Article
pubs.acs.org/est
Increase in NOx Emissions from Indian Thermal Power Plants during
1996−2010: Unit-Based Inventories and Multisatellite Observations
Zifeng Lu* and David G. Streets
Decision and Information Sciences Division, Argonne National Laboratory, Argonne, Illinois, United States
S Supporting Information
*
ABSTRACT: Driven by rapid economic development and growing
electricity demand, NOx emissions (E) from the power sector in India
have increased dramatically since the mid-1990s. In this study, we
present the NOx emissions from Indian public thermal power plants
for the period 1996−2010 using a unit-based methodology and
compare the emission estimates with the satellite observations of NO2
tropospheric vertical column densities (TVCDs) from four spaceborne instruments: GOME, SCIAMACHY, OMI, and GOME-2.
Results show that NOx emissions from Indian power plants increased
by at least 70% during 1996−2010. Coal-fired power plants, NOx
emissions from which are not regulated in India, contribute ∼96% to
the total power sector emissions, followed by gas-fired (∼4%) and oilfired (<1%) ones. A number of isolated NO2 hot spots are observed
over the power plant areas, and good agreement between NO2 TVCDs and NOx emissions is found for areas dominated by
power plant emissions. Average NO2 TVCDs over power plant areas were continuously increasing during the study period. We
find that the ratio of ΔE/E to ΔTVCD/TVCD changed from greater than one to less than one around 2005−2008, implying
that a transition of the overall NOx chemistry occurred over the power plant areas, which may cause significant impact on the
atmospheric environment.
■
INTRODUCTION
Nitrogen dioxide (NO2) and nitric oxide (NO), together
known as nitrogen oxides (NOx), are two of the most
important gaseous species in the atmosphere, and they are
emitted from both anthropogenic sources (e.g., fossil-fuel
combustion and human-induced biomass burning) and natural
sources (e.g., wildfires, soil release, lightning, ammonia
oxidation). NOx plays a crucial role in the formation of
ozone and secondary aerosol and is involved in the chemical
transformation of other atmospheric species (e.g., CO, CH4,
VOCs) through feedback on HOx (OH + HO2). Consequently,
NOx can have broader effects on human health, atmospheric
composition, acid deposition, air/water quality, visibility,
radiative forcing, etc. As the second largest NOx emitting
country in Asia (∼16% of total Asian emissions1−5), India is of
increasing concern. Thermal power plants are the most
important point sources in India, accounting for more than
30% of the national NOx emissions (transportation, industry,
and other sectors contribute ∼35%, ∼20%, and ∼15%,
respectively).6 Due to the rapid economic development, the
growing electricity demand, and the absence (or weak
enforcement) of regulations, NOx emissions in the Indian
power sector have been reported to increase at a remarkable
rate since the mid-1990s.1−3,6 Although some previous studies
have reported NOx emissions from Indian power plants,1−7
they have various shortcomings. First, none of them present
year-by-year trends with up-to-date activity rates, especially for
© 2012 American Chemical Society
the period after 2000. Second, NOx emission factors are
strongly dependent on combustion conditions, fuel quality,
boiler size, and NOx control technology; however, previous
studies simply treated all power plants as one single sector (or
subsectors by fuel type) and applied default NOx emission
factors prescribed by the IPCC.8 Third, few of them used
activity rates at the plant or unit level but instead at the country
or state level, which cannot yield spatially accurate emissions.
To overcome these disadvantages, we use a unit-based
methodology to develop new NOx emission inventories for
Indian power plants.
