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Atmospheric Environment 109 (2015) 217e227

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Atmospheric Environment

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Time-resolved inorganic chemical composition of fine aerosol andassociated precursor gases over an urban environment in westernIndia: Gas-aerosol equilibrium characteristics

A.K. Sudheer*, R. RengarajanGeosciences Division, Physical Research Laboratory, Ahmedabad, 380 009, India

h i g h l i g h t s

� Time-resolved aerosol composition and trace gas concentrations were measured.� We examine gas-aerosol equilibrium characteristics using ISORROPIA II model.� NH3eNH4

þ are in equilibrium with measured particulate and gas composition.� HCl and HNO3 deviate from thermodynamic equilibrium possibly due to uptake by dust.

a r t i c l e i n f o

Article history:Received 14 October 2014Received in revised form10 March 2015Accepted 12 March 2015Available online 13 March 2015

Keywords:Aerosol compositionThermodynamic equilibriumTrace gases

* Corresponding author.E-mail address: sudheer@prl.res.in (A.K. Sudheer).

http://dx.doi.org/10.1016/j.atmosenv.2015.03.0281352-2310/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

Inorganic ionic constituents (Naþ, NH4þ, Kþ, Mg2þ, Ca2þ, Cl�, NO3

� and SO42�) of PM2.5 and associated trace

gases (NH3, HNO3 and HCl) were measured simultaneously by Ambient Ion Monitor e Ion Chromato-graph (AIM-IC) system with a time resolution of one hour at an urban location in semi-arid region ofwestern India during summer and winter. The average NH3, HNO3 and HCl concentrations were11.6 ± 5.0, 2.9 ± 0.8 and 0.15 mg m�3, respectively, during winter. During summer, NH3 and HNO3 con-centrations were of similar magnitude, whereas HCl concentration was less than ~0.03 mg m�3. NH3

concentration exhibited a distinct diurnal variation during both seasons. However, HNO3 did not show aspecific diurnal trend during the observation period in both seasons. The data obtained were used tostudy gas-aerosol equilibrium characteristics using a thermodynamic equilibrium model, ISORROPIA II.The results suggest that NH3 exists in equilibrium between measured fine-mode particle and gas phasewith a systematic bias of ~14%, whereas HCl and HNO3 deviate significantly from the modelled data.These observations have implications on thermodynamic equilibrium assumptions used for estimatingvarious aerosol parameters such as liquid water content, pH, etc., thus causing significant bias inchemical transport model results over the study region.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Atmospheric particulatematter with aerodynamic diameter lessthan 2.5 mm (PM2.5) has the potential to alter the radiative balanceof the Earth by scattering or absorbing radiation and influencecloud microphysics. Furthermore, fine aerosols reduce visibility,provide surface for heterogeneous chemical reactions, affect hu-man health and impact ecosystems through deposition of varioustrace constituents (Finlayson-Pitts and Pitts, 2000; Poschl, 2005;

Seinfeld and Pandis, 2006; Mannucci, 2010). For a better under-standing of climate forcing, air quality and for taking mitigatingmeasures, knowledge of chemical constituents and their phases inatmospheric PM2.5 is essential. The bulk of dry, fine-particle mass isinorganic (typically 25e75%; Heitzenberg, 1989), with NH4

þ, SO42�

and NO3� asmain components. In addition, Naþ, Cl�, Ca2þ, Mg2þ and

Kþmay also be present, associated with crustal and sea-salt sourcesdepending on the location. These species exist in aqueous phase orsolid form depending on ambient temperature and relative hu-midity, and some can get partially volatilized as NH3, HNO3 and HClvapours. Hence, the gas and the particle phases of these semi-volatile constituents are related through thermodynamic equilib-rium partitioning. Consequently, many aerosol thermodynamic

Table 1Limit of detection (LOD) for the measured aerosol ionic constituents and trace gasspecies. All concentrations are in mg m�3.

Gas species HCl HNO3 NH3

LOD 0.02 0.03 0.02

Particle component Cl� NO3� SO4

2� Naþ NH4þ Mg2þ Kþ Ca2þ

LOD 0.02 0.03 0.05 0.02 0.02 0.02 0.02 0.02

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227218

equilibrium models such as EQUIL, MARS, AIM, E-AIM, SCAPE,EQUISOLV, ISORROPIA have been developed to simulate the processof aerosol dissolving in water, forming ions and equilibrating withgas phase (Bassett and Seinfeld, 1983; Saxena et al., 1986; Wexlerand Seinfeld, 1991; Kim et al., 1993; Jacobson et al., 1996; Neneset al., 1998; Wexler and Clegg, 2002).

