ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-6153-2016Temporal variations of atmospheric CO2 and CO at Ahmedabad in western IndiaChandraNaveenLalShyamshyam@prl.res.inVenkataramaniS.PatraPrabir K.https://orcid.org/0000-0001-5700-9389SheelVarunPhysical Research Laboratory Ahmedabad 380009, IndiaIndian Institute of Technology, Gandhinagar 382355, IndiaDepartment of Environmental Geochemical Cycle Research, JAMSTEC, Yokohama, 2360001, JapanShyam Lal (shyam@prl.res.in)20May201616106153617329September201517November201526April201628April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/6153/2016/acp-16-6153-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/6153/2016/acp-16-6153-2016.pdf
About 70 % of the anthropogenic carbon dioxide (CO2) is emitted from the
megacities and urban areas of the world. In order to draw effective emission
mitigation policies for combating future climate change as well as
independently validating the emission inventories for constraining their
large range of uncertainties, especially over major metropolitan areas of
developing countries, there is an urgent need for greenhouse gas measurements
over representative urban regions. India is a fast developing country, where
fossil fuel emissions have increased dramatically in the last three decades
and are predicted to continue to grow further by at least 6 % per year
through to 2025. The CO2 measurements over urban regions in India are lacking.
To overcome this limitation, simultaneous measurements of CO2 and carbon monoxide
(CO) have been made at Ahmedabad, a major urban site in western India, using a
state-of-the-art laser-based cavity ring down spectroscopy technique from November
2013 to May 2015. These measurements enable us to understand the diurnal
and seasonal variations in atmospheric CO2 with respect to its sources
(both anthropogenic and biospheric) and biospheric sinks. The observed annual
average concentrations of CO2 and CO are 413.0 ± 13.7 and
0.50 ± 0.37 ppm respectively. Both CO2 and CO show strong
seasonality with lower concentrations (400.3 ± 6.8 and
0.19 ± 0.13 ppm) during the south-west monsoon and higher
concentrations (419.6 ± 22.8 and 0.72 ± 0.68 ppm) during the
autumn (SON) season. Strong diurnal variations are also observed for both the
species. The common factors for the diurnal cycles of CO2 and CO are
vertical mixing and rush hour traffic, while the influence of biospheric
fluxes is also seen in the CO2 diurnal cycle. Using CO and CO2
covariation, we differentiate the anthropogenic and biospheric components of
CO2 and found significant contributions of biospheric respiration and
anthropogenic emissions in the late night (00:00–05:00 h, IST) and evening
rush hours (18:00–22:00 h) respectively. We compute total yearly emissions
of CO to be 69.2 ± 0.07 Gg for the study region using the observed
CO : CO2 correlation slope and bottom-up CO2 emission inventory. This
calculated emission of CO is 52 % larger than the estimated emission of CO
by the emissions database for global atmospheric research (EDGAR) inventory. The
observations of CO2 have been compared with an atmospheric
chemistry-transport model (ACTM), which incorporates various components of
CO2 fluxes. ACTM is able to capture the basic variabilities, but both
diurnal and seasonal amplitudes are largely underestimated compared to the
observations. We attribute this underestimation by the model to uncertainties
in terrestrial biosphere fluxes and coarse model resolution. The fossil fuel
signal from the model shows fairly good correlation with observed CO2
variations, which supports the overall dominance of fossil fuel emissions
over the biospheric fluxes in this urban region.
Introduction
Carbon dioxide (CO2) is the most important anthropogenically emitted
greenhouse gas (GHG) and has increased substantially from 278 to 390 parts
per million (ppm) in the atmosphere since the beginning of the industrial era
(circa 1750). It has contributed to more than 65 % of the radiative forcing
increase since 1750 and hence leads to a significant impact on the climate
system . Major causes of CO2 increase are
anthropogenic emissions, especially fossil fuel combustion, cement production
and land use change. Land and oceans are the two important sinks of
atmospheric CO2, which remove about half of the anthropogenic emissions
. The prediction of future climate change and its
feedback rely mostly on our ability to quantify fluxes of greenhouse
gases, especially CO2, at regional (100–1000 km2) and global scales.
Though the global fluxes of CO2 can be estimated fairly well, the regional-scale fluxes are associated with quite high uncertainty especially over
southern Asia; the estimation uncertainty being larger than the value
itself . Detailed scientific
understanding of the flux distributions is also needed for formulating
effective mitigation policies.
Along with the need for atmospheric measurements for predicting future
levels of CO2, quantifying the components of anthropogenic emissions of
CO2 is likewise important for providing an independent verification of
mitigation strategies as well as understanding the biospheric component of
CO2. CO2 measurements alone would not be helpful
due to the large role of biospheric fluxes in its atmospheric distributions.
The proposed strategy for the quantification of the anthropogenic component of
CO2 emissions is to simultaneously measure the anthropogenic tracers
. CO can be used as a surrogate tracer for detecting
and quantifying anthropogenic emissions from burning processes, since it is a
major product of incomplete combustion . The vehicular as well as industrial
emissions contribute large fluxes of CO2 and CO to the atmosphere in urban
regions. Several simultaneous ground-based and aircraft-based studies of CO
and CO2 have been carried out in the past in different parts of the world
but such a study has not been done in India except for recently reported results
from weekly samples for three Indian sites by .
Measurements in different regions (e.g. rural, remote, urban) and at
different frequencies (e.g. weekly, daily, hourly) have their own
advantages and limitations. For example, taking measurements at remote locations at weekly
intervals can be useful for studying seasonal cycles, growth rates and
estimating the regional carbon sources and sinks after combining their
concentrations with inverse modelling and atmospheric tracer transport
models. However, some important studies, like on diurnal variations,
temporal covariance etc., are not possible from these measurements due to
their limitations. An analysis on temporal covariance of atmospheric mixing
processes and variation of flux along shorter timescales, e.g. sub-daily, is
essential for understanding local-to-urban scale CO2 flux variations
. Urban regions contribute about 70 % of
global CO2 emissions from anthropogenic sources and are projected to
increase further over the coming decades . Hence,
measurements over these regions are very helpful for understanding emissions
growth as well as verifying the mitigation policies. The first observations
of CO2, CO and other greenhouse gases started in February 1993 from Cape
Rama (CRI: a clean site) on the south-west coast of India using flask samples
. Since then, several other groups have
initiated the measurements of surface-level greenhouse gases
.
Most of these measurements are made at weekly or fortnightly time intervals.
Two aircraft-based measurement programmes, namely, Civil
Aircraft for the regular Investigation of the atmosphere Based on an
Instrument Container (CARIBIC) and
Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL)
have provided an important first look at the southern Asian
CO2 budget, but these data have their own limitations
. It is pertinent to mention here
that until now, there have been no reports of CO2 measurements over an urban location in India.
Sampling the urban regions could be very useful for understanding the role of the Indian
subcontinent in the global carbon budget as well as for mitigation purpose, since
anthropogenic activities are growing strongly over this region. Hence, the present
study is an attempt to reduce this gap by understanding the CO2 levels
in light of its sources and sinks at an urban region in India.
In view of the above, simultaneous continuous measurements of CO2 and CO
have been made since November 2013 from an urban site, Ahmedabad, located in
western India, using a highly sensitive laser-based technique. The
preliminary results of these measurements for a 1-month period have been
reported in . These detailed measurements are utilized
for studying the temporal variations (diurnal and seasonal) of both gases,
their emission characteristics on diurnal and seasonal scales using their
mutual correlations, estimating the contribution of anthropogenic and
biospheric emission components in the diurnal cycle of CO2 using the
ratio of CO to CO2 and roughly estimating the annual CO emissions from
the study region. Finally, the measurements of CO2 have been compared with
simulations using an atmospheric chemistry-transport model to discuss roles
of various processes contributing to CO2 concentration variations.