Due to the short lifetime of NOx in the atmosphere, satellite
NO2 observations are closely correlated to the land surface
NOx emissions.9−11 In the past few years, tropospheric NO2
columns retrieved from satellites have been successfully applied
to monitor and quantify the spatial distribution (local to
global),9,10,12,13 temporal variation (diurnal to seasonal),14−18
and interannual trends11,19,20 of NOx emissions from both
anthropogenic and natural sources. Especially, because of their
high spatial resolution, the new generation of instruments (e.g.,
SCIAMACHY and OMI) have proved to be able to identify
and constrain NOx emissions from large point sources (LPS)
Received:
Revised:
Accepted:
Published:
7463
March 1, 2012
June 18, 2012
June 25, 2012
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such as thermal power plants.9,12−14 However, there have been
very few applications of satellite retrievals to India. Ghude et
al.21,22 and Prasad et al.23 studied the interannual trends and
seasonal variations of major NO2 hotspots over India using
NO2 retrievals from GOME, SCIAMACHY, and OMI.
However, they only gave a qualitative or semiquantitative
analysis of the relationship between NOx emissions and satellite
retrievals over NO2 hotspots, partially due to the lack of reliable
NOx emission inventories for LPS. In this study, we examine
the India NOx emission trend of thermal power plants during
the Indian financial years (FY, from 1 April of one year to 31
March of the next year) 1996−2010 from the viewpoints of
both unit-based inventories and multisatellite observations. We
compare the satellite NO2 data with bottom-up NOx emission
estimates over power plant areas in India. The results are
intended to provide a better understanding of the large NOx
releases from the Indian power sector for recent years. In a
more general sense, the results contribute to our ongoing
attempts to assess the usefulness of satellite retrievals for
quantifying LPS emissions.
■
Ei =
∑ ∑ ∑ ∑ (Gi ,j ,k × SFCi ,j ,k × NCVj × EFj ,l ,m
j
k
l
m
−9
× 10 )
(1)
where j, k, l, and m stand for fuel type, generation unit, boiler
size, and emission control technology, respectively. G
represents the electricity generation (kWh yr−1); SFC
represents the specific fuel consumption per unit electricity
generation (kg kWh−1 for coal- and oil-fired units, and m3
kWh−1 for gas-fired units); and NCV represents the net
calorific value (MJ kg−1 for coal- and oil-fired units, and MJ m−3
for gas-fired units). EF is the NOx emission factor (g GJ−1).
NOx emission factors of power generating units vary with
fuel type, fuel quality, boiler size, and NO x control
technology.15 However, due to the absence of India-specific
measurement data, nearly all previous studies2−4,6 used default
EF values prescribed by the IPCC8 to estimate NOx emissions
from the Indian power sector. In India, more than 95% of the
coal-fired boilers are pulverized coal (PC) burning type.2,26 In
this study, boiler-size-specific and emission-control-specific EFs
with PC combustion technology were applied to all coal-fired
units (see Table 1), and they were derived from not only the
METHODS AND DATA SETS
Table 1. Summary of NOx Emission Factors and Scenarios
for Coal-Fired Power Units
Unit-Based Methodology for NOx Emission Estimation. A bottom-up, unit-based methodology is used to develop
the NOx emission inventory for Indian public thermal power
plants during FY 1996−2010. Captive (privately owned) power
plants and public coal-fired units with capacity smaller than 20
MW are not included due to the lack of detailed data at unit or
plant level. In total, information on more than 800 units was
compiled, including geographical location, boiler size (capacity), fuel type, electricity generation, specific fuel consumption, the exact time when the unit came into operation
and/or retired, etc. Most of the information was derived from a
series of the Performance Review of Thermal Power Stations24
and various reports published by the Central Electricity
Authority (CEA), Ministry of Power of India. Where unitlevel information was not available, corresponding plant-level
information was used. The exact longitude and latitude of each
power plant was obtained from the Global Energy Observatory
(http://globalenergyobservatory.org/index.php), and verified
through Google Earth. As shown in Figure S1 of the
Supporting Information (SI), the electricity generation of
thermal power plants in India increased dramatically from 321.9
to 684.5 TWh during FY 1996−2010, with an annual average
growth rate (AAGR) of 5.5%. On average, about 87% of
thermal electricity was generated from coal-fired power units,
followed by gas-fired (∼12%) and oil-fired (∼1%) ones. Coalfired units with capacity larger than 200 MW are the main
boiler type in India, contributing ∼72% to the total thermal
electricity generation. Figure S1 also shows the official
electricity generation statistics from the Government of
India,24 the United Nations Statistics Division (http://
unstats.un.org/unsd/default.htm), and the International Energy
Agency (IEA).25 They match reasonably well with the data
derived from our unit-based database (differences < ±3%),
indicating that the current database covers nearly all public
generating units in India.
