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Aerosol characteristics over Delhinational capital region: a satellite viewParul Srivastavaa, Sagnik Deya, P. Agarwala & George Basilaa Centre for Atmospheric Sciences, Indian Institute of TechnologyDelhi, Hauz Khas, New Delhi 110016, IndiaPublished online: 15 Jul 2014.

To cite this article: Parul Srivastava, Sagnik Dey, P. Agarwal & George Basil (2014) Aerosolcharacteristics over Delhi national capital region: a satellite view, International Journal of RemoteSensing, 35:13, 5036-5052

To link to this article: http://dx.doi.org/10.1080/01431161.2014.934404

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Aerosol characteristics over Delhi national capital region:a satellite view

Parul Srivastava, Sagnik Dey*, P. Agarwal, and George Basil

Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi110016, India

(Received 21 October 2013; accepted 24 April 2014)

Multi-sensor aerosol data sets are analysed to examine the aerosol characteristics overthe Delhi national capital region. Both the Multiple-angle Imaging Spectroradiometer(MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) capture theseasonal cycle of aerosol optical depth (AOD) as observed by ground-based measure-ments. However, AOD from MISR shows a low bias relative to AOD from MODIS,which increases linearly at high AOD conditions. A large difference (by >25 W m–2

per unit AOD) in the top-of-atmosphere direct radiative forcing efficiency derived fromMODIS and MISR-retrieved AOD is observed during the winter and pre-monsoonseasons relative to the other seasons. The ubiquitous presence of dust (as indicated bynon-spherical particle fraction to AOD and linear depolarization ratio values) isobserved throughout the year. The aerosol layer is mostly confined to within 2 kmof surface in the winter and post-monsoon seasons, while it expands beyond 6 km inthe pre-monsoon and monsoon seasons. Columnar AOD is found to be highly sensitiveto aerosol vertical distribution. The applicability of multi-sensor data sets and climaticimplications are discussed.

1. Introduction

Aerosols affect the Earth’s climate directly by scattering and absorbing solar radiation(Haywood and Boucher 2000) and indirectly by modifying cloud properties (Twomey1974, 1977; Albrecht 1989; Lohmann and Feichter 2005). Aerosol optical depth (AOD, ameasure of the columnar extinction of solar radiation by aerosols) is the primary opticalproperty that is used to quantify aerosol loading in the atmosphere and the resultingradiative forcing. Launches of new generation sensors capable of retrieving AOD overboth ocean and land have provided unprecedented opportunity to use the data to examinethe aerosol–climate interaction at various space–time scales (Reid et al. 2013; Feng andChristopher 2013; De Meij and Lelieveld 2011; Kokhanovsky et al. 2007). The impor-tance of satellite data in examining the variability of aerosol characteristics increasesmanifold at the regions (e.g. Indian subcontinent) having limited long-term ground-basedmeasurements by passive (e.g. radiometers) and active (e.g. lidar) remote sensing. Eversince the Indian Ocean Experiment revealed high aerosol loading and demonstrated thepossible climatic impacts (Ramanathan et al. 2001), the Indian subcontinent has receivedglobal attention. In recent years, several studies (e.g. Di Girolamo et al. 2004; Jethva,Satheesh, and Srinivasan 2005; Prasad and Singh 2007; Ramachandran and Cherian 2008;Dey and Di Girolamo 2010; De Meij and Lelieveld 2011) have used satellite data toexamine the spatial and temporal variability of AOD over the subcontinent.

*Corresponding author. Email: sagnik@cas.iitd.ac.in

International Journal of Remote Sensing, 2014Vol. 35, No. 13, 5036–5052, http://dx.doi.org/10.1080/01431161.2014.934404

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The satellite-based studies revealed high AOD over the Indo-Gangetic Basin (IGB)and Himalayan foothills within the subcontinent, while the spatiotemporal variability isinfluenced by seasonal cycles of anthropogenic and natural sources and meteorology. Allthese studies agreed to the observed spatiotemporal distribution of AOD qualitatively, butthe absolute values of AOD show a lot of discrepancy (e.g. Mishchenko et al. 2009; Kahnet al. 2009). The Indian subcontinent, more particularly the IGB, is one such region whereAOD from the two most popular sensors, moderate resolution imaging spectroradiometer(MODIS) and multiangle imaging spectroradiometer (MISR), do not agree well becauseof the complex aerosol characteristics that the algorithms fail to account for (Kahn et al.2009). These issues demand further investigation about the applicability (and the possibleimplications) of multisensor data for climate studies in the Indian subcontinent.

