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Atmos. Chem. Phys., 17, 313–326, 2017

www.atmos-chem-phys.net/17/313/2017/

doi:10.5194/acp-17-313-2017

© Author(s) 2017. CC Attribution 3.0 License.

Characteristics of brown carbon in the urban Po Valley atmosphere

Francesca Costabile1, Stefania Gilardoni2, Francesca Barnaba1, Antonio Di Ianni1, Luca Di Liberto1,

Davide Dionisi1, Maurizio Manigrasso3, Marco Paglione2, Vanes Poluzzi4, Matteo Rinaldi2, Maria Cristina Facchini2,

and Gian Paolo Gobbi1

1Institute for Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Rome, Italy

2Institute for Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Bologna, Italy

3INAIL, Rome, Italy

4ARPA ER, Bologna, Italy

Correspondence to: Francesca Costabile (f.costabile@isac.cnr.it)

Received: 30 December 2015 – Published in Atmos. Chem. Phys. Discuss.: 1 February 2016

Revised: 13 December 2016 – Accepted: 17 December 2016 – Published: 5 January 2017

Abstract. We investigate optical–microphysical–chemical

properties of brown carbon (BrC) in the urban ambient at-

mosphere of the Po Valley. In situ ground measurements of

aerosol spectral optical properties, PM1 chemical compo-

sition (HR-ToF-AMS), and particle size distributions were

carried out in Bologna. BrC was identified through its

wavelength dependence of light absorption at visible wave-

lengths, as indicated by the absorption Ångström exponent

(AAE). We found that BrC occurs in particles with a narrow

monomodal size distribution peaking in the droplet mode,

enriched in ammonium nitrate and poor in black carbon

(BC), with a strong dependance on OA-to-BC ratios, and

SSA530 of 0.98 ± 0.01. We demonstrate that specific com-

plex refractive index values (k530 = 0.017 ± 0.001) are nec-

essary in addition to a proper particle size range to match the

large AAEs measured for this BrC (AAE467−660 = 3.2 ± 0.9

with values up to 5.3). In terms of consistency of these find-

ings with literature, this study

i. provides experimental evidence of the size distribu-

tion of BrC associated with the formation of secondary

aerosol;

ii. shows that in the lower troposphere AAE increases with

increasing OA-to-BC ratios rather than with increasing

OA – contributing to sky radiometer retrieval techniques

(e.g., AERONET);

iii. extends the dependence of AAE on BC-to-OA ratios

previously observed in chamber experiments to ambi-

ent aerosol dominated by wood-burning emissions.

These findings are expected to bear important implications

for atmospheric modeling studies and remote sensing obser-

vations as regards the parametrization and identification of

BrC in the atmosphere.

1 Introduction

Aerosol has an important role in the Earth’s climate with both

direct and indirect effects; in addition, it affects air quality

and atmospheric chemistry. At present, our understanding of

the light-absorbing aerosol types is incomplete (see reviews

by Laskin et al., 2015; Moise et al., 2015). An important

absorber of solar radiation in the UV–vis region is atmo-

spheric carbonaceous aerosol (IPCC, 2013). In the classifi-

cation of its components proposed by Pöschl (2003), visible-

light-absorbing properties ranged between two extremes. On

one side, there is black carbon (BC), refractory material

that strongly absorbs light over a broad spectral range. On

the other side, there is colorless organic carbon (OC), non-

refractory material, with no absorption or little absorption in

the UV–vis spectral range. There is a gradual decrease of

thermochemical refractiveness and specific optical absorp-

tion going from BC graphite-like structures to non-refractive

and colorless OC (Laskin et al., 2015). A broad range of col-

ored organic compounds has recently emerged in the scien-

tific literature for their possible role in the Earth’s radiative

transfer and therefore its climate (Laskin et al., 2015; Moise

et al., 2015).

Published by Copernicus Publications on behalf of the European Geosciences Union.

314

F. Costabile et al.: Brown carbon in the Po Valley atmosphere

The term brown carbon (BrC) has emerged to describe

this aerosol with an absorption spectrum smoothly increas-

ing from the visible (vis) to the near-UV wavelengths, with

a strong wavelength dependance of the light absorption co-

efficient (λ−2 − λ−6) (Andreae and Gelencsér, 2006; Moos-

müller et al., 2011; Bond et al., 2013; Laskin et al., 2015;

Moise et al., 2015). BrC lacks a formal analytical defini-

tion (Bond et al., 2013). In this study, we will identify BrC

through its high values (2–6) of the absorption Ångström ex-

ponent (AAE), a parameter describing the wavelength (λ) de-

pendent absorption coefficient (σa) of light by aerosol, writ-

ten as

AAE(λ) = −

dln(σa)

dln(λ)

.

