Introduction
Carbonaceous aerosols, defined as black carbon (BC) and also known as elemental carbon (EC) and organic carbon (OC) , form a significant
and highly variable component of atmospheric aerosols. Neither BC nor OC has
a precise chemical definition. OC includes numerous organic compounds, some
of which are found to be carcinogenic, such as poly-aromatic hydrocarbons
(PAHs) . The Intergovernmental Panel on
Climate Change (IPCC) defines BC as “Operationally defined aerosol species
based on measurement of light absorption and chemical reactivity and/or
thermal stability” . BC is released from incomplete
combustion of carbonaceous fuels such as agricultural and forest biomass,
coal, diesel, etc. The type of combustion greatly affects the BC emission
rates; notably, inefficient combustion emits more BC than efficient
combustion for the same type of fuel. Aside from air quality and health
effects, there are a number of climate impacts of BC emissions including
alterations to temperature through atmospheric adsorption, modifications to
precipitation timing and increased melting of snow , all of which are
consequential to global warming. BC has been implied to be the second-largest contributor to global warming after CO2 .
There is a current debate that due to the short life span of BC, the BC
atmospheric concentration will drop quickly if emissions are reduced, thereby
potentially offering a rapid means to slow down global warming
.
India is a rapidly growing economy with massive future growth potential. The
total energy and coal consumption has almost doubled from 2001 to 2011
. The emissions of particulate matter or aerosols have been
rising over the last few decades and are expected to increase in the future
as well, due to rapid industrial growth and slower emission control measures
. Recent studies have shown
that the deposition of BC in the Himalayan glaciers has accelerated their
melting. While BC is a source of warming on a global scale, on a regional
scale, it has adverse effects on air quality and human health. BC is a major
part of particulate matter, with a size less than 2.5 micron (PM2.5), and
like other PM2.5 particles, it is small enough to be inhaled. According
to the World Health Organization (WHO), exposure to BC can lead to
cardiopulmonary morbidity and mortality. WHO also suggests that BC may act as
a universal carrier of chemicals of varying toxicity to lungs
. Understanding the sources of BC, their emissions and
spatial distribution is important both for policy making and improving
climate modeling. Preparation of an accurate emission inventory is the first
step towards developing robust air pollution control strategies. Air quality
measurement stations are installed at limited locations and are unable to
provide a measure of spatial variability. However, observations coupled with
air quality models can provide comprehensive information about the impact of
various sources on ambient air quality and their spatial variability. The
greatest benefit of these models is gained after preparing an accurate
emission inventory, validating the models with observations and thereby enabling
a tool for improved control measures.
Although there have been several emission inventories developed for BC in the
last decade, the estimates are variable without any knowledge of
uncertainties. Model-predicted BC concentrations over India are 2 to 6 times
lower than the observed concentrations
. Further the current
estimates vary considerably. The Reanalysis of tropospheric chemical composition
(RETRO) emission inventory estimated BC
emissions in 2010 as 697 Gg yr-1; the System of Air Quality Weather Forecasting
and Research (SAFAR) emission inventory estimated them as
1119 Gg yr-1 for the year 2011; report BC emissions as 1104 Gg yr-1
for the year 2010, and reported them as 1015 Gg yr-1 for the year 2010. Not
only is there a need to get a meaningful total estimate but there is also a need to assess
the uncertainty and spatial variability associated with these estimates. Most
of the emission inventories provide yearly emissions and do not account for
sub-annual temporal emission variability, which leads to inaccurate impact
assessments. To improve the nature of advanced numerical forecasts of impacts
from aerosol pollution, we have developed an emission inventory at a monthly
resolution.
Methodology for national emissions.
Methodology for preparing gridded emissions.
The objective of this study is to prepare a sub-annual, high spatial
resolution, comprehensive spatially gridded emission inventory of BC
emissions for India for the base year 2011. The approach is a ground-up
inventory based on activity data from various sectors, combined with emission
factors. While results are provided for 1 year, the frequency and
distribution should be general enough such that coupled with growth
forecasts, multiyear use could be valid. In this study, we have prepared a
district-wise emission inventory available on a 40×40 km2 grid. We
have accounted for all the major sources of BC emissions in India. For
example, emissions from kerosene lamps and forest fires, which
were previously unaccounted for in many emission inventories, have been
included. Monthly variation of BC emissions has also been estimated to
provide better input for air quality models. We employ a unique approach to
quantify uncertainty in the emissions by considering variability in (i)
activity data from various sources and (ii) emission factors (EFs).
Specifically, probabilistic distributions were assigned to both activity data
and EFs. By employing a Monte Carlo simulation, several activity levels and EFs
were generated to arrive at emissions (by multiplying generated activity data
and EF), which could be interpreted in terms of a mean value and associated
uncertainty.
In Sect. we present the methods used in our analysis. Sect. describes the source sectors and activity data
we considered. A description of the magnitude of emissions from each sector
is presented in Sect. .
Methods
Our approach may be divided into two parts. Figure presents the methodology for developing national
emissions and their uncertainty, and Fig. presents the
approach for extracting gridded emissions. For estimating national emissions,
a thorough review of multiple national activity data and EFs for each source
was conducted from available published and unpublished sources (Table and Table ).
We fit a probability distribution function (PDF) to both national activity
data and EFs from a pool of distributions on the basis of a Kolomogorov–Smirnov
test (KS statistic) using Mathwave Technologies EasyFit© software
. Using the optimal PDF for both variables (EFs and activity data) for each source, we generated 1000
estimates of each variable from each of the two distributions. Further
increasing the number of generations did not change the mean and the variance
of the emissions.
For activity data that had only one source of information, a normal
distribution with a mean as the data point and standard deviation of 20 % of
the data point was assumed based on the experience regarding other data sets (Table ). Best-fit distributions were only determined from the KS
statistic if the number of data points exceeded five; in other cases, a uniform
distribution was assumed.
For preparing the gridded inventory, the emissions were first estimated
within a Geographic Information System (GIS) using polygons at the district
level. Polygons were subsequently divided into 40×40 km2 grid
elements and were proportionally assigned emissions based on the area. The area
for grid elements spanning a district border was accounted for. Emissions
from industry (point data) were added directly to the overlying grid based on
available location coordinates for the source. For the road transport
(network) sector, the data from at the district level were distributed along
the road network and then assigned to overlying grids, proportionally to the
length of road in the grid element. Interpolation of the data was not
conducted, as this would lead to erroneous georeferencing of emissions,
particularly in the case of point data. More details are found in the
subsections below.