Here we focus on the megacity Delhi and the surrounding area, described as thenational capital region (NCR), which is one of the most polluted urban regions in theworld (Kumar, Chu, and Foster 2007; Lodhi et al. 2013). Previous studies have usedground-based measurements (primarily spectral AOD and black carbon mass concentra-tion) to constrain simulation of aerosol optical properties for the entire shortwave (SW)spectrum. First, these studies are limited to a particular season (e.g. Singh et al. 2005;Pandithurai et al. 2008) or a short period of time spanning a few years (e.g. Srivastavaet al. 2012; Singh et al. 2010; Soni et al. 2010). More recently, Lodhi et al. (2013)reported an 11-year climatology of spectral AOD and its relation to the seasonal air mass.In many of these studies, the columnar spectral optical properties were estimated assum-ing aerosol vertical profiles representative of a tropical region. The seasonal change ofaerosol vertical distribution and its relation to columnar aerosol properties need to bequantified to improve estimates of aerosol direct radiative forcing (DRF, defined as thechange in net radiative flux due to aerosols). Moreover, ground-based measurements atparticular locations are sometimes influenced by local sources and may not alwayscorrespond to regional aerosol characteristics. We address some of these issues byanalysing multi-sensor aerosol products over Delhi NCR. Columnar aerosol propertiesfrom MODIS (on-board Terra and Aqua) and MISR (on-board Terra) and verticaldistribution from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization, on-board the CALIPSO satellite) are analysed, and the applicability of such multi-sensordata sets in examining aerosol SW DRF is discussed.

2. Satellite data and analysis

All analyses are presented as statistics over a domain bounded within 77°–78° E and 28°–29° N covering the Delhi NCR region. We note that AOD may vary within the cityperimeter (Kumar, Chu, and Foster 2008). Our objective is to examine the applicability ofmultisensor data for studying regional aerosol characteristics in the Indian region. Wechoose Delhi NCR for a case study and present the results for the entire NCR to maximizethe sample numbers in the statistics covering the centre of the city as well as the suburbanarea and to minimize the spatial heterogeneity. First, AOD by two different sensors,MODIS and MISR, are compared. Further, the aerosol DRF has been derived bycombining top-of-the-atmosphere (TOA) radiative flux data from Clouds and the Earth’sRadiant Energy System (CERES) and AOD data. The study has been carried out for a 11-year period (March 2000–February 2011). However, the MODIS retrieval on-board theAqua satellite and the CALIOP retrieval on-board the CALIPSO satellite are availableonly from July 2002 and July 2006, respectively.

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MISR, on-board the NASA Earth Observing System’s Terra satellite, is an imagingsensor operating at four spectral bands centred at 446, 558, 672, and 867 nm in each ofnine separate cameras oriented along the orbital track with surface viewing zenith anglesranging from +70.5° to −70.5°. Aerosol retrievals are performed on 16° × 16° patches of1.1 km sub-regions, yielding an aerosol product at 17.6 × 17.6 km spatial resolution(referred to as the ‘level 2’ product) that includes AOD, single scattering albedo (SSA),fractions of ‘fine’ (fs, particle radius < 0.35 µm), ‘medium’ (fm, radius between 0.35 and0.7 µm), and ‘large’ (fl, radius > 0.7 µm) and ‘spherical’, fsp, and ‘non-spherical’, fnsp,particles. The details of the aerosol retrieval algorithm are discussed elsewhere (Kahnet al. 2010). The MISR version 22, level 2 aerosol product has been extensively evaluatedfor its applicability in examining aerosol climatology in the Indian subcontinent (Dey andDi Girolamo 2010, 2011). MISR AOD is biased low relative to AERONET observationsand the bias increases with an increase in AOD. Thus, MISR underestimates AOD inhighly polluted conditions (Kahn et al. 2010; Dey and Di Girolamo 2010). For the presentstudy, we have analysed level 3 data (0.5° × 0.5° resolution) of AOD at 558 nm, fs, fm, fl,fsp, and fnsp, which are averaged over the 1° × 1° domain for the monthly statistics. TheLevel 3 AOD data were corrected for bias following Dey and Di Girolamo (2010).