Site description, local emission sources and meteorology
(A1) Spatial distribution of total anthropogenic CO2
emissions from the EDGARv4.2 inventory over Ahmedabad and surrounding
regions. (A2) The Ahmedabad city map showing location of the
experimental site (PRL). (A3: a–d) Monthly average temperature with
monthly maximum and minimum values, relative humidity (RH), rainfall, wind
speed, PBL height and ventilation coefficient (VC) over Ahmedabad during the
year 2014. Temperature, RH and wind speed are taken from Wunderground weather
(www.wunderground.com), while rainfall and PBLH data are used from
the Tropical Rainfall Measuring Mission (TRMM) satellite and MERRA reanalysis
data. (A4) Wind rose plots for Ahmedabad for the four seasons of
2014 using daily averaged data from Wunderground.
The measurement facility is housed inside the campus of the Physical Research
Laboratory (PRL), situated in the western part of Ahmedabad
(23.03∘ N, 72.55∘ E, 55 m a.m.s.l.) in the state of
Gujarat, India (Fig. ). It is a semi-arid, urban region in western India
and has a variety of large- and small-scale industries (textile mills and
pharmaceutical companies) in the eastern and northern outskirts. The
institute is situated about 15–20 km away from these industrial areas and
surrounded by trees on all sides. The western part is dominated by the
residential areas. The city has a population of about 5.6 million (Census
India, 2011) and has a large number of automobiles (about 3.2 million), which
are increasing at the rate of about 10 % per year. Most of the city buses
and auto-rickshaws (three-wheelers) use compressed natural gas (CNG) as
fuel. The transport-related activities are the major contributors of various
pollutants . An emission inventory for this city,
which has been developed for all known sources, shows the annual emissions (for
year 2010) of CO2 and CO at about 22.4 million tons and 707 000 tons
respectively
(http://www.indiaenvironmentportal.org.in/files/file/Air-Pollution-in-Six-Indian-Cities.pdf).
Of these emissions, the transport sector contributes about 36 % in CO2
emissions and 25 % in CO emissions, power plants contribute about 32 % in
CO2 emissions and 30 % in CO emissions, industries contribute about
18 % in CO2 emissions and 12 % in CO emissions and domestic sources
contribute about 6 % in CO2 emissions and 22 % in CO emissions. The
Indo-Gangetic Plain (IGP), situated to the north-east of Ahmedabad, is a
very densely populated region and has high levels of pollutants, emitted from
various industries and power plants along with anthropogenic emissions from
fossil fuels and traditional biofuels (wood, cow-dung cake etc.).
The Thar Desert and the Arabian Sea are situated to the north-west and
south-west of Ahmedabad respectively.
Figure shows the average monthly variability of temperature, relative
humidity (RH) and wind speed data taken from Wunderground
(http://www.wunderground.com); rainfall data from Tropical Rainfall
Measuring Mission (TRMM) and planetary boundary layer height (PBLH) data from the
Modern-era Retrospective Analysis for Research and Applications (MERRA). The
wind rose plot shows the surface-level wind speed and direction during
different seasons over Ahmedabad in 2014. Large seasonal variations are
observed in the wind speed and direction over Ahmedabad. During the summer
monsoon season (June–July–August), the intertropical convergence zone (ITCZ)
moves northwards across India. It results in the transport of moist and
cleaner marine air from the Arabian Sea and Indian Ocean to the study
location by south-westerly winds or the so-called south-west monsoon (summer
monsoon). The first shower due to the onset of the south-west monsoon occurs
in July and retreats in mid-September over Ahmedabad. Due to heavy
rain and winds, mostly from the oceanic region, RH shows higher values in July,
August and September. The highest RH value of about 83 % is observed in September.
The long-range transport of air masses from the north-eastern part of the Asian
continent starts to prevail over India when ITCZ moves back southwards
in September and October. These months are regarded as a transition period for
the monsoon. During autumn (September–October–November), the winds are calm
and undergo a change in their direction from south-west to north-east. When
the transition of winds from the oceanic to the continental region takes place in
October, the air gets dryer and RH decreases until December. The winds are
north-easterly during winter (December–January–February) and transport
pollutants mostly from continental region (IGP region). During the spring
season (March–April–May), winds are north-westerly and a little south-westerly
which transport mixed air masses from continent and oceanic regions. The
average wind speed is observed higher in June and July while lower in October
and March when transition of wind starts from oceanic to continental and
continental to oceanic region respectively. The monthly averaged temperature starts
increasing from January and attains maximum (34.6 ± 1.4 ∘C) in
June, followed by a decrease until September and temperature is slightly
warmer in October compared to the adjacent months. The monthly variation in
planetary boundary layer height (PBLH) closely resembles the temperature
pattern. Maximum PBLH of about 1130 m is found in June and it remains in the
lower range at about 500 m during July to January. The ventilation
coefficient (VC) is obtained by multiplying wind speed and PBL height, which
gradually increases from January and attains the maximum value in June, followed by a decrease until November.
Experiment and model detailsExperimental method
The ambient measurements of CO2 and CO concentrations have been made using
the wavelength-scanned cavity ring down spectroscopic technique (CRDS)-based
analyser (Picarro-G2401) at 0.5 Hz. CRDS offers highly sensitive and precise
measurements of trace gases in the ambient air, due to its three main
characteristics .
(1) It provides very long sample interaction path length (around 20 km), by utilizing a 3-mirror configuration,
which enhances its sensitivity over other conventional techniques like
Non-dispersive Infrared Spectroscopy (NDIR) and Fourier Transform Infrared
Spectroscopy (FTIR). (2) The operating low pressure (140 Torr) of cell
allows to isolate a single spectral feature with a resolution of
0.0003 cm-1, which ensures that the peak height or area is linearly
proportional to the concentration. (3) The measurements of trace gases using
this technique are achieved by measuring the decay time of light intensity
inside the cavity while the conventional optical absorption spectroscopy
technique is based on absorption of light intensity. Hence, it increases the
accuracy of measurements because it is insensitive to the fluctuations of
incident light. The cell temperature of the analyser is maintained at
45 ∘C throughout the study period.
Schematic diagram of the experimental set-up. We additionally introduce
a Nafion dryer upstream of the inlet of the instrument for removing
the water vapour. Three calibration mixtures from NOAA, USA are used to
calibrate CO2 measurements and one calibration mixture from Linde, UK is
used to calibrate CO measurements. The red-coloured box covers the analyser
system received from the company, while two blue-coloured boxes cover the
2-stage moisture-removing systems, designed at our lab in PRL.
Figure shows the schematic diagram of the experimental
set-up, which consists of the analyser, a glass bulb, a Nafion dryer, a heatless
dryer, other associated pumps and a set of calibration mixtures.
Atmospheric air is sampled continuously from the terrace of the building
(25 m a.g.l.) through 1/4 inch PFA Teflon tube via a glass manifold. An
external pump is attached on one side of the glass manifold to flush the
sample line. Water vapour affects the measurements of CO2 by diluting its
mole fractions in the air and by broadening the spectroscopic absorption
lines of other gases. Although, the instrument has the ability to correct for the
water vapour interference using an experimentally derived water vapour
correction algorithms , but it has an absolute
H2O uncertainty of ∼ 1 % and can introduce
a source of error using a single water vapour correction algorithm
. This error can be minimized either by generating the
correction coefficients periodically in the laboratory or by removing the
water vapour from the sample air. Conducting the water vapour correction
experiment is bit tricky and needs extra care as discussed by
. Hence, we prefer to remove water vapour from the
sample air by introducing a 50-strand Nafion dryer (Perma Pure, p/n
PD-50T-24MSS) upstream of the analyser. The Nafion dryer contains a bunch
of semi-permeable membrane tubing separating an internal sample gas stream
from a counter sheath flow of dry gas in stainless steel outer shell. The
partial pressure of water vapour in the sheath air should be lower than the
sample air for effectively removing the water vapour from the sample air. A
heatless dryer generates dry air using a 4-bar compressor (KNF, MODEL:
NO35ATE), which is used as a sheath flow in the Nafion dryer. After drying, sample
air passes through the PTFE filter (polytetrafluoroethylene; 5 µm
Sartorius AG, Germany) before entering the instrument cavity. This set-up
dries the ambient air near to 0.03 % (300 ppm) concentration of H2O.