Total NOx emissions (E, Mg yr−1) from thermal power
plants for year i are estimated by the following equation:
emission scenarios
boiler size
not classified
<100 MW
100−300
MW
≥300 MW
LNB
EF (g
GJ−1)
w/o
w/
w/o
300a
308b
177c
330b
w/
w/o
w/
188d
410e
236b,d
S5
S1
S2
S3
S4
<1996
√
√
√
√
√
√
f
S5
g
>1996
√
√
√
√
√
√
√
√
√
√
a
From IPCC.8 bFrom Zhao et al.,28 assuming the NCV of bituminous
coal in China is 21.2 MJ kg−1.25 cEstimated, assuming the average
removal efficiency of the LNB devices is 43%. dFrom Zhao et al.,29
assuming the NCV of bituminous coal in China is 21.2 MJ kg−1.25
e
From IPCC EFDB,27 average EF of sub-bituminous coal for boilers
size ≥300 MW. fFor units built before 1996. gFor units built after
1996.
IPCC Emission Factor Database (EFDB),27 but also 209 field
measurements of Chinese bituminous and lignite coal reported
by Zhao et al.28,29 (Indian coal is of mostly sub-bituminous
rank, followed by bituminous and lignite.) We convert the unit
of Zhao et al.’s EFs from g kg−1 to g GJ−1 on the basis of NCV
supplied in the IEA Energy Statistics25 to take into account the
difference in coal heating values between the two countries.
Currently, NOx emissions are not regulated in India for coalfired power plants. Although some new plants were reported to
be equipped with low-NOx burners (LNB), the actual
installation rate, operation, and performance of LNB devices
in India are unknown.26 To reflect possible alternative NOx
emission situations in the Indian power sector during FY
1996−2010, we generate five emission scenarios (Table 1). As
used in previous studies,2−4,6 we first generate the baseline
emission scenario (S1), in which the default IPCC EF of 300 g
GJ−1 is applied to all coal-fired boilers. In scenarios S2−S4, we
assume that no boiler (S2), all boilers with capacity ≥300 MW
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(S3), and all boilers with capacity ≥100 MW (S4) are equipped
with LNB, respectively. In scenario S5, we utilize the
assumption made in the GAINS inventory2 that no boiler
built before 1996 was equipped with LNB, but LNB devices
were installed in all new boilers built after 1996. These
scenarios are also used to analyze the sensitivities and
uncertainties introduced by the LNB assumptions. For gasfired and oil-fired power plants, India has NOx emission
standards varying with the age and size of the unit.30 Taking
into account both the standards and the EFs prescribed by the
IPCC,8 EFs of gas-fired units are set to be 150 g GJ−1 for units
built before June 1, 1999, and 100 g GJ−1, 75 g GJ−1, and 50 g
GJ−1 for newly installed units with capacity <100 MW, 100−
400 MW, and ≥400 MW, respectively. For naphtha-fired units,
EFs are 150 g GJ−1 and 100 g GJ−1 for units commissioned
before and after June 1, 1999. We assume an EF of 200 g GJ−1
for all other oil-fired power units.
Satellite Retrievals of Tropospheric NO2. To compare
with our emission estimates, tropospheric vertical column
densities (TVCDs) of NO2 from GOME, SCIAMACHY, OMI,
and GOME-2 are used. The combined operation time of these
instruments covers the entire period of Indian FY 1996−2010.