MODIS, on-board the EOS-Terra and Aqua satellites, uses two visible bands (0.47 and0.65 µm) and one shortwave infrared band (2.1 µm) to retrieve aerosols (Levy et al.2010). These bands are chosen because they are nearly transparent to gaseous absorptionand demonstrate a consistent relationship over vegetated land surfaces (Kaufman et al.1997). The vegetated surface is not ‘dark’ at the green band (550 nm) and therefore is notused directly in the AOD retrieval. AOD is retrieved by matching the sensor-measuredreflectance at these three wavelengths with the look-up table reflectance, which assumesaerosol as a mixture of fine and coarse particle types (Remer et al. 2005), and the value isreported at 550 nm wavelength. The retrieval algorithm and its global validation havebeen discussed in detail elsewhere (Remer et al. 2005; Levy et al. 2010). For the presentstudy, C005 level 3 data (at 1° × 1° resolution) of AOD at 550 nm wavelength were used.This version was previously evaluated in India by several researchers (e.g. Jethva,Satheesh, and Srinivasan 2007; Misra, Jayaraman, and Ganguly 2008; Aloysius et al.2009), where it was found that about 70% of AOD values fall within the pre-launchuncertainty of 0.15 × AOD ± 0.05 over India (Levy et al. 2010).

CALIOP is a lidar, with very narrow swath, which measures elastic laser backscatter at1064 nm and the parallel and cross-polarized components of the 532 nm return signal(Hunt et al. 2009), from which the linear depolarization ratio (LDR) is derived. Thevertical resolution of 532 nm backscatter signal is about 30 m between 0.5 and 8.2 kmaltitude and 60 m between 8.2 and 20.2 km altitude ranges. Extinction coefficients areretrieved in three steps. First, the backscatter profiles are used to identify layers withhorizontal averaging in the range 0.333–80 km. Second, the layers are classified as‘aerosol’ or ‘cloud’, and finally, the aerosol and cloud extinction profiles are retrievedstarting with the highest detected layer (Winker et al. 2009). Misclassification of ‘aerosol’and ‘cloud’ layers and failure to properly detect an aerosol layer are the two dominantsources of errors in the retrieved AOD values (Winker et al. 2009). Unlike MODIS andMISR, CALIOP, being an active sensor, retrieves AOD during day and night and in‘clear’ as well as ‘cloudy’ conditions.

We used CALIOP version 3.1 level 2 data to study the vertical distributions of theextinction coefficient (bext) and LDR over the study area during the day and night. Globalvalidation of CALIOP retrieval is an ongoing task for the CALIOP science team.Recently, Misra et al. (2012) evaluated the CALIOP backscatter profiles with near-

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coincident profiles from ground-based lidar at Kanpur (about 500 km west of DelhiNCR). CALIOP retrievals show a large uncertainty at altitude close to the surface(below 400 m), whereas retrieval errors above 400 m may be attributed to cloudcontamination and differences in backscatter-to-extinction ratio used by CALIOP andground-based lidar (Misra et al. 2012). Since no ground-based lidar data are available forDelhi NCR, we proceeded to analyse the CALIOP data keeping in mind the aboveuncertainties. Aerosol scale height (H) is a good approximation of the aerosol verticaldistribution on regional and global scales, particularly for satellite retrievals, modelsimulations, and model–model and model–satellite inter-comparison (Yu et al. 2010).The conventional exponential relationship of H is not valid in a vertically heterogeneousatmosphere (Fernández-Gálvez et al. 2013). Hence, it is defined as the height aboveground level below which 63% of total columnar extinction (i.e. AOD) is present (i.e. theheight at which bext is reduced by a factor of e). Following Hayasaka et al. (2007),

ZH

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bext dz ¼ 1� e�1� �� ðAODÞ ¼ 0:63� ðAODÞ; (1)

where z is the aerosol layer thickness. The scale height has been estimated using the aboveequation by integrating the extinction profiles. However, we neglected the contribution ofstratospheric aerosols to AOD and assumed a homogeneously mixed boundary layer(Fernández-Gálvez et al. 2013). Monthly statistics are derived by averaging all CALIOPoverpasses in that particular month during the study period within the Delhi NCR, andresults are presented separately for daytime and night-time.