The measurement system is equipped with three high-pressure aluminium
cylinders containing gas mixtures of CO2 (350.67 ± 0.02,
399.68 ± 0.01 and 426.20 ± 0.01 ppm) in dry air from NOAA,
Bolder USA, and one cylinder of CO (970 parts per billion (ppb)) from Linde
UK. These tanks were used to calibrate the instrument for CO2 and CO. An
additional gas standard tank (CO2: 338 ppm, CO: 700 ppb), known as the
“target”, is used to monitor the instrumental drift and to assess the data
set accuracy and repeatability. The target tank values are calibrated against
the CO2 and CO calibration mixtures. The target tank and calibration gases
were usually measured in the middle of every month (Each calibration gas is passed for 30 min and the
target tank for 60 min). The target gas is introduced into the instrument
for a period of 24 h once every six months, for checking the diurnal
variability of instrument drift. Maximum drift for 24 h has been calculated
by subtracting the maximum and minimum values of 5 min averages, which were
found to be 0.2 and 0.015 ppm for CO2 and CO. For all calibration
mixtures, the measured concentration is calculated as the average of the last
10 min. The linearity of the instrument for CO2 measurements has been
checked by applying the linear fit equation of the CO2 concentration of
the calibration standards (350.67, 399.68 and 426.20 ppm), measured by the
analyser. The slope is found in the range of 0.99–1.007 with a correlation
coefficient (r) of about 0.999. Further, linearity of the instrument for CO
is also checked by diluting the calibration mixture from 970 to 100 ppb. The
calibration mixture is diluted with pure air (free from water vapour,
particles, carbon monoxide (CO), sulphur dioxide (SO2), oxides of nitrogen
(NOx), ozone (O3) and hydrocarbons (HC)) from an ECO Physics zero-air
generator. The flows of calibration mixture and pure air were regulated using
two separate mass flow controllers from Aalborg. For increasing the
interaction times of the gases (zero air and calibration mixture) and to
ensure a homogeneous mixing, the spring-shaped dead volume is used. Each
diluted mixture is passed for 30 min in the instrument and the data averaged
from the last 10 min is used. The instrument shows excellent linearity for
CO and the slope is observed to be 0.98. The accuracy of the measurements is
calculated by subtracting the mean difference of measured CO2 and CO
concentrations from the actual concentration of both gases in target gas. The
accuracies of CO2 and CO are found to be in the range of 0.05–0.2 ppm
and 0.01–0.025 ppm respectively. The repeatability of both gases are
calculated using the standard deviation of the mean concentration of target
gas measured by the analyser over the period of observations and found to be
0.3 and 0.04 ppm for CO2 and CO respectively.
Description of AGCM-based chemistry-transport model (ACTM)
This study uses the Center for Climate System Research/National Institute for
Environmental Studies/Frontier Research Center for Global Change
(CCSR/NIES/FRCGC) atmospheric general circulation model (AGCM)-based
chemistry-transport model (ACTM). The model is nudged with reanalysis
meteorology using a Newtonian relaxation method. The U and V components of
horizontal winds are from the Japan Meteorological Agency Reanalysis
(JRA-25; ). The model has
1.125∘× 1.125∘ horizontal resolution (T106 spectral
truncation) and 32 vertical sigma-pressure layers up to about 50 km. Three
components, namely anthropogenic emissions, monthly varying ocean exchange
with net uptake and terrestrial biospheric exchange of surface CO2 fluxes
are used in the model. The fossil fuel emissions are taken from the EDGAR
inventory for the year 2010. Air–sea fluxes from
have been used for the oceanic CO2
tracer. The oceanic fluxes are monthly and are linearly interpolated between
mid-months. The terrestrial biospheric CO2 tracers are provided by the
Carnegie–Ames–Stanford approach (CASA) process model
, after introducing a diurnal variability using
2 m air temperature and surface short wave radiation from the JRA-25 as per
. The ACTM simulations have been extensively used in
TransCom CO2 model intercomparison studies .
Results and discussionTime series and general statistics
(a, c) Time series of 30 min averaged values of CO2 and CO
measured at Ahmedabad for the study period. (b, d) The frequency
distribution in CO2 and CO concentrations for the study period using
a 30 min mean of the gases. (e, f) The polar plots show the
variation of 30 min averaged CO2 and CO at this site with wind direction
and speed during the study period except July, August and September due to
unavailability of meteorology data.
Figure a and c show the time series of 30 min averaged CO2
and CO concentrations for the periods from November 2013 to February 2014 and
July 2014 to May 2015. Large and periodic variations indicate the stronger
diurnal dependence of the gases. Concentrations and variability of both
gases were observed at their lowest in the months of July and August, while maximum
scatter in the concentrations and several plumes with very high levels of the
gases have been observed from October 2014 to mid-March 2015. Almost all
plumes of CO2 and CO have one-to-one correlations and are mostly found during
evening and late night rush hours. Figure e and f show the
variations of CO2 and CO concentrations with wind speed and direction for
the study period except for July, August and September, due to non-availability of
wind data. Most of the high and low concentrations of the gases are found to
be associated with low and high wind speeds. There is no specific direction
associated with the high levels of these gases. This probably indicates that the transport sector
is an important contributor to local emissions since the measurement site
is in the midst of an urban city.
Figure b and d show the probability distributions or
frequency distributions of CO2 and CO concentrations during the study
period. Both gases show different distributions from each other. This
difference could be attributed to the additional impact of the biospheric cycle
(photosynthesis and respiration) on the levels of CO2 apart from the
common controlling factors (local sources, regional transport, PBL dynamics
etc.) responsible for distributions of both gases. The control of the boundary
layer is common for the diurnal variations of these species because their
chemical lifetimes are longer (> months) than the timescale of PBL height
variations (∼ h). However, biospheric fluxes of CO2 can have strong
hourly variations. During the study period the CO2 concentrations varied
between 382 and 609 ppm, with 16 % of data lying below 400 ppm, 50 % lying
in the range 400–420 ppm, 25 % between 420 and 440 ppm and 9 % in the
range of 440–570 ppm. Maximum frequency of CO2 is observed at 402.5 ppm
during the study period. The CO concentrations lies in the range of
0.071–8.8 ppm with almost 8 % of data lying below the most probable frequency
of CO at 0.2 ppm, while 70 % of data lies between the concentrations of 0.21
and 0.55 ppm. Only 8 % of data lies above the concentration of 1.6 ppm and
the remaining 14 % lies between 0.55 and 1.6 ppm. The annual mean
concentrations of CO2 and CO are found to be 413.0 ± 13.7 ppm and
0.50 ± 0.37 ppm respectively, after removing outliers beyond 2σ
values.
Seasonal variations of CO2 and CO
The seasonal cycles of CO2 and CO are mostly governed by the strength of
emission sources, sinks and transport patterns. They follow almost identical
seasonal patterns, but the factors responsible for their seasonal behaviours are distinct. We calculate the
seasonal cycles of CO2 and CO using two different approaches. In the first
approach, we use the monthly mean of all measurements and in the second
approach we only use the monthly mean of measurements from the afternoon
period (12:00–16:00 h). The seasonal cycles from the first approach will
present the overall variability in both gases. On the other hand, the second
approach removes the auto-covariance by excluding CO2 and CO data mainly
affected by local emission sources and represent seasonal cycles at the
well-mixed volume of the atmosphere. The CO2 time series is detrended by
subtracting a mean growth rate of CO2 observed at Mauna Loa (MLO), Hawaii,
i.e. 2.13 ppm year-1 or 0.177 ppm month-1
(www.esrl.noaa.gov/gmd/ccgg/trends/) for clearly depicting the seasonal
cycle amplitude. Figure a and b show the variations of
monthly average concentrations of CO2 and CO using all daily (0–24 h)
data and afternoon (12:00–16:00 h) data.