A comparison of the characteristics of each instrument is shown
in Table S1 of the SI. In this work, we used the monthly level-3
products of GOME (TM4NO2A v2.0), SCIAMACHY
(TM4NO2A v2.0), OMI (DOMINO v2.0), and GOME-2
(TM4NO2A v2.1) developed at the Royal Netherlands
Meteorological Institute (KNMI),31,32 which are available
from the Tropospheric Emission Monitoring Internet Service
(TEMIS) at www.temis.nl. The retrieval processes are similar
for all four instruments. First, NO2 slant columns were
retrieved using the differential optical absorption spectroscopy
(DOAS) technique; second, TVCDs were derived with the
KNMI combined retrieval-assimilation-modeling approach.31,32
the estimates are different under different scenarios, they all
show similar increasing trends. In the baseline scenario S1, NOx
emissions increase rapidly by 97% from 1213 Gg in FY 1996 to
2391 Gg in FY 2010, with an AAGR of 5.0%. In other scenarios,
the AAGRs are in the range of 3.9% to 5.1%, in agreement with
values reported in other inventories (2.8%−6.0%).1−3,6 The
dramatic change of NOx emissions can be viewed in the context
of a 113% increase of electricity generation, an 84% increase of
generating capacity, and a 95% increase of fuel consumption in
the thermal power sector during FY 1996−2010, reflecting
rapid economic and social development driven by growing
fossil-fuel use and relatively lax emission control.33 Coal-fired
power plants dominate the total NOx emissions from the power
sector (93%−98%), followed by gas-fired (2%−6%) and oilfired (0−1%) plants. In terms of boiler size, coal-fired units
with capacity larger than 200 MW are the main contributors,
accounting for ∼85% of the NOx emission growth.
A Monte Carlo approach is used to determine the
uncertainties of the emission estimates in different scenarios.
Unless specified otherwise, the term “uncertainty” in this article
refers to a 95% confidence interval (CI) around the central
estimate. Input parameters and their corresponding probability
distributions were incorporated into a Monte Carlo framework
with the Crystal Ball software and 10 000 simulations were
performed. For fuel consumption data, we applied normal
distributions and assigned uncertainties of 5% and 10% to coal
and other fuels. Uncertainties of coal EFs were set to be 15%
with normal distribution, on the basis of field measurements of
bituminous coal and lignite by Zhao et al.29 Normal
distributions with 30% uncertainties were assumed for EFs of
other fuels based on the authors’ judgment. LNB-related
parameters (e.g., application rate, operation rate, and performance) were not included in the Monte Carlo simulation, since it
is not possible to obtain the necessary information. Instead, we
examined the sensitivity of the LNB assumptions through
different scenarios to analyze uncertainties introduced by them.
Results show that due to uncertainties in activity rates and EFs
alone, the uncertainties of estimated NOx emissions are
between ±11% and 15% under all scenarios (uncertainties of
scenarios S2 and S4 are shown in Figure 1). The uncertainties
are lower than those reported in the TRACE-P inventory
(±21%),4 and mainly attributed to the detailed unit-based
methodology and reliable unit-level information gathered in
this study. Compared to activity rates and EFs, LNB-related
parameters seem to be a more crucial factor that influences the
accuracy of NOx emission estimates in India. As shown in
Figure 1, the difference between emissions estimated in
scenario S2 (no boilers with LNB) and S4 (boiler capacity
≥100 MW with LNB) is ∼63%, much higher than the
uncertainties introduced by fuel consumption data and EFs.
Figure 1 also compares the emissions estimated in this study
to those of other inventories, including GAINS,2 REAS,3
TRACE-P,4 INTEX-B,5 HTAP-EDGAR,7 EDGAR4.2,1 and
Garg et al.6 Taking into account the uncertainties in all input
parameters, scenarios S2 and S4 can be treated as the upper and
lower bounds of NOx emissions from thermal power plants in
India. Clearly, most previous results are within this “broad”
uncertainty range, except for emission estimates in the
EDGAR4.2 inventory.