Radiation data (TOA SW flux) in clear-sky conditions are obtained from CERES,flying on-board Terra spacecraft along with MISR and MODIS. This sensor is a key partof NASA’s earth observation system serving as continuation for the Earth RadiationBudget Experiment. Accuracy of the flux measurements has improved because of thedevelopment of empirical angular distribution models (ADMs) that use the angulardependence of the Earth’s radiance and better scene identification (ADMs are derivedusing a MODIS-based scene-type parameter) (Loeb et al. 2005).

3. Results

3.1. Aerosol characteristics

The time series of mean monthly AOD over Delhi NCR from MODIS (at 555 nmwavelength) and MISR (at 558 nm wavelength) is shown in Figure 1 along with theupper and lower bounds of error displayed as shaded regions. Both the sensors capture theseasonal cycle of AOD (peak during May–August and low during February–March) andinter-annual variability in the region as discussed by Lodhi et al. (2013) based on ground-based measurements, but a large discrepancy is observed in the absolute values. Forexample, the mean (±1 standard deviation, σ) AOD in January are 0.50 ± 0.07,0.56 ± 0.08, and 0.33 ± 0.08 as retrieved by Terra-MODIS, Aqua-MODIS, and MISRrespectively. On an annual scale, AODMISR is 34% lower than AODMODIS. However, thetwo values are very close to each other when the bias is corrected for MISR. Thesegeneral observations led to the important issue – the question of which AOD should betrusted or used for estimating aerosol DRF or any other application. AODTerra-MODIS andAODAqua-MODIS have excellent correlation (correlation coefficient, r = 0.94, which is

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significant at 99% confidence interval, CI, using the least-square technique) with near-perfect slope (0.96 ± 0.03) and intercept (0.03 ± 0.005). Very few outliers (points awayfrom 1:1 line in Figure 2(a)) are confined to the monsoon season. This may be attributedeither to a larger error in AOD retrieval in cloudy conditions or to larger diurnal variations(since Terra crosses at 10.30 am and Aqua crosses at 1.30 pm) in the monsoon or both.A recent study (Tiwari et al. 2013) has suggested a large diurnal variation in surface PM2.5

(particulate matter with diameter less than 2.5 μm) and black carbon concentration inDelhi. Excellent match between AODTerra-MODIS and AODAqua-MODIS implies that thediurnal variability of columnar AOD at Delhi NCR is not captured because the valuesmostly fall within the retrieval uncertainty. This further implies that either of these twodata sets will provide similar conclusions about aerosol DRF.

AODMISR is also well-correlated (r = 0.81, significant at 99% CI) with AODTerra-MODIS,but with a low bias (Figure 2(b)). The bias (ΔAOD = AODMODIS – AODMISR) linearlyincreases with an increase in AODMODIS following the relation:

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Figure 1. Temporal variation of AOD over Delhi NCR from MODIS-Terra (red) and MISR (blue)during March 2000–February 2011 and MODIS-Aqua (green) during July 2002–February 2011. Theshaded region around each solid line represents the error envelopes based on the global validation ofthe satellite aerosol products against AERONET.

1.6

N = 104, p < 0.001R = 0.94Y = (0.96 ± 0.03) x + 0.03

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Figure 2. Scatter plots between (a) AODMISR and AODMODIS-Terra and (b) AODMODIS-Terra andAODMODIS-Aqua over Delhi NCR. Correlation coefficients (r), equations of best-fit lines, number ofsamples (N), and statistical significance of r are also given. The error bars represent the uncertaintyin the reported values.

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ΔAOD ¼ 0:51� AODMODIS � 0:11; (2)

which shows that the low bias in AODMISR starts at AODMODIS = 0.21. A large differencein AODMISR and AODMODIS is not unexpected (Figure 2(b)). Neither MODIS nor MISRrealistically accounts for the aerosol absorption observed in this region (Kahn et al. 2009).Evaluation of AODTerra-MODIS and AODAqua-MODIS against coincident AERONET mea-surements revealed a high bias in presence of dust (Jethva, Satheesh, and Srinivasan 2007;More et al. 2013). These studies have drawn conclusions similar to global evaluation ofMODIS products (e.g. Levy et al. 2010). Evaluation of AODMISR against coincidentAERONET-retrieved AOD revealed similar systematic low bias in the IGB, whichincreases with an increase in true AOD field (Dey and Di Girolamo 2010). The bias-corrected values (following Dey and Di Girolamo 2010) of mean monthly AODMISR showa closer match with AODTerra-MODIS values (Table 1). The slope of the best-fit line of bias-corrected AODMISR increases to 1.2 (not shown here) from 0.49 (Figure 2(b)) with asimilar correlation (r = 0.81) and smaller intercept (0.05). A larger temporal coverage ofMODIS than of MISR may also introduce a sampling bias in the monthly statistics.Comparison of monthly statistics of bias-corrected MISR-AOD and MODIS-AOD within situ measurements (Lodhi et al. 2013) reveals that satellite-based climatological valuesare smaller than those from in situ measurements (Table 1), particularly during the winterseason. We note that the in situ observations reported in Lodhi et al. (2013) were takeninside the city, while the satellite data are averaged over a larger region. Past studies (e.g.Kumar, Chu, and Foster 2008) have shown a large gradient in AOD with a high valueinside the city perimeter and low in the outskirts that explain the larger climatologicalvalues from in situ observations. In the months dominated by dust particles (large size andnon-spherical shape), satellite observations are larger than the in situ observations(Table 1). Nonetheless, bias-corrected AOD values can be used for air quality application(e.g. Dey et al. 2012).

