Aerosol optGangetic Basianalyses of Cvertically elevwith maximumMODIS and Ato Kanpur. Thdust/burning amicrophysical(Type I, Typecoming via lowhereas a mixas Type II aerndustrial/biom
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Keywords: Ae
INTRODUCT
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ar Mishra1
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tical and microin (IGB) durinCALIOP, MOvated aerosol pm loading in AERONET obhe high aerosoactivities are l parameters ce II and Typeong-range tranxture of absorbrosols. The prmass burning ombination ofbiomass burn
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distribution.
osols (i.e., blabon/pollution) vated Heat Ping of dust lon advancemen
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biomass bucontinent. SimRamanathan euction in surfosols over the nhe north-south
Identifica
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dia te of Science,
rial cities (DeM: March–MayALIOP-deriveust particles d
ed with columcal depth (AOocations can br the IGB. Te presence of characterized ra, Iran, Afghnsula and the ype III aerosoed by agricultd-based measue IGB during
nificant effect region.
ack carbon, mmay intensify
Pump hypotheoading with abnt of the sum
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2012) have ren precipitationurning (BB) milar inferencet al. (2005), wface solar radnorthern Indiah SST (sea su
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mixture of duy the Indian sumesis) and suggbsorbing pollu
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Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 768
thereby weakening the Indian summer monsoon. These ambiguities in the results related to the effect of aerosols on climate mainly arise due to lack of information on variability of the vertical, geographical and temporal distributions of aerosols over the Indian region.
In terms of aerosol research, the Indo-Gangetic Basin (IGB) region has drawn significant attention over the past two decades. The geological setting and synoptic meteorological conditions coupled with high population density make the IGB a “natural laboratory” to study aerosol properties and their effect on regional climate. Previous studies on the IGB region have focused on both natural and anthropogenic aerosols, whose dominance is seasonally dependent (Singh et al., 2004; Jethva et al., 2005; Dey et al., 2008; Mishra and Shibata 2012a; Srivastava et al., 2012). The significant aerosol types during the pre-monsoon (PrM) season are urban-industrial (U-I) pollution (locally generated and regionally transported secondary organic carbon and black carbon) and dust particles from nearby arid agricultural land and the Thar Desert (western India), along with some long-range transported mineral dust from the deserts of Sahara, the Arabian peninsula and Iran (Middleton, 1986; Chu et al., 2003; Bond et al., 2004; Singh et al., 2005; Gautam et al., 2009, 2011; Arola et al., 2011). Chinnam et al. (2006) and Prasad and Singh (2007) have also reported that the dust aerosols over the IGB originate from three major sources (Oman, the southwest Asian basins and the Thar Desert) during the PrM season. They have also found evidence of the mixing of dust with other anthropogenic pollution during the transport process. The mixing of these different aerosols changes the optical and microphysical properties of existing aerosols (Eck et al., 2010) which leads to uncertainty in the calculation of aerosol radiative forcing, thereby having an impact on climate and aerosol-monsoon related studies (Pandithurai et al., 2008). Thus, an in-depth knowledge about the optical and microphysical properties during the PrM season is needed to assess the variability in aerosol types and their mixing over the IGB.
Many efforts have been devoted to measure aerosol optical and microphysical properties during the PrM season (Dey et al., 2004; Singh et al., 2004; Ganguly et al., 2009; Gautam et al., 2010; Srivastava et al., 2011) using ground- and satellite-borne remote sensors. Recently, Gautam et al. (2011) reported the distribution of aerosol properties and their radiative effects using several ground-based radiometric measurements in the IGB region as well as over the southern slopes of the Himalayas during the PrM season. A positive gradient in aerosol optical depth (AOD) and Ångström Exponent (AE) from central IGB to eastern IGB with the advancement of the PrM season has been reported by Srivastava et al. (2011). They have found the dominance of coarse mode particles over central IGB and of fine mode pollution over eastern IGB as the main components of absorbing type aerosols during this season. Also the influence of drought monsoon condition and a dry PrM period on aerosol properties has been studied by Kaskaoutis et al. (2012). They have found the drastic increase in dust loading during these anomalous dry conditions over Kanpur and Delhi during 2002–2003.
The classification of aerosol types over the IGB and other Indian cities has been extensively studied using various methodologies in recent years. Srivastava et al. (2012) have studied the aerosol type discrimination (e.g., polluted continental, polluted dust, non-absorbing aerosols) using fine mode fraction (FMF) of AOD and single scattering albedo (SSA), which reveals higher contribution of polluted dust over Kanpur, while the polluted continental and carbonaceous aerosols dominate over Gandhi College (eastern IGB) during the PrM period. Recently, aerosol classifications were reported using the relationship between AE440–870 and the difference in AE [ΔAE = AE440–675 – AE675–870] including the FMF over a southern city (Hyderabad) of India (Sinha et al., 2012). Pathak et al. (2012) have classified five different categories (viz. continental, marine continental, urban-industrial (U-I) and biomass burning (BB), desert dust and mixed type) of aerosols over a city of North-Eastern India using the relationship between AOD500 and AE. Using a similar methodology, Kaskaoutis et al. (2009) show dominance of mixed type of aerosols (44.3–72.9%) during the PrM and monsoon season over Hyderabad. The relationship between AE and AOD500 (from ship-borne sunphotometer measurements) for discriminating aerosol types has also been well discussed over the oceanic environment of the Arabian Sea and Bay of Bengal (Kaskaoutis et al., 2010, 2011). Vijayakumar and Devara (2012) have also attempted to see the aerosol variation over a high altitude station of the Western Ghats (Sinhagad) using Microtop observation of AOD and AE values.