(1)

What is known about BrC aerosol is that it is organic

matter with both primary and secondary sources (Laskin

et al., 2015). Primary BrC can be emitted together with BC

from low-temperature combustion processes, like wood com-

bustion (Andreae and Gelencsér, 2006). Secondary organic

aerosol (SOA) formed in the atmosphere also contributes to

light-absorbing-carbon (Moise et al., 2015, and references

therein), but only a few field measurement studies have ana-

lyzed the BrC associated with SOA (Zhang et al., 2011; Saleh

et al., 2013; Zhang et al., 2013; Gilardoni et al., 2016).

Numerous evidences indicate increased absorption to-

wards UV for aerosol particles high in nitrate (e.g., Jacobson,

1999; Zhang et al., 2013), sulfate (Lee et al., 2013; Song et

al., 2013; Powelson et al., 2014; Lin et al., 2014), and ammo-

nium (Shapiro et al., 2009; Noziere et al., 2009; Bones et al.,

2010; Noziere et al., 2010; Sareen et al., 2010; Updyke et al.,

2012; Flores et al., 2014; Lin et al., 2015). Lin et al. (2014)

reported the formation of light-absorbing SOA constituents

from reactive uptake of isoprene epoxydiols onto preexisting

acidified sulfate seed aerosol as a potential source of sec-

ondary BrC under tropospheric conditions. Powelson et al.

(2014) discussed BrC formation by aqueous-phase carbonyl

compound reactions with amines and ammonium sulfate. Lee

et al. (2013) studied the likely but uncertain effect of sulfate

on the formation of light-absorbing materials and organo-

nitrogen via aqueous glyoxal chemistry in aerosol particles.

Song et al. (2013) observed significant light absorption at

355 and 405nm for SOA formed from an α-pinene + O3

+ NO3 system only in the presence of highly acidic sulfate

seed aerosols under dry conditions. Several studies demon-

strated the importance of ammonium, both as a catalyst and

as a reactant, in the formation of light-absorbing products

(Powelson et al., 2014; Laskin et al., 2015). SOA formation

can occur in both the gas and condensed phases. Recently, ef-

ficient SOA production has been recognized in cloud and fog

drops and water-containing aerosol: water-soluble products

of gas-phase photochemical reactions may dissolve into an

aerosol aqueous phase and form SOA through further oxida-

tion, this SOA being referred to as “aqSOA” (Ervens et al.,

2011; Laskin et al., 2015). AqSOA formation impacts total

SOA mass and aerosol size distributions by adding mass to

the so-called “droplet mode” (Ervens et al., 2011). Meng and

Seinfield (1994) showed that the aerosol “droplet mode” in

urban areas is the result of activation of smaller particles

to form fog, followed by aqueous-phase chemistry and fog

evaporation. It was demonstrated that aqSOA formation can

affect aerosol optical properties by adding light-absorbing

organic material at UV wavelengths (Shapiro et al., 2009;

Ervens et al., 2011; Gilardoni et al., 2016). Gilardoni et al.

(2016) demonstrated that in the ambient atmosphere the aq-

SOA from biomass burning contributes to the BrC budget

and exhibits light absorption wavelength dependence close to

the upper bound of the values observed in laboratory experi-

ments for fresh and processed biomass-burning emissions.

Despite the efforts made, relations between optical prop-

erties and chemical composition of organic compounds with

spectrally variable light absorption (high AAE) are poorly

understood (Laskin et al., 2015). A number of previous

works (Shinozuka et al., 2009; Russell et al., 2010; Arola

et al., 2011) studied how the organic aerosol (OA) mass frac-

tion (fOA) relates to AAE and to single scattering albedo

(SSA), the ratio of scattering to extinction, a key parame-

ter in understanding aerosol warming or cooling effect. Re-

sults from in situ measurements on the C-130 aircraft (Cen-

tral Mexico) during MILAGRO (Russell et al., 2010) showed

that both organics and dust increase AAE values. Russell

et al. (2010) showed a direct correlation between AAE and

fOA. On the basis of the same data, Shinozuka et al. (2009)

showed that AAE generally increases as fOA or SSA in-

creases. Saleh et al. (2014) burnt a selection of biomasses in

a combustion chamber, varying the combustion parameters

to obtain a range of BC-to-OA ratios. This ratio, the relative

proportions of BC and OA mass, depends on fire character-

istics and plume age, and it determines aerosol color from

black to brown to white as the ratio decreases. Saleh et al.

