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Atmospheric Environment 44 (2010) 1237e1243

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Atmospheric Environment

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Technical note

Coupling between meteorological factors and ambient aerosol load

Ankit Tandon, Sudesh Yadav, Arun K. Attri*

School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110 067, India

a r t i c l e i n f o

Article history:Received 8 May 2008Received in revised form25 July 2009Accepted 24 December 2009

Keywords:AerosolsMeteorologyAPCSPMFDFTCoupling

* Corresponding author. Tel.: þ91 11 26704309.E-mail address: attri@mail.jnu.ac.in (A.K. Attri).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2009.12.037

a b s t r a c t

The coarser (CPM) and respirable (RPM) fractions of aerosol loads collected in a time sequence, duringthe onset of winter season in Delhi region, were subjected to Principal Component Analysis(15 elemental variables, 39 samples); the absolute mass contributed by each identified source to the CPMand RPM was quantified by using Absolute Principal Component Score (APCS) and Positive MatrixFactorization (PMF) method. Interestingly, the mass contributed by the local crustal source (material) toboth fractions manifested undulating periodic behavior, a dominating harmonic corresponding to 24-hperiod was detected by using Discrete Fourier Transform (DFT). The corresponding harmonics, of varyingstrengths, were also detected in the recorded meteorological factors: Planetary Boundary Layer (PBL),Surface Level Temperature (T), Surface Level Relative Humidity (RH) and Wind Speed (WS). The analysisof the respective harmonic strength within the CPM, RPM, and meteorological factors suggested thatthe undulation observed in both size fractions of aerosol load from the local crust was affected by themeteorological factors. The large proportion of undulating loads (CPM and RPM), explained by thedominating harmonic, was fully accounted for by the empirical relation involving the discrete couplingparameters, and the recorded meteorological factors: PBL, T, RH and WS. The analysis suggests that themagnitude and the direction (‘positive’ load increase and ‘negative’ the reverse) of coupled meteoro-logical factors'(s) effect on ambient CPM, RPM load is determined by the phase difference betweenthe harmonic explaining the aerosol fraction's load and the corresponding harmonic present in therespective meteorological factor. The absolute mass contributions arising from the identified sources(APCS and PMF) allowed us to calculate the baseline ambient concentrations of undulating CPM and RPMloads, in the region of this study, affected by meteorological factors only.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Aerosols, suspended particulate matter in air, with diversephysical and chemical attributes, arise from many sources (Pittsand Pitts, 2000). Aerosols interact with and have considerableimpact on physical as well as biological components of the envi-ronment (Dickerson et al., 1997; Pope, 2000; Satheesh and KrishnaMoorthy, 2005). The concentrations of different size fractionsof aerosols in the ambient environment depend, to a large extent,upon the nature and the strength of source(s); the physico-chem-ical properties of the aerosols, and the regional and local climatic(meteorological) factors (Hinds, 1999; Seinfeld and Pandis, 2000).Many studies have used source apportionment approach to identifythe sources contributing to aerosol load arising from crustalmaterial, fossil fuel burning, industrial emissions and biologicalactivities; the characteristics and quantum of these emissions varysignificantly from one location to another (Artaxo et al., 1998; Choi

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et al., 2001; Kumar et al., 2001; Karar and Gupta, 2007). However,there is little information with regard to the effects of meteoro-logical factors as variables in modulating the ambient aerosols’concentration; at best there exists a gross sketchy understanding(Fine et al., 2004; Suresh and Desa, 2005).

The capital region of Delhi, located in the northern part of India,having semi-arid climatic conditions, is afflicted with unusuallyhigh concentration of aerosol load in the lower atmosphere, andthe onset of winter season further amplifies the load on account ofthe calm wind regime and low planetary boundary layer (PBL)conditions. Apart from the health concerns, the undulation ofaerosol load in winter season severely impairs atmospheric visi-bility in this region. Studies done in the past have focusedmainly onthe time basedmonitoring of the aerosol load and the identificationof the sources involved. To a limited extent few investigationsdone in Delhi region (Balachandran et al., 2000; Srivastva and Jain,2007a,b, 2008) have analyzed aerosol load, their elementalcomposition and determined source(s) identity. These investiga-tions, however, did not address the question of dynamic relation-ship, if any, between the different size fractions of the aerosol loadin terms of the absolute mass contributions arising from the

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e12431238

identified sources, particularly, in relation to the diurnal variationsin the local and regional meteorological factors.