To that end, this study was carried out using synergistic analyses of aerosol optical and microphysical properties observed from AERONET (Aerosol Robotic NETwork), MODIS (MODerate resolution Imaging Spectroradiometer), CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), and PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations over the IGB region during three consecutive (2007–2009) PrM periods. The main highlights of this study consist of 1) inter-seasonal changes of aerosol properties during the PrM seasons, 2) intra-seasonal changes in pre-monsoonal aerosol characteristics due to major event days (e.g., dust outbreaks, biomass-burning, urban/industrial pollution etc.), and 3) classification of dominant aerosol types using various optical and microphysical properties (Russell et al., 2010) and their contributions over two industrial cities (Kanpur and Delhi) of central IGB. EXPERIMENTAL SETUP Study Area
The Indian part of the Indo-Gangetic Basin (IGB), one of the largest drainage basins in the world, is bordered by the Himalayas to the north and Vindhyan-Satpura ranges to the south (Fig. 1). The central region of IGB (24°–29°N; 76°–84°E) is represented by the black rectangle in Fig. 1. The IGB is dominated by urban/industrial aerosols (Bond et al., 2004; Dey and Tripathi 2007), which show strong seasonal variability based on the mixing of anthropogenic and natural aerosols (Dey et al., 2004; Mishra and Shibata,
Fib(Cb
2ghtvcaaacatgsFtw( DC
CLvpt(
Fig. 1. The stus represented
by a dotted lin(Delhi and KCentral regionblack color rec
2012a), especgrowing urbanhave resulted the last two devariability ovcities, Kanpuraccount, whichareas) as well aerosols duringconditions oveal. (2012) fortemperature ggradient are pseasons. EuroForecasts (ECMthat the IGB iwesterly and (Srivastava et
Data Sets andCALIOP
We used CALIOP onboLidar Infraredvertical distribproducts (Youthe vertical di(2007–2009) o
udy area i.e., d by the blackne. The groun
Kanpur) in thn of IGB (24°–ctangle.
ially in the Pnization and in a substantiecades. To stuer the IGB,
r and Delhi (shh experience bas anthropog
g the PrM seaser the IGB arer PrM 2006–gradient and pronounced oopean CentreMWF)-derivedis generally csouth-westerlal., 2012).
d Methodology
data collecteoard the satel
d Pathfinder Sabution. CALI
ung and Vaughistribution of over the IGB.
Mishra et a
the Indo-Gank-dot shaded nd based meashe IGB are d–29°N; 76°–8
PrM and monseconomic gr
ial increase inudy the pre-mtwo big andhown in Fig. both natural (penic (urban/inson. The synope well present–2009. In geneast to west over the IGBe for Mediumd monthly reancharacterized bly during the
y
ed by the llite CALIPSatellite ObservIOP level 2 Vhan, 2008) weaerosol durinFollowing th
al., Aerosol and
ngetic Basin (region surrousurement locadepicted by 4°E) are show
soon seasons.rowth in the
n air pollution monsoonal aerd highly indu1) were takenproximity to dndustrial polluptic meteoroloed in Srivasta
neral, west tospecific hum
B during the m-Range Wenalysis data reby north-west
e all PrM sea
space-borne O (Cloud-Aevation) for aeVersion 3.01 ere used to anang the PrM pe method of L
d Air Quality Re
(IGB) unded ations stars.
wn by
. The IGB
over rosols ustrial n into desert ution) ogical ava et o east midity
nes (β532,p, ⊥(z)re β and z areude height. Thparallel compect to the por pulses. Theicles (aerosolshe color ratifficient of partnm, which is
efined as:
(z) = β1064,p (z)
he particle dolarization onlicles) can be c
R (z) = β532,p, ⊥
he variation obeen presenteussed the fre
or ratio, depolvarious aeroso
osol vertical dral region of Ithree consecuved backscatters (Misra et ara et al. (2012LIOP and Mved aerosol vetamination as
RONET AERONET is a/sky scanningvelengths, typi, 940 and 10zed in the dire
0, 675, 870 anctral AOD is RONET site vation: 123m50000°E; Elev
w onwards willruary to 31st Mrequired perio75100°E; Elestudy. The u
RONET sites hlben et al., 19he inversion p
h as single sc
7–782, 2014
ighttime CALstudy. It pro
ficient profile32,p (z) and β10
) and β532,p, ǁ e respectivelye signs ⊥ andponents of thlarization plansubscript “p
s or clouds). o (CR) is thticles at the ws a parameter
)/β532,p (z)
depolarizationly by particlescalculated as f
⊥ (z)/β532,p, || (z)
of CR and PDed in Omar et equency distriarization ratiool types. In thdistribution hGB [Fig. 1: (2tive PrM seaser profiles hasal., 2012 and r) have reporte
Micro Pulse Lertical distributa possible sou
a globally distrg radiometerscally centered
020 nm. All oect Sun measu
nd 1020 nm) aobtained fromof Kanpur (2
m) and Guvation: 384 ml be written asMay. Due to tod, New Delhievation: 240 muncertainties ohave been estim98; Eck et al.,
products of othcattering albe
LIPSO overpaovides data o
es at two wa064,p (z)) and tw(z)) at 532 n
y, backscatter d ǁ represent thhe backscatterane of the linep” indicates th
he ratio of twavelength 106
for the size o
n ratio (PDRs (parameter ffollows:
)
DR for varioual. (2009), w
ibution of CAo and attenuahis study, mo
have been pre24°–29°N; 76°son. Validatios been reportereference ther
ed a good correLidar Networution over Kanurce of error.