(2014) findings link the extent of absorbance to the BC-to-

OA ratio for aged and fresh biomass-burning aerosols. If

confirmed, this link has the potential to be a strong predic-

tive tool for light-absorbing properties of biomass-burning

aerosols (Bellouin, 2014; Moise et al., 2015). Following the

approach of Saleh et al. (2014), Lu et al. (2015) reviewed

available emission measurements of biomass-burning and

biofuel combustion and found similar results indicating that

AAE of the bulk OA decreases with the increasing BC-to-

OA ratio. They conclude that the absorptive properties of OA

from biomass/biofuel burning depend strongly on burning

conditions and weakly on fuel types and atmospheric pro-

cessing.

In this study, we investigate optical–microphysical–

chemical properties of BrC in the ambient urban atmosphere.

In situ ground ambient data of chemical (high-resolution

time-of-flight aerosol mass spectrometer, HR-ToF-AMS),

optical (3λ nephelometer and 3λ particle soot absorption

photometer, PSAP), and microphysical (SMPS and APS)

aerosol properties were taken during two field measurements

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F. Costabile et al.: Brown carbon in the Po Valley atmosphere

315

in Bologna, Po Valley. BrC is identified through the AAE

of the non-dust bulk aerosol. First (Sect. 4.1), we investigate

BrC properties by relating the AAE to the aerosol size, and in

particular to the aerosol types with known size distribution –

e.g., the droplet mode. We match AAE patterns measured for

BrC to those theoretically expected for BrC in the ambient

aerosol (based on the Mie theory). The AAE and aerosol size

are then related to PM1 major chemical components (nitrate,

OA, BC, sulfate, and ammonium) and to the BC-to-OA ratio.

Then, we show a case study to illustrate the major features of

BrC (Sect. 4.3). Finally, findings are discussed in comparison

with the literature to explore their general validity (Sect. 4.4).

2 Experimental

Optical, chemical, and microphysical aerosol properties were

measured, in the framework of the Supersite project funded

by the Emilia-Romagna region, at the urban background site

of Bologna (44◦31 29 lat, 11◦20 27 long), in the Po Valley

(Italy). Two measurement campaigns lasting 1 month were

carried out: 22 October–13 November 2012 (fall campaign)

and 1–27 February 2013 (winter campaign). Measurement

setup is described below.

2.1 Measurement cabins and sampling lines

Equipment was set up in two different cabins, located side

by side. Optical properties and coarse fraction size distribu-

tions were measured in the same cabin, all the instruments

set up on the same inlet system equipped with a PM10 head.

In the cabin, external air was pumped into a stainless steel

tube (length of 4.0 m) by an external pump ensuring a lam-

inar flow (Reynolds number < 2000). The cabin was kept at

a temperature of 20–25 ◦C. The difference between air tem-

perature and dew point was enough to dry the sampled air.

Chemical properties and fine and ultrafine particle number

size distribution (PNSD) were measured through a separate

stainless steel inlet tube equipped with a PM1 head.

2.2 Optical measurements

Spectral optical properties in the visible range were mea-

sured online with 5 min time resolution. Dry aerosol absorp-

tion coefficients, σa(λ), at three wavelengths (λ = 467, 530,

660 nm) were measured by a three-wavelength PSAP (Radi-

ance Research), together with dry aerosol scattering coeffi-

cients (σs(λ)) at 450, 525, and 635 nm, measured by an inte-

grating nephelometer (Ecotech, mod. Aurora 3000). Like all

filter-based methods, PSAP suffers from a number of mea-

surement artifacts, including an overestimate of absorption

due to light scattering effects, and a dependence of mea-

surements on filter transmittance (Tr) (Lack et al., 2008;

Virkkula, 2010; Bond et al., 2013; Backman et al., 2014).

We corrected raw PSAP data after the iterative procedure de-

scribed by Virkkula (2010), where only data with Tr > 0.7

were retained. The wavelength-resolved σs(λ) necessary to

correct PSAP raw data were taken from nephelometer data

corrected for truncation (Anderson and Ogren, 1998; Bond,

2001; Müller et al., 2011). The scattering error after the trun-

cation error correction is

δ(σs)

σs

= 0.02 (Bond et al., 2013).

The uncertainty of σa(λ) derived from PSAP data after these

corrections has been estimated to be

δ(σa)

σa

= 0.2 (Virkkula,

2010; Lack et al., 2008; Bond et al., 1999; Virkkula, 2010;

Cappa et al., 2008). The PSAP-derived σa(λ) can be consid-

ered an upper limit of the “true” value (Subramanian et al.,

2007; Lack et al., 2008).