Mean activity data, standard deviation and best-fit probabilistic distribution.
Subsector
Activity level
Mean ± SD
Distribution
Open burning (Mt yr-1)
Crop residue burning
99.931,2, 89.791,3, 90.941,4
93.56 ± 4.96
Uniform
Forest fire*
47.835
47.83 ± 9.56
Normal
Garbage burning
3.902,6, 2.512,6,7,8,9
3.2 ± 0.76
Uniform
Industry (Mt yr-1)
Brick*
47410
474 ± 237
Normal
Steel*
40.0511
40.05 ± 8.01
Normal
Sugar*
77.112,13
77.1 ± 15.42
Normal
Cement*
28.0614
28.06 ± 5.61
Normal
Power coal*
380.9115
380.91 ± 76.18
Normal
Power diesel*
0.7115
0.71 ± 0.01
Normal
Transport (billion km yr-1)
Bus
38.7716,17, 39.391616,18,32.831616,19
35.46 ± 3.94
Uniform
Car
12816,17,196.2716,18, 130.8516,19, 167.8716,21
155.06 ± 29.76
Uniform
LMV
104.2216,17, 131.5116,18, 65.7516,19
105.32 ± 22.74
Uniform
LCV
78.5516,17, 99.1216,18, 74.3416,19
111.37 ± 38.70
Uniform
Truck
122.9916,17, 87.3016,20, 125.5016,21
109.60 ± 16.72
Uniform
Taxi
38.2516,17, 40.2616,18, 48.3216,19, 16.9116,21, 60.4416,22, 51.0116,23
42.52 ± 14.86
Gumbel
Two wheeler
450.816,17, 764.0716,18, 481.3616,21, 2062.8916,22, 313.2616,23
814.50 ± 716.18
Uniform
Tractor and trailer
11.0816,17, 26.3816,18
18.73 ± 8.38
Uniform
Railway coal (kt yr-1)*
124
1 ± 0.02
Normal
Railway diesel (kt yr-1)*
21.0624
21.06 ± 42.10
Normal
Shipping HSDO (kt yr-1)*
0.1125,26
0.11 ± 0.02
Normal
Shipping fuel oil (kt yr-1)*
8025,26
80 ± 16
Normal
Shipping LDO (kt yr-1)*
0.3625,26
0.36 ± 0.07
Normal
Aviation LTO (kt yr-1)*
514.162,25,27,28
514.16 ± 102.83
Normal
Aviation cruise (kt yr-1)*
1505.832,25,27,28
1505.83 ± 301.16
Normal
Domestic fuel (Mt yr-1)
Dung cake
144.8429, 75.6230
110.23 ± 37.91
Uniform
Agriculture residue
125.3429, 81.2530
103.30 ± 24.14
Uniform
Firewood
209.9931, 281.9929, 193.8730
228.62 ± 41.96
Uniform
Coal*
4.7731
4.77 ± 0.95
Normal
Kerosene cooking*
4.5731,32
4.57 ± 0.91
Normal
LPG*
12.3731
12.37 ± 2.47
Normal
Kerosene lamps
1.6832,1.2131,32
1.45 ± 0.25
Uniform
Others (Mt yr-1)
Irrigation pumps*
2.1125
2.11 ± 0.42
Normal
Diesel generators (mobile towers)*
1.1225,33
1.12 ± 0.22
Normal
Diesel generators (other)*
2.2825,33
2.28 ± 0.45
Normal
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10 Industry experts.
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* Normal distribution assumed.
Mean EFs, standard deviation and best-fit
probabilistic distribution.
Subsector
EFs used
Mean EF ± SD
Best-fit distribution
Open burning (g kg-1)
Crop residue burning
0.691, 0.782, 0.733, 0.474, 0.752
0.69 ± 0.19
Dagum
Forest fire
0.561, 0.984, 0.995, 0.566
0.76 ± 0.21
Error
Garbage burning
0.657, 0.378
0.51 ± 0.15
Uniform
Industry (g kg-1)
Brick
0.119, 0.279, 0.099
0.16 ± 0.09
Uniform
Steel
0.323, 1.1–1.5810, 0.22411, 0.23–0.1312, 0.065, 0.009513
0.45 ± 0.51
Log Pearson 3
Sugar
1.214, 0.715
0.95 ± 0.27
Uniform
Cement
0.323, 1.1–1.5810, 0.22411, 0.23–0.1312, 0.065, 0.009513
0.45 ± 0.51
Log Pearson 3
Power coal
0.003–0.03216, 0.07713, 0.002911, 0.0025, 0.065
0.03 ± 0.03
Gamma (3P)
Power diesel
0.2511, 0.158, 0.0613
0.15 ± 0.08
Uniform
Transport (g km-1)
Bus
0.3517,18, 0.818,19, 0.22518,20, 0.6118,21
0.49 ± 0.24
Uniform
Car
0.1622, 0.1717,18, 0.0518,19, 0.0718,20, 0.1618,21
0.09 ± 0.06
Uniform
LMV
0.1622, 0.13817,18, 0.1718,21
0.15 ± 0.01
Uniform
LCV
0.2717,18, 0.1318,19, 0.1618,21
0.19 ± 0.07
Uniform
Truck
0.6117,18, 0.2618,19, 0.1918,20, 0.3118,21
0.34 ± 0.17
Uniform
Taxi
0.0122, 0.0617,18, 0.07618,20, 0.05718,21
0.05 ± 0.03
Uniform
Two wheeler
0.01323, 0.01217,18, 0.03818,19, 0.02318,20
0.02 ± 0.01
Uniform
Tractor and trailer*
1.2423
1.24 ± 0.25
Normal
Railway coal (g kg-1)
1.8313, 38
2.415 ± 0.33
Uniform
Railway diesel (g kg-1)
1.5324, 0.518, 0.2913
0.78 ± 0.59
Uniform
Shipping HSDO (g kg-1)
0.8525, 1.198, 1.3226, 0.3625
0.78 ± 0.49
Gen. extreme value
Shipping fuel oil (g kg-1)
0.3825, 0.3625, 0.9725, 0.8525, 1.198, 1.3226
0.72 ± 0.40
Wakeby
Shipping LDO (g kg-1)
0.8525, 1.198, 1.3226
0.89 ± 0.46
Uniform
Aviation LTO (g kg-1)
0.08–0.127
0.09 ± 0.01
Uniform
Aviation cruise (g kg-1)
0.02–0.127
0.06 ± 0.02
Uniform
Domestic fuel (g kg-1)
Dung cake
0.538, 114, 0.828, 0.254, 0.4929, 0.1830, 0.1231, 0.415
0.47 ± 0.31
Gen. extreme value
Agriculture residue
0.4332, 0.6611, 0.752, 0.474, 0.3729, 18, 1.333, 0.2430, 1.3834, 0.631, 0.931
0.74 ± 0.37
Gen. extreme value
Firewood
13, 0.591, 0.414, 0.732, 1.214, 128, 0.858, 0.631, 0.3529, 1.133, 0.2530, 0.8335, 1.336, 0.736
0.78 ± 0.32
Gen. extreme value
Coal
1.913, 2.8410, 1.834, 58, 0.2837, 2.29524, 0.811, 0.311, 0.6911, 0.7911, 0.3211, 0.49711, 0.0736, 5.415
1.64 ± 1.73
Pearson 6 (4P)
Kerosene cooking
0.164, 0.0215
0.18 ± 0.02
Uniform
LPG
0.06711, 0.0115
0.04 ± 0.03
Uniform
Kerosene lamps
6638, 8938, 7238, 11038, 7938, 9438, 8938, 7638
84.37 ± 14.05
Pearson 6 (4P)
Others (g kg-1)
Irrigation pumps
3.1824, 3.968
3.56 ± 0.22
Uniform
Diesel generators
3.4124, 3.968
3.68 ± 0.16
Uniform