In general, total mean values of CO2 and CO are observed to be lower in
July, having concentrations of 398.78 ± 2.8 and 0.15 ± 0.05 ppm
respectively. During summer monsoon months the predominance of south-westerly
winds, which bring cleaner air from the Arabian Sea and the Indian Ocean over
to Ahmedabad (Fig. ), and high VC are mostly responsible for the
lower concentration of the total mean of both gases. CO2 and CO
concentrations are also at their seasonal low in the northern hemisphere due
to net biospheric uptake of CO2 and seasonally high chemical loss of CO through reaction with
OH. In addition to this, deep convection efficiently transports the emitted pollutants
(CO, hydrocarbons.etc) and biospheric uptake signals (of CO2) from the surface to the upper troposphere during the summer monsoon, resulting in lower
concentrations at the surface in the summer compared to the winter months
. During autumn and early
winter (December), lower VC caused trapping of anthropogenically
emitted CO2 and CO, and is the major cause for high concentrations of both
gases during this period. In addition to this, wind changes from the cleaner
marine region to the polluted continental region, especially from the IGP region,
could be an additional factor for higher levels of CO2 and CO during
these seasons (autumn and winter). Elevated levels during these seasons are
also examined in several other pollutants over Ahmedabad as discussed in
previous studies . Maximum
concentrations of CO2 and CO are observed to be 424.8 ± 17 and
0.83 ± 0.53 ppm respectively during November. From January to May
the total mean concentration of CO2 decreases from 415.3 ± 13.6 to
406.1 ± 5.0 ppm and total mean concentration of CO decreases from
0.71 ± 0.22 to 0.22 ± 0.10 ppm. Higher VC and predominance of
comparatively less polluted mixed air masses from oceanic and continental
region result in lower concentrations of both gases during this
period. There are some clear differences which are observed in the afternoon
mean concentrations of CO2 compared to daily mean. The first distinctive
feature is that a significant difference of about 5 ppm is observed in the
afternoon mean of CO2 concentrations from July to August compared to the
difference in total mean concentrations of about ∼ 0.38 ppm for the same
period. Significant differences in the afternoon concentrations of CO2
from July to August are mainly due to the increasing sink by net biospheric
productivity after the Indian summer monsoonal rainfall. Another distinct
feature is that the daily mean concentration of CO2 is found to be highest in
November, while the afternoon mean concentration of CO2 attains maximum
value (406 ± 0.4 ppm) in April. A prolonged dry season combined with
high daytime temperatures (about 41 ∘C) during April–May create a tendency for the
ecosystem to become a moderate source of carbon exchange
and this could be responsible for the elevated mean
noontime concentrations of CO2. Unlike CO2, seasonal patterns of CO
from total and afternoon mean concentrations are identical, although levels
are different. It shows that the concentrations of CO are mostly governed by
identical sources during day- and night-time throughout the year.
The average amplitude (max–min) of the annual cycle of CO2 is observed
at around 13.6 and 26.07 ppm from the afternoon mean and total mean
respectively. Different annual cycles and amplitudes have been observed from
other studies conducted over different Indian stations. Similarly to our
observations of the afternoon mean concentrations of CO2, maximum values
are also observed in April at Pondicherry (PON) and Port Blair (POB) with
amplitudes of mean seasonal cycles at about 7.6 ± 1.4 and
11.1 ± 1.3 ppm respectively . Cape Rama (CRI), a
costal site on the south-west coast of India shows seasonal maxima one
month before our observations in March with an annual amplitude of about 9 ppm
. The Sinhagad (SNG) site located over the
Western Ghats mountain range, show much larger seasonal cycles with annual
amplitude at about 20 ppm . The amplitude of the mean
annual cycle at the free tropospheric site, Hanle, at an altitude of 4500 m is
observed to be 8.2 ± 0.4 ppm, with maxima in early May and minima
in mid-September . Distinct seasonal amplitudes and
patterns are due to differences in regional controlling factors for the
seasonal cycle of CO2 over these locations, e.g. Hanle is remotely
located from all continental sources, at the Port Blair site
predominantly marine air is sampled, Cape Rama observes marine air in the summer and
Indian flux signals in the winter, and Sinhagad represents a forested
ecosystem. These comparisons show the need for CO2 measurements over
different ecosystems for constraining its budget.
The seasonal variation of CO2 and CO from July 2014 to May 2015
using their monthly mean concentrations. The blue dots and red rectangles
show the monthly average concentrations of these gases for the total
(0–24 h) and noontime (12:00–16:00) data respectively with 1σ
spread.
The annual amplitudes in afternoon and daily mean CO concentrations are
observed to be about 0.27 and 0.68 ppm. The seasonal cycle of CO over PON
and POB shows a maximum in the winter months and minimum in the summer months
with annual amplitudes of 0.078 ± 0.01 and 0.144 ± 0.016 ppm
respectively, which are similar to our results. So the seasonal levels of CO
are affected by large-scale dynamics, which changes air masses from marine to
continental and vice versa, and by photochemistry. The amplitudes of annual
cycles at these locations differ due to their climatic conditions and
source/sink strengths.
Diurnal variation
The diurnal patterns for all months and seasons are produced by first
generating the time series from the 15 min averages and then averaging the
individual hours for all days of the respective month and season after
removing the values beyond 2σ standard deviations for each month as
outliers.
Diurnal variation of CO2
(a) Average diurnal variation of CO2 over Ahmedabad
during all four seasons. (b) Monthly variation of average
diurnal amplitude of CO2 during from July 2014 to May 2015. All times are
in Indian Standard Time (IST), which is 5.5 h ahead of Universal Time (UT).
Figure a shows the mean diurnal cycles of atmospheric CO2
and associated 1σ standard deviation (shaded region) during all
four seasons. All times are in Indian Standard Time (IST), which is 5.5 h
ahead of Universal Time (UT). Noticeable differences are observed in the
diurnal cycle of CO2 from season to season. In general, maximum
concentrations have been observed during morning (07:00–08:00) and evening
(18:00–20:00) hours, when PBL is shallow, traffic is dense and vegetation
respiration dominates due to the absence of photosynthesis activity. The minimum
of the cycles occurs in the afternoon hours (14:00–16:00) when PBL is
deepest and well mixed, as well as when vegetation photosynthesis is
active. There are many interesting features in the period of 00:00–08:00.
CO2 concentrations start decreasing from 00:00 to 03:00 and increase
slightly afterwards until 06:00–07:00 during summer and autumn. Respiration
of CO2 from vegetation is mostly responsible for this night-time
increase. During winter and spring seasons CO2 levels are observed
constant during night hours and small increase is observed only from 06:00 to
08:00 during the winter season. In contrast to this, the subsequent section shows
a continuous decline in the night-time concentrations of the main anthropogenic
tracer CO, which indicates that there is enough vertical mixing of low CO air
from above that once the CO source is turned off, its concentration drops.
Hence, constant levels of CO2 at night during these seasons give
evidence of a continued but weak source (such as respiration) in order to
offset dilution from mixing low CO2 air from aloft. Dry soil conditions
could be one of the possible causes of weak respirations. Further, distinct
timings have been observed in the morning peak of CO2 during different
seasons. It is mostly related to the sunrise time, which decides the
evolution time of PBL height and the beginning of vegetation photosynthesis.
Sunrise occurs at 05:55–06:20, 06:20–07:00, 07:00–07:23 and 07:20–05:54
during summer, autumn, winter and spring respectively. During spring and
summer, rush hour starts after sunrise, so the vehicular emissions occur when
the PBL has been already high and photosynthetic activity has begun. The
CO2 concentration is observed lowest in the morning during the summer
monsoon season compared to other seasons. This is because CO2 uptake by
active vegetation deplete entire mixed layer during daytime and when the
residual layer mixes to the surface in the morning, low-CO2 air is mixed
down. In winter and autumn, rush hour starts parallel with the sunrise, so
the emissions occur when the PBL is low and hence concentration build-up is much
stronger in these seasons than in spring and summer.
The diurnal amplitude is defined as the difference between the maximum and
minimum concentrations of CO2 in the diurnal cycle. The amplitudes of
a monthly averaged diurnal cycle of CO2 from July 2014 to May 2015 are shown
in Fig. b. The diurnal amplitude shows large month-to-month
variation with increasing trend from July to October and decreasing trend
from October onwards. The lowest diurnal amplitude of about 6 ppm is
observed in July while the highest amplitude at about 51 ppm is observed in
October. The amplitude does not change largely from December to March and is
observed in the range of 25–30 ppm. Similarly from April to May the
amplitude varies in a narrow range from 12 to 15 ppm. The jump in the
amplitude of the CO2 diurnal cycle is observed to be highest (around 208 %)
from July to August. This is mainly due to a significant increase in biospheric
productivity from July to August after the rains in Ahmedabad. It is observed
that during July the noontime CO2 levels are found in the range of
394–397 ppm, while in August the noontime levels are observed in the range
of 382–393 ppm. The lower levels could be due to the higher PBL height
during the afternoon and the cleaner air, but in the case of CO (to be discussed in
next section), average daytime levels in August are observed to be higher than in
July. It rules out that the lower levels during August are due to the higher
PBL height and presence of cleaner marine air, and confirms the higher
biospheric productivity during August.