After all corrections, data were checked (by visual inspec-

tion) to find any outlier/low values that could significantly

influence data statistics. These values could be due to vari-

ability in the measurements or to experimental errors. Ac-

cording to manufacturers, (i) PSAP sensitivity is < 1 Mm−1

and measurement range is 0–50 Mm−1, and (ii) the lower de-

tectable limit of the nephelometer is 0.3Mm−1, with cali-

bration tolerance of ±4 Mm−1 and measurement range 0–

2000 Mm−1. A few data (124 records with σa < 1 Mm−1,

less than 20 records with σs < 10 Mm−1, and some points

with σs > 700 Mm−1) were discarded, as they were consid-

ered dubious values (comparing to data variability during the

field campaigns, illustrated in Fig. S1 of the Supplement).

2.3 Chemical measurements

Chemical composition of atmospheric aerosol particles were

characterized online with an HR-ToF-AMS (Aerodyne Re-

search Inc., Billerica) (DeCarlo et al., 2006). The HR-ToF-

AMS measured the chemical composition of non-refractory

PM1 (nr-PM1), i.e., sulfate, nitrate, ammonium, chloride, and

organic aerosol. The instrument alternated acquisition in V

mode (higher sensitivity and lower mass spectral resolution)

and W mode (lower sensitivity and higher mass spectral res-

olution) every 2.5min. Quantitative information discussed

here corresponds to the data collected in V mode. While op-

erating in V mode, the instrument measures particle size dis-

tribution based on time of flight (Jimenez et al., 2003). HR-

ToF-AMS data were analyzed using SQUIRRELL v1.51 and

PIKA v1.10 software (D. Sueper, University of Colorado,

Boulder, CO, USA) within Igor Pro 6.2.1 (WaveMetrics,

Lake Oswego, OR). Collection efficiency was calculated ac-

cording to Middlebrook et al. (2012) based on aerosol chem-

ical composition and relative humidity. Data validation was

performed by comparison with offline measurements of sul-

fate, ammonium, and nitrate concentrations in PM1 aerosol

samples. The HR-ToF-AMS aerosol sample line was dried

below 40 % RH with a Nafion drier. The uncertainty of the

AMS-derived OA was assumed to be

δ(OA)

OA

= 0.2 according

to Quinn (2008).

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F. Costabile et al.: Brown carbon in the Po Valley atmosphere

2.4 Particle number size distributions

PNSDs were measured by combining a commercial scan-

ning mobility particle sizer (SMPS, TSI model 3080, with

long-DMA, TSI model 3081, equipped with a water-based

condensation particle counter, CPC, TSI model 3787) and

a commercial aerodynamic particle sizer (APS, TSI). Parti-

cles from 14 to 750 nm of mobility diameter were sized and

counted by the SMPS; particles from 0.5 to 20 µm of aero-

dynamic diameter were sized and counted by the APS (the

procedure to fit the two PNSDs is described in Sect. 3.2).

SMPS data were corrected for penetration errors through the

sampling line, penetration efficiency due to diffusion losses

(calculated according to Hinds, 1999) being higher than 98 %

for particles bigger than 14 nm. An impactor (50 % cutoff di-

ameter = 0.677 µm) was used to remove larger particles from

the SMPS sampling line.

3 Data analysis

Data measured by all the instruments were merged in a single

5 min time resolution dataset. This dataset includes the time

series of the following variables: σa(λ) and σs(λ), OA, NO

−

3

,

SO2

4

−, NH+

4

, and PNSD. Raw data were subjected to vari-

ous “cleaning” processes as described in Sect. 2 and then an-

alyzed as described in this section. The time series subjected

to data analysis includes 11 910 time points covering 40 days

of measurements (5317 time points in the fall and 5650 time

points in the winter). This time series includes missing val-

ues of some variables, related to instrument failures or data

filtering as described above. The length of the complete time

series (i.e., with no missing value) varies from variable to

variable (10 897 time points for OA, 10 361 time points for

NO−

3

, 8999 time points for SO2

4

−, 9677 time points for NH+

4

,

2656 time points for the PNSD, 2367 time points for AAE,

SSA, and BC, 1820 time points for fBC, fOA, fNO3 , fSO4 ,

fNH4 , and OA-to-BC). Overall, about 1500 data points have

the complete set of coincident measurements of all the vari-

ables addressed.