Studies from various geographical locations of the Indian sub-continent have revealed that particulate NH4

þ, SO42�, and NO3

�

constitute as much as 25e40 % of PM2.5 mass (Kumar and Sarin,2010; Deshmukh et al., 2011; Rengarajan et al., 2011). The rest ofthemass consists of generally organicmatter, elemental carbon andmineral dust. An important aspect is to verify whether equilibriummodels can adequately predict the equilibrium partitioning ofsemi-volatile inorganic species over a representative region withreasonable confidence level. Though several studies have docu-mented inorganic constituents of aerosol over the region of west-ern India, data on simultaneous measurement of precursor gasesand assessment of gas-aerosol equilibrium are not available. Mostof the available information from various locations in Indiaregarding gas phase constituents and aerosol ionic species is basedon off-line analytical techniques with extended integration time upto a few days, thereby making impossible to elucidate the changesoccurring on time scales of hours. If measurements are made byaveraging for long duration, significant changes in temperature,relative humidity (RH) and precursor gases can occur during sam-pling and the data would be inappropriate for equilibrium calcu-lations. In particular, northern region of the Indian subcontinent is aperennial source of both natural and anthropogenic aerosol andwitnesses seasonal wind reversal, transporting marine air massestowards the continental regions during summer. Studies to char-acterize the influence of high dust and anthropogenic emissions onequilibrium partitioning of semi-volatile species are not reportedfrom this region.

In this paper, results of simultaneous on-line measurement ofwater-soluble ionic constituents of PM2.5 and associated gaseousspecies over an urban environment in western India along with ananalysis of gas-aerosol partitioning of HNO3, NH3 and HCl usingISORROPIA II are presented. ISORROPIA II is a computationallyefficient model and widely used in global chemical transportmodels as well as regional climate models (Fountoukis and Nenes,2007; Knote and Brunner, 2013; Heald et al., 2014). The temporaland average diurnal variations of particulate ionic constituentsduring the two sampling campaigns in summer and winter arereported in our earlier publication (Sudheer et al., 2014).

2. Experimental

2.1. Measurement site and general meteorology

Themeasurement site is located at Physical Research Laboratorybuilding at Ahmedabad, India. Ahmedabad is an urban locationsituated in western India (23.03�N, 72.54�E) with a population ofmore than 6 million. The sampling location is surrounded byvarious emission sources both anthropogenic and natural. This re-gion witnesses seasonal reversal of trade winds on annual scale.Typically, during DecembereFebruary winds are north-easterly,whereas during JuneeAugust south-westerly. A detailed descrip-tion of sampling site and meteorological characteristics is availablein Sudheer et al. (2014).

2.2. Measurement of PM2.5 ionic constituents and related gaseousspecies

The inorganic constituents of PM2.5 (Naþ, NH4þ, Kþ, Mg2þ, Ca2þ,

Cl�, NO3�, SO4

2�) and associated precursor gases (HCl, HNO3 and

NH3) were measured by the Ambient Ion Monitor e Ion Chro-matograph (AIM-IC, URG 9000D). The AIM-IC system provides acontinuous, near-real-time, hourly online measurement of thewater-soluble inorganic constituents of PM2.5 and precursor gases.A detailed description of technique and performance evaluation ofAIM-IC system is reported in our earlier publication as well as byvarious other groups (Markovic et al., 2012; Sudheer et al., 2014;Markovic et al., 2014). Briefly, ambient air was taken at a flowrate of 3 L min�1 and inlet was connected to a cyclone for 2.5 mmparticle size cut off. Water extraction was performed with amodified steam jet collector (Khlystov et al., 1995) after gaseouscomponents were removed from the sampled air with a continu-ously regenerated parallel plate wet denuder. 5 mM H2O2 indeionised water was used as denuder solution. The water extractaccumulated in steam jet collector as well as denuder solutionwereanalysed for major cations and anions with two ion-chromatographs (DIONEX 1100) with a time resolution of onehour. For analysis of anions, Dionex AS-14A and AG-14A columnswere used and for cations, CS-12A and CG12Awere used along withthe appropriate electrolytic suppressors. Ambient concentrations ofparticulate constituents were ascertained from analysis of waterextract from steam jet collector. NH4

þ, NO3� and Cl� content in

denuder solution were used to calculate NH3, HNO3 and HCl con-centrations. AIM system was operated during winter fromDecember 22, 2011 to January 16, 2012 and during summer fromJune 5 to July 4, 2012.