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* Normal distribution assumed.
For the national level annual inventory, Monte Carlo simulations were
undertaken to specifically estimate mean emissions and uncertainties,
whereas at the district level the mean of the EFs and district level
activity data were used to arrive at average emission levels. An image of the
political map of India was georeferenced using
Google Earth, and 640 districts were digitized as polygons to generate a
national level shapefile. This shapefile had an attribute table containing
all the districts, and yearly emission quantities were recorded for each
district. The shapefile and polygon data were resampled to a 40×40 km2 grid by calculating the area of each portion of the districts within a
grid element and attributing that portion of the emissions to the grid. As a
grid cell may overlay over more than one district, the overall emission in
each cell was calculated by summing up part of emissions from each
contributing portion from the district, based on area of the district within
the grid cell and emission density for the district:
Ecell=∑i=1n(ρi⋅Ai),
where n is the total number of districts within each grid cell, ρ is
the emission density (g s-1 m-2) for each district and A is the area of the district (m2) within
the grid. Emission density (mass / time area) was calculated by dividing
the BC emission in the district with the total area of the district.
Flow chart for the calculation of forest fire emissions based on
MODIS products.
Source sectors and activity data
The emissions sources considered in this study can be broadly categorized
into five sectors: open burning, industry, transport, domestic fuel and
others. In the following section we define the activity data and emission
sources considered within each sector. All the emission sources identified by
and were included in this
study. Also, some of the highly emitting sources identified in the recent
literature (kerosene lamps, diesel generators and irrigation pumps) were
also considered. Tables and
provide an overview of activity data and EFs for the sources considered.
Open burning
The open-burning sector includes forest fire emissions, open solid-waste burning and agriculture residue burning.
Forest fire
According to the 2013 Forest Survey of India (FSI), around 50 % of the forest
area of India is prone to forest fires . There is a strong
seasonality associated with forest fires in India, with the majority of fires
occurring in the months from February to July. The causes of forest fire in
India are both man-made and natural, natural causes being the high
temperature and low humidity. Man-made causes include accidental fires and
forest burnt for shifting cultivation. The forest fire burnt area in this
study was determined using the MODIS (Moderate Resolution Imaging
Spectroradiometer) monthly burnt-area product MCD45A1, which has a resolution
of 500 m . MODIS product MCD12Q1 (500 m resolution) was used to
define forest cover. The burnt-area and land cover products were retrieved
from the LP DAAC website (https://lpdaac.usgs.gov/).
Land cover and burnt area for March 2011.
The methodology used for emission estimation is presented in Fig. . The burnt-area (MCD45A1) and land cover product
(MCD12Q1) are available in Hierarchical Data Format – Earth Observing System
(HDF-EOS) and have an Earth gridded tile area of 1200 km × 1200 km.
They were stitched to cover the whole geographical extent of India. The
stitched products were converted to GeoTIFF image format and clipped to the Indian domain using the ESRI shapefile of the boundary of India. The same
methodology was used for the burnt-area product as well as the vegetation
cover. Monthly burnt area GeoTIFF images were overlayed on land cover images
to determine the monthly forest burnt-area pixels and subsequently the
forest area burnt. Dry mass per unit area of forest burnt was taken to be
5.2 kg m-2 . Emissions were distributed district-wise according to
the number of incidents of forest fire occurring in that district in 2011.
The data of district-wise incidents of forest fire were taken from the most
recent forest survey . Figure
shows the land cover image and burnt-area image used for estimating the
forest fire burnt area in January 2011. It can be noted that the emissions
from this subsector can easily be updated for future years using the
latest MODIS burnt area and land cover products and following the
aforementioned methodology.
Municipal solid-waste open burning
The dry weight content of Indian municipal solid waste (MSW) was estimated
using the MSW composition in India and the dry matter
content of MSW components per . Indian MSW is primarily
composed of vegetables (40 %), stones (42 %) and grass (4 %), which have a dry
matter content of 40, 100 and 40 %, respectively. Dry matter content was
estimated to be 67.6 %.