Near-surface diurnal amplitude of CO2 has also been documented at the humid
subtropical Indian station, Dehradun, and a dry tropical Indian station, Gadanki
. In comparison to Ahmedabad, both stations
show distinct seasonal change in the diurnal amplitude of CO2. The maximum
CO2 diurnal amplitude of about 69 ppm is observed during the summer
season at Dehradun (30.3∘ N, 78.0∘ E, 435 m), whereas
maximum of about 50 ppm is observed during autumn at Gadanki (13.5∘ N,
79.2∘ E, 360 m).
Diurnal variation of CO
(a) Diurnal variation of CO over Ahmedabad during all
four seasons. (b) Monthly variation of the diurnal amplitude of CO.
Scatter plots and regression fits of excess CO (CO(exc))
vs. excess CO2 (CO2(exc)) during morning (06:00–10:00), noon
(11:00–17:00), evening (18:00–22:00) and night (00:00–05:00) hours for all
four different seasons. Excess values of both species are calculated
after subtracting their background concentrations. Each data points are
averaged for 30 min. Emission ratios range of CO / CO2 for different
sources from the literature are also plotted in each figure.
Figure a shows seasonally averaged diurnal variation of CO.
In general, the mean diurnal cycle of CO shows lower concentration during noon
(12:00–17:00) and two peaks in the morning (08:00 to 10:00) and in the
evening (18:00 to 22:00) hours. This cycle exhibits the same pattern as the
mean diurnal cycle of traffic flow, with maxima in the morning and at the end
of the afternoon, which suggests the influence of traffic emissions on CO
measurements. Along with the traffic flow, PBL dynamics also play a critical
role in governing the diurnal cycle of CO. The amplitudes of the evening peak
in diurnal cycles of CO are always greater than the morning peaks. It is
because the PBL height evolves side by side with the morning rush hour
traffic and hence increased dilution, while during evening
hours, PBL height decrease along with evening dense traffic and favours the
accumulation of pollutants until the late evening under the stable PBL
conditions. The noontime minimum of the cycle is mostly associated with the
deepest and well-mixed PBL. In general, the average diurnal cycle patterns of
both gases (CO2 and CO) are similar, but have a few noticeable
differences. The first difference is observed in the timing of the
morning peaks: CO2 peaks occur slightly before the CO peak due to the
triggering photosynthesis process by the sunrise. On the other hand, the
morning peaks of CO mostly depend on the rush hour traffic and are consistent
at 08:00–10:00 in all seasons. The second difference is that the afternoon
concentrations of CO show little seasonal spread compared to the afternoon
concentrations of CO2. Again, this is due to the biospheric control on the
levels of CO2 during the afternoon hours of different seasons, while CO
levels are mainly controlled by dilution during these hours. The third
noticeable difference is that the levels of CO decrease very fast after
evening rush hours in all seasons, while this feature is not observed in
the case of CO2 since respiration during night hours contributes to the
levels of CO2. The continuous drop of night-time concentrations of CO
indicates that there is enough vertical mixing of low CO air from above once
the CO source is turned off. The average morning (08:00–09:00) peak values
of CO are observed at a minimum of (0.18 ± 0.1 ppm) in summer and maximum
of (0.72 ± 0.16 ppm) in winter, while evening peak shows minimum value
(0.34 ± 0.14 ppm) in summer and maximum (1.6 ± 0.74 ppm) in
autumn. The changes in CO concentrations show large fluctuations from morning
peak to afternoon minima and from afternoon minima to evening peak. From
early morning maxima to noon minima, the changes in CO concentrations are
found in the range of 20–200 %, while from noon minima to late evening
maxima the changes in CO concentrations are found in the range of 85 to
680 %. Similar diurnal variations with two peaks have also been observed in
earlier measurements of CO as well as NOx at this site
.
The evening peak contributes significantly to the diurnal amplitude of CO.
The largest amplitude in CO cycle is observed in autumn (1.36 ppm) while the
smallest amplitude is observed in summer (0.24 ppm). The diurnal amplitudes
of CO are observed to be about 1.01 and 0.62 ppm respectively during winter
and spring. Like CO2, the diurnal cycle of CO (Fig. b)
shows the minimum (0.156 ppm) amplitude in July and maximum (1.85 ppm) in
October. After October the diurnal amplitude keeps on decreasing until summer.
Correlation between CO and CO2
The relationship between CO and CO2 can be useful for investigating the CO
source types and their combustion characteristics in the city region of
Ahmedabad. The measurements are generally affected by dilution due to the
boundary layer dynamics, but their ratios will cancel this effect. Further,
the interpretation of correlation ratios in terms of their dominant emission
sources needs to isolate first the local urban signal. For this, the
measurements have to be corrected from their background influence. The
background concentrations are generally those levels which have an almost
negligible influence from the local emission sources. The continuous measurements
of these gases at a cleaner site can be considered as background data, but
due to the unavailability of such measurements for our site and study period, we use the
fifth percentile value of CO2 and CO for each day as the background of these
gases for the corresponding day. It is observed that the mixing ratios of both gases at low
wind speed, which show the influence of local urban signal, are significantly
higher than the background levels and hence confirm that the definition of
background will not significantly affect the derived ratios
. This technique of measuring the background is
extensively studied by and found to be suitable for
both CO and CO2, even having the role of summer uptake on the levels of
CO2. The excess CO2 (CO2(exc)) and CO (CO(exc))
above the background for Ahmedabad city are determined for each day after
subtracting the background concentrations from the hours of each day
(CO2(exc)= CO2(obs)- CO2(bg),
CO(exc)= CO(obs)- CO(bg)).
Correlation slopes
(ΔCO(exc)/ΔCO2(exc) in ppb ppm-1)
measured during different time intervals of distinct seasons. Coefficient of
determination (r2) is given inside the brackets.
We use a robust regression method for the correlation study. It is an
alternative to the least squares regression method and more applicable for
analysing time series data with outliers arising from extreme events
(http://www.ats.ucla.edu/stat/stata/dae/rreg.htm).
Figure illustrates the correlations between
CO(exc) and CO2(exc) for the four seasons at different
time windows of the day. Based on the dominance of different atmospheric
processes and different emission sources as discussed in Sect. ,
the measurements are divided into four different time windows: (1) morning
period (06:00–10:00), when PBL height is slowly evolving and rush hour
traffic is there, (2) afternoon period (11:00–17:00), when atmosphere is
well mixed and traffic volume is relatively low, (3) evening period
(18:00–22:00), when influence of rush hour traffic is significantly high,
and (4) night period (00:00–05:00), when the atmosphere is calm and the
anthropogenic sources of both gases are switched off. The measured slope
values for these time intervals are given in Table . The ranges
of the emission ratios of CO / CO2 for transport, industrial and
domestic sources, as given in Table , are also plotted in
the figures for broadly showing the dominance of different sources. The
ΔCO(exc)/ΔCO2(exc) ratios are observed to be
lowest during the summer, with a range varying from 0.9 ppb ppm-1 in
the morning to 19.5 ppb ppm-1 in the evening period. The lowest
coefficient of determination is also observed during this season, which
suggests that the levels of CO and CO2 are controlled by different
factors. As discussed previously, higher biospheric productivity during this
season mostly controls the CO2 concentrations while CO concentrations are
mostly controlled by the long-range transport. During the winter season
ΔCO(exc)/ΔCO2(exc) ratios are observed at
their highest and vary from 14.3 ppb ppm-1 in the morning to
47.2 ppb ppm-1 in the evening period. Relatively higher ratios during
winter compared to the other three seasons indicate a contribution of CO
emissions from additional biofuel-burning sources. From day to night, the
highest coefficient of determination is observed during spring. As
illustrated by the diurnal cycle, CO2 is not significantly removed by the
biosphere during spring. Along with this, higher VC during this season will
result in very fast mixing. Therefore, very fast mixing will mostly regulate
their relative variation and will result in higher correlation in this
season. Other factors like soil and plant respiration during this period may
also control CO2 concentrations due to which the correlation coefficient
is not equal to one. Except for the monsoon, the
ΔCO(exc)/ΔCO2(exc) ratios and their
correlations are fairly comparable in the other seasons in the evening rush
hours, which indicates stronger influence of common emission sources. Ratios
during this time can be considered as fresh emissions since dilution and
chemical loss of CO can be considered negligible for this time. Most of these
data fall in the domestic and transport sector emission ratio lines, which
indicate that during this time interval these sources mostly dominate
(Table ). On the other hand, during other time intervals
most of the data is scattered between emission ratio lines of the industrial
and transport sectors. Hence, we can conclude that during evening hours,
transport and domestic sources mostly dominate, while during other periods
transport and industrial emission sources mostly dominate. The observed
ratios are similar to the air mass influenced by both fossil fuel and biofuel
emissions as discussed by over Pondicherry. Using
CARIBIC observations, also reported the
ΔCO /ΔCO2 ratio in the range of
15.6–29.3 ppb ppm-1 from the air mass influenced by both biofuel and
fossil fuel burning in the Indochinese Peninsula.