3.1 Inference of the optical BC mass concentration

The wavelength (λ) dependent BC absorption coefficient

(σaBC(λ)) and equivalent BC mass concentration were cal-

culated using the AAEBC attribution method. The measured

absorption coefficient at 660nm (σa(660)) was used to de-

rive σaBC(530) and then the BC mass concentration, assum-

ing (i) BC is the only light-absorbing species at 600nm,

(ii) a known value of AAEBC at 530–660nm (see below),

and (iii) a BC mass absorption efficiency at 530nm of

10 m2 g−1 (as indicated by PSAP manufacturer). In litera-

ture, AAEBC = 1 is a commonly used value for both exter-

nally mixed BC and internally mixed BC. In fact, AAEBC for

externally mixed BC was predicted to be 1 for particles with

diameter < 50 nm (e.g., Bergstrom et al., 2002; Moosmüller

et al., 2011) but can range from 0.8 to 1.1 for particle diame-

ters of 50–200 nm (Gyawali et al., 2009). For ambient parti-

cles, which can be internally or externally mixed, AAEBC at

visible wavelengths has often been observed to be larger than

1 (Lack and Langridge, 2013; Shinozuka et al., 2009, and ref-

erences therein). Theoretical calculations have shown that the

AAEBC for internally mixed BC can vary from 0.55 (e.g., Ba-

hadur et al., 2012) to an upper limit of ∼ 1.7 (e.g., Lack et al.,

2008) depending on particle size, coating, core, and wave-

length. In Fig. S2 of the Supplement we show numerically

simulated (Mie theory) values of AAE(dp,λ,m) resolved by

particle diameter (dp) and complex refractive index (m(λ))

at visible wavelengths (λ) for BC (Sect. 3.4). It is shown that

the AAE of BC tends to 1 for the smaller BC particles only

and can differ significantly from 1 for the larger BC parti-

cles. Based on these results and on previous works, we de-

cided to use AAEBC = 1.1. The uncertainty δ(AAEBC) was

set to 22% (

δ(AAEBC)

AAEBC

= 0.22) according to Lack and Lan-

gridge (2013). BC uncertainty (δ(BC)) was derived propa-

gating this δ(AAEBC) together with the uncertainty of PSAP-

derived σa (see Sect. 2.2).

We discarded data possibly affected by desert dust

(43 records over 5 days of measurements) to ensure that the

equivalent BC mass concentration is not affected by desert

dust contamination (assumption (i) above). Dust-free aerosol

conditions were identified based on the analysis of aerosol

spectral optical properties, increasingly applied to gather in-

formation on aerosol type (e.g., Bergstrom et al., 2002; Shi-

nozuka et al., 2009; Russell et al., 2010; Arola et al., 2011;

Costabile et al., 2013). In particular, we followed the method-

ology proposed by Costabile et al. (2013) which identifies the

aerosol dominated by dust by a distinctive combination of

scattering and absorption Ångström exponents (SAE, AAE)

and SSA spectral variation. Data points of the time series

fulfilling this distinctive combination (indicated in Table 3 in

Costabile et al., 2013) were excluded from the analysis.

3.2 Fitting procedure for the PNSD

PNSDs were measured by two different instruments (SMPS

and APS, Sect. 2). These data were merged to obtain one

PNSD based on particle electrical mobility diameters (dm)

ranging from 14 nm to 14 µm. PNSDs measured by APS are

based on aerodynamic diameters (da); these data were con-

verted to PNSDs based on dm according to Eq. (2) (Khlystov

et al., 2004; Seinfeld and Pandis, 2006):

dm = χ

Cc(dm)

Cc(da)

da

(

ρp

ρ0χ

)1/2

,

(2)

where χ is the shape factor, Cc(dm) and Cc(da) are the slip

correction factors based on dm and da, respectively, ρp is the

particle density, and ρ0 is the unit density (1 g cm−3). In ap-

plying Eq. (2) to convert APS data, we assumed that dm rep-

resents the true particle diameter, Cc(dm) = 1 and Cc(da) = 1

Atmos. Chem. Phys., 17, 313–326, 2017

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F. Costabile et al.: Brown carbon in the Po Valley atmosphere

317

(continuum regime), χ = 1 (spherical particles), and ρp con-

tinuously varies from 1.6 to 2 g cm−3.

PNSDs (i.e., nN (log10dm) =

dN

dlog10(dm)

) measured by

SMPS and APS (PNSDSMPS, PNSDAPS) overlap for dm

ranging from 460 to 593 nm. In this size range, PNSDSMPS

and PNSDAPS were replaced by PNSDfitted. PNSDfitted was

assumed to vary according to a power-law function (Junge

size distribution) (Khlystov et al., 2004; Seinfeld and Pandis,

2006) (Eq. 3):

nN (log10dm) =

c

dα

m

.