State-wise generated and collected MSW was derived from the Central Pollution
Control Board (CPCB) Municipal Solid Waste Management Report 2012
. The MSW generated was distributed among districts according
to their urban population. For the states where MSW collected volume was not
available, a value of 60 % of the total MSW generated was assumed
. The total MSW openly burnt was taken to be 10 % of the
collected waste and 2 % of the uncollected waste
. To provide a
second approach for the uncertainty analysis, per capita waste generation in
India and the fraction burnt were taken from . The 2011 census
population data were used to provide the urban population of the district.
From this, the total MSW burnt for each district was taken as the product of
the IPCC guideline results and the urban population.
Agricultural residue burning
India generates a large amount of agricultural residues (e.g., waste biomass)
every year after harvesting crops. These residues are used as domestic and
industrial fuel, fodder for animals, etc., but a large amount remains
unutilized in the fields. The quickest and easiest way for the farmers to
manage this waste is to burn it. Figure shows a
flowchart for estimating emissions from crop residue burning.
The state-wise production of cotton, jowar, barley, jute, ragi, rice, maize,
bajra, groundnut, sugarcane, wheat and rapeseed and mustard in 2011 was taken
from the Ministry of Agriculture (2012) (http://eands.dacnet.nic.in/). The crop
production was distributed among districts of that state according to the net
sown area in that district. Emission from
crop residue burning was calculated using the following equation as suggested
by .
ECRB=∑i=1D∑j=1C(P⋅Q⋅R⋅S⋅T⋅EFBC),
where ECRB is the emissions from crop residue burning. The summation is done
over the districts, D, and for each type of crop, C. The emission is then
calculated from the product of crop production (P), residue-to-crop ratio
(Q), dry matter fraction (R), the fraction burnt (S), the fraction
actually oxidized (T) and finally the EF for BC. Three estimates of crop
residue burnt (P⋅Q⋅R⋅S⋅T) were obtained by varying
Q, R and S, while holding P and T constant for all the three
estimates. In the first estimate, the residue-to-crop ratio (Q), dry matter
fraction (R) and fraction burnt (S) were taken from . In
the second estimate, the residue-to-crop ratio and dry matter fraction was
kept the same and the fraction burnt was taken as 0.25 for all the crops
. In the third estimate, the residue-to-crop ratio and dry
matter fraction was taken from , and the fraction burnt
was kept as 0.25 . This provided us with three estimates
of the total crop residue burnt in the fields (Table ).
Flow chart for the calculation of agricultural waste burning.
Industry
The industrial sector includes brick production, cement, steel plants, sugar
mills and powerplants. In general, emissions and activity data for these
sectors are derived from available published reports and scientific
literature. We then use location information from each of the facilities to
develop district-wise emissions. In order to construct the gridded inventory,
industrial units were geolocated precisely using the provided GPS coordinates
wherever available. In general, geolocated coordinate data are available for
iron and steel manufacturing, cement, sugar mills and power production. Where
exact information regarding facility locations cannot be obtained directly,
the district-wise distribution is a function of population density. Within
the industry sector, this is the case for brick kilns, adding a source of
uncertainty to the analysis, but also a novel emission, which previous studies have not included.
Brick industry
The Indian brick industry has more than 100 000 brick kilns producing 250
billion bricks and consuming about 25 million tons of coal annually
. Bricks in India are produced locally in small
enterprises on a rural scale . It is a seasonal industry
operating predominantly from October to June .
Brick kilns can be classified into two major categories based upon firing
practice: intermittent and continuous kilns. Intermittent kilns include
clamp, scove, scotch and downdraft kilns (DDK). In these kilns bricks are
fired in batches. In continuous kilns brick heating and cooling takes place
simultaneously in different parts of the kiln. Several types of kilns, including the Bull trench kiln (BTK), Hoffmann kiln, zigzag kiln, tunnel kiln
and vertical shaft brick kiln , operate continuously.
In India a majority of the bricks are produced from fixed-chimney Bull trench kilns (FCBTKs) and clamps . There are around
60 000 small-scale clamp kilns in India. Located all over India – mostly
near or in villages and using biomass, coal and lignite as fuel
– these represent an important source of BC emissions.
No account of their location, production, fuel consumption and emission
factors have been published. For this study, emissions only from FCBTKs
are used, which account for 70 % of the total bricks produced from India,
and these kilns use coal as the primary fuel . The state-wise brick
production (in kg) through these kilns was compiled from consultation with
industry experts. It was distributed district-wise according to the
population of the districts in the state. The quantity produced was assumed
to be normally distributed with 50 % standard deviation .
Cement manufacturing
The plant-wise cement production in 2011 was taken from the Cement Manufacturers
Association, Government of India . India had around 150 major
cement plants in 2011, which produced 180 million tons of cement and consumed
28 million tons of coal. Cement being a transport-expensive product, plants
are evenly distributed across India. Since the plant-wise coal consumption
was not available, the national consumption by cement industry was taken from
the same source. The fuel consumption was distributed using available
location data and based on cement production in 2011.
Iron and steel production
India produced 68.6 million tons of total finished steel in 2010–11, consuming
40 million tons of coal . The plant-wise steel
production was taken from the , Government of India. The coal
consumption was distributed among plants according to their level of steel
production. District-wise coal consumption in steel plants was subsequently
determined from the location of these plants.
Sugar mills
India ranks second globally in terms of sugar production. Significant BC
emissions result from sugar mills due to the usage of bagasse as a fuel.
Bagasse is the fibrous residue obtained from sugarcane juice extraction and
consists of cellulose (50 %), hemicellulose (25 %) and lignin (25 %)
. India has a total of around 550 sugar
mills, which produced 26.3 million tons of sugar by crushing 361 million tons
of sugarcane (Indian Sugar Mills Association, ). Specific
geolocated data are available and were used to distribute the emissions in the
gridded data set. The mill-wise sugarcane crushing capacity was taken from
the Department of Food & Public Distribution . The total
sugarcane crushed was distributed among mills according to their crushing
capacity. The bagasse generated was taken as 30 % of the total sugarcane
crushed .
Powerplants
The Indian Central Electricity Authority reports the
plant-wise fuel consumption for coal and diesel powerplants in India. In
2011, India had an installed capacity of 112 GW of coal- and 1.2 GW of
diesel-based thermal powerplants. There are around 100 coal-based and 14
diesel-based major thermal powerplants located across India, with location data
available from government reports. District-wise fuel consumption was
estimated by the location of these plants using the data contained in the
report.