PM2.5 samples were collected on quartz filters using a highvolume air sampler and subsequent analysis revealed that the AIM-IC generated data are in good agreement with filter based mea-surements. Balance between the sum of cations and anions sug-gested that themeasurements are consistent with respect to chargeneutrality (Sudheer et al., 2014). To verify the collection efficiencyof gaseous NH3, HCl and HNO3 by the parallel plate denuder, a filterwas fixed at the inlet and the solution from steam jet collector wasanalysed for both cations and anions. Cl� and NO3

� in anion channelwere not detected and NH4

þ signal was less than two orders ofmagnitude compared to that observed during sampling, confirmingthat the measured gaseous species were removed from thesampled air almost quantitatively. In order to minimize the loss, ashort aluminium inlet (1 m length) was used for sampling theambient air. The ion chromatographs were calibrated before andafter the observation period. Standard solutions were injectedintermittently to verify consistency of ion chromatograph perfor-mance. Markovic et al. (2014) described the comparison of analysisof gaseous species NH3, HNO3 and SO2 with independent mea-surement techniques, demonstrating that the AIMeIC systemyieldsmeasured parameters with an uncertainty ranging from 10 to20%.

The limit of detection (LOD) of NH3 was determined as0.02 mg m�3 based on reproducibility of cation blank runs on ionchromatograph (3s). Minimum peak areas of Cl� and NO3

� identi-fied by ion chromatography software (Chromeleon®) correspond to0.02 mg m�3 and 0.03 mg m�3 of HCl and HNO3 in ambient air,respectively, based on calibration with standards. LOD for particu-late constituents were also estimated from minimum peak areas

Table 2Summary of aerosol ionic constituents and gas phase NH3, HNO3 and HCl concen-trations during winter and summer at Ahmedabad. All concentrations are in mg m�3.

Average Winter Summer

Min Max Average Min Max

Cl� 2.6 <0.02 19.5 1.9 0.18 10.3NO3

� 5.0 0.86 17.1 2.0 0.58 11.7SO4

2� 5.7 1.7 16.4 4.7 1.2 35.6Naþ 0.24 0.03 1.69 1.3 0.05 2.19NH4

þ 4.7 0.83 16.8 1.2 0.18 17.6Kþ 0.71 0.16 7.8 0.21 0.07 2.3Mg2þ 0.05 <0.02 0.24 0.21 <0.02 0.30Ca2þ 0.66 0.08 2.3 0.60 0.10 1.1NH3 11.5 4.3 43.4 10.5 3.8 33.4HNO3 2.8 1.7 5.6 1.7 0.48 4.1HCl 0.15 <0.03 0.75 e e e

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227 219

identified by the software based on calibration. LOD of all constit-uents are summarised in Table 1. Generally, ambient concentrationsof all the species are significantly higher (by more than a factor of 5or 10) than the LOD, except for HCl (Table 2). HCl concentration dataless than 0.03 mg m�3 are not considered for this study. Conse-quently, HCl data obtained during summer are not included in thisanalysis.

2.3. Data analysis

A statistical summary of measured species is given in Table 2.There are total 568 data sets available for winter season inwhich allparameters were obtained. Some data for particulate compositionwere omitted from previous set considered in Sudheer et al. (2014),where all the gaseous constituents were not available either due toinjection of calibration standard in that slot or improper injection ofdenuder solution. During summer, data from June 20 to July 4, 2012were taken for this study. Gaseous sample data were interrupteddue to improper injection of sample solution and hence discardedfrom June 5 to 19. Total 350 data sets were analysed for summer.Data used for ISORROPIA II model run were only those in which allparameters including relative humidity and temperature are

Fig. 1. Measured concentrations of a) NH4þ and NH3 and b) NO3

� and HNO3 durin

available and 566 and 343 data sets were used for winter andsummer, respectively, for thermodynamic equilibrium modelling.The meteorological parameters for Ahmedabad were taken fromhttp://www.wunderground.com for which the data were collectedat a location within 10 km from our observation site.

3. Results and discussion

3.1. NH4þ, NH3, NO3

� and HNO3 concentrations

NH3 concentration in continental atmosphere typically rangesfrom ~0.07 mgm�3 to ~7 mgm�3 (Seinfeld and Pandis, 2006). Kumaret al. (2004) reported average NH3 concentration of 11 mg m�3

during summer and that during winter was 9 mg m�3 at Agra, anurban location in India. Sharma et al. (2007) reported average NH3concentration of 19 mg m�3 over Kanpur, an urban location in Indo-Gangetic plain. Most of the reported works from Indian region arebased on off-line analytical techniques involving collection ofsamples with impingers or filters for extended duration, integratingtypically for more than 10 h Fig. 1 depicts the measured NH4