In a recent study, done during the onset of winter season, in thesemi-arid climate of Delhi region, it was reported that two sources(local crustal material and fireworks) contributed to the coarseraerosol load (aerodynamic diameter > 10 mm); whereas foursources [(a) fireworks, (b) local crust, (c) wind transported crustalmaterial and (d) vehicular traffic] contributed to the respirableparticulate load (aerodynamic diameter <10 mm)1 (Tandon et al.,2008). The prior knowledge of the fireworks (emissions) event wasused as a tracer to assist in the appropriation of source(s) identity,and in understanding of the surface deposited aerosol re-suspen-sion. In both, coarser (CPM) and respirable (RPM) particulatematter, a large proportion of ambient aerosol load was tracked tothe local crustal material (LCM). It was also evident that the massproportion of CPM in the collected samples wasmany fold than thatof RPM share, and the time-series plots of the resolved absoluteloads of CPM and RPM from LCM source manifested a systematicperiodic behavior: shown in Fig. 1(AeD), and Supplementary Figs.S1(AeB) and S2(AeD).

In this paper we report that the large fraction of the aerosol loadin the atmosphere is driven by the diurnal coupling, which existsbetween the LCM and the meteorological factors: PlanetaryBoundary Layer (PBL), Surface Level Temperature (T), Surface LevelRelative Humidity (RH) and wind speed (WS). The analysis pre-sented in this paper, to identify the sources and to determine theirrespective mass contributions, was done by using (a) AbsolutePrincipal Component Score (APCS) method (Thurston and Spengler,1985), and (b) Positive Matrix Factorization (PMF) method (Paateroand Tapper, 1993, 1994; Paterson et al., 1999). The approach tocouple the diurnal variation observed in the meteorological factors,with the similar variation shown by particulate matter from theLCM is presented. This enabled us to quantify the baselineconcentrations attributed to meteorological factors, of the CPM andRPM fractions, in Delhi's ambient atmosphere in early winterseason. “Baseline concentration” is defined as ambient aerosolconcentration arising from LCM, and driven by the variations in themeteorological factors only (PBL, T, RH and WS).

2. Materials and methods

Aerosol samples (39 each) in two different size fractions,i) Coarse Particulate Matter of >10 mm aerodynamic diameter(CPM), ii) Respirable Particulate Matter of <10 mm (RPM) werecollected by using Respirable dust sampler (Envirotech, model 460BL), during earlywinter at a receptor site, in the time sequence of 8 hintervals over thirteen days (Tandon et al., 2008). For better reso-lution itwas desirable to have sample collection in less than8hourlyintervals (e.g. 3 h) sequence, but the amount ofmaterial required forchemical analysis was a constraint, consequently requisite mass ofsamplewas collected in 8 hourly sequence. Analysis for the presenceof 15 elements by using inductively coupled plasma atomic emis-sion spectrometer ICP-AES (JY France, model Ultima 2) was done onthe collected aerosol fractions. The identity of the sources and theabsolute load (mgm�3) contributed to CPM and RPMwas calculatedby principal component analysis (PCA) of the mass concentrations(ng m�3) of 15 elemental variables (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg,Mn, Na, P, Pb, Sr, Zn), and absolute principal component scoremethod (Thurston and Spengler, 1985; Wilks, 2006). The same

1 The Figs. S1(AeB) and S2(AeD) of the supplementary material show theresolved loads from the sources for CPM and RPM aerosol load; APCS and PMFmethods were used to resolve the sources. The results on APCS resolved RPMfraction were taken from Tandon et al. (2008).

analysis was also done by using positive matrix factorization (PMF)method (Paatero and Tapper, 1993, 1994). Prior knowledge of theonset of tracer source (fireworks) associated emissions, and theability of APCS and PMF to clearly identify and resolve this sourceindicated the adequacy of the number of collected samples. Iden-tified sources, APCS and PMF based are shown in SupplementaryFigs. S1(AeB) for CPM and S2(AeD) for RPM load. The resolved CPMand RPM fractions arising from LCM by APCS and PMF were inagreement; Figs. S1(AeB) and S2 (AeD).