Aerosol DRF depends on aerosol composition (i.e. relative abundance of variousindividual species such as water-soluble scattering particles, absorbing carbonaceousparticles, mineral dust, etc.), their mixing state, and number concentrations. In the absenceof robust chemical data, two hybrid approaches are typically adopted to estimate aerosolDRF. In the first approach, representative aerosol composition is constructed by matchingtheoretically simulated spectral AOD through tuning of the number concentrations ofvarious individual species with satellite or ground-based AOD (Satheesh, Srinivasan, andMoorthy 2006). This approach further provides spectral single scattering albedo and phasefunctions, which along with spectral AOD are used as input to the radiative transfer-model(e.g. Dey and Tripathi 2008) to estimate the DRF. This approach relies on absolute AODvalues and hence the composition obtained through iteration could be different fordifferent absolute values of AOD. Second, theoretical estimation of AOD: this approachalso requires information of aerosol vertical distribution (H and aerosol layer thickness, z).Thus any DRF estimation constrained by satellite-retrieved AOD and assumption of Hand z may have large uncertainty. In the second approach, coincident measurements TOAflux and AOD can be used (Chen et al. 2009). The intercept of the best-fit line of thescatter plot between CERES-TOA flux and AOD may be considered as the TOA flux‘without aerosol’ (Chen et al. 2009). By definition, the difference between TOA flux ‘withaerosol’ (i.e. CERES measured TOA flux) and ‘without aerosol’ (i.e. subtracting theintercept from each measured value) represents aerosol DRF at TOA. Aerosol DRFover the study area has been estimated using both AODTerra-MODIS and AODMISR,which show excellent agreement (not shown here) within ±1 W m–2. This is attributed

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to almost similar intercepts (50 W m–2 and 49 W m–2, respectively, for MODIS andMISR). However, aerosol DRF efficiency (DRFE, defined as DRF per unit AOD) is themore intrinsic property of aerosol radiative effect and should not change for the sameaerosol composition. Mean (±1σ shown as error bars) seasonal aerosol DRFE fromMODIS and MISR are compared (Figure 3). Here we have defined the pre-monsoonseason from March to June, as onset of monsoon in Delhi NCR takes place at the end ofJune. The monsoon season is defined for July–September, post-monsoon season forOctober–November, and winter season for December–February. Since both MODIS andMISR are measuring AOD at the same time, ideally all the points should lie on the 1:1line. Maximum deviation is observed in the winter season followed by the pre-monsoon

Table 1. Mean (±1σ) monthly statistics of AOD over Delhi NCR for the period 2001–2012 fromLodhi et al. (2013) (1st row in AOD column), bias-corrected AOD (2nd row in AOD column), andparticle microphysics from MISR (remaining columns) for March 2000–February 2011 and AODfrom Terra-MODIS (3rd row in AOD column).