þ, NH3,NO3

� and HNO3 concentrations during winter at Ahmedabad in thisstudy. Ammonia concentration ranges from 4.3 mg m�3 to43.4 mg m�3 with an average of 11.5 ± 5.0 mg m�3. Particulate NH4

þ

varies from 0.8 to 16.8 mg m�3, with an average value of4.7 ± 2.6 mg m�3 (Table 2). Though temporal variations of NH3 andNH4

þ concentrations exhibit a similar trend during the observationperiod in winter, correlation between the two parameters is notsignificant, if entire data are considered. A strong association ofammonia emission from animal husbandrywas reported by Goebeset al. (2003). Marshy wetlands, agricultural waste and biomassburning are also reported as prominent sources for gaseous NH3.Gujarat state is known for its surplus dairy production and elevatedammonia emission is expected in this region throughout the year.HNO3 concentration varies from 1.7 to 5.6 mg m�3 with an averagevalue of 2.8 ± 0.8 mg m�3 which does not show any similarity in thetemporal trend of particulate NO3

� concentrations (Fig. 1). NO3�

concentration shows a systematic daily variation and ranges from0.86 to 17.1 mg m�3 with an average value of 5.0 ± 2.7 mg m�3.Kumar et al. (2004) reported HNO3 concentration of

g winter demonstrating the temporal variability during observation period.

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227220

2.1 ± 0.8 mg m�3 over Delhi during winter. Gupta et al. (2003) re-ported HNO3 concentration of 1.1 mg m�3 over a rural location nearAgra. The concentration levels observed during our study overAhmedabad are significantly higher compared to these reportedvalues.

During summer, NH3 varies between 3.8 mg m�3 to 33.4 mg m�3

with an average value of 10.5 ± 3.5 mgm�3. Average particulate NH4þ

mass concentration is 1.2 ± 2.0 mg m�3, ranging from 0.18 to17.6 mg m�3. The temporal trends of NH3 and NH4

þ indicate thatwhile NH4

þ concentration is increasing, NH3 tends to decrease(Fig. 2). The average concentrations of NH3 and NH4

þ are lowercompared to those observed during winter. NH3 is lower by ~10%,possibly due to shallow boundary layer during winter. Kumar et al.(2004) reported higher concentration of NH3 during summercompared to winter. Clarisse et al. (2009) reported that the repre-sentative concentration of NH3 in California's San Joaquin Valleywas highest in the spring and summer. The higher concentrationduring summer in these studies is attributed to higher temperaturein summer, which favours emission of NH3 from agricultural fieldsand dairy farms. At Ahmedabad, all the pollutant levels remainlower during summer compared to winter, partially due to dilutioneffect initiated by atmospheric boundary layer dynamics. AverageHNO3 concentration observed during summer is 1.7 mg m�3, with arange of 0.5e4.1 mg m�3, and is relatively low compared to that inwinter. Conspicuous similarity or trend is not observed in temporalvariation of both HNO3 and NO3

� concentrations during summer.Markovic et al. (2014) demonstrated a significant temperature

dependence of NH3 mixing ratios over Bakersfield, Canada, sug-gesting that the predominant emission source is sensitive toambient temperature. Behera and Sharma (2010) reported NH3 andHNO3 concentrations vary in annual scale and that there exists arelationship between product of their mixing ratios and tempera-ture, indicating dissociation/formation of NH4NO3 that is temper-ature sensitive. A similar analysis of NH3 and HNO3 mixing ratiosover Ahmedabad does not show a dependence on temperature,suggesting that measured mixing ratios are not sensitive toambient temperature changes. Several studies reported thatwestern region of India is known for the presence of mineral dust inaerosol throughout the year (Rastogi and Sarin, 2009; Kumar andSarin, 2010; Sudheer and Rengarajan, 2012). Mineral dust

Fig. 2. Measured concentrations of a) NH4þ and NH3 and b) NO3

� and HNO3 durin

abundance varies from 60 to 70% of total suspended particles overAhmedabad during different seasons (Rastogi and Sarin, 2009).Hence, gas phase HNO3 is likely to undergo uptake by alkalinemineral dust instead of forming NH4NO3 and significant amount ofcoarse mode NO3

� is expected to be present over this region. Thiscould be a possible reason for HNO3 and NH3 being independent ofambient temperature changes.