The data-set for meteorological parameters: Planetary Bound-ary Layer (PBL), Surface Level Temperature (T), Surface Level Rela-tive Humidity (RH) andWind Speed (WS) recorded on 3 h basis wasobtained from Air Resources Laboratory, National Oceanicand Atmospheric Administration (http://www.arl.noaa.gov/READYamet.php). The eight hourly absolute load (mg m�3) contributionsarising from different sources in both the size fractions of aerosolalong with the three hourly meteorological data were analyzed forthe presence of dominant harmonic by Discrete Fourier Transform(DFT) Analysis (Equation (1)): where n is the number of samples,j represents the mass of jth sample in the sequence, the term k/ndenotes the frequency.

loadj ¼ a0 þXðn�1Þ=2

k¼1

�ak cos

�2pj

kn

�þ bk sin

�2pj

kn

��(1)

The coefficients a0, ak and bk were determined from regressionand used to estimate kth harmonic amplitude and the phase angle,q (Wilks, 2006). The power spectrum (variance) plots showed thattwenty four hourly dominant harmonics were present in theaerosol load arising from LCM: CCPM (CPM), CRPM (RPM), and inthe meteorological parameters PBL, T and RH: where, CCPM andCRPM represent the periodic load of CPM and RPM (from LCM) asdeciphered by DFT. The aerosol load coming from other identifiedsources, in both the size fractions and the WS did not show pres-ence of any comparable dominant 24 h harmonic. To investigate thecoupling, present between the aerosol load arising from LCM, inboth the size fractions and the meteorological parameters, thedata-sets of aerosol load arising from LCM and the meteorologicalparameters were interpolated using the DFTequation. The presenceof most dominant harmonic (24 h period) was detected fromrespective power spectrum plots: Supplementary Figs. S3(AeD)and S4(AeD). Calculations of the coupling factors are covered innext section.

3. Results and discussion

3.1. Calculations of the harmonic strength, phase angle differencepresent in aerosol loads and the respective meteorological factors

To ascertain the association between the recorded meteoro-logical factors and the resolved crustal aerosol load, Pearson'scorrelation co-efficients were calculated between the hourlyaerosol load arising from LCM and that explained by the 24-hdominant harmonic (i.e. CCPM_24 and CRPM_24) and hourlyvalues of meteorological factors explained by the 24-h dominantharmonic (PBL_24, T_24, RH_24 and WS_24). A strong correlationwas observed between CCPM_24: PBL_24 (0.70), CCPM_24: T_24(0.82), CCPM_24 : RH_24 (�0.89) and CCPM_24: WS_24 (0.79). Onthe other hand relatively weak correlation between CRPM_24:PBL_24 (0.14), CRPM_24: T_24 (0.32), CRPM_24: RH_24 (�0.45) andCRPM_24 : WS_24 (0.27) was observed.

It is apparent that, both, the amplitude and phase angle (q) ofthe discerned 24-h harmonic varied significantly with respect tothe aerosol load arising from LCM (CCPM and CRPM), and the

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A

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Fig. 1. [AeD]: APCS based resolved load (CCPM and CRPM) arising from the local crustal source (dotted black line) shown in panel A and C. The same, as resolved by using PMF(dotted grey lines) is shown in panel B and D. Solid black curve drawn in panel A and C represents contributions made by 24-h dominant harmonic to CCPM and CRPM load; solidgrey curves drawn in panels B and D also represent the 24 h harmonic present in CCPM and CRPM load resolved by PMF.