Transport
From the transportation sector emissions from road vehicles, railways,
shipping and aviation have been accounted for individually. For road
vehicles, to prepare gridded data from district level emissions, road network
data from OpenstreetMap© were utilized. The data
provide a high-resolution road network in vector format. The district
shapefile, grid polygons and road network shapefile were resampled to a 40×40 km2 grid by calculating the total road length in each portion
of the districts within a grid element and attributing that portion of the
emissions to the grid. For non-road vehicles, the methodology discussed in
Sect. 2 was used.
Road vehicles
Road vehicles have been divided into seven categories: two wheelers, cars,
light motor vehicles (LMVs), light commercial vehicles (LCVs), taxies, trucks,
buses, tractors and trailers.
The state-wise number of registered vehicles in the aforementioned categories
was taken from the . The vehicles were distributed among
districts of that state according to the population of that district. In
determining the emissions for 2011, we needed an estimate of the number of
vehicles on the road for that year. The reported number of registered vehicles
represents the cumulative number of first registrations .
In India, vehicles are neither deregistered when they are no longer in use
nor are double registrations deducted. The actual number of vehicles on the
road is significantly smaller than the number of registered vehicles.
determined the rolling fleet in 2005 using survival
functions. The category-wise number of vehicles on the road as a fraction of
registered vehicles was taken from . Emissions from the road
were estimated using the number of vehicles on the road and the annual distance
traveled per vehicle type.
EVdistrict=∑i=1n(Ni⋅AKTi⋅EFi),
where EV is the total BC emissions from vehicles for a district
(g district-1 year-1), i is type of vehicle, N is the number of vehicles,
AKT is the annual kilometer traveled for the vehicle type (km year-1) and
EF is the vehicle type emission factor (g km-1).
The annual average distance traveled is difficult to quantify and is a source of
uncertainty in the emissions. The annual distance traveled by various vehicle
types was derived from seven different studies (Table ). This provided us with multiple estimates of the total
distance traveled by a vehicle type in a year. For some vehicle types only
few BC EFs were available. To compensate for lack of information, EFs were
derived from PM2.5 emission factors using the BC / PM2.5 fraction given by
.
Railways
Railways in India are primarily powered by electricity and diesel. The use of
coal has decreased over the years and is negligible now. The annual report
(2010–11) of Indian railways details the consumption of diesel and coal
. The state-wise allocation of fuel consumed was
performed according to the railway track length in the state
and finally district-wise according to the
population of the district.
Shipping
The Ministry of Petroleum and Natural Gas (MoPNG) reports the total
consumption of fuel oil (FO), high-speed diesel oil (HSDO) and light diesel oil (LDO) by the shipping subsector in 2011 . According
to IPCC guidelines , the fuel used in international bunkers
is not counted in the national emission inventory and their estimate is
recorded separately. The proportion of shipping fuel used domestically was
assumed from the European Environment Agency . Due to
the nonavailability of a spatial proxy, the emissions from ships have not
been distributed district-wise and have only been accounted for in the
national emissions.
Aviation
The total aviation turbine fuel (ATF) consumption in India was taken from
. Domestic operations account for 38 % of the total
fuel consumption . Domestic fuel consumption was divided into that
used for landing and takeoff (LTO) and for cruise operations. The
Directorate General of Civil Aviation (DGCA) reports the total number of
domestic scheduled and nonscheduled aircraft departures in 2011
. The fuel consumption per LTO was taken from
. The LTO ATF consumption was distributed district-wise
according to the number of flights landing and taking off from the airports
in that district. The cruise emission was not distributed and was only
counted in national emissions.
Domestic fuel
The domestic fuel sector includes emissions from firewood, agricultural
residue, coal, liquid petroleum gas (LPG), kerosene (cooking and lighting)
and dung cake.
Energy sources used for cooking in rural India, 2009–2010.
(a–d) Proportion of subcategories to the major sector
emissions and (e) contribution of major sector emissions to the national
emissions total.
India faces a crucial challenge of providing clean and affordable energy
sources to its rural households, especially in the cooking sector.
Eighty-five percent of the rural households are still dependent upon
traditional biomass fuel for their cooking needs . Figure
shows the distribution of rural households on the
basis of the energy source used for cooking .
The stoves used for cooking are inefficient, causing incomplete combustion and hence releasing more BC than would result from efficient combustion. In the
year 2000, domestic fuels contributed 64 % to the total BC emissions in Asia
. State-wise per capita consumption (rural and urban) of
firewood, LPG and coal was taken from a National Sample Survey
, which releases a report of household consumption of various commodities using
a large sample of households every 5 years. Apart from this,
report the state-wise total consumption of firewood,
agriculture residue and dung cake in 1985. We extrapolated the fuel
consumption data to 2011 by using the growth rate of rural population from
1985–1991 and the change in the number of households using these fuels for cooking
from 1991 to 2011. also report the
state-wise consumption of firewood, dung cake and agricultural residue in
1991. We extrapolated the data to 2011 using the change in number of
households using these fuels for cooking from 1991 to 2011. Using data from
, and
, three estimates of domestic fuels
consumed in 2011 were prepared and used within the uncertainty analysis.
According to the , 25 % of the Indian population does
not have access to electricity. As a result kerosene-fueled lamps are the
only source of lighting after sunset for a large part of the population. In
2011, over a billion liters of kerosene was consumed to fuel these lamps
. The information on kerosene consumed was available from
two sources: and . The National Sample
Survey reports the state-wise per capita (rural and urban)
kerosene consumption. The proportion of kerosene used for cooking versus
lighting in India was taken from . Another estimate of
kerosene consumed in lamps was derived following the methodology described in
.
Other
The sector “other” incorporates emissions from the use of diesel in power generation
sets. One of the largest consumers of diesel are irrigation pumps. In
addition, diesel is used in power generation for mobile towers, private
households, small industry and commercial enterprises.
Irrigation pumps
Agriculture is a core economic activity of India, with about 60 % of the
population involved in the activity. In 2011 India used around 2.4 billion
liters of diesel for irrigation pumps . The use of dug
wells and tube wells is very common for irrigation purposes in India. Diesel
powered pumps are used for mini irrigation schemes in farms with minimal or
no access to electricity. The diesel consumed was distributed district-wise
according to the net sown area in that district
.