3.2. NH4þ, NH3, NO3

�and HNO3 diurnal variations

The average diurnal variations of NH3 and NH4þ observed during

both seasons are depicted in Fig. 3. NH3 concentrations exhibit areverse trend with that of particulate NH4

þ in summer. NH3 de-creases to minimum values at 18:00e19:00 h and increases tobackground level by 23:00 h. Overall increased production of NH4

þ

salts during late afternoon hours may cause a reduction in gasphase NH3. Based on NH4

þ/SO42� ratios, Sudheer et al. (2014) re-

ported that summer time represents an ammonium-poor envi-ronment, whereas excess ammonium is present during winter. Onthe other hand, when the total ammonia (TA¼NH3þNH4

þ) is takeninto account, both summer and winter are characterized by excessammonium over this region. Since the dry deposition velocity ofNH3 is relatively higher than NH4

þ, ratio of NH3 to TA can be used asan indicator for local sources of ammonium (Walker et al., 2004).Higher the ratio, stronger will be the influence of local emissionsources on ambient concentrations. The observed NH3/TA ratioranges from 0.38 to 0.93 with an average value of 0.71 ± 0.1 inwinter. During summer, the ratio is substantially higher with anaverage value of 0.90 ± 0.08, ranging from 0.50 to 0.99. Hence, localemission sources dominate the NH3 concentration as well asobserved diurnal variation. On July 4, NH3 and NH4

þ concentrationswere exceptionally high as well as there were large daytime vari-ation compared to all other days. During winter, diurnal variation ofNH3 remained the same as that during summer. A significant pos-itive correlation is observed between average NH3 and NH4

þ con-centrations on a diurnal cycle during winter. However, an inversetrend is observed during summer.

The major pathway for formation of HNO3 is through the reac-tion with hydroxyl radical in daytime, whereas during night it isproduced by hydrolysis of N2O5. HNO3 concentrations do not

g summer demonstrating the temporal variability during observation period.

Fig. 3. Diurnal variation of NH3 and particulate NH4þ during a) winter and c) summer. Scatter plots between NH3 and NH4

þ during b) winter and d) summer. NH3 exhibits a distinctdiurnal trend in both seasons with a characteristic decrease in concentration in afternoon hours.

Fig. 4. Diurnal variation of HNO3 and NO3� concentrations during a) winter and c) summer. Scatter plots between HNO3 and NO3

� during b) winter and d) summer. Diurnal variabilityof HNO3 is not prominent.

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227 221

exhibit a distinct general diurnal trend in contrast to particulateNO3

� concentration during summer (Fig. 4). Hence, it can be infer-red that production of HNO3 during daytime and nighttime isalmost similar in magnitude. During winter, the minimum con-centration of HNO3 is at 17:00e18:00 h, similar to that of NH3 aswell as NO3

�, though the extent of variability is not as high as NH3.This could be due to dilution effect, caused by daytime increase inboundary layer height, prominently observed during afternoonhours. Markovic et al. (2014) reported maximum mixing ratio ofHNO3 during noontime at Bakersfield, CA, attributing to gas-aerosolpartitioning of semi-volatile NH4NO3 and local photochemical

production. Similarly, Morino et al. (2006) reported maximumHNO3 during noon hours at Tokyo during summer. They suggestedthat the daytime photochemical production rate dominates thecause of diurnal variation as evidenced by O3 concentration and O3photolysis rate. Particulate NO3

� mass concentration was only aquarter of HNO3 during their observation period. At Ahmedabad, onan average, HNO3 contributes ~40% of total NO3

� during winter aswell as summer (range: 10e75%), which is higher compared to thatreported at Tokyo. The HNO3/(HNO3 þ NO3

�) ratio is found lowerduring afternoon hours compared to nighttime during winter,indicating that temperature dependent NH4NO3 partitioning is not

Table 3Statistical evaluation of observed and modelled gaseous constituents using ther-modynamic equilibrium model, ISORROPIA II.

Winter Summer

NH3 HNO3 NH3 HNO3

meas-M mg m�3 11.5 2.8 10.4 1.3mod-M mg m�3 13.1 2.6 11.2 1.4MB (mg m�3) 1.47 �0.74 0.70 0.13NMB (%) 14.0 �58.3 5.91 �3.68ME(mg m�3) 1.51 2.33 0.73 1.43NME (%) 14.4 183 6.9 112N 566 566 343 343R 0.99 0.24 0.99 0.07

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227222

the dominant factor in controlling observed HNO3 concentrationlevel.