NOx Emissions of Thermal Power Plants Observed
from Space. Because of the short lifetime of NOx in the lower
mixed layer, surface NOx emissions and NO2 TVCDs are
closely correlated, and changes in NO2 columns are expected to
■
RESULTS AND DISCUSSION
NOx Emissions of Thermal Power Plants during FY
1996−2010. Annual trends of NOx emissions from Indian
thermal power plants during FY 1996−2010 are shown in
Figure 1, and detailed results by fuel type, unit size, and major
power plants are provided in Tables S2−S3 of the SI. Although
Figure 1. Comparison of NOx emission estimates of Indian thermal
power plants.
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Figure 2. Spatial distribution of (a, c−f) average NOx emissions from thermal power plants (203 plants, based on baseline scenario S1), (b) defined
power plant areas, and (a, b) annual and (c−f) seasonal average of OMI NO2 TVCDs over India during FY 2005−2010.
always used in previous studies to quantitatively analyze NOx
emissions in the U.S.9,13 and China.12,35 The short NOx
lifetime, low snow cover, small solar zenith angle, and strong
actinic flux in summer make the retrievals more reliable and the
link between emissions and the NO2 columns more direct than
in the other seasons in these two countries.15 However, for
India, although the NOx lifetime is short during the monsoon
period, late June to September is the worst period to observe
NO2 from satellites (Figure 2c−f and Figures S2−S4). In India,
more than 70% of the annual precipitation occurs in the
monsoon season.36 The frequent cloud cover and heavy rainfall
results in insufficient satellite observation samples and
extremely low NO2 surface concentrations, and makes the
satellite retrievals highly uncertain. However, the fact that India
is closer to the equator (the latitude is ∼20° lower) than the
U.S. and China brings tremendous advantages to India for the
use of other months’ NO2 retrievals in the quantitative analysis.
First, due to the low latitude, temperature and actinic flux are
high in India, resulting in a relatively short lifetime of NOx all
year round. Second, low snow cover and favorable satellite
measurement geometry at small solar zenith angle make the
NO2 retrievals very reliable. Third, compared to the U.S. and
China, the seasonality of temperature, actinic flux, NOx lifetime,
and NO2 retrievals in India is weaker, which means that all-year
data can be used in the analysis and the NO2 retrievals’
uncertainties are further reduced. Taking into account all the
above factors, we select all nonmonsoon months as our study
period for India, in contrast to the summer-only NO2 retrievals
used in previous studies of the U.S. and China. An additional
be approximately proportional to changes in NOx emissions.9−11 This makes it possible to identify the NOx sources
from NO2 column maps. Figure 2a and Figures S2a−S4a show
the spatial distribution of average NOx emissions from India,
highlighting thermal power plants and all-month averages of
NO2 TVCDs over India from four instruments. A number of
satellite NO2 hot spots (especially for OMI, SCIAMACHY, and
GOME-2, which have finer footprints) are observed over India,
and they match the locations and the amounts of NOx
emissions of thermal power plants reasonably well. This is
consistent with the findings of Ghude et al.21,22 and Prasad et
al.23 because thermal power plants are the major local NOx
emitters and power plants in India were often built in
populated areas where other anthropogenic NOx emissions
(e.g., industrial and vehicular) are also high. Influenced by the
South Asia monsoon meteorology, India is largely subject to
four seasons: winter (December−February), summer/premonsoon (March−June), monsoon (late June−September), and
postmonsoon (October−November).34 The spatial distributions of average NO2 TVCDs by season for each instrument are
shown in Figure 2c−f and Figures S2−S4. NO2 columns are
high in winter and low in the monsoon season due to the
seasonal variation of NOx lifetime and the effective wet
scavenging during the monsoon season.15,21
Three major factors may impact the comparison between
satellite NO2 retrievals and emission estimates: uncertainty in
the satellite retrievals, seasonal variation of NOx lifetime, and
incomplete conversion of NO to NO2.14 These factors are the
reasons summertime (June−August) NO2 retrievals were
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Figure 3. Scatter plots of annual NOx emissions from thermal power plants (based on baseline scenario S1) against annual (corresponding to Indian
FY, excluding monsoon period) average TVCDtotal (top) and TVCDpower (middle) over power plant areas for four NO2 instruments. R2 is the square
of linear correlation coefficient between NOx emissions and TVCDs. (bottom) Relationship between R2 and the proportion of power plants’
emissions to total NOx emissions ( f power). Error bars express the ranges of R2 in each 0.1 interval of f power. Data points are arbitrarily fitted by
quadratic polynomials, and the goodness of fit is shown as r2.