Further, the ΔCO /ΔCO2 ratio is also observed at about
13 ppb ppm-1 in the south-eastern Asian outflow during
February–April 2001 during the TRACE-P campaign and it suggests the combined
influence of fossil fuel and biofuel burning . The
overall ratios (using all data) from autumn to spring
(8.4–12.7 ppb ppm-1) suggest the dominance of local emission sources
during these seasons, and this range corresponds to the range of
anthropogenic combustion sources (10–15 ppb ppm-1) in developed
countries . This suggests that the overall emissions of CO over
Ahmedabad are mostly dominated by the anthropogenic combustion during these
seasons.
Top-down CO emissions from observations
Emission ratios of CO / CO2 (ppb ppm-1),
derived from emission factors (gram of gases emitted per kilogram of fuel burned).
If the emissions of CO2 are known for a study location, the emissions of CO
can be estimated by multiplying the correlation slopes and molecular mass
mixing ratios . Final emissions of CO
will depend on choosing the values of the correlation slopes. The slopes should
not be biased by particular local sources, chemical processing and PBL
dynamics. We exclude the summer monsoon season data, as the CO2 variations
mainly depend on the biospheric productivity during this season. As discussed
previously, the morning and evening rush hour data are appropriate for
tracking vehicular emissions, while the afternoon data are affected by
other environmental factors, e.g. the PBL dynamics, biospheric activity and
chemical processes. The stable, shallow night-time PBL accumulates emissions
since the evening and hence the correlation slope for this period can be used
as a signature of the city's emissions. Hence, we calculate the slopes from
the data corresponding to the period of night-time (23:00–05:00) and evening
rush hour (19:00–22:00). The CO emission (ECO) for Ahmedabad is
calculated using the following formula.
ECO=αCOMCOMCO2ECO2,
where, αCO is the correlation slope of CO(exc) to
CO2(exc) ppb ppm-1, MCO is the molecular mass of
CO in g mol-1, MCO2 is the molecular mass of CO2 in
g mol-1 and ECO2 is the CO2 emission in Gigagram (Gg)
over Ahmedabad. The EDGARv4.2 emission inventory reported annual emissions of
CO2 at 0.1∘× 0.1∘ for the period of 2000–2008
(EDGAR Project Team, 2011). It reported an annual CO2 emission of
6231.6 Gg CO2 yr-1 by EDGARv4.2 inventory over the box
(72.3 < longitude < 72.7∘ E,
22.8 < latitude < 23.2∘ N) which contain Ahmedabad
coordinates in the centre of the box. We assume that the emissions of CO2
are linearly changing with time, and using increasing rates of emissions from
2005 to 2008, we extrapolate the emissions of CO2 for 2014 over the same
area. The bottom-up CO2 emissions for Ahmedabad is thus estimated of about
8368.6 Gg for the year 2014. Further, to compare the estimated emissions
with inventory emissions, we also extrapolated the CO emissions for the year
2014 using the same method that was applied for CO2. The slope values and corresponding estimated emissions of CO are given in
Table .
Estimates of emissions of CO using CO2 emissions from the
EDGAR inventory over the box (72.3 < longitude < 72.7∘ E,
22.8 < latitude < 23.2∘ N) and observed
CO(exc): CO2(exc) slopes for different time periods. The
correlation coefficient for corresponding slopes are given inside the
brackets in the slope column. Data for the summer monsoon season are not included for
calculating slopes.
Further, the uncertainty in total emission due to uncertainty associated with used slope
is also calculated. Using this slope and CO2 emissions from the EDGAR
inventory, the estimated fossil fuel emission for CO is observed at
69.2 ± 0.7 Gg (emission ± uncertainty) for the year 2014. The
EDGAR inventory underestimates the emission of CO as they give an estimate of
about 45.3 Gg extrapolated for 2014. The slope corresponding to the evening rush hours (19:00 - 21:00) gives the highest estimate of CO. Using combinations of
slopes for other periods also, the derived CO emissions are larger than the
bottom-up EDGAR emission inventory. The EDGAR inventory estimates the relative
contributions of CO from the industrial, transport and slum/residential sectors to
be about 42, 42 and 10 % respectively. The possible cause for
underestimation of CO by the EDGAR inventory could be the underestimation of
residential emissions, since other inventories, particularly for major urban
Indian cities
(http://www.indiaenvironmentportal.org.in/files/file/Air-Pollution-in-Six-Indian-Cities.pdf),
show large relative contributions from the residential sector. The uncertainty
associated with the emission factors for different sectors could be another
cause for the underestimation of CO emissions, since these are important
parameters for developing the inventory .
Diurnal tracking of CO2 emissions
(a) Diurnal cycle of excess CO2 over background levels
during all four seasons. (b) Correlation between excess CO and
CO2 for evening hours (18:00–21:00) during the study period.
Contributions of fossil fuel (c) and biosphere (d) in the
diurnal variation of excess CO2 in all four seasons.
CO has virtually no natural source in an urban environments except for
oxidation of hydrocarbons and hence can help to disentangle the relative
contributions of anthropogenic (from transport, power plant, industrial etc.)
and biospheric (mainly from respiration) sources of CO2, by serving as a
tracer of combustion activity on a shorter timescale .
Several studies have used simultaneously measured concentrations of CO2
and CO to segregate the contributions of anthropogenic and natural biospheric
sources in the total atmospheric concentrations of CO2. The observed
results are extensively validated using the carbon isotope (14CO2) method.
.
This quantification technique is more practical, less expensive and less time
consuming in comparison to the 14CO2 method .
For performing this analysis, the background concentrations of CO and CO2
and the emission ratio of CO/CO2 from anthropogenic emissions are
required. The methods for calculating the background concentrations of CO2
and CO are already discussed in Sect. . The observed
concentrations of these gases can also be directly used for calculating the
emission ratio, provided that the measured levels are not highly affected by
natural sources as well as sharing the same origin. We have used the evening
time (19:00–21:00) data of CO2(exc) and CO(exc) for the
whole study period to calculate the emission ratio of CO / CO2 from
the predominantly anthropogenic emission sources. The emission ratio for this
time is calculated to be 47 ± 0.27 ppb ppm-1 with very high
correlation (r=0.95) (Fig. b), after excluding those
data points for which the mean wind speed is greater than 3 ms-1 in
order to avoid the effect of fast ventilation. The tight correlations imply
that there is not a substantial difference in the emission ratio of these
gases during the measurement period from November 2013 to May 2015.