tributed netwos that measud at 340, 380, of these specurements, whiare used for thm CIMEL Sun26.512778°N,ual Pahari
m; southern outs Delhi-AEROthe unavailabii AERONET m) site is notof AODs datamated to be w, 1999; Sinyukher aerosol opedo (SSA) an
76
asses have beeon aerosols avelengths, 53wo polarizationm wavelength
coefficient anhe perpendiculared signal witearly polarizehe atmospheri
the backscatte64 nm to that aof particles an
(1
R) that is thfor sphericity o
(2
s aerosol typewhere they havALIOP-deriveted backscatte
onthly averageesented for th°–84°E)] durinon of CALIOPed in numerourein). Recentlyelation betweek (MPLNET
npur, with clou
rk of automatures at severa
440, 500, 675ctral bands arile four of them
he sky radiancnphotometer a 80.231639°E(28.426390°Ntskirts of DelhONET) from 1ility of data fo(28.630467°Nt considered ia derived from
within 0.01–0.0k et al., 2012)tical propertiend the colum
69
en as 32 on h, nd ar th ed ic
er at
nd
1)
he of
2)
es ve ed er ed he ng P-us y, en )-
ud
ic al 5, re m e. at E; N, hi: 1st or N, in m 02 ). es, mn
Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 770
integrated aerosol size distributions above the measurement sites (over Kanpur-AERONET and Delhi-AERONET sites) are provided at the sky radiance wavelengths (Holben et al., 1998; Dubovik and King, 2000). Uncertainty associated with inversion products from sky radiance measurement were reported to be less than 5% in Holben et al. (1998). In this study, level 1.5 data (cloud screened) of AOD500, volume size distribution, and SSA at four wavelengths (440, 675, 870 and 1020 nm) are used for both cities. Also level 2.0 data (quality assured) of daily AOD and SSA, for five years (2006–2010) over Kanpur-AERONET and for two years (2009–2010) over Delhi-AERONET sites, are used to show the seasonal dependency of aerosol types over Kanpur and Delhi. Following Russell et al. (2010), absorption aerosol optical depth (AAOD) is obtained as: AAOD (λ) = [1 – SSA (λ)] AOD (λ) (3)
In general, AAOD follows a relatively smooth decrease with wavelength and can be approximated with a power-law wavelength dependence (AAOD ~ λ–AAE). By convention, AAE (absorption Ångström exponent) is the negative of the slope of the absorption AOD on a log-log plot. In the same way, extinction Ångström exponent (EAE) is calculated by power-law wavelength approximation (AOD~ λ–EAE), which is used as a size parameter of aerosol particles. MODIS
MODIS onboard EOS-Terra (Earth Observing System) measures AOD and other optical properties of aerosols on a global scale. MODIS-derived aerosol properties over land have been validated by numerous studies across the world (Ichoku et al., 2002; Remer et al., 2005; Tripathi et al., 2005, Mishra et al., 2008) using different ground-based observations. MODIS Collection 5.1 (Terra and Aqua satellite product) level-3 AOD (at 550 nm) and AE (470–660) datasets are used for characterizing the spatial and temporal variation of aerosol loading during three PrM seasons (2007–2009) over the IGB (Remer et al., 2005; Levy et al., 2007). These data are provided by the Giovanni-MODIS Online Visualization and Analysis system site (http://gdata1. sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=MODIS_ DAILY_L3). All MODIS-AOD550 and MODIS-AE470–660 datasets presented in this study are averaged mean of Aqua and Terra measurements for the central IGB region. RESULTS AND DISCUSSION Aerosol Properties from Space- and Ground-Based Remote Sensors
Study of aerosol vertical distribution is needed to assess the long-range transport of aerosols and their effects on thermal structure/stability of the atmosphere (Satheesh et al., 2009). Fig. 2 presents (a) CALIOP-derived monthly averaged vertical distribution (up to 4 km) of aerosol backscatter (β532), and (b) scatter plot between PDR and CR for the PrM season of 2007, 2008 and 2009 (left to right) over central IGB. Vertical profile of aerosol backscatter shows maximum aerosol concentration during May and minimum
during March for all altitude levels in almost every year. Vertically extended (up to 4 km altitude) aerosol profiles have been found during each month of PrM season, which are generally associated with the increased thermal/mechanical convective activities and long-range transportation of dust particles during this time period (Gautam et al., 2011; Mishra and Shibata, 2012a). Similar role of prevailing meteorology on aerosol vertical distribution has been found during the monsoon season over Delhi using CALIOP observations by Srivastava et al. (2012a). Misra et al. (2012) have also reported higher extinction values at altitudes of 2–4 km during dust loading period of April-June over Kanpur using MPLNET and CALIPSO datasets. In another study, Kuhlmann and Quaas (2010) have shown a net atmospheric heating (1–2 K/day) due to elevated (4–5 km) dust and polluted dust aerosols over eastern IGB during the PrM seasons of 2007–2009.