(3)

The coefficients c and α were calculated by an iterative

procedure: (i) c was randomly initialized from 0 to 1000 and

(ii) α was calculated by Eq. (3) constraining values from 2 to

5, as typically found for atmospheric aerosols (Seinfeld and

Pandis, 2006). PNSDfitted replaced PNSDAPS and PNSDSMPS

when their relative difference (δ(PNSD), Eq. 4),

δ(PNSD) =

|PNSDSMPS − PNSDAPS|

max[PNSDSMPS,PNSDAPS]

,

(4)

was larger than 0.1cm−3. This procedure was considered

acceptable if (i) the minimum mean squared error between

PNSDfitted and PNSDAPS was less than 1% or (ii) correla-

tion coefficients between PNSDfitted and PNSDSMPS and be-

tween PNSDfitted and PNSDAPS were larger than 0.8 (98 of

the records did not verify these conditions and were checked

by visual inspection: 94 of them were discarded and 4 ac-

cepted). The final dataset contained PNSD data based on dm

from 14.1 to 429.4 nm measured by the SMPS, from 446.1

to 699nm generated by the fitting procedure, and from 0.7

to 14µm measured by the APS. The particle surface size

distribution (PSSD, i.e., nS(log10dm) =

dS

dlog10(dm)

) and par-

ticle volume size distribution (PVSD, i.e., nV (log10dm) =

dV

dlog10(dm)

) were calculated from this PNSD under the hy-

potheses of spherical particles (Hinds, 1999; Seinfeld and

Pandis, 2006).

3.3 Principal component analysis (PCA) of PNSD

PNSDs were statistically analyzed through PCA to iden-

tify key aerosol types with known modality. The relevant

methodology was described in a previous study by Costabile

et al. (2009). In short, principal components (PCs) retained

in the analysis were arranged in decreasing order of vari-

ance explained ( k, called eigenvalue of PCk), PC1 being the

component explaining the largest k. The kth eigenvector is

composed of scalar coefficients describing the new PCk as a

linear combination of the original variables (the original vari-

ables are the time series of dN/ dlog(dp)). Factor loadings of

PCk represent the relative weight of the original variables in

the PCk re-scaled. Loadings thus show the “mode” of the

PNSD associated with the PCs. Factor scores of PCk repre-

sent the time series of PCk values in the new coordinates of

the space defined by the PCs. Scores thus represent the PCk

values in the time series of the original variables.

PCA retained three principal components (PC1–PC3) ex-

plaining approximately 80 % of the variance. Factor loadings

and diurnal cycles of scores for PC1–PC3 are illustrated in

Fig. S3 of the Supplement, while Pearson’s correlation co-

efficients r between these PCs and the other variables mea-

sured are shown in Table 1. Tables 1 and S2 of the Supple-

ment show relevant r values in the winter and in the fall and

relevant Bonferroni adjusted probabilities (p values), respec-

tively. These PCs were interpreted as follows:

– PC1 is the largest component in terms of variance ex-

plained (51%). Loadings peak in the 80–300nm size

range. Factor scores correlate to OA and BC. Weekly

diurnal cycles of these scores are higher on working

days and in the road-traffic rush hour. This PC repre-

sents the aerosol enriched in OA originating in the traf-

fic rush hour in the urban area due to local emissions

(e.g., Costabile et al., 2009; Brines et al., 2015, and ref-

erences therein).

– PC2 explains 14% of the variance. Loadings peak in

the ultrafine size range (approx. 100 nm). Factor scores

show diurnal cycles higher at night and in the winter,

with a slightly larger contribution during the weekends.

It correlates (inversely) to aerosol size and (directly) to

fOA; in the winter it correlates to both fOA and fBC

(0.40, p < 0.001; Table S1 of the Supplement). This PC

should represent the nocturnal urban aerosol related to

residential heating emissions.

– PC3 explains 13% of the variance. Loadings peak in

the larger accumulation-mode size range (from 0.3 to

1µm). Diurnal cycles of scores are higher in daytime.

It is inversely correlated to fBC and directly to nitrate,

sulfate, and ammonium. This PC represents the droplet-

mode aerosol poor in BC, previously found to origi-

nate in fog droplets, cloud droplets, and wet aerosol

particles, due to aqueous-phase processing (John, 1990;

Meng and Seinfield, 1994; Seinfeld and Pandis, 2006;

Ervens et al., 2011).