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e1243 1239

respective meteorological factors; the amplitude was �59 mg m�3

for CCPM,�6 mgm�3 for CRPM,�471m for PBL,�3 �C for Tand�4%for RH and �0.2 knots for WS; the phase angle q too variedconsiderably (Fig. 1(AeD), Fig. 2(AeD) and Fig. 3(AeD)). Thestrength of 24-h dominant harmonic in CCPM, CRPM and themeteorological factors was calculated as the fraction of the exper-imental observations explained by the respective harmonic(resolved loads and meteorological factors). For example the 24-hperiod dominant harmonic strength (<H24>) present in CCPM orCRPM load will be the ratio between the aerosol load explained bythe most dominant harmonic to the total load: < H24 >CCPM ¼Pn

j¼1 jxj;24j=Pn

j¼1 jxjjwhere xj,24 represents the jth value of the 24-h harmonic time series and xj is the jth value of APCS resolved

CCPM load time series. The fraction of load explained by the <H24>

for CCPM and CRPM was 0.37 and 0.14 respectively from APCSbased analysis (Fig. 1(A and C)). The same (<H24>) calculationsdone on PMF based analysis for CCPM and CRPM was 0.20 and 0.13(Fig. 1(Band D)). The <H24> ¼ 0 implies the absence of the 24-hharmonic and its value ¼ 1 implies that the total CCPM (or CRPM)load would be explained by the 24-h harmonic. The correspondingharmonic strength calculated for PBL, T, RH and WS was 0.51, 0.07,0.09 and 0.03 respectively (Fig. 2(AeD)). This show that changesin PBL will have much larger influence in introducing periodicityseen in the ambient aerosol load. As stated earlier, the selectionof the most dominant cycle was done on the basis of the Periodo-grams generated by the Discrete Fourier Transform Analysis

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Fig. 2. [AeD]: Light grey curves drawn in respective panels represent recorded meteorological variables: Planetary Boundary Layer (PBL), Surface level Temperature (T), Surfacelevel Relative Humidity (RH) and Wind Speed (WS) respectively. The data was obtained from National Oceanic and Atmospheric Administration (NOAA). The estimated 24-hharmonics present in the respective meteorological variables (PBL_24[A], T_24[B], RH_24[C] and WS_24[D]) is plotted as solid black curve in respective panels.

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e12431240

(see Supplementary Figs S3 and S4), and 24 h harmonic was foundto be dominant for CCPM and CRPM calculated, both, by using APCSand PMF. The samewas the case for meteorological variables (PBL, Tand RH), except for WS.

Two hour phase difference (30�) between the 24-h harmonicspresent in the CCPM (CCPM_24) and CRPM (CRPM_24) indicatedthat the ambient concentration of each of these size fractions,temporally, will behave differently. The calculated phase differ-ences between the 24-h harmonics representing the two aerosolfractions, i.e. (CCPM_24 and CRPM_24) with PBL (PBL_24), T (T_24),RH (RH_24) and WS (WS_24) are shown in Fig. 3(AeH). Thedirection of the harmonic strength (positive or negative) withrespect to the affecting meteorological factor (PBL, T, RH and WS)on the affected variable (CCPM and CRPM) is determined by thisphase difference. The phase difference of>12 h (180�) between, e.g.CCPM and RH, will result in the lowering of aerosol load (negativedirection of the harmonic strength) from the atmosphere (Fig. 3(Cand G)). On the other hand a phase difference<180 [e.g. 3 (45�) and5 (75�) hours between PBL with respect to the CCPM and CRPMload] will assist the periodic undulation of the respective loadsarising from LCM to the ambient environment (positive direction ofharmonic strength).

The presence of a (24-h period) harmonic in aerosol load and themeteorological data alluded to the role of the meteorological

factors in affecting the aerosol load. It could be argued that theambient concentrations of aerosols (CCPM and CRPM) arising fromthe local crustal material are coupled with the periodic variationspresent in the climatic variables. If this be the case then one canrepresent the CCPM or CRPM load, explained by the dominantharmonic (24-h harmonic in this case), as a linear combination ofthe corresponding harmonics discerned in the respective meteo-rological factor as Equation (2).