favorable condition for quantitative analysis in India is that area
NOx emissions are smaller and individual NOx sources and
NO2 hotspots are more isolated and discernible all over the
country (except for the region around Delhi) in contrast to the
dense regional NO2 plumes over the northeastern U.S. and
eastern central China.
To compare the NOx emissions from thermal power plants
with NO2 TVCDs of different instruments, 81 power plant
areas were defined in this study (Figure 2b).Typically, the size
of an area with a single plant is 0.625° × 0.625°. We combined
the adjacent plants and adjusted the boundaries of the areas
based on the shapes of NO2 plumes with the intent of capturing
the major satellite signals over all thermal power plants and
excluding signals from nearby sources. The smallest study area
is close to or larger than one pixel of SCIAMACHY, GOME-2,
and OMI, but smaller than one pixel of GOME in the acrosstrack direction (∼320 km). This implies that GOME may be
insufficient to detect the power-plant signals, and GOME
results may contain larger uncertainties than the other
instruments. However, since GOME is the only instrument
covering the period 1996−2002, we still include it in the
analysis.
For power plant area n, we define the NO2 TVCD attributed
to emissions from thermal power plants (TVCDpower) as
TVCDpower, n = fpower, n × TVCDtotal, n
=
Epower, n
Etotal, n
× TVCDtotal, n
(2)
where TVCDtotal,n is the TVCD averaged over area n, and
f power,n is the proportion of power plants’ emissions (Epower,n) to
total NOx emissions (Etotal,n) in area n. In this study, gridded
emissions of other anthropogenic sources were taken from
EDGAR4.21 for the year 2005 and scaled to 1996−2010 based
on the GAINS inventory.2 Emissions from natural sources were
also derived from EDGAR4.2 for 2005, but kept constant
throughout the years 1996−2010. Lightning emissions are not
included in total emissions, because they are negligible
compared to anthropogenic emissions and they are intensive
in the monsoon season, which is excluded from this analysis.
Figure 3 presents the scatter plots of annual Epower (based on
baseline scenario S1) versus annual (excluding monsoon
period) averaged TVCDtotal and TVCDpower over power plant
areas for the different NO2 instruments. The results based on
the other scenarios are similar (Figures S5−S8). We exclude
data with an average TVCDtotal smaller than 0.5 × 1015
molecules cm−2 since they are lower than the errors of NO2
retrievals.31,32 For all data points, Epower correlates better with
TVCDpower (R2 = 0.52−0.71) than TVCDtotal (R2 = 0.34−0.50),
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implying that the treatment of TVCD using eq 2 is reasonable.
Among the four instruments, GOME has the worst performance because of the large pixel size and the relatively low NOx
emissions before 2003. Positive intercepts are observed in all
scatter plots, and they are probably caused by a combined effect
of the exclusion or underestimation of NOx emissions from
sources other than power plants, the import of NO2 from
nearby areas, and the uncertainties in both satellite retrievals
and the emission inventory. The correlations are even better for
areas dominated by power plant emissions. As shown in Figure
3e−h, R2 values are significantly improved to 0.69−0.91 for
areas with f power > 0.85 (∼ 12 power plant areas). To further
explore the relationship between NO2 columns and NOx
emissions over power plant areas, we show the variation of
R2 with f power in Figure 3i−l. The higher the proportion of
power plant emissions to total NOx emissions, the better the
agreement between NO2 columns and NOx emissions. This is
consistent with previous modeling results of NO2 columns over
power plant areas in the western U.S. and China,13,37 indicating
that area emissions (normally allocated using various spatial
proxies such as population density and road networks) are not
as well understood as power plant emissions.