Diesel generator sets
In 2011–12, India faced an overall power deficit of 8.5 % and peak power
shortage of 10.6 % . To deal with this deficit, there were
prolonged power cuts throughout the country especially during the peak
consumption period. Increasingly, private households, commercial enterprises
and industries are using diesel generators to maintain consistent power
supply during power outages. Although there is no official estimate of the
amount of diesel consumed by diesel generators, ICF International estimates
that 4.51 billion liters of diesel was used in the year 2012–13 . The growth rate of the power deficit in India was used to adjust this value for
2011 . The telecom industry is one of the largest users of diesel
generators. In 2011, India had more than half a million cell towers
. Most of these towers are located in villages where
grid-connected electricity is not available. They use small generators fueled
by diesel for their power needs. The total diesel consumption by cell towers
was taken from . The fuel consumed was distributed
state-wise according to the number of mobile towers in that state. It was
then distributed district-wise according to the population of the district.
Diesel consumed by generators in mobile towers was deducted from the total amount
of 4.51 billion liters consumed by diesel generators to estimate the
remaining amount. Due to the paucity of data it was not possible to spatially
distribute the remaining emissions to grids, so they have only been accounted
for in the total national emissions.
Results and discussion
Tables and present the
probabilistic best-fit distributions, mean and standard deviation for
activity data and EFs for sources considered in this study. The mean district
level activity data and EFs were used to estimate the district-wise
emissions. It may be noted that kerosene lamps have the highest EF among all
sources considered in this study; these lamps convert 8.5 % of the fuel
directly into BC. In the open-burning sector, forest fires have the highest
EF. In the industry sector, EF is highest for the sugar industry, as the industry
uses bagasse as a fuel in a very inefficient combustion process. For the transport sector, EFs for diesel-operated vehicles (railways, ships, bus,
truck, tractor and trailer, LCV) are higher than that for gasoline-operated
vehicles (two wheeler, LMV, car).
Total BC emissions for the year 2011 have been estimated to be 901 ± 152 Gg
(Table ), of which 47 % (425 Gg) originated from
domestic fuel consumption, 22 % (198 Gg) from industry, 17 % (154 Gg) from
the transport sector and 12 % (103 Gg) from open burning. Diesel use in
mobile towers and irrigation pumps contributed 2 % (20 Gg) to total BC
emissions (Fig. ). Firewood with emissions of 177 Gg is
the single most emitting source. It emits more than transportation (154 Gg)
and open-burning (103 Gg) categories. As shown in Fig. , 76.3 % of the 140 million rural households
use firewood as the primary source of energy for cooking.
(a–d) Maps of major sector emissions and (e) spatial
variability of national emissions total for BC.
Gen. extreme value distribution fit for the national BC emissions.
The spatial distribution of national emissions is presented in Fig. . From the map it can be easily concluded that the Indo-Gangetic Plain (IGP) is the
main contributor to national BC emissions. This can be attributed to the very
high population density and presence of major BC emitting industries like
sugar and brick production in this region. Some of the states in the IGP are
among the least developed in India, with little access to even basic amenities
like electricity, clean cooking fuels, sanitation, health care, etc. More than
90 % of the rural households in Uttar Pradesh and Bihar use biomass fuels as
their primary source of cooking, and more than 65 % are dependent upon
kerosene lamps as their primary source of lighting . The high
dependence on biomass fuels and the presence of brick and sugar industry
accentuates the emissions from this region. With annual emissions of 140 Gg,
the state of Uttar Pradesh emits the most in the IGP followed by West Bengal
(57.67 Gg), Bihar (47.8 Gg), Punjab (34.01 Gg), Haryana (26.82 Gg) and the
National Capital Territory (NCT) of Delhi (6.74 Gg). The major emissions
sources in Uttar Pradesh are kerosene lamps (12 %), biomass cooking fuels
(30 %), brick kilns (20 %) and sugar mills (17 %). High emissions from IGP
and its vicinity to the Himalayas potentially pose a serious threat to water
security in the region, resulting from impacts on the cryosphere from BC
deposition and atmospheric heating.
Sectorial emission histograms and associated best-fit PDFs.
As discussed in Sect. , best-fit probabilistic
distributions were obtained for EF and activity data (for each source) using
the KS statistic. A sample of 1000 numbers was generated from each of the
two distributions (EF and activity data), the product of which provides over 1 million emission points. The mean and standard deviation were determined for
each source using the obtained emission points. The emission points were
added up for all the sources to get, overall, 1000 national emission points
and, subsequently, the national mean emission and standard deviation. A best-fit probabilistic distribution curve was obtained for the national emission
points on the basis of the KS statistic. The probabilistic distribution for
overall national emissions was found to be a general extreme value distribution
with KS statistics of 0.01 (Fig. ). Figure presents the sector-wise optimally fit distributions
for the BC emissions.
Open burning
The national level emissions from this sector contribute 12 % (103 Gg) to the
total emissions. Burning of crop residue has been the major contributor
(62 %), followed by forest fires (36 %). MSW burning contributed only 2 % to
the open-burning emissions. The source-wise emission contribution and
spatially distributed open-burning emissions are presented in Figs. and . The emissions from open burning
are highest from the northwest states of Punjab and Haryana (crop residue
burning) and the northeastern states of Nagaland, Manipur, Mizoram and Tripura
(forest fires). Punjab and Haryana are the main food-producing states of
India. In April, May, October and November, the crop residue is
burnt to clear the land for the next crop. In the northeast, open-burning
emissions arise primarily from forest fires; however, some tribal communities
also practice slash and burn agriculture in this region as well.
Industry
National level industry sector emissions account for 22 % (198 Gg) of the
total emissions. In this sector, brick and sugar production contribute the
maximum emissions (37 % each), followed by steel production (11 %), cement
(8 %) and powerplants (7 %) (Fig. ). Spatially
distributed emissions from the industry sector are presented in Fig. . The hotspots of industrial emissions are the states in the IGP, as most of the brick and sugar industries lie in this area. It is also
evident and expected from Fig. that metropolitan
cities contribute significantly to the sector as they have major industrial
belts on the periphery. High emissions from the brick and sugar industry
result from the use of low-grade fuels and from dated and inefficient systems
and processes. While powerplants account for 75 % of coal consumption,
their BC emissions are just 7 % of the total industrial emissions, due to the
higher efficiency of combustion in these systems. An acknowledged source of
uncertainty in our approach is the lack of specific geolocated coordinate
data for the two largest emission sources, brick and sugar.