CCPM 24t ¼ CPBL � PBL 24t þ CT � T 24t þ CRH � RH 24t

þ CWS �WS 24t (2)

In Equation (2) CCPM_24t represents the load at time t (h)explained by the 24-h dominant period harmonic in mg m�3;PBL_24t (m), T_24t (�C), RH_24t (%) and WS_24t (knots) are therespective meteorological observations at time t. The couplingfactors (CPBL, CT, CRH and CWS) in the equation will have the unitsmg m�4, mg m�3 �C�1, mg m�3 %�1 and mg m�3 knot�1 respectively.Equation (2) can also be written for respirable fraction of theaerosol load, CRPM_24t. The estimate of the coupling factors wasdone by linear regression of the time series data explaining therespective aerosol's size fraction load by the 24-h harmonic. Theestimated respective coupling factors, along with the fit statisticson APCS based results are given in Table 1, and the same done on

CCPM_24 Vs PBL_24

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Fig. 3. [AeH]: The phase difference between (24 h) cycle detected in CCPM_24 load (light grey curve) with respect to the corresponding harmonic (solid black curve) present in themeteorological variables (PBL_24, T_24, RH_24 and WS_24) is shown in panels AeD. The same for the CRPM is shown in panels EeH. The calculated phase difference is highlightedby the two vertical black lines passing through the crest of the respective harmonics in each panel.

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e1243 1241

PMF based results are given in Table 2. The sign and the magnitudeof the determined coupling factor will determine the character ofmodulation, which exists between the load-harmonic and theconcerned meteorological factor. Low harmonic strength observedfor WS factor is apparent from the plots of the correspondingharmonic (Figs. 2D and 3H). It is expected that coupling factorrelating the effect of WS on two load fractions should reflect that,and the same can be seen in terms of the presence of large spread inthe 95% confidence limit standard deviation (�2s) for the fitted

CWS. A regression fit excluding the WS did not affect the quality offit and the fit statistics (Tables 1 and 2). In absolute load terms themeteorological factor's coupling explains 37% and 14% of the totalload of CCPM and CRPM calculated using APCS. The same, calcu-lated using PMF, was 20% and 13%. Calculated values of averagemass per sample of CPM, CCPM, CCPM_24, and RPM, CRPM,CRPM_24 are given in Table 3.

The relevance of the estimated coupling factors will hold onlyfor the onset of winter season's ambient aerosol load. However, as a

Table 1Statistics of the determined coupling parameters relating the meteorological factors with the CCPM24,t and CRPM24,t ambient aerosol loads (mg m�3) accounted by thedominant 24-h harmonic. The model explaining the relationship is the sum of linear combination of respective coupling factor and meteorological factor's harmonic. The poorstatistics of CWS show the weak coupling for wind speed. The fit where WS was excluded does not affect the quality of fit and the estimates of other coupling parameters. Thevalues given are calculated on the basis of APCS method applied on the elemental analysis of collected aerosol samples.

CCPM24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;t þ CWS �WS24;t CRPM24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;t þ CWS �WS24;t

c2 ¼ 0.077 c2 ¼ 0.001Sum of squares ¼ 0.077 Sum of squares ¼ 0.001COD ¼ 1 COD ¼ 1Correlation coefficient ¼ 1 Correlation coefficient ¼ 1MSC ¼ 12.86 MSC ¼ 12.25

CPBL [95% confidence band] ¼ �0.22 [�0.24 �0.19] CPBL [95% confidence band] ¼ �0.044 [�0.048 �0.040]CT [95% confidence band] ¼ 18.07 [7.28 28.86] CT [95% confidence band] ¼ 3.144 [1.679 4.609]CRH [95% confidence band] ¼ �23.15 [�27.23 �19.07] CRH [95% confidence band] ¼ �3.101 [�3.655 �2.202]CWS [95% confidence band] ¼ 4.56 [�5.73 14.84] CWS [95% confidence band] ¼ 1.151 [�0.245 2.547]

CCPM24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;t CRPM24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;tc2 ¼ 0.080 c2 ¼ 0.001Sum of squares ¼ 0.080 Sum of squares ¼ 0.001COD ¼ 1 COD ¼ 1Correlation coefficient ¼ 1 Correlation coefficient ¼ 1MSC ¼ 12.90 MSC ¼ 12.30

CPBL [95% confidence band] ¼ �0.21 [�0.23 �0.19] CPBL [95% confidence band] ¼ �0.0417 [�0.0447 �0.0388]CT [95% confidence band] ¼ 15.08 [6.71 22.46] CT [95% confidence band] ¼ 2.39 [1.197 3.584]CRH [95% confidence band] ¼ �24.33 [�27.40 �21.25] CRH [95% confidence band] ¼ �3.398 [�3.836 �2.959]

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e12431242

concept ‘coupling factors’ will be an important handle to quantifythe ambient aerosol load associated with local crustal material indifferent seasons, with local meteorological variables, though it isimportant to stress that the coupling factors will change with thechange of season and the characteristic nature of the local crustalmaterial; e.g. loose crustal material present in desert regions.