3.3. Aerosol equilibrium model vs. observations

Thermodynamic gas-aerosol equilibrium characteristics duringsummer and winter were examined using ISORROPIA II model(Fountoukis and Nenes, 2007). This equilibriummodel is one of themost widely used module in various global chemical transportmodels such as Geos-Chem and regional chemistry climate models(Knote and Brunner, 2013; Heald et al., 2014). Measured concen-trations of inorganic constituents were used along with gas phaseNH3, HNO3 and HCl. Themodel was run in forwardmode using totalammonia (NH4

þ þ NH3) and total nitric acid (HNO3 þ NO3�) con-

centrations as the sum of particulate and gaseous forms of

a)

b)

Fig. 5. Diurnal variation of observed and modelled NH3 concentrations depicting good agreeISORROPIA II model.

ammonia and nitrate. Particulate Cl� was taken as total, whereverHCl is below detection limit. Since RH ranged from ~15% to ~100%during our observation, both stable (deliquescent state) andmetastable conditions were used for model runs (Fountoukis et al.,2009). The model output for gas phase NH3, HNO3 and HCl wereconsidered for the comparison with AIM-IC measurement andassessment of gas-aerosol partitioning of these species with PM2.5.

The statistical analysis of the agreement between observationsand model predictions was performed by calculating model mean(mod-M), measured mean (meas-M), correlation coefficient, meanbias (MB), mean error (ME), normalized mean bias (NMB) andnormalized mean error (NME):

MB ¼ 1N

xXN

i¼1Mi � Oið Þ

ME ¼ 1N

xXN

i¼1

����Mi � Oi

����

NMB ¼PN

i¼1 Mi � Oið ÞPN

i¼1Oix 100%

NME ¼PN

i¼1

���Mi � Oi

���PN

i¼1Oix 100%

where,Mi and Oi are modelled and observed data, respectively. N isthe number of data points. The results of statistical analyses of thecomparison for deliquescent state are shown in Table 3. These

ment during both a) winter and b) summer. Concentration is overestimated by ~14% by

a)

b)

Fig. 6. Diurnal variation of observed and modelled HNO3 concentrations during a) winter and b) summer depicting a discrepancy in both diurnal trend as well as concentration.

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227 223

metrics are commonly used to evaluate a model performance(Simon et al., 2012). Comparisons between the measurements andthe model output for gas phase NH3, HNO3 and HCl yield negativevalues for MB and NMB when the model is predicting values lowerthan those observed and positive values for over-prediction. MEand NME give an estimation of overall discrepancy between pre-diction and observation. When themagnitude of NME and NMB arevery close to each other, the discrepancy is explained as a sys-tematic bias.

ISORROPIA II was able to predict the appropriate range of NH3mixing ratios (within the measurement uncertainty) during bothseasons. The correlation coefficient between observed and pre-dicted NH3 is 0.99 (Table 3) for winter and summer suggesting anexcellent agreement. NH4

þ concentrations also exhibit a similarcorrelation between modelled and observed values. Diurnal profileof NH3 (Fig. 5) demonstrates a very good agreement between pre-dicted NH3 in equilibrium with particulate matter and measuredambient NH3. The statistical comparison of measured andmodelledNH3 showed an MB of 1.47 mg m�3 and NMB and NME of 14.0 and13.8%, respectively, indicating that, in general, model is simulatingmoderately higher concentration than observed during winter witha systematic error. This systematic error of ~14% may be due to thepresence of organic acids that leads to partitioning of more NH3 toparticulate NH4

þ as their salts which are not accounted for in theseequilibrium calculations. BothMB andME for summer are the same(0.7 mg m�3) and NMB and NME are 5.9 and 6.9%, respectively. Ingeneral, NH3 and fine particulate NH4

þ exist in thermodynamicequilibrium as represented in ISORROPIA II model.

On the contrary, modelled and observed values of HNO3 exhibita poor agreement (NME of 183% for winter and 112% for summer,Table 3). NMB for winter is �58%, suggesting that on an average,

observed HNO3 concentration is higher than that predicted by themodel. The correlation coefficients for the summer and winter are0.07 and 0.24, respectively. Unlike NH3, the observed discrepancy isnot due to systematic bias. Diurnal variation of HNO3 predicted byISORROPIA II also disagrees with the observed trend (Fig. 6). Fromthe diurnal trend of modelled HNO3, it can be noted that HNO3 islargely influenced by temperature and RH conditions. A typical highconcentration during day and almost zero level at nighttime formost of the observation period is predicted by the model, butdisagree with the observations. A possible reason for thedisagreement between observed and modelled HNO3 during thecampaign could be the presence of reactive dust particles in coarsemode. HNO3 can undergo reactions with CaCO3 and NaCl in parti-cles and displace the corresponding week acids to form Ca(NO3)2 orNaNO3. These constituents exist largely in the coarse mode and isnot sampled with the AIM-IC (Cwiertny et al., 2008; Sullivan et al.,2009; Vlasenko et al., 2006, 2009; Wu and Okada, 1994). The up-take coefficients of these acids suggest that the reactions can bevery efficient. The heterogeneous uptake coefficient (g) for the re-action of HNO3 with CaCO3 ranges from 0.003 to 0.21 depending onambient RH (Liu et al., 2008). The value of g varies between 0.026and 0.2 for the reaction with NaCl, which is a function of ambientRH, particle size, and particle composition (Liu et al., 2007).Vlasenko et al. (2006) reported that, for the uptake of HNO3 onArizona Test Dust, g increases from 0.022 to 0.113 with increase inambient RH from 12% to 73%, showing that dust samples uptakeHNO3 with efficiency similar to pure CaCO3 and NaCl. Hence, theeffects of dust chemistry on the thermodynamic equilibrium andthe abundance of gas phase HNO3 over urban environment, wherealkaline dust constitutes a major fraction, can be very significant. Inthe case of NH3, most of NH4