AOD fs (%) fm (%) fl (%) fsp (%) fnsp (%)

January 0.82 ± 0.17 48 ± 5 16 ± 6 36 ± 7 90 ± 5 10 ± 50.50 ± 0.190.50 ± 0.07

February 0.64 ± 0.10 46 ± 5 16 ± 4 38 ± 5 86 ± 8 14 ± 80.52 ± 0.230.43 ± 0.06

March 0.61 ± 0.13 47 ± 5 18 ± 4 35 ± 5 81 ± 10 19 ± 100.45 ± 0.180.43 ± 0.07

April 0.70 ± 0.07 42 ± 3 21 ± 3 37 ± 5 74 ± 8 26 ± 80.61 ± 0.200.53 ± 0.06

May 0.88 ± 0.16 42 ± 3 23 ± 4 35 ± 4 71 ± 9 29 ± 91.00 ± 0.280.85 ± 0.10

June 0.92 ± 0.22 41 ± 7 22 ± 4 38 ± 8 78 ± 10 22 ± 101.23 ± 0.381.02 ± 0.22

July 0.88 ± 0.26 53 ± 12 19 ± 8 28 ± 17 91 ± 7 9 ± 71.28 ± 0.451.16 ± 0.18

August 0.72 ± 0.22 42 ± 10 21 ± 3 37 ± 12 87 ± 7 13 ± 71.14 ± 0.360.77 ± 0.23

September 0.64 ± 0.13 50 ± 5 19 ± 4 31 ± 6 91 ± 5 9 ± 50.59 ± 0.120.47 ± 0.07

October 0.90 ± 0.23 50 ± 5 19 ± 3 31 ± 4 83 ± 7 17 ± 70.88 ± 0.300.73 ± 0.12

November 0.91 ± 0.15 50 ± 4 17 ± 3 33 ± 4 89 ± 3 11 ± 30.66 ± 0.250.64 ± 0.12

December 0.85 ± 0.10 51 ± 3 15 ± 4 34 ± 4 90 ± 4 10 ± 40.49 ± 0.150.55 ± 0.06

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and monsoon seasons. Higher DRFE in the case of MISR relative to MODIS implies alarger TOA cooling estimated using MISR relative to MODIS for the same AOD. Thismay happen if the aerosol absorption in the retrieval algorithm is underestimated, which isthe case for MISR in this region (Kahn et al. 2009). Using AERONET observations,Srivastava et al. (2012) found that the dust in the IGB is mainly ‘polluted dust’ (i.e.mixture of pure mineral dust with other components), particularly in the pre-monsoonseasons. This type of mixture is not considered in the MISR or MODIS look-up-table,which may lead to the observed discrepancy in DRFE.

Mean monthly relative fractions of fine (fs), medium (fm), and large (fl) particles tototal AOD and spherical (fsp) and non-spherical (fnsp) particles to total AOD are shown inFigures 4 and 5 and summarized in Table 1. The presence of non-spherical particles(indicating mineral dust) throughout the year is noteworthy. Even the post-monsoon andwinter seasons are dominated by anthropogenic particles (Lodhi et al. 2013), mean (±1σ)fnsp are 14.3 ± 6% and 11.6 ± 6.3%, respectively. The value further increases to24.1 ± 9.8% in the pre-monsoon season, when the desert dusts are transported from theGreat Indian Desert, West-Asian, and West African sources (Chinnam et al. 2006; Lodhiet al. 2013). Subsequently, in the monsoon season, fnsp reduces to 10.3 ± 6.4%. The meanseasonal relative fractions of fs are 47.8 ± 4.9%, 42.9 ± 5.1%, 48.6 ± 10.6%, and49.9 ± 4.3%, respectively, for the winter, pre-monsoon, monsoon, and post-monsoonseasons. Remaining contributions are by medium-sized particles. In the MISR algorithm,the variability in the medium-sized fraction may also be partially attributed to dust(Kalashnikova and Kahn 2006). Mean seasonal fm is 15.9 ± 4.4%, 21.0 ± 4.1%,19.7 ± 5.3%, and 17.8 ± 3.3%, respectively, for the four seasons. By combining theinformation about size and shape of the particles from MISR, it can be interpreted thatdust particles are persistent in Delhi NCR throughout the year. This is supported by theclimatological variation of the Angstrom Exponent (AE) derived from ground-basedmeasurements in the spectral range 340–1020 nm (Figure 5 of Lodhi et al. 2013),which shows that AE never exceeds 1.0. The meteorology suggests that the transportfrom desert sources is mostly confined to the pre-monsoon season (Lodhi et al. 2013). Inother seasons, local sources (such as road transport, construction activities, etc.) mighthave contributed to the dust loading. Another important observation is the reduction of

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fnsp during the pre-monsoon season below 25% after the year 2006, which indicatessuppressed dust loading at later years. This is in accordance with the ground-basedmeasurement reported by Lodhi et al. (2013), where the slight decreasing trend in AODduring the last decade was attributed to the larger dust load in the first half of the lastdecade.