3.2. Baseline concentration of coarser and respirable aerosolsin Delhi region during the onset of winter season

Different fractions of collected ambient aerosol load in thisstudy can be viewed as the sum of contributions made by theidentified sources. In case of CPM, mass contributions to the loadwere from two sources (fireworks and local crust; Fig. S1). CPMarising from fireworks falls under anthropogenic process (episodic).On the other hand, CPM load from LCM may involve manyprocesses, of these; the coupling of meteorological factors explains

Table 2Statistics of the determined coupling parameters relating the meteorological factors wdominant 24-h harmonic. The model explaining the relationship is the sum of linear combstatistics of CWS show the weak coupling for wind speed. The fit whereWSwas excluded dof PMF resolved crustal source in CPM and RPM (PM10) with 24 h harmonic present in m

CCPM PMF24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;t þ CWS �WS24;t

c2 ¼ 0.163Sum of squares ¼ 0.163COD ¼ 1Correlation coefficient ¼ 1MSC ¼ 12.18

CPBL [95% confidence band] ¼ �0.40 [�0.44 �0.36]CT [95% confidence band] ¼ 26.10 [9.99 42.21]CRH [95% confidence band] ¼ �33.03 [�39.11 �26.94]CWS [95% confidence band] ¼ 6.65 [�8.72 22.01]

CCPM PMF24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;tc2 ¼ 0.170Sum of squares ¼ 0.170COD ¼ 1Correlation coefficient ¼ 1MSC ¼ 12.23

CPBL [95% confidence band] ¼ �0.39 [�0.42 �0.35]CT [95% confidence band] ¼ 21.75 [9.26 34.24]CRH [95% confidence band] ¼ �34.74 [�39.33 �30.14]

37% of the CCPM load. The rest (63%) may involve other processes,we suspect anthropogenic in nature. Baseline CPM fraction's loadwould be the load which only arises from the natural factors(meteorological in the present case).

RPM load involves four sources (fireworks, local crust, windtransportedmineral dust and vehicular emissions; Fig. S2). Baselineaerosol concentration, in this size fraction, would be from windtransported material and the fraction of LCM (14% of CRPM)involving meteorological coupling. This approach presented in thispaper allows one to have the reasonable baseline estimates ofaerosol load at any location. Of course, the calculated values willvary as a function of changes in the meteorological variables. Thisemphasizes the importance of multivariate analysis (APCS, PMF)methods to get the absolute mass contributions arising from allidentified sources, which in turn can be used to develop newscientific insight to understand the effect of non-anthropogenicfactors on ambient aerosol load.

ith the CCPM24,t and CRPM24,t ambient aerosol loads (mg m�3) accounted by theination of respective coupling factor and meteorological factor's harmonic. The pooroes not affect the estimates of coupling parameters. The values given are on the basiseteorological parameters.

CRPM PMF24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;t þ CWS �WS24;t

c2 ¼ 0.002Sum of squares ¼ 0.002COD ¼ 1Correlation coefficient ¼ 1MSC ¼ 12.20

CPBL [95% confidence band] ¼ �0.051 [�0.055 �0.046]CT [95% confidence band] ¼ 3.412 [1.691 5.134]CRH [95% confidence band] ¼ �3.613 [�4.263 �2.963]CWS [95% confidence band] ¼ 0.777 [�0.864 2.418]

CRPM PMF24;t ¼ CPBL � PBL24;t þ CT � T24;t þ CRH � RH24;tc2 ¼ 0.002Sum of squares ¼ 0.002COD ¼ 1Correlation coefficient ¼ 1MSC ¼ 12.23

CPBL [95% confidence band] ¼ �0.050 [�0.053 �0.047]CT [95% confidence band] ¼ 2.904 [1.564 4.244]CRH [95% confidence band] ¼ �3.814 [�4.306 �3.321]

Table 3Collected and estimated average aerosol load in CPM, CCPM, CCPM_24 (column 2); RPM, CRPM and CRPM_24 (column 4). Estimates are based on APCS analysis of collectedsamples as explained in Section 2.

