house without partition using traditional
stove. The exposure to chief cook (RD1) is maximum when cooking is done indoors
or outdoors using biofuels. One of the important findings is that the individuals who
are inside the house during the cooking activity denoted by variable RD2 too face high
exposures. The regression analysis also reveals a positive correlation between the type
of fuel and type of stove used for cooking, since every fuel has a specific type of stove
and variations among types of traditional stove was rarely found. As a result we had
to drop one of the variable. The significance of the variable ‘stove’ improved
dramatically by removing fuel variable. The variable meals per day was found to be
insignificant. The most important variable found was location of kitchen followed by
type of stove and open air emissions (RD3). The exposure to biofuels is more a
problem of poor to middle class income group than the very poor because the very
poor generally cook in the open air, cook fewer meals and eat less number of dishes.
For open air emissions (RD3) only explained variable relevant was number of meals
cooked per day because concentration of respirable dust increases with the increase in
number of meals cooked by the households.
The results from the regression analysis suggest that opting for good
ventilation, cleaner fuels, rational cooking technology and practices can substantially
reduce the level of pollution. Another feasible policy would be to make available
clean fuels especially kerosene, which is the next clean fuel in the energy ladder after
22
wood, for those who can afford to buy it at market rate. Marketing and promotion of
Improved cook stoves (IC) can also reduce exposures. Presently improved cook
stoves are not very popular in the study area. Our socio-economic study of 5000
households reveals that even those who have IC are not using it because it is
inconvenient to use.
For future analysis we wish to incorporate other confounding variables like
type of housing (mud or cement), ventilation, income of the households, female
education, etc. A better analysis is expected if one has at-least 8 hour or 24-hours of
exposure monitoring with some control group using clean fuels and efficient stoves.
The major strength of the study has been determination of actual exposure
under a variety of exposure situations prevailing in the rural houses. The study
therefore can lay down the framework for the evaluation and even the design of
interventions to further establish definitively the linkages between bio-fuel smoke
exposure and health.
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Barnes, D.F.; Openshaw, K.; Smith, K.R.. van der Plas, R. What makes
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Reddy, A.K.N.; Williams, R.H.; Johansson, T.B. Energy after Rio: Prospects
and Challenges, United Nations Publications: New York, 1996.
23
3.
Smith, K.R., Biofuels, air pollution and health. A global review. Plenum
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4.
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