3.4 Numerical simulations of AAE

Values of AAE(dp,λ,m) resolved by diameter dp, radia-

tion wavelength (λ), and complex refractive index (m(λ) =

n(λ) − ik(λ)) were numerically simulated according to Mie

theory (e.g., Bohren and Huffman, 1983; Moosmüller et al.,

2011). The aim was to reproduce patterns expected for BrC,

BC, and the urban background aerosol impacted by biomass-

burning emissions, as these were abundant in the study area.

Simulations are illustrated in Fig. S2 of the Supplement, the

relevant methodology being described in a previous study by

Costabile et al. (2013).

To simulate patterns expected for BrC in the urban ambi-

ent aerosol, we used λ dependent complex values of m(λ) in-

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318

F. Costabile et al.: Brown carbon in the Po Valley atmosphere

Table 1. Statistically significant (p < 0.001) Pearson’s correlation coefficients (r) between absorption Ångström exponent (AAE) at 467–

660nm; scores of major aerosol types identified by PCA (PC1 is the road-traffic-related aerosol, PC2 is the residential heating related

aerosol, and PC3 is the droplet-mode aerosol); mass concentration and mass fractions (fx) of black carbon (BC), organics (OA), nitrate

(NO

−

3

), sulfate (SO

2−

4

), and ammonium (NH

+

4

); optically relevant aerosol size representative of the entire aerosol population (calculated as

median mobility diameter of the particle surface size distribution, dmed(S)); OA-to-BC ratios (see Tables S1 and S2 of the Supplement for

additional relevant values).

r

AAE

dmed(S)

BC

fBC

OA

fOA

OA-to-BC

NO3

fNO3

SO4

fSO4

NH4

fNH4

dmed(S)

0.60

1

−0.24

−0.38

–

−0.68

0.37

0.48

0.50

0.30

0.25

0.49

0.69

AAE

1

0.60

−0.26

−0.52

0.40

−0.34

0.78

0.60

0.40

0.67

0.18

0.65

0.44

PC1

–

–

0.56

–

0.83

–

–

0.52

–

–

–

0.52

–

PC2

–

−0.52

–

–

–

0.31

–

−0.19

–

–

–

−0.20

−0.27

PC3

0.66

0.60

−0.38

−0.53

0.12

−0.52

0.54

0.46

0.50

0.45

–

0.49

0.58

ferred during CAPMEX for an air mass with AAE405−532 =

3.8 (standard deviation = 3.4), characterized by high OC

to sulfate (SO

2−

4

) ratio and high nitrate (NO−

3

) to SO

2−

4

ratio (Flowers et al., 2010; Moise et al., 2015). These

are m467 = 1.492–0.026i, m530 = 1.492–0.017i, and m660 =

1.492–0.014i, the uncertainty for n(λ) and k(λ) being set to

±0.01 and ±0.001, respectively.

To simulate patterns expected for BC we used λ inde-

pendent complex values of m estimated by Alexander et al.

(2008) for soot carbon particles at 550 nm: n = 1.95–0.79i at

λ = 467, 530, 660 nm. Note in Fig. S2 of the Supplement the

resulting variability with dp of AAE467−660 for BC: values of

AAE = 1 are only obtained for dp ≪ 100 nm.

To simulate patterns expected for the urban background

aerosol impacted by biomass-burning emissions we used val-

ues of m(λ) inferred in a previous study for the smaller

accumulation-mode particles enriched in BC from biomass-

burning smoke (Costabile et al., 2013): m467 = 1.512–

0.027i, m530 = 1.510–0.021i, and m660 = 1.511–0.022i, the

uncertainty for n(λ) and k(λ) being set to ±0.01 and ±0.001,

respectively.

4 Results and discussion

In this section we first identify BrC and characterize its

optical–microphysical–chemical properties (Sect. 4.1), then

illustrate a case study (Sect. 4.2), and finally discuss the find-

ings in comparison with the literature (Sect. 4.3).