Seasonal averaged loading of aerosols is found to be maximum in 2008 with highest aerosol backscatter (averaged for lower 4 km altitude) in May (4.73 ± 1.24 × 103/km/sr) and lowest in March (2.18 ± 0.62 × 103/km/sr). The space-borne and ground-based measurements of monthly averaged columnar AODs show a similar pattern of aerosol loading in all three PrM seasons (Fig. 3). Similar to CALIOP-backscatter observation, MODIS- and AERONET-AOD also show maximum values in May, 2008. Fig. 2(b) shows different features in 2008 as compared to other two years, where all the PDR and CR values are concentrated in the following range: (0.15 < PDR < 0.25) and (0.8 < CR < 1.2). PDR and CR values can be effectively used in differentiation between spherical and non-spherical as well as smaller and larger particles (Liu et al., 2008). A higher value of CR (~1.0) and PDR (~0.2) indicates the dominance of larger dust particles in the atmosphere, whereas relatively lower values suggest the presence of BB or U-I pollution particles (Mishra and Shibata, 2012a). Also columnar AE values derived from both MODIS and AERONET observations show minimum value during May, 2008 and are found to be well corroborated with CALIOP-derived microphysical parameters (Table 1). The minimum values of AE (AERONET: 0.45 ± 0.25 and MODIS: 0.61 ± 0.05) and maximum values of AOD (AERONET: 0.68 ± 0.18 and MODIS: 0.69 ± 0.14) found during May, 2008 indicates dominance of coarse particles compared to other months in the PrM season (Fig. 3). These results show relatively strong dust loaded PrM season of 2008 as compared to the other two years. Similar type of inferences have been reported by Gautam et al. (2009), where they have mentioned that the PrM of 2008 was strikingly different from 2007 in terms of dust loading over IGB. They have found more than 50% of AOD associated with higher depolarization ratio (> 0.15) in PrM of 2008 as compared to only 20% AOD in PrM of 2007.
Table 2 shows the monthly averaged β532, CR and PDR (averaged for 0.3 to 4 km altitude) values for three consecutive PrM season over central IGB. Seasonal (PrM) averaged aerosol backscatter, CR and PDR were found to be highest (3.29 ± 1.30 × 103/km/sr, 0.94 ± 0.04 and 0.21 ± 0.02 respectively) in 2008 and lowest (2.47 ± 1.03 × 103/km/sr, 0.87 ± 0.04 and 0.18 ± 0.01 respectively) in 2009. Also the
Fp
FptvaAoe
ircT(s
Fig. 2. (a) CAplot between P
Fig. 3. Monthpanel) and AEthree consecutvalues are meaveraged overAERONET-Aonly. Error bareach month.
ncreased backrange were focontribution oThe average v(coupled withsection indica
ALIOP-derivedPDR and CR,
hly mean valERONET-AOtive PrM seasoean values or central IGB (OD500 values rs represent re
kscatter valueound to be asof total AOD values of aer
h columnar Aate the presen
Mishra et a
d monthly averfor the pre-m
ues of MODOD500 (lower ons (2007–09f Terra and (24°–29°N; 76are monthly m
espective stand
es between thessociated withduring all th
osol backscatAOD and AE)nce of dust a
al., Aerosol and
raged vertical onsoon season
IS-AOD550 (upanel) during). MODIS-AOAqua observ6°–84°E) whemean over Kadard deviation
e 1–3 km altith more than hree PrM seatter, CR and ) presented inaerosols durin
d Air Quality Re
distribution (n of 2007, 200
upper g the OD550 vation ereas, anpur ns for
tudes 50%
asons. PDR
n this ng all
PrMaverseasaerointraPrMover(pre2009both
Fiat 50overFebrshowsomvaluthat perio(1.49respoverThe (AOaeroevenwhical. (showAE vin A
esearch, 14: 767
up to 4 km) o08 and 2009 (
M seasons overraged dataset onal variabili
osol propertiea-seasonal var
M seasons, we r two industrisented in follo9) is done on h cities for a coig. 4 shows d00 nm (AOD5
r Delhi-AEROruary, 2009 tow almost the s
me event daysues are found
over Kanpur od. The maxi9 and 0.16) anectively. Ther Kanpur (0.76similarity in in
OD and AE) oosol source regn similarity inch has been re2012). Thougw any pronouvalues is quite
AE values ar
7–782, 2014
of aerosol backleft to right) o
r the IGB. Homay someti
ity (including s in these seriability of aehave intensifiial cities (Kanowing section
the basis of omparative stuaily variation
500) and (b) ÅnONET and Kano 31 May, 200ame type of ps. The meanto be more ov(0.51 ± 0.19)
imum and mind (1.06 and 0e mean AEs 6 ± 0.37) than tntra-seasonal vover Kanpur gions, either nthe atmosphereported in a reh intra-season
unced variatione peculiar in boound 21st Fe
kscatter (β532),over the centra
owever, monthimes lead to g different eveeasons. In orerosol parameied our analysnpur and Del
n). The selectioavailability oudy.