Thus, the multi-sensor analysis of aerosol characteristics provides complementaryinformation (provided the biases are taken care of) about long-term aerosol microphysicalproperties in Delhi NCR to ground-based measurements. Furthermore, understanding the

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Figure 4. Monthly variation of relative contributions (in %) of fine (particle diameter, D < 0.7 µm),medium (0.7 < D < 1.4 µm), and large (D > 1.4 µm) particles to total AOD at 558 nm wavelengthover Delhi NCR as retrieved by MISR.

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Figure 5. Monthly variation of relative contributions (in %) of spherical and non-sphericalparticles to total AOD at 558 nm wavelength over Delhi NCR as retrieved by MISR.

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temporal variation of aerosol vertical distribution becomes necessary to fully understandevolution of aerosol properties during transport in this region.

3.2. Aerosol vertical distribution

Previous studies used values of H representative of tropics to estimate aerosol DRFfollowing the first approach in absence of coincident aerosol vertical distributions (e.g.Singh et al. 2005; Srivastava et al. 2012). To illustrate the sensitivity of computed AOD tovertical distribution for any aerosol composition, we consider data published in Singhet al. (2005). The number concentrations of various individual components representativefor the period April–June 2003 were provided in Table 1 of Singh et al. (2005) at 50%relative humidity. Mean (±1σ) H and aerosol layer thickness (z) are found to be2.0 ± 0.6 km and 5.1 ± 0.2 km, respectively, for the period April–June from CALIOPdata. We carried out two sensitivity studies using the same composition and numberconcentrations by the same OPAC model (Hess, Koepke, and Schult 1998) as reported inSingh et al. (2005). First, we calculated AOD at the mean value of H by varying z values.We note that the deviation of AOD (in %) from the AOD value reported in Singh et al.(2005) is not very high (<3%) for a wide range (±30%) of z. In the second sensitivitystudy, the deviation of AOD is calculated by using mean z (5.1 km) and varyingH (Figure 6). We found out that a value of H = 2.8 km is required at z = 5.1 km tomatch the AOD value observed during April–June 2003. AOD is underestimated by 20%if H reduces to 2.0 km (i.e. the mean H for this period). Further reduction in H to 1.4 km(mean – 1σ value of H) leads to an underestimation in AOD by 42%. This analysisemphasizes the need to account for accurate H in estimating aerosol radiative properties,not only in Delhi NCR, but also in any region. We also note that additional uncertaintymay creep in the estimations of aerosol optical properties using OPAC from the assump-tion of spherical particles, particularly in the presence of a large fraction of non-sphericaldust.

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Figures 7(a) and (b) show the mean monthly statistics of H in daytime and night-timeover Delhi NCR. During the daytime, H increases from 0.6 km in the winter season to2.3 km in May along with an increase of z from 1.9 km to 6 km. In the night-time, theoverall seasonal variation is similar to that of the daytime, with a lower value of Hthroughout the year. This is expected because of the reduced boundary layer height innight-time confining the aerosol layer to a smaller altitude range. Although H may beuseful for the modellers to evaluate the simulated vertical distributions, a detailed verticalstructure of aerosols over the region was examined by mean seasonal extinction profiles(Figures 8(a) and (b)). Most of the aerosols are confined to a 2 km altitude in both day andnight-time during the winter (i.e. DJF) and post-monsoon (i.e. ON) seasons. A daytimesecondary peak in bext (>0.2 km–1) is observed at 2.5 km altitude in the winter season thatmigrates to about 3.8 km at night-time. The spike at 6.2 km altitude in the night-time bextprofile in the monsoon season is attributed to a dust event in the month of July 2009during the CALIPSO overpass over Delhi NCR. In general, bext in the altitude range1 km–3 km is higher by >50% during the dust-dominated pre-monsoon and monsoonseasons relative to the other two seasons.