CO2(exc) and CO(exc) will be poorly correlated with each
other if their emission ratio varies largely with time, assuming the
correlation is mainly driven by emissions. Since anthropogenic emissions are
very high for this period, a contribution of respiration sources to the
levels of CO2 can be considered negligible during this period. This ratio
can be considered to be representative of anthropogenic sources, as discussed
in the previous section. We define it as RCO/CO2(ant). The
standard deviation shows the uncertainty associated with the slope, which is
very small. The contribution of the transport sector (CO2(ant)) to
the diurnal cycle of CO2 is calculated using the following formula.
CO(Ant)=COobs-CObgRCO/CO2(ant),
where CO(obs) is the observed CO concentration and CO(bg)
is a background CO value. Uncertainty in the CO2(ant) is dominated
by the uncertainty in the RCO/CO2(ant) and by the choice of
CO(bg). The uncertainty in CO2(ant) due to the
uncertainty in the RCO/CO2(ant) is about 0.5 % or
0.27 ppm and can be considered negligible. As discussed in
Sect. , the uncertainty in the measurements of
CO(bg) is very small and can also be considered negligible.
Further, the contributions of CO2 from the other major sources are
calculated by subtracting the CO2(ant) from the excess
concentrations of CO2. These sources are those which do not emit
significant amounts of CO and can be mostly considered as natural sources
(respiration), denoted by CO2(bio).
The average diurnal cycles of CO2 above the background for each season are
shown in Fig. a. In Sect. , we have
discussed qualitatively the role of different sources in the diurnal cycle of
CO2. With the help of the above method, the contributions of
anthropogenic (CO2(ant)) and biospheric sources
(CO2(bio)) are now discussed quantitatively. Due to the unavailability of
PBL measurements, we cannot disentangle the contributions of boundary layer
dynamics. The diurnal pattern of CO2(ant)
(Fig. c) reflects the pattern of CO because we are using
constant RCO/CO2(ant) for all seasons. Overall, this analysis
suggests that the anthropogenic emissions of CO2, mostly from transport
and industrial sectors during early morning between 06:00 and 10:00, varied from
15 to 60 % (4–15 ppm). During afternoon hours (11:00–17:00), the
anthropogenic-originating (transport and industrial sources, mainly) CO2
varied between 20 and 70 % (1–11 ppm). During evening rush hours
(18:00–22:00), the highest contributions of combined emissions of anthropogenic
sources (mainly transport and domestic) are observed. During this period the
contributions vary from 50 to 95 % (2–44 ppm. During night/early morning
hours (00:00–07:00) non-anthropogenic sources (mostly biospheric
respiration) contribute from 8 to 41 ppm of CO2
(Fig. d). The highest contributions from 18 to 41 ppm are
observed in the autumn from the respiration sources during night hours, since
there is more biomass after the southern Asian summer
monsoon. During the afternoon hours, the lower biospheric component of CO2
could be due to a combination of the effects of afternoon anthropogenic
emissions, biospheric uptake of CO2 and higher PBL height.
Comparison of the model and observationsComparison of diurnal cycle of CO2
Residual of the diurnal cycle of CO2 (in ppm) for (a)
observations and (b) model simulation over Ahmedabad in all four
seasons. Please note that the scales of the model and observational diurnal
cycles are different. (c) Correlation between observed and the model
simulated monthly mean diurnal cycle amplitudes.
We first evaluate the ACTM in simulating the mean diurnal cycle of CO2
over Ahmedabad by comparing the model-simulated surface-layer mean diurnal
cycle of CO2. The atmospheric concentrations of CO2 are calculated by
adding the anthropogenic, oceanic and biospheric component from the CASA process
model. Figure a and b show the residuals (Hourly
mean minus daily mean) of diurnal cycles of CO2 based on the observations and
the model simulations respectively. The model shows very little diurnal amplitude
compared to the observations. Larger differences and discrepancies in night-time
and morning CO2 concentrations between the model and observations
might be contributed to by diurnal cycles of the anthropogenic fluxes from local
emissions and biospheric fluxes as well as by uncertainties in the estimation of PBL
height by the model . Hence, there is a need for efforts
in improving the regional anthropogenic emissions as well as a module for
estimating the PBL height. It may be pointed out that the model's horizontal
resolution (1.125∘× 1.125∘) is too coarse for analysing
local-scale observations. However, the model is able to capture the trend of
the diurnal amplitude, highest in autumn and lowest in the summer monsoon
season. Figure c shows better agreement (r=0.75)
between the monthly change in modelled and observational diurnal amplitude of
CO2 from monthly mean diurnal cycle however slope (m=0.17) is very
poor. We include the diurnal amplitudes of CO2 for November and
December 2013 also for improving the total number of data points. The model
captured the spread in the daytime concentration of CO2 from summer to
spring with a difference that the model shows a lower concentration of CO2
during noon hours in autumn while observations show the lowest concentration in the summer
monsoon season.
Diurnal variation of biospheric fluxes from the CASA ecosystem
model.
The monthly average diurnal cycles of the biospheric net primary productivity
from the CASA model for Ahmedabad and for the year 2014 are shown
Fig. . The details of CASA flux are given in the
Sect. . It is clear from Fig. that the
CO2 flux diurnal cycle as modelled by CASA show minimum day-night
variations amplitude during the summer monsoon time (June-July-August). Given
that biosphere over Ahmedabad is water stressed for all other three seasons
(except the summer monsoon time, Fig. A3), the behaviour of CASA
model simulated diurnal variation is not in line with biological capacity of
the plants to assimilate atmospheric CO2. Due to this underestimation of
CO2 uptake in the summer monsoon season, we also find very large
underestimation of the seasonal through by ACTM in comparison with
observations (Fig. ). Hence, there is a discrepancy
in the diurnal flux of CO2 simulated by CASA model. Similar discrepancy in
the timing of maximum biospheric uptake is also discussed earlier by
using inverse model CO2 fluxes and CASA biospheric
fluxes. It clearly suggests that there is a need for improving the biospheric
flux for this region. It should be mentioned here that the CASA model used a
land-use map from the late 1980s and early 1990s, which should be
replaced by rapid growth in urbanized area in Ahmedabad (area and population
increased by 91 and 42 % respectively, between 1990 and 2011). The model
resolutions may be another factor for discrepancy. As
show that a regional model WRF-CO2 is able to
capture both diurnal and synoptic variations at two closely spaced stations
within 25 km. Hence the regional models could be helpful for capturing these
variabilities.
Comparison of seasonal cycle of CO2
(a) The red circles and blue triangles show the mean
seasonal cycles of CO2 (in ppm) using afternoon values only, calculated
from measurements over Ahmedabad and the model. The green triangles show the
seasonal cycles of CO2 flux over southern Asia, calculated from
TDI64/CARIBIC-modified inverse model as given in
(Fig. 3d). (b) Blue bar and red bar show the correlation
coefficient (r) of model CO2 concentration of biospheric tracer and
fossil fuel tracer component with observed concentrations of CO2, taking
the entire annual time series of daily mean data. The green bar
shows the correlation coefficient between the monthly residuals of afternoon
mean only and the CO2 flux over southern Asia.
Performance matrices used to quantify the level of agreement between
the model simulations and observations. These statistics are based on hourly
values for each day.
Figure a shows the performance of an ACTM-simulating
mean seasonal cycle of CO2 over Ahmedabad by comparing it to the model-simulated
mean surface seasonal cycle of CO2. Due to the unavailability of data from
March to June 2014, we plotted the monthly averages of the year 2015 for the same
periods to visualize the complete seasonal cycle of CO2. The seasonal
cycles are calculated after subtracting the annual mean from each month and
are corrected for growth rate using the observations at MLO. For comparison, we
used the seasonal cycle calculated from afternoon average monthly
concentrations, since the model is not able to capture the local fluctuations
and produce better agreements when boundary layer is well mixed. In
Table we present the summary of the comparisons of the model
and observations. The model reproduces the observed seasonal cycle in CO2
fairly well but with low seasonal amplitude at about 4.15 ppm compared to
the 13.6 ppm observed. Positive bias during the summer monsoon season depicts
the underestimation of biospheric productivity by the CASA model. The root
mean square error is observed to be 3.21 % at its highest in the summer monsoon
season. To understand the role of the biosphere, we also compared the
seasonal cycle of CO2 from noontime mean data with the seasonal cycle of
CO2 fluxes over the southern Asian region, which is taken from
, where they calculated it using a inverse model with
CARIBIC data and shifted a sink of 1.5 Pg C year-1
from July to August and termed it “TDI64/CARIBIC-modified”. Positive and
negative values of flux show the net release and net sink by the land
biosphere over southern Asia. This comparison shows an almost one-to-one
correlation in the monthly variation of CO2 and suggests that the lower
levels of CO2 during July and August and the higher levels in April are mostly due
to the moderate source and sink of the southern Asian ecosystem during these months.