Local Crustal Material (LCM) in coarser aerosol Average load (mg m3) Local crustal Material (LCM) in respirable aerosol Average load (mg m3)

CPM: Collected aerosol sample 233 � 193 RPM: Collected aerosol sample 209 � 121CCPM: APCS resolved periodic load of CPM 102 � 131 CRPM: APCS resolved periodic load fraction of RPM 28 � 17CCPM_24: Fraction n of CCPM load modulated by

meteorological factor's (PBL, T, RH, WS) coupling38 � 16 CRPM_24: Fraction of CRPM load modulated by

meteorological factor's (PBL, T, RH, WS) coupling4 � 2

A. Tandon et al. / Atmospheric Environment 44 (2010) 1237e1243 1243

4. Conclusion

It was important to note that aerosols arising from the localcrustal material manifested a strong periodic undulation in terms ofthe ambient concentrations of CPM and RPM load. The dominant24-h harmonic explaining significant proportion of CPM and RPMload, suggested that this load is influenced by the correspondingharmonics present in the meteorological parameters: PBL, T, RHandWS. It was shown that the aerosol load undulations are coupledwithmeteorological factors, each factor having its discrete couplingparameter. Introduction of coupling parameters was able to explainfully the periodic undulations observed in the CPM and RPM frac-tions of aerosol load from local crust. The coupling parameter, asa concept, is relevant to explain the variability of ambient aerosolload often recorded in tropical regions and it also allows the esti-mation of the contributions arising from meteorological factor'svariability. Adequacy of the sample size for APCS and PMF analysiswas facilitated by knowing beforehand the temporal activity of thetracer source (fireworks). Both methods were able to discern thetiming and the source identity of the tracer source.

Acknowledgements

The authors thank National Oceanic and Atmospheric Admin-istration (NOAA) for providing data on meteorological variables.Financial assistance provided by the Council of Scientific andindustrial Research (CSIR) India, in the form of a research project, isacknowledged. We thank Prof. V. Rajamani for his suggestionsand help in the analysis of samples by extending the facility ofICP-AES. Authors extend their appreciations to the two anonymousreviewers of this manuscript for providing valuable comments andsuggestions, which were helpful in improving the quality of thismanuscript.

Appendix. Supplementary material

Fig. S1 [AeB]: Ambient load of Coarser Particulate Matter (CPM)arising from two sources (Local crustal source and fireworks)identified by Principal Component Analysis (PCA) and quantified byAbsolute Principal Component Score Method (APCS) is shown assolid black curves (panel AeB). Light grey curves drawn in eachpanel represent the estimates from PMF method.

Fig. S2 [AeD]: Ambient load of Respirable Particulate Matter(RPM) emitted by the four identified sources in each sample,resolved by Principal Component Analysis (PCA) and AbsolutePrincipal Component Score Method (APCS), are plotted in panelAeD (solid black Curves). The emission profile of tracer source(fireworks) is plotted in panel A; local crustal source in panel B; thewind transported crustal material source in panel C; and emissionsfrom vehicular source in panel D. PMF method based contributionsmade by each source is shown in light grey curves.

Fig. S3 [AeD]: Periodograms generated by the Discrete FourierTransform Analysis for CCPM, CRPM aerosol fractions, which wereresolved by using APCS (panels A and C) and PMF (panels B and D)

method. The most dominant cycle of 24 h is clearly present in bothfractions.

Fig. S4 [AeD]: Periodograms generated by Discrete FourierTransform analysis of meteorological factors: PBL (panel A), RH(panel B), T (panel C), and WS (panel D). Except for WS, all othermeteorological factors manifest the presence of strong 24 h cycle.

Note: Supplementary data associated with this article can befound in the online version at doi:10.1016/j.atmosenv.2009.12.037.

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