þ exists in fine particles and existence of

Fig. 7. Difference between predicted and observed HNO3 concentrations as a function of RH and temperature during winter, indicating a dependence of model bias.

Fig. 8. Difference between predicted and observed HNO3 concentrations as a function of RH and temperature during summer, indicating model bias is independent of meteo-rological input.

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227224

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227 225

NH4NO3 is unlikely as evident from the above discussion. This leadsto achieve a thermodynamic equilibrium predictable with ISO-RROPIA II for NH3. Fountoukis et al. (2009) reported a largediscrepancy in predicting gas phase HNO3 with ISORROPPIA IIwhile excellent agreement with gas phase NH3 at Mexico withparticles less than 1 mm during MILAGRO field campaign. It ispertinent to state that, in a recent study, Schiferl et al. (2014) usedISORROPIA II for gas-aerosol partitioning in the GEOS-Chem modeland found that it is inadequate to simulate total nitrogen parti-tioning between gaseous and fine particulate phases, where sig-nificant uptake of gas phase HNO3 by coarser particles can occur.

3.4. Effect of RH and temperature on HNO3 equilibrium

Ambient temperature and relative humidity are crucial param-eters for the gas-aerosol equilibrium. During our observations, RHvaried from ~15% to ~100%, allowing to analyse the equilibriumcharacteristics for awide range of RH. During summer, temperaturewas generally above 30 �C and during winter, it is below 30 �C anddrops to ~6 �C. A conspicuous difference between bias of modelledHNO3 mixing ratios during winter and summer has been observedin our study period. Plots of (Mi � Oi) against RH and temperature(Fig. 7) show a significant correlation during winter indicating apossible dependence of model bias. On the contrary, similar plotsexhibit a scatter for summer data (Fig. 8). When the relative hu-midity is high, the observed HNO3 mixing ratio is higher than thepredicted levels. This clearly demonstrates further that theobserved HNO3 is not in thermodynamic equilibrium with themeasured fine particulate matter composition and model biaslargely depends on RH and temperature. The range of variation inRH is similar during winter as well as summer, however, the tem-perature is significantly lower during winter. Hence, it is likely thatthe temperature is more influencing the bias in model outputduring winter. These characteristics are found for metastable con-dition as well, where crystallization of NO3

� salts is not allowed at

a)

b)

Fig. 9. Temporal variation of a) observed and b) modelled HCl concentration duringwinter. Substantial levels of HCl concentration is predicted during initial few days ofobservation when the ambient concentrations were below detection limit.

lower RH for both summer and winter.Fountoukis et al. (2009) reported an inverse trend between RH

and (Mi � Oi) for particulate NH4þ as well as NO3

�. This dependenceis attributed to the hysteresis in deliquescenceeefflorescence cyclein the observed RH range, but our analysis indicates that the modeldiscrepancy in predicting HNO3 vapours is largely temperaturedependent. Bias is observed for the model runs for both stable andmetastable conditions and influenced by temperature and RH inboth seasons. Hence, it is obvious that the treatment of deli-quescenceeefflorescence properties of salts in aqueous solutions isnot the factor that causes the observed trend in difference betweenmodelled and measured HNO3 concentrations. One possible reasoncould be RH and/or temperature dependence of g for HNO3 oncoarse mode mineral dust during our observation period, which isnot possible to analyse in this study because data onmineral dust incoarse particles are not available.