Transport
Transportation sector emissions account for 17 % (154 Gg) of the national BC
emissions in 2011. In the transport sector trucks have been found to emit the most (24 %), followed by tractor and trailers (15 %). Emissions from bus,
car, LCV, LMV and two wheelers contributed 12, 10, 13, 11 and 13 % to
national transport sector BC emissions, respectively. Railways contributed
0.2 % to BC emissions in 2011; shipping and aviation combined emitted less
than 0.05 % (Fig. ). The spatial distribution of
transportation emissions is presented in Fig. . The
main contributors are the metropolitan cities, the NCT of Delhi, Mumbai and
Bangalore. The results also indicated that the majority of the emissions from the
transport sector originate from diesel road vehicles (truck, tractors and
trailers, bus, LCV and LMV).
Domestic fuel
Domestic fuels account for almost half of the national BC emissions (47 %,
425 Gg). Within the sector, firewood contributes most significantly, (42 %),
followed by kerosene lamps (26 %). Agricultural residue, dung cake and coal
used for cooking contributed 17, 13 and 2 %, respectively (Fig. ). Figure shows the spatially
distributed emissions of domestic fuel usage. Here also, the majority of
emissions arise from the IGP due to the high population density in this area.
Also, the poverty levels are high in this region, so a larger proportion of
the population tends to use cheaper biofuels for cooking. The biofuel used
in handmade stoves has low combustion temperatures leading to an inefficient
combustion process, and consequently the domestic fuel sector has higher BC
emissions. Also, these are uncontrolled emission sources. Kerosene lamps
(109 Gg) are the second-highest emitting source as a result of the very high EF of
kerosene lamps. While the emissions from kerosene lamps are more than the
entire open-burning sector combined, studies must be conducted to evaluate
the potential impact and transport of this source of BC. It likely has
extremely significant health impacts due to the emissions being contained
within homes, but the climate impact is likely as large as for open burning.
Other
Emissions from this category account for slightly more than 2 % (20 Gg) of
the national BC emissions. Within this category emissions from use of diesel
in irrigation pumps contribute 8 Gg, and its use in mobile generators
contributes 12 Gg. Among diesel generators, their use in mobile towers
contributes 4 Gg and other applications (private households, small commercial
enterprises and industry) account for the remaining 8 Gg.
Uncertainty analysis
Figure shows the mean and standard deviations
based on best-fit probabilistic distributions of emissions from the major
sectors. Based on the Monte Carlo simulations using the multiple emissions
estimates and available information on uncertainty, the PDFs for each of the sectors is calculated as shown
in Fig. . The best-fit distribution for the domestic fuels sector was found to be a Burr distribution with a KS statistic of 0.01; for
industrial emissions, it was a gamma distribution with KS statistics of 0.02; for open-burning emissions, it was a Johnson SU distribution with a KS statistic of 0.02;
and it was log-logistic (3P) for the transport sector, with a KS statistic of 0.03. The
uncertainty is highest for emissions from the domestic fuels sector. The EFs and
activity data for the sources in the domestic fuel sector show a large variation
leading to high uncertainty in the BC emissions as there is no accurate
database of the population using cookstoves, of the quantity of fuel consumed and
the stoves' efficiency.
Mean and standard deviation for each of the major sectors of
emissions for India, 2011.
Comparison with prior estimates
Emissions in this study have been determined using a Monte Carlo simulation of
multiple activity data and emission factors. As previous studies have used
point estimates for these highly uncertain quantities, the results are bound
to differ. Figure presents the comparison of the
results of this study (Table ) with emission
inventories developed in the past. For the base year 2011, the estimate is
about 80 % of that reported in the SAFAR emission inventory (1119 Gg yr-1). For
inventories with base year 2010, total national emissions estimated in this
study are a factor of 1.3 higher than RETRO (697 Gg yr-1), factor of 0.8 of that estimated in (1104 Gg yr-1), a factor of 0.9 of that
estimated in (1015 Gg yr-1), and they were in agreement with emissions
determined by (862 Gg yr-1). All prior national emission
estimates lie within 2 standard deviations of our mean estimate.
Mean national emissions and standard
deviation.
Sector/subsector
Emissions (Gg yr-1)
Open burning
102.84 ± 27.56
Crop residue burning
64.31 ± 17.19
Forest fire
36.90 ± 12.85
Garbage burning
1.63 ± 0.62
Industry
198.5 ± 83.391
Brick
74.11 ± 61.38
Steel
21.09 ± 32.18
Sugar
72.76 ± 25.05
Cement
15.45 ± 22.26
Power
15.09 ± 23.88
Transport
154.34 ± 56.14
Bus
17.64 ± 8.72
Car
14.69 ± 10.54
LMV
17.01 ± 25.03
LCV
20.62 ± 10.51
Truck
37.46 ± 20.49
Taxi
2.13 ± 1.44
Two wheeler
20.11 ± 39.50
Tractor and trailer
22.79 ± 11.41
Railway
1.60 ± 1.32
Shipping
0.15 ± 0.07
Aviation
0.14 ± 0.04
Domestic fuel
425.36 ± 111.97
Dung cake
54.79 ± 48.15
Agriculture residue
74.38 ± 44.17
Firewood
177.34 ± 83.88
Coal
9.02 ± 14.622
Kerosene cooking
0.83 ± 0.19352
LPG
0.47 ± 0.39
Kerosene lamps
108.53 ± 27.10
Others
20.08 ± 2.59
Irrigation pumps
7.55 ± 1.73
Diesel generators (mobile towers)
4.14 ± 0.85
Diesel generators (other)
8.39 ± 1.73
Total
901.11 ± 151.56
Emissions estimates from the domestic fuels sector (425 ± 112 Gg yr-1) are lower
by a factor of 0.7–0.9 than (488 Gg yr-1),
(628 Gg yr-1) and (579 Gg yr-1). For the
transport sector our emission estimate (154 ± 56 Gg yr-1) is almost identical
to that presented in (144 Gg yr-1) and a factor of
1.1–1.3 higher than the emissions determined by (111 Gg yr-1),
(123 Gg yr-1) and (136 Gg yr-1). In the
industry sector our emissions (198 ± 83 Gg) are 10–20 % lower in view of the
inclusion of only higher emitting industries in this study. The combined
industrial emission estimate of (formal industry) and
(informal industry) (212 Gg yr-1) is in good agreement with
our emission estimate (factor 0.94). Estimated industrial emissions are a
factor of 0.8–0.9 lower than (227 Gg yr-1) and
(261 Gg yr-1). Emissions from open crop residue burning (64 ± 17 Gg yr-1) are in close agreement (factor 0.8–1) with
(68 Gg yr-1), (74 Gg yr-1) and (80 Gg yr-1). Forest
fire emissions (37 ± 13 Gg) are almost identical to those determined in
(39 Gg yr-1). As with the national emission estimate, for
all sectors prior emission estimates are within 1 or 2 standard
deviations from our mean emission estimate.