Significant correlation (r=0.88) between southern Asian CO2
fluxes and monthly mean CO2 data for the daytime only suggest that the daytime levels of CO2 are mostly controlled by the seasonal cycle of
biosphere (Fig. b).
Separate correlations of each CO2 tracer with the observations are helpful
for determining the relative importance of each flux component in the CO2
variation . Hence, we perform a separate correlation
study between the measurements and biospheric, anthropogenic and oceanic
components of CO2, estimated by the model using CASA 3 h fluxes
, EDGAR v4.2 inventory and
air–sea fluxes from respectively. The
correlation coefficient indicates dominating controlling
factors for deriving the levels of CO2. Figure b
shows the resulting correlations for a separate flux component with respect
to measurements. We did not include the oceanic tracer and observed CO2
correlation results, since
no correlation has been observed between them. The comparison is based on the daily mean of the entire time series. The correlation between biospheric tracers and
observed CO2 has been found to be negative. This is because during the growing season, biospheric sources act as a net sink for CO2. A correlation of observed
CO2 with the fossil fuel tracer has been identified fairly well (r=0.75). Hence, a correlation study of individual tracers also gives evidence of the overall
dominance of fossil flux in overall concentrations of CO2 over Ahmedabad for the entire study period and by assuming fossil fuel CO2 emissions we can
derive meaningful information on the biospheric uptake cycle.
Seasonal mean concentrations and diurnal amplitudes (max–min) of
CO2 and CO over Ahmedabad.
This study suggests that the model is able to capture
seasonal cycles at lower amplitude for Ahmedabad. However, the model fails to
capture the diurnal variability since local transport and hourly daily flux
play important roles for governing the diurnal cycle and hence there is a
need for improving these features of the model.
Conclusions
Atmospheric concentrations of CO2 were measured along with an
anthropogenic tracer CO at Ahmedabad, a semi-arid urban region in western
India, using a laser-based CRDS technique during 2013–2015. The air masses,
originating from both polluted continental and cleaner marine
regions over the study location during different seasons, make this study
most important for studying the characteristics of both types of air masses.
The observations show a large range of
variability in CO2 concentrations (from 382 to 609 ppm) and CO
concentrations (from 0.07 to 8.8 ppm), with averages of
416 ± 19 ppm and 0.61 ± 0.6 ppm respectively. Higher
concentrations of the gases are recorded for lower ventilation and
winds from a north-easterly direction, while the lowest concentrations are observed for
higher ventilation and the cleaner south-westerly winds from the Indian
Ocean. Along with these factors, the biospheric activity also
controls the seasonal cycle of CO2. The lowest daytime CO2
concentrations, ranging from 382 to 393 ppm in August, suggest a stronger biospheric
productivity during this month over the study region in agreement with an
earlier inverse modelling study. This is in contrast to the terrestrial flux
simulated by the CASA ecosystem model, showing highest productivity in
September and October. Hence, the seasonal cycles of the gases
reflect the seasonal variations of natural sources and sinks, anthropogenic
emissions and seasonally varying atmospheric transport. The annual amplitudes
of CO2 variation after subtracting the growth rate based on the Mauna Loa,
Hawaii data are observed to be about 26.07 ppm using the monthly mean of all
data and 13.6 ppm using the monthly mean of the afternoon (12:00–16:00)
data only. Significant differences between these amplitudes suggests that the
annual amplitude from the afternoon monthly mean data only does not give a true
picture of the variability. It is to be noted that most of the CO2
measurements in India are based on daytime flask samplings only.
Significant differences in the diurnal patterns of CO2 and CO are also
observed, even though both gases have major common emission sources and
undergo PBL dynamics and advection. Differences in their diurnal variability
are probably the effect of the terrestrial biosphere on CO2 and chemical loss
of CO due to reaction with OH radicals. The morning and evening peaks of CO
are affected by rush hour traffic and PBL height variability, and they occur
at almost the same time throughout the year. However, the morning peaks in CO2
change their time slightly due to a shift in photosynthesis activity according
to change in sunrise time during different seasons. The amplitudes of annual
average diurnal cycles of CO2 and CO are observed at about 25 and 0.48 ppm
respectively (Table ). Both gases show highest
amplitude in the autumn and lowest in the summer monsoon season. This shows
that major influencing processes are common for the gases, specific to
the city and the Indian monsoon.
The availability of simultaneous and continuous measurements of CO2 and CO
have made it possible to study their correlations at different time windows
(during morning (06:00–10:00), noon (11:00–17:00), evening (18:00–22:00)
and night (00:00–05:00) hours) of distinct seasons. Using the correlation
slopes and comparing them with the emission ratios of different sources,
contributions of distinct sources are discussed qualitatively. It is observed
that during the evening hours, measurements over the study region are mostly
affected by transport and domestic sources, while during other periods the
levels of both gases are mostly dominated by emissions from transport and
industrial sources. Further, using the slope from the evening rush hour
(18:00–22:00) data as anthropogenic emission ratios, the relative
contributions of dominant anthropogenic emissions and biospheric emissions
have been disentangled from the diurnal cycle of CO2. At rush hour, this
analysis suggests that 90–95 % of the total emissions of CO2 are
contributed by anthropogenic emissions. The total yearly emission of CO for
Ahmedabad has also been estimated using these measurements. In this
estimation, fossil-fuel-derived emissions of CO2 from the EDGAR v4.2 inventory
are extrapolated linearly from 2008 to 2014 and it is assumed that there are
no year-to-year variations in the land biotic and oceanic CO2 emissions.
The estimated annual CO emission for Ahmedabad is estimated to be
69.2 ± 0.7 Gg for the year 2014. The extrapolated CO emission from
the EDGAR inventory for 2014 shows a value smaller than this estimate by about
52 %.
The observed results of CO2 are also compared with a general atmospheric
circulation model based on chemistry-transport model-simulated CO2
concentrations. The model captures some basic features like the trend of
diurnal amplitude, seasonal amplitude etc. qualitatively but not
quantitatively. The model captures the seasonal cycle fairly well but the
amplitude is much lower compared to the observations. Similarly,
performance of the model capturing the change in monthly averaged diurnal
amplitude is quite good (r=0.72), however the slope is very poor. We also
examined the correlation between the hourly averaged observed CO2 and
tracer of fossil fuel from model simulation and found fairly good correlation
between them. However, no significant correlation has been observed between
observed CO2 and biospheric tracer. It suggests that the levels of CO2
over Ahmedabad are mostly controlled by fossil fuel combustion throughout
the year.
This work demonstrates the usefulness of simultaneous measurements of CO2
and CO in an urban region. The anthropogenic and biospheric components of
CO2 have been studied from its temporally varying atmospheric
concentrations, and validity of the “bottom-up” inventory has been assessed
independently. Use of CO(exc): CO2(exc) ratios avoids
some of the problems with assumptions that have to be made with modelling.
These results represent a major urban region of India and will be helpful in
validating emission inventories, chemistry-transport and terrestrial
ecosystem models. However, a bigger network of sites is needed to elucidate
more accurate distributions of emissions and their source regions and run
continuously over multiple years for tracking the changes associated with
anthropogenic activities and emission mitigation policies. The corresponding
author may be contacted for the data published in this article.
Acknowledgements
The authors greatly acknowledge the PRL and ISROGBP-ATCTM for funding and
support. We acknowledge the support of T. K. Sunil Kumar in making the
measurements. We thank the European Commission for the provision of the EDGAR
inventory data used in this study. We thank the reviewers for their
exhaustive comments and detailed suggestions in getting the MS to its present
form. We are grateful to the editor for his support throughout the review
process.
The corresponding author may be contacted for the data published in this
article.Edited by: C. Gerbig
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