3.5. HCl mixing ratios and equilibrium with particulate Cl�

The concentration of gas phase HCl during summer is less than0.03 mg m�3 for most of the observation period and not reported inthis study. In the initial few days of sampling duringwinter, HCl wasless than LOD, however, higher concentrations were recorded fromDecember 30, 2011 onwards (Fig. 9). The highest observed HClconcentration (0.75 mg m�3) was on January 10, 2012. Averageconcentration for the whole observation period is 0.15 mg m�3. HClconcentrations exhibit a distinct diurnal trend with maximumconcentration during afternoon hours and minimum duringnighttime. Trebs et al. (2004) reported ambient HCl ranging fromless than 0.15 mg m�3 to ~1.2 mg m�3 at a location in the AmazonBasin during biomass burning season. The HCl diurnal variationreported in this study also show afternoon hour maximum andnighttimeminimum. Such diurnal pattern is the typical signature ofturbulent mixing during daytime and deposition to wet surfaces athigher RH during nighttime, since HCl has a strong affinity towardswater. HCl is known to be emitted from biomass burning activitieswhich account for a large fraction of total chlorine emission(Andreae et al., 1996). Biomass burning is one of the major factorscontributing to the observed particulate matter during winter atthe sampling location as reported earlier (Rengarajan et al., 2011;Sudheer et al., 2014). Hence, the observed concentration level ofHCl may largely be associated with biomass burning emissions overAhmedabad. Another potential source is sea-salt aerosol, on whichacid displacement reaction emits a significant amount of gaseousHCl to the ambient atmosphere. For example, Kumar et al. (2012)reported widespread chloride deficit in marine aerosol samplescollected from the north eastern Arabian Sea, while the meteoro-logical conditions are conducive for transport of polluted

Fig. 10. Diurnal variation of observed and modelled HCl concentrations during winterdepicting overestimation of HCl equilibrium concentration and discrepancy in diurnalvariation.

A.K. Sudheer, R. Rengarajan / Atmospheric Environment 109 (2015) 217e227226

continental air masses to marine boundary layer. Nonetheless,during winter, wind direction is not favourable to carry air parcelfrom marine atmosphere to our sampling site as north-easterlywinds prevail during this period. Hence, HCl originating from sea-salt particles will be negligible to the observed concentrations.

ISORROPIA II model output for gas phase HCl shows a widerange of concentrations from less than 0.02 to 6.8 mg m�3, with anaverage value of 0.75 mgm�3, indicating an overall higher predictedgas phase concentration than the measured. It may also be notedthat while observed HCl is less than detection limit, high concen-trations are predicted (Fig. 9). The diurnal trend depicted in Fig. 10shows minimum modelled HCl during afternoon hours and adistinct peak at ~10:00 h which are contrary to the observations.Accordingly, the thermodynamic equilibrium condition is notapplicable for HCl partitioning with fine particle composition overthis region during our observation. Unlike HNO3, the differencebetween modelled and measured HCl concentrations does notexhibit any trend with ambient RH or temperature, suggesting thatthese parameters may not be the major controlling factor of modelbias.

Generally, the thermodynamic equilibrium model is an integralcomponent of several other studies wherein aerosol parameterssuch as liquid water content, aerosol acidity, etc. are estimated(Meskhidze et al., 2003; Bian et al., 2014). Our analysis of aerosolcomposition and related trace gas constituents indicates that asubstantial deviation from equilibrium conditions is possible in thisgeographical region. This may cause significant bias in derivedparameters of aerosol from models where thermodynamic equi-librium is assumed and needs further studies to improve repre-sentation of gas-aerosol system in models.

4. Conclusions

Simultaneous measurements of water-soluble PM2.5 inorganicionic constituents (Naþ, NH4

þ, Kþ, Mg2þ, Ca2þ, Cl�, NO3�, SO4

2�) andassociated precursor gases (NH3, HCl and HNO3) were carried outwith an hourly time resolution using AIM-IC system over an urbanlocation in western India during winter and summer. Thermody-namic equilibrium modelling of the fine particulate and relatedtrace gas data using ISORROPIA II reveals that HNO3 is not inequilibrium with fine particulate matter, whereas NH3 is in excel-lent agreement with equilibrium prediction. The deviation fromequilibrium conditions is attributed mainly to substantial amountof mineral aerosol over this region and significant amount of NO3

�

residing on coarse mode particles. Gas phase HNO3 is efficientlyscavenged by alkaline mineral dust causing the total nitrate todeviate from the equilibrium with the fine particles. HCl predictedby the equilibrium model also disagrees with the observations,suggesting that the equilibrium representation of these constitu-ents in fine mode and gas phase in the ISORROPIA II model is notrealistic over this region.

The findings reported in this study have implications on usingthermodynamic equilibrium conditions for estimating variousaerosol parameters such as liquid water content, pH, etc. Deviationfrom equilibrium conditions may cause a significant bias inchemical transport model outputs for the study region. Furtherinvestigation with extensive field observation of aerosol constitu-ents and related trace gases with similar time resolution is essentialto improve the gas-aerosol representation in models.

Acknowledgements

The AIM-IC system used in this study was procured underGEOTRACES programme funded by Ministry of Earth Sciences,Govt. of India, New Delhi.

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