n of (a) aerosongström Exponpur-AERONE09. The AOD pattern over bon AERONETver Delhi (0.5) during the ainimum value0.17) over Delshow slightlythat over Delhvariation of aeand Delhi su
natural or antric conditions oecent study bynal variation oon pattern, daioth cities. A s
ebruary–9th M
77
, and (b) scatteal IGB.
hly or seasonasmooth intra
ent periods) oder to addres
eters during thsis in PrM 200hi) in the IGon of year (i.e
of datasets ove
ol optical deptonent (AE or αET sites from and AE value
oth cities excep-derived AOD
58 ± 0.24) thaaforementionees of AOD arlhi and Kanpuy higher valuhi (0.73 ± 0.32erosol propertieuggests similathropogenic, oover the regiony Kaskaoutis eof AOD did noily variation osudden decreas
March is foun
71
er
al a-of ss he 09
GB e., er
th α) 1
es pt D an ed re
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Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 772
Table 1. Monthly mean values of Angstrom Exponent (AE ± SD) during the PrM seasons for three consecutive years (2007–2009). The subscript associated with AE show the interval of wavelength used in calculation.
Fig. 4. Daily variation of (a) AOD500 and (b) Ångström exponent (AE or α) over Delhi and Kanpur using AERONET measurements from 1st February, 2009 to 31st May, 2009.
been found duellipse in Fig. (i.e., very lowMarch and Apthunderstormsof large partic(Puranik and K
Fig. 5(a) de(PrM 2009) Ashows peculiarIGB in terms oet al. (2012) hn western IGBn eastern IGB
MODIS AODsimilar patternFig. 5(b)) whicoading from A
the enhanced producing highthe IGB. Also(0.6–1.2) withhas been reportal., 2005).
The main fin
Fig. 5. (a) Spaand (b) daily vMay, 2009.
tted ellipse inly variable And April. Relauring mid-May4(b)) than tha
w and very hipril can be unds (with rain) focles is the chKarekar, 2004epicts spatial AOD over thr differences bof aerosol loadhave reported B and abundanB. The daily vD over Delhi n like AERONch also presenApril to May. Gdust transporther AOD (> 0o in an earlieh significant sted over the IG
ndings of this
atial variationvariation of A
Mishra et a
n Fig. 4(b)) ovAE values are
atively highery (10–20 May)at in April monigh values) oderstood as freollowed by drharacteristic tr4).
variation of he Indian subbetween easterding and theirthe dominanc
nce of U-I/carbariation (1:30and Kanpur
NET observatnt a consistent Gautam et al. t from South-0.6 April–Juneer study, a hspatial and te
GB during Apr
section conclu
n of three monAOD550 over D
al., Aerosol and
ver both the cfound throug
r AE values ) (shown by dnth. The variab
of AE during equently occuy and wet remrait of this p
seasonal averbcontinent, wrn IGB and we
source. Srivace of pollutedbonaceous aer PM local timshows some
tion (Fig. 4(a)increase in ae(2011) have shAsian arid reg
parameter (En widely usedhra and Shibal. (2010) hav
ustrial (U-I) amass burning (ues for Sahara
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7–782, 2014
hi than over Krogressed. Hoseason is not by fine-mode aon).
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Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 774
black dotted lines are used to separate seasons. A pronounced seasonal variation is found in AAE and EAE values over both locations, which indicates the seasonal dependency of aerosol types over the IGB (Mishra and Shibata, 2012a). In winter, AAE values are almost constant (0.95–1.1) with decrease in EAE values from December (~1.35) to February (~1.15) for both cities. AAE values near 1 and larger EAE (fine mode dominance) values indicate the presence of U-I pollution during the winter season. Relatively larger AAE (~1.3) and larger EAE (1.16–1.36) values during the post-monsoon season, show the dominance of fine mode BB aerosols over Kanpur, whereas AAE ~1.0 in September indicates the presence of U-I pollution over Delhi. The difference between these two aerosol types (U-I pollution and BB aerosols) can be done on the basis of AAE values, as AAE for organic species (brown carbon) is larger than that for black carbon (Bond, 2001; Schnaiter et al., 2003). Largest AAE (1.2–1.58) values and lowest EAE (< 0.8) values show a distinct scenario in the PrM and monsoon season. Bergstrom et al. (2004) have identified the mixture of mineral dust and black carbon with high AAE and lower EAE values over the Asian region. The AAE and EAE variation during the PrM and monsoon seasons indicate the presence of mixed dust particles over both locations.