4.1 Brown carbon: identification and features

Several literature studies identify BrC based on the high

AAE values in the bulk aerosol, i.e., from 2 to 6 (e.g., An-

dreae and Gelencsér, 2006; Bond et al., 2013). At a certain

range of wavelengths (λ), these high AAE values depend

on several factors, including aerosol size, chemical compo-

sition, and aerosol mixing state. First, we analyzed the de-

pendence of AAE on aerosol size. We used two different

approaches. We calculated the median mobility diameter of

the PSSD (Sect. 3.2) – dmed(S) – to obtain the optically rele-

vant aerosol size representative of the entire particle popula-

tion. We then analyzed the PNSD to find major aerosol types

(i.e., PCs; Sect. 3.3). We found two components (PC1 and

PC2) related to smaller particles and originating from local

emissions (road traffic and residential heating, respectively),

and one component (PC3) related to larger particles (droplet

mode) originating from the aerosol processing. Second, we

analyzed the relation between AAE, these PCs, and dmed(S)

and related these to PM1 major constituents. Relevant sta-

tistically significant Pearson’s correlation coefficients (r) are

shown in Table 1, while r values observed in the winter and

in the fall and the matrix of Bonferroni probabilities (p) as-

sociated with these r values are shown in Tables S1 and S2

of the Supplement.

The AAE correlates well with the dmed(S) of the aerosol

population (r = 0.60, p < 0.001). In Fig. 1 we analyze this

relation by comparing field measurements to numerical sim-

ulations results (these simulations are based on the Mie the-

ory and are described in detail in Sect. 3.4). Measurements

show that the AAE increases with the increase of the dmed(S)

(grey markers). We show the AAE and dmed(S) representative

of the three different aerosol types identified (PC1, PC2, and

PC3). To interpret these measurements we add patterns theo-

retically expected (through numerical simulations) for three

aerosol types: BrC in the ambient aerosol (brown line), a pure

BC particle (black line), and an urban background aerosol

impacted by wood-burning emissions (grey line). The lowest

AAEs measured in the present study tend to values expected

for pure BC (black line), but they are similar to values calcu-

lated for the urban background aerosol impacted by wood-

burning emissions (grey line) – both aerosol types which

were related to local emissions (PC1 and PC2) match these

patterns. Larger AAEs (3.2 ± 0.9) correspond to the droplet-

mode aerosol (PC3) and are similar to the AAE expected for

BrC (brown line). Note that Fig. 1 suggests that the threshold

value of AAE467−660 distinguishing BrC is AAE > 2.3.

The dotted black line in Fig. 1 represents the best fit to

all the data points of the measurements. This line shows

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F. Costabile et al.: Brown carbon in the Po Valley atmosphere

319

Figure 1. Experimentally measured and numerically simulated

(Mie theory) relation between absorption Ångström exponent

(AAE) at 467–660 nm and aerosol size (d). For measurements, (i) d

is represented by the mobility median diameter of the PM10 particle

surface size distribution (dmed(S)); (ii) all the data measured are in-

dicated by light grey markers, the dotted black line representing the

best fit to these data; (iii) major aerosol types identified (Sect. 3.3)

are indicated by darker markers (median ± standard deviation). For

numerical simulations (Sect. 3.4), patterns theoretically expected

for BrC, BC, and urban biomass burning are indicated by brown,

black, and grey thick lines, respectively, the dotted thinner lines in-

dicating the uncertainty of the refractive index (m(λ) = n(λ)−ik(λ))

set to ±0.01 for n(λ) and ±0.001 for k(λ)).

the increase of the AAE with the increase of the dmed(S)

in the measurements. To interpret this pattern, we note that

the brown and grey lines (referring to the simulations of

BrC and of the wood-burning-related aerosol, respectively)

show the theoretical increase of the AAE with increasing

only the aerosol size – i.e., when m(λ) is constant. Com-

paring these three lines, it is evident that specific values

of m(λ) are necessary in addition to a proper particle size

range to match the large AAE measured for the droplet-

mode BrC aerosol. These values are k(467) = 0.026 ± 0.001,

k(530) = 0.017 ± 0.001, k(660) = 0.014 ± 0.001, and n467 =

1.47 ± 0.01 (Sect. 3.4). These are λ dependent values con-

sistent with BrC in the ambient atmosphere (Moise et al.,

2015) in an air mass with high OC to SO

2−

4

ratio and NO−

3

Figure 2. Physicochemical features of brown carbon. Absorption

Ångström exponent at 467–660nm (AAE) plotted against mass

fractions (fx) of (a) organic aerosol (OA), (b) black carbon (BC),

(c) sulfate (SO

2−

4

), (d) nitrate (NO

−

3

), and (e) ammonium (NH

+

4

).

Grey markers show the longest available time series for AAE and

fx, while marker color and size indicate data points for which the

droplet-mode aerosol scores and dmed(S) were available (Sect. 3).

Data indicated by dark grey “o” show case study values illustrated

in Fig. 4.

to SO

2−

4

ratio (Flowers et al., 2010). This finding emphasizes

that BrC properties depend on both aerosol size distribution