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Further analysis was carried out to estimate the relative contributions of dust particles onthe seasonal aerosol vertical structure using mean seasonal LDR profiles (Figures 9(a) and(b)) for both day and night. Using a ground-based Raman lidar, Shin et al. (2013) observedthat LDR at a 532 nm wavelength exceeded 0.2 for pure mineral dust, while it varied in therange 0.1–0.14 for the mixed dust and about 0.05 for spherical particles. Similarly, largevalues of LDR were also observed at European Aerosol Research Lidar Network(EARLINET) stations during Saharan dust outbreaks (e.g. Ansamann et al. 2003). Duringthe pre-monsoon season, LDR > 0.2 was observed throughout the atmospheric column. Inthe monsoon season, a dusty layer is observed above 3 km altitude. It is interesting to notethat in the other two seasons also, LDR exceeded 0.2 above 2 km altitude in both day andnight-time, suggesting a ubiquitous presence of dust in accordance to the MISR observa-tions. Uniform or a slight increase in LDR values aloft along with a reduction in bext suggestan increasing relative fraction of non-spherical dust to total aerosol load as elevated layers inthis region throughout the year. The bext and LDR profiles are more or less similar in dayand night-time.

4. Discussion and conclusions

In the present study, multi-sensor aerosol products are analysed to understand the aerosolcharacteristics in the Delhi NCR. Our results have several climatic implications. A theore-tical study by Mishra, Dey, and Tripathi (2008) showed that TOA DRF may deviate by 10–20% for non-spherical particles containing 4–8% haematite (typical of the Indian region,Mishra and Tripathi 2008) relative to estimates assuming only spherical particles. Previousestimates of aerosol DRF in Delhi NCR were carried out considering only sphericalparticles (e.g. Singh et al. 2005; Srivastava et al. 2012). The statistics of non-sphericalfractions to AOD presented here should be used for improved estimates of DRF. Second,the optical properties of dust may vary from season to season. For example, the dusttransported from the West Asian sources and Great Indian Desert in the pre-monsoonseason may mix with anthropogenic particles (interpreted as ‘polluted dust’ by Srivastavaet al. 2012), which may lead to enhanced absorption (Dey and Tripathi 2008). Such mixingshould be considered to estimate the DRF and also be included in the retrieval look-up tableof the sensors. In other seasons, dust may be of local origin arising from various

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anthropogenic activities (such as construction activities, road dust, etc.). Hence, ‘anthropo-genic dust’ should be further considered in source characterization based on chemical datawith a proper inventory. Furthermore, such quantification (local vs. transported dust) isrequired for air quality policy regulations. Third, the climatology of aerosol verticaldistribution presented here (in form of scale height) may be useful in evaluating simulationsby chemical transport models. Unless the models simulate the vertical distribution accu-rately, examination of aerosol–cloud interaction and dynamic feedback of aerosols onclimate system may have a large uncertainty. Finally, local effects may influence ground-based measurements, while satellite data (at a coarser resolution) may provide a moreregional picture. Hence, a combination of the two is always better to understand the regionalscale aerosol characteristics for megacities such as Delhi NCR, where aerosol properties arestrongly influenced by both anthropogenic and natural sources.

The key conclusions of the present study are summarized below.

● Both MODIS and MISR capture the seasonal cycle of aerosol loading in DelhiNCR with a systematic bias. A large difference (>25 W m–2 per unit AOD) isobserved in MODIS- and MISR-derived direct radiative forcing efficiency at TOA.This may be attributed to aerosol characteristics in Delhi NCR, very different froma typical urban region (e.g. ubiquitous presence of dust throughout the year alongwith anthropogenic aerosols), which is not properly represented in the retrievallook-up table.

● Non-spherical particles have contributed 24.1% to total AOD in the last decade(March 2000 to February 2011) with the relative contribution reducing after theyear 2006, suggesting a suppressed dust loading during peak dust transport season.

● Aerosol vertical structure over Delhi NCR shows a strong seasonal variation withaerosols mostly confined below 2 km during the post-monsoon to winter seasons.In the other two seasons, the aerosol layer expands beyond 6 km.

● The elevated aerosol layer throughout the year is dominated by non-spherical dustparticles. Seasonal variation in aerosol vertical structure is similar in day and night-time with a lower scale height in night relative to daytime. These vertical structuresmay be helpful in evaluating climate model simulations (e.g. Das et al. 2013).

AcknowledgementsMISR, MODIS, CERES, and CALIOP aerosol data are distributed by the NASA Langley ResearchAtmospheric Science Data Center. We acknowledge the comments from two anonymous reviewersthat helped in improving the earlier version of the manuscript.

FundingThe work is supported by a research grant from Department of Science and Technology, Govt. ofIndia under Fast Track Scheme [SR/FTP/ES-191/2010] through a research project operational at IITDelhi [IITD/IRD/RP02509].

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