Fig. 7(a) and 7(b) show scatter plot of daily averaged AAE vs. EAE during the PrM season (21st February–31st
May) of 2009 over Kanpur and Delhi, respectively. Three different clusters of (AAE, EAE) points shown in Fig. 7(a) and Fig. 7(b) indicate the presence of three different aerosol types (Type I, Type II and Type III are marked in the figures) during the PrM season over both the cities. Type I shows AAE values in the range of 1.3 to 2.2 and EAE values in between 0.05 to 0.8. These values of AAE and EAE could characterize the Type I aerosols as dust dominated particles partially mixed with U-I pollution (Bergstrom et al., 2004). Relatively lower values of AAE (0.7–1.3) and EAE (0.45–0.76) indicates the presence of black carbon (BC) coated dust/sea-salt particles in Type II aerosols (Gyawali et al., 2009). BC coating over dust particles (core-shell mixing) have been seen as most probable state of mixing during the PrM season over the IGB (Dey et al., 2008; Srivastava and Ramachandran, 2012). Type III can be characterized as fine mode dominated (larger EAE values) mixture of U-I pollution and BB aerosols (AAE: 0.7–1.4).
In order to get some more information about the type of aerosol loading, we plotted the scatter diagram of AOD500 and AE440–870 over Kanpur and Delhi (Figs. 7(c) and 7(d)). An inverse relationship between AOD and AE (dotted rectangular boxes) is found over both locations (Delhi and Kanpur) i.e., AOD decreases as AE increases and vice-versa. This type of inverse relationship shows a trend of increased dust contribution over turbid atmosphere, which
Fig. 6. Monthly variation of AAE and EAE over (a) Kanpur [five year (2006–2010) monthly averaged], and (b) Delhi [two year (2009–2010) monthly averaged] using AERONET data. Vertical error bars represent standard deviation and vertical black dotted lines are used to separate seasons. Seasonal variation of AAE/EAE shows the dominance of industrial-urban pollution in the winter, biomass-burning aerosols in the post-monsoon and dust dominated aerosols during the PrM and monsoon seasons (refer the text for explanation).
0.0
0.2
0.4
0.6
0.8
1.0
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1.6
1.8
2.0AAEEAE
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E
Urban‐Industrial pollution
Dust dominated aerosols
Biomass‐burning aerosols
0.0
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EAEAAE(b) Delhi
Winter Pre‐monsoon Monson Post‐monsoon
Urban‐Industrial pollution
Dust dominated aerosols
Biomass‐burning aerosols
AAE/EA
E
(a) Kanpur
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 775
Fig. 7. Scatter plot of daily averaged AAE vs. EAE over (a) Kanpur and (b) Delhi and AOD500 vs. AE440–870 over (c) Kanpur and (b) Delhi during the PrM season (21st February–31st May) of 2009. Different ellipses and rectangles are used to show the presence of three different types (Type I, Type II and Type III) of aerosols in Figs. 7(a) and 7(b). In Figs. 7(c) and 7(d), dotted rectangular boxes show an inverse relationship between AOD and AE whereas; the dotted ellipses show different association. Total number of data-points is presented in each respective plot.
also agrees with earlier observations by Kaskaoutis et al. (2009) and Pathak et al. (2012) for coarse mode dominated aerosol particles. However, some different relationships are also found over both locations (shown in dotted ellipses) which indicate presence of some other aerosol loading sources during the observation period. If we compare the upper and lower panels of Fig. 7, it is found that all Type I and Type II aerosols show an inverse relationship between AOD and AE, whereas Type III aerosols show a different association between these two parameters.
The different aerosol types could be the result of variations in the intensity of the emissions, in source region, in atmospheric and meteorological dynamics and in the mixing state of the atmosphere. This result also generates an interest about the differences in optical and microphysical properties of these three types of aerosols over both locations and an assessment of their source region.
Specific Case Studies
To analyze the optical and microphysical properties and source region of these three types of aerosols, we will present the observation of a representative day for each aerosol type. We considered 10th May for Type I, 5th March for Type II and 13th May for Type III as best representative days amidst all observed days over both locations. Fig. 8 represents daily averaged (a, b) volume size distribution and (c, d) wavelength dependence of single scattering albedo
(SSA) for three different aerosol types over Kanpur and Delhi, respectively. Both these parameters show different behaviors for all three types of aerosols over both locations. Volume size distributions show bimodal distribution with maximum coarse mode concentration (0.32 µm3/µm2 at 3.85 µm) over Kanpur and sufficiently large coarse mode concentration (0.88 µm3/µm2 at 2.24 µm) over Delhi for Type I aerosols. Type II aerosols show relatively less coarse mode concentration than that of Type I and also comparable at both locations, Kanpur (0.20 µm3/µm2) and Delhi (0.26 µm3/µm2). These results suggest that Type I aerosols are dominated by dust particles with relatively bigger coarse particle size whereas Type II aerosols show lesser dust dominance with smaller coarse particle size. The large difference in dominant coarse mode concentrations (Type I) between two locations can be explained by the fact that Delhi is much closer to dust source regions, i.e., Thar desert and Arabia, than Kanpur (Kaskaoutis et al., 2012). Type III aerosols show very different volume size distribution (tri-modal) from the other two types over both the cities. The presence of three modes with relatively small differences in mode concentrations for Type III aerosols corresponds to well-mixed aerosol type with similar coarse and fine contributions.
The spectral shape of SSA coupled with AE values will give a more clear idea about absorption properties of different aerosol types and thus their composition. The
0.5
0.7
0.9
1.1
1.3
1.5
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1.9
2.1
2.3
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
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1.3
1.5
1.7
1.9
2.1
2.3
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
N = 86 N = 87
Type IType I
Type II Type II
Type III Type IIIAAE
EAE EAE
Kanpur (PrM 2009) Delhi (PrM 2009)
0.0
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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.40.0
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1.2
0 0.2 0.4 0.6 0.8 1 1.2 1.4
AOD50
0
AE440‐870 AE440‐870
(a) (b)
(c) (d)
N = 96 N = 87
Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 776
Fig. 8. Daily averaged (a, b) volume size distribution and (c, d) wavelength dependence of single scattering albedo (SSA) for three different aerosol types over Kanpur and Delhi respectively. Corresponding Ångström Exponent (AE) values are shown in SSA plots.
spectral shape of Type I indicates dominance of dust with lowest SSA, 0.86 (0.88) at 440 nm to increasing values 0.94 (0.97) at longer wavelengths over Kanpur (Delhi). The lower AE values for Type I aerosols (0.49 and 0.22 for Kanpur and Delhi, respectively) also complemented our above inference. Similar to Type I, Type II aerosols also show increasing trend of SSA with wavelengths at both locations, but relatively gentle slope, indicating the presence of absorbing aerosols as suggested by the lower SSA at 440 nm. However, SAA > 0.9 for higher wavelength ranges (675–1020 nm) also suggests the relative dominance of scattering particles (large Sea-salt/sulfate or dust aerosols) mixed with absorbing aerosols in the atmosphere, which is also found in volume size distribution result for Type II aerosols over both locations. Similar type of spectral dependency of SSA has been reported for dominant dust and mixed aerosol types over nine AERONET sites (including Kanpur) across the world (Giles et al., 2012). On the contrary, the spectral dependence of SSA does not show much variation over Delhi (relatively flat for Kanpur) for Type III aerosols. This shape of the SSA spectrum (SSA < 0.9 at all wavelengths) coupled with AE values (AEIII = 1.19 and 1.03 for Kanpur and Delhi respectively) suggests carbonaceous aerosols associated with U-I pollution as well as BB aerosols (Srivastava et al., 2011). The above observations are also complimented by tri-modal size distribution found for Type III aerosols over both locations. However, the lower
values of SSA over Delhi than that of Kanpur suggest the presence of more absorbing Type III aerosols over Delhi. The lower values of SSA could be a result of mixing of dust with pollution over Delhi, which is also reported by Gautam et al. (2011) for north-west IGB (SSA550 < 0.9).
In order to add to robustness of our discussion section, the aerosol classification results of this study have been compared with Srivastava et al. (2012). They have characterized five different aerosol types including polluted dust (PD: higher dust + low U-I pollution), polluted continental (PC: low dust + high U-I pollution), carbonaceous particles having high absorbing (black carbon: BC) and low absorbing (organic carbon: OC) aerosols, and non-absorbing aerosols over Kanpur and Gandhi college (site located in eastern IGB) for the PrM seasons (2004–2009). On the basis of aerosol properties Type I aerosols can be compared as polluted dust, Type II as polluted continental and Type III as a mixture of BC and OC. Table 3 presents the comparison (of this study with that of Srivastava et al., 2012) of occurrence frequency, AE, AAE and AOD values for three dominant aerosol types over the IGB region. Almost all parameters (for three dominant aerosols types) show good consistency in the case of the study by Srivastava et al. (2012) over Kanpur. However, some differences between AAE values (other parameters) for different aerosol type from both studies may have arisen due to different time scales, and different methodologies used in classification. One striking observation
(a) (b)
0.00
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0.30
0.40
0.50
0.60
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0.80
0.90
1.00
0.01 0.1 1 10 100
5‐Mar
10‐May
13‐May
dV/dlnr(µm
3 /µm
2 )
Radius (µm)
(Type II)(Type I)
(Type III)
0.85
0.87
0.89
0.91
0.93
0.95
0.97
0.99
440 674 870 1020
5‐Mar
10‐May
13‐May
Single Scattering Albed
o (SSA)
Wavelength (nm)
(Type II)
(Type I)
(Type III)
0.0
0.1
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0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.01 0.1 1 10 100
5‐Mar10‐May13‐May
(Type II)(Type I)(Type III)
Radius (µm)
Wavelength (nm)
(Type II)(Type I)(Type III)
Kanpur
Kanpur
Delhi
Delhi
(c) (d)
0.85
0.87
0.89
0.91
0.93
0.95
0.97
0.99
440 674 870 1020
5‐Mar
10‐May
13‐MayAEI = 0.49
AEII = 0.59
AEIII = 1.19
AEI = 0.22
AEII = 0.49
AEIII = 1.03
Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 777
Table 3. Comparison of this study with that of Srivastava et al. (2012) for occurrence frequency, AE, AAE and AOD values for three dominant aerosol types during the PrM seasons over the IGB region.