Fuel balance
A fuel balance approach has been used to ensure that no major emission source
has been overlooked in our study. Since biomass consumption data in India are highly uncertain, this approach was only employed for emissions arising from
combustion of fossil fuels. Emissions from combustion of diesel, gasoline,
fuel oil, ATF, LDO and coal were estimated using emission factors from
and . In 2011, emission from these
fuels was estimated to be 281 Gg (Table ). This was
very close to emissions estimated from our methodology (304 Gg), considering
the emission sources which use these fuels as a combustion source.
Comparison of current BC emissions estimate with previously published
results for India. A: Streets et al. (2003) (base year 2000); B: Reddy and
Venkataraman (2002a, 2002b) (base year 1997); C: Sahu et al. (2008) (base
year 2011); D: Schultz et al. (2008) (base year 2010); E: Lu et al. (2011)
(base year 2010); F: Klimont et al. (2009) (base year 2010); G: Ohara et al.
(2007) (base year 2010); H: this study (base year 2011).
Seasonality of emissions
There is a strong seasonality associated with BC emissions in India. Crop
residue burning, forest fires, and the brick and sugar industry have a seasonal
dependence in emissions. Forest fires are predominant from February to July. Monthly BC emissions from forest fires were estimated using MODIS burnt-area
data. The brick industry becomes active after the monsoon season from October to June ; the sugar industry operates from November
to June , and the emissions are equally distributed among the
months of operation. Burning of crop residues generally occurs in the
harvesting months, which are October–November for kharif crop and April–May
for rabi crop. Emissions of agricultural open burning are equally distributed
among the months of April, May, October and November. For all the other
sources, emission rates are assumed to be uniform throughout the year. Using
these data, monthly variation of BC emissions has been estimated and is shown
in Fig. .
The emissions in April are highest due to the burning of crop residues.
Despite the absence of crop residue burning, emissions in March are also high
because of the emissions from forest fires. As we have shown that a considerable
amount of the emissions comes from the IGP, which is in close proximity to the
Himalayas, this causes further concern regarding the potential cryospheric impact of
these aerosols as they are strongest during the period when the seasonal snowmelt period is beginning and they could be incorporated into the snowpack.
Fuel balance.
Sector/fuel
Activity
EF
Emission
(Mt)
(g kg-1)
(Gg)
Coal
535.881
0.3282
175.77
Gasoline/petrol
14.4423
2.7954
40.37
Diesel
63.5043
1.024
64.77
Fuel oil
6.6243
0.044
0.26
ATF
5.3243
0.034
0.16
Total
281.33
1. 2. 3. 4.
Conclusions
A spatially resolved BC emission inventory for 2011 has been
developed, considering major sectors and with careful consideration of subsector
sources. The sources were classified into five major sectors: (i) open
burning, including forest fire emissions, open solid-waste burning and
agriculture residue burning; (ii) industry, including brick industry, cement,
steel plants, sugar mills and powerplants; (iii) transport, including two
wheelers, cars, light motor vehicles passenger, light commercial vehicles,
taxies, trucks, buses, tractors and trailers, railways, shipping, and airways;
(iv) domestic fuel, including firewood burning, agricultural residue, coal,
liquid petroleum gas, kerosene (cooking and lighting) and dung cake; and (v)
“other”, including use of diesel in irrigation pumps and for other power
generation in diesel generators.
This is a first-of-its-kind comprehensive study which included sources such as
kerosene lamps and forest fires that were not part of earlier emission
inventories. Furthermore, for each sector, source uncertainties in emissions
have been estimated based on variability in available activity data and
emission factors. Lastly, and significantly, we provide our estimate of
emissions at a monthly temporal resolution on a spatially distributed 40×40 km. grid.
The national BC emissions for India in 2011 are estimated to be
901 ± 152 Gg yr-1, with domestic fuels contributing the most (47 %), followed by industries
(22 %), transport (17 %), open burning (12 %) and others (2 %). Large
emission in the domestic fuels sector stems from the extensive use of biomass for
cooking in India. Firewood is the single largest emitter, with 177 Gg (20 %)
of BC emissions in 2011. The emissions from firewood are more than the entire
transportation sector combined. Kerosene lamps surprisingly contribute 12 %
to the national BC emissions. The emissions have been found to be have a
significant seasonality, varying from 55 Gg in July to 90 Gg in April 2011.
Time series of monthly emissions for India, 2011. Note the strong
seasonality of the open-burning and industry sector. In the latter case, the
seasonality results predominately from sugarcane production.
The results of the study could be used to assess the contribution of
different sources to national and regional emissions. The spatial resolution
of the inventory should be useful for modeling the black carbon processes in
the atmosphere through air quality models. Monthly gridded emission data sets can
also be prepared for finer temporal-resolution input. To improve the future
BC emission estimates, local emission factors and activity data should be
improved, especially for domestic fuels and the brick industry. The emission
inventory can be improved nationally, regionally and temporally by comparing
the modeled emission estimates (providing the inventory as input to air
quality models) with the observed data.