Trends of Tropospheric NO2 over Thermal Power
Plant Areas in India during FY 1996−2010. We calculate
the total NO2 burden (Btotal) and NO2 burden attributed to
power plant emissions (Bpower) over all Indian power plant areas
as
Btotal(or power) =
∑ (TVCDtotal(or power), n × A n)
n
Figure 4. Monthly variations and annual evolutions of NO2 burden
(Btotal and Bpower, left axis) and corresponding average NO2 TVCD
(right axis) over all Indian power plant areas for four NO2 instruments
during FY 1996−2010. Annual values are averaged according to Indian
FY, and months in monsoon period are excluded. R2 values shown are
the square of correlation coefficients of NO2 burden with unit-based
NOx emission estimates. The dependence of OH concentration and
NO2 lifetime (top axis) on NO2 TVCD (right axis) is derived from a
HOx−NOx steady-state model, and is replotted according to Figure 1
of Valin et al.39.
(3)
where An is the surface area of power plant area n. When
divided by ∑An, Btotal (or power) can be further converted to an
average NO2 TVCD over all power plant areas. Figure 4 shows
the monthly variations and annual evolution of Btotal and Bpower
(as well as the corresponding average NO2 TVCD) for the four
NO2 instruments during FY 1996−2010. Theoretically, the
temporal overlap of operation periods provides the possibility
of cross-validation between different instruments; however, due
to the differences in the satellite characteristics, measurement
time, and retrieval algorithms, a comprehensive cross-validation
is problematic and beyond the scope of this study. Here, we
only focus on the trends of NO2 retrievals. As shown in Figure
4, in line with the dramatic increase of NOx emissions in the
Indian power sector, tropospheric NO2 increased during FY
1996−2010 with AAGRs of 1.5−3.2% for Btotal and 3.3−5.4%
for Bpower, depending on the instrument. Although power plants
contributed only ∼50% to the NO2 burden, they accounted for
more than 90% of the burden increment over power plant areas
and dominated the NO2 burden trends. Figure 4 also lists the
correlation coefficients between NO2 burden and unit-based
NOx emission estimates for different instruments. The trends of
NO2 burden are in good agreement with the trends of NOx
emissions from the power sector, especially for Bpower (R2 =
0.74−0.98), further confirming the close relationship between
NO2 columns and NOx emissions over power plant areas.
The lifetime of NOx depends on its own concentration,
because reacting with OH is the main sink of NOx, while OH
concentration in the atmosphere depends strongly on NOx
concentration. Taking into account the nonlinear feedback of
NOx emissions on NOx chemistry, Lamsal et al.38 used a
dimensionless factor β to establish the relationship between
changes in surface NOx emissions and changes in NO2 TVCD:
β=
ΔEtotal /Etotal
ΔTVCDtotal /TVCDtotal
(4)
β tends to be greater than one in clean (low NO2) regions,
since an increase in NOx emissions increases the OH
concentration through chemical feedback and thus decreases
the NOx lifetime. In polluted (high NO2) regions, β tends to be
less than one, since increased NOx emissions consume OH and
increase the NOx lifetime. In this study, we calculated the β
values in different periods for four instruments, and the results
are listed in Table 2. Although there are discrepancies in β
between different instruments for a certain period, β values
decreased from ∼2 to ∼0.7 during FY 1996−2010. This implies
that the overall NOx chemistry over Indian power plant areas
has changed considerably in the past few years due to the
dramatic increase in NOx emissions from power plants, as well
as an increase from transportation since many of the power
plants in India are located in the populated places where
vehicular emissions are high.21,33 It should be noted that the
change of VOCs emissions might also affect the NOx−VOCs−
O3 chemistry, and further affect β. However, increasing the