Location Aerosol Types Occurrence frequency AE AAE AOD
Delhia Type I 30% 0.41 1.45 0.70 Type II 51% 0.64 1.12 0.50 Type III 19% 0.99 1.09 0.64
Kanpura Type I 50% 0.53 1.62 0.53 Type II 24% 0.67 0.99 0.42 Type III 26% 1.09 1.16 0.53
(BC + OC) 21% 1.18 1.04 0.75 a This study (PrM 2009), b Srivastava et al. (2012) (PrM 2004–2009).
is that occurrence frequency of Type II (polluted continental) decreases from Delhi to Kanpur (towards eastern IGB); whereas, it increases from Kanpur to Gandhi college (again towards eastern IGB). However, total effect of dust [(PD + PC)/(Type I + Type II)] decreases consistently from north-west IGB to eastern IGB (Delhi-Kanpur-Gandhi collage), which is also reported in earlier studies (Srivastava et al., 2011; Kaskaoutis et al., 2012). The differences between Delhi and Kanpur regarding frequency of dominant aerosol type (Fig. 7) could be seen as a background for further studies using long-term datasets from ground-based and space-borne observations.
To get information about the history of different aerosol types, we analyzed the 4-day backward trajectories at 500, 1000 and 2000 m altitudes from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003) for both cities, Kanpur and Delhi. Fig. 9 shows that origin of all arrived air masses at different heights are somewhat same for both Delhi and Kanpur in every case (Type I or Type II or Type III), which supports our earlier observation (i.e., similar source region over both cities). However, the origin of air reaches at 2000 m altitude for Type I aerosols are different for Delhi (Sahara Desert) and Kanpur (Thar Desert). Type II aerosols over the IGB are found to be coming from the Arabian Peninsula through the Arabian Sea and the Thar Desert of western India, and are well interacted with boundary layer aerosols during transport path (right panel). In general, Type I aerosols (dominated by dust particles) originate from long-range transported dust from major dust sources (Sahara, Iran, Afghanistan and the Indian Desert). Fig. 9 (right-middle panel) is also showing that high altitude elevated dust (~6000 meters) of Sahara is carried away by the westerlies over Delhi by long-range transportation. A very different wind pattern has been found for Type III aerosols (Fig. 9, lower panel), where almost all air masses are locally originated in the IGB. Also the dominance of U-I/BB in Type III aerosols can be explained as all air masses are mainly local in origin with significant interaction with boundary layer (Fig. 9) polluted aerosols. The great absorbing nature of Type III aerosols draws special attention to study
its spatial and temporal variation during the month of May. Fig. 10 shows the spatial variation of anthropogenic
columnar aerosol concentration (PARASOL Fine Mode AOD550) averaged for (a) 1st May–10th May, (b) 11th May–20th May, (c) 21st May–31st May and (d) 12th May–16th May in 2009 over the Indian subcontinent. The used gridded fine mode (18 × 18 km spatial resolution) PARASOL-AOD550 data is provided by ICARE website: http://www.icare.univ-lille1.fr/ (Xuehua et al., 2009). The fine mode optical depth is resulting from the fine mode of the aerosols size distribution (particle sizes smaller than ~0.35 µm), which is derived from the polarized radiances.
Increased fine mode AOD (0.45–0.85) over the IGB during mid-May as compared to the fine mode AOD (0.2–0.4) of the other two periods strengthened our earlier ground-based observations. Particularly, increased amount of fine mode aerosols over the north-west and eastern IGB along the foothills of Himalayas (shown by dotted ellipse in Fig. 10(d)) during 12 May–16 May emphasized the occurrence of some special anthropogenic or natural events (burning activities). This sudden increase in fine mode aerosol concentration can be attributed to the smoke originating from agricultural crop residue (wheat straw) burning activities or vegetation fire as reported by Vadrevu et al. (2011, 2012) and Mishra and Shibata (2012b). To confirm our hypothesis, we used level 2 version 3.01 data (night-time) for the aerosol subtypes and aerosol backscatter (β532) from space-borne lidar CALIOP (Omar et al., 2009) on 12th May, 2009 (Fig. 11). The aerosol subtype shows the dominance of elevated smoke plumes (along with polluted dust and polluted continental) over the IGB. Vertical profile of aerosol backscatter (Fig. 11(b)) also shows the increased values (0.005–0.008 km/sr) for smoke plumes over the IGB. Now, the synergetic analyses indicate a concluding remark that Type III aerosols are dominated by biomass-burning aerosols with less contribution of dust/polluted dust.
CONCLUSION
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and satellite oover the IGB highlighted uscomparing thnstrumentation
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s in the PrM sosol propertieur and Delhi.properties of dust aerosolhigher conc
ns (2007–2009IS and AEROd/space-borne ng the late Prattributed to twheat straw) bGB. The analy(AAE, EAE,
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Mishra et al., Aerosol and Air Quality Research, 14: 767–782, 2014 780
ACKNOWLEDGMENTS
The authors are highly thankful to the Monbuka-gakusho (MEXT) Japanese Government Fellowship to pursue the research at Nagoya University, Japan. MODIS data used in this study were procured from the Giovanni online data system, developed and maintained by the NASA GES DISC. The efforts of PIs of Kanpur (R.P. Singh, S.N. Tripathi and B. Holben) and Gual Pahari (Gerrit de Leeuw) AERONET sites are appreciated. Authors would also like to appreciate Dr. A.P. Dimri for his valuable suggestions. The CALIPSO data were obtained from the NASA Langley Research Centre Atmospheric Science Data Center. We would also like to thank the ICARE center for providing the POLDER/PARASOL level 3 data.
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Received for review, March16, 2013 Accepted, August 18, 2013