VOCs emissions by 15% only increases β by 2.8%,38 implying
that β values are not sensitive to VOCs emissions. Also, based
on the GAINS model,2 VOCs emissions in India increased by
only 6.5% during 2000−2010. Therefore, the overall NOx
chemistry change over Indian power plant areas is mainly
attributed to the dramatic increase of NOx emissions. Table 2
shows that the transition took place approximately between
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Table 2. Estimated β Values for Four NO2 Instruments in Different Emission Scenarios
β
period
instrument
S1
S2
S3
S4
S5
FY 1996−2002
FY 2003−2005
FY 2005−2007
GOME
SCIAMACHY
SCIAMACHY
OMI
SCIAMACHY
OMI
GOME-2
1.62
2.29
1.04
2.38
0.69
0.84
0.69
1.65
2.41
1.09
2.48
0.71
0.86
0.71
1.65
2.20
1.02
2.36
0.68
0.84
0.68
1.49
2.22
0.97
2.21
0.65
0.81
0.65
1.60
2.24
1.00
2.40
0.56
0.71
0.57
FY 2007−2010
■
ACKNOWLEDGMENTS
This work was sponsored by the National Aeronautics and
Space Administration (NASA) as part of the Air Quality
Applied Sciences Team (AQAST) program. The India emission
inventory was partially funded in support of the Ganges Valley
Aerosol Experiment (GVAX) by the Office of Biological and
Environmental Research in the U.S. Department of Energy,
Office of Science. We acknowledge the free use of tropospheric
NO2 column data from www.temis.nl. Argonne National
Laboratory is operated by UChicago Argonne, LLC, under
Contract DE-AC02-06CH11357 with the U.S. Department of
Energy.
2005 and 2008, and this can be confirmed by the dependence
curve of OH concentration and NO2 lifetime on NO2 TVCD
shown in Figure 4, which is derived from a HOx−NOx steadystate model.39 Increasing NO2 at low NO2 condition (β > 1)
results in an increase in OH and a decrease in NO2 lifetime,
while the situation is opposite at high NO2 condition (β < 1).
The transition occurred at a NO2 TVCD of ∼2.7 × 1015
molecules cm−2, exactly falling in the period 2005−2008. This
discussion is focused on the overall atmospheric characteristics
over all power plant areas. The NOx chemistry of individual
areas with high/low NOx emissions could have changed
before/after 2005−2008.
Our results clearly demonstrate a very high growth of surface
NO2 caused by a continuous increase in NOx emissions from
power plants in India during 1996−2010. More importantly,
the overall NOx chemistry over Indian power plants changed
around 2005−2008. Increasing the same proportion of NOx
emissions leads to a greater surface NO2 increase now than in
the past, implying that NOx pollution in India becomes more
and more serious. Undoubtedly, it has led to a change in the
tropospheric chemistry relating ozone and aerosol formation
and consequently has affected human health, air quality,
atmospheric physics, climate forcing, etc. This should be
further investigated with atmospheric chemical transport
models at local and regional scales. A further implication of
both the growth in emissions and the change in NOx chemistry
is that regulations on NOx emissions from coal-fired power
plants are urgently needed in India, and high-efficiency NOx
control technologies (e.g., selective catalytic reduction) are
strongly recommended to policy makers.
■
■
ASSOCIATED CONTENT
S Supporting Information
*
(1) Electricity generation of thermal power plants (Figure S1);
(2) Spatial distribution of power plants NOx emissions and
NO2 TVCDs (Figures S2−S4); (3) Relationship between
power plants NOx emissions and NO2 TVCDs (Figures S5−
S8); (4) Characteristics of four NO2 instruments (Table S1);
(5) Detailed results of NOx emissions (Tables S2−S3). This
material is available free of charge via the Internet at http://
pubs.acs.org.
■
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AUTHOR INFORMATION
Corresponding Author
*Phone: (630) 252-9853; fax (630) 252-9868; e-mail: zlu@anl.
gov.
Notes
The authors declare no competing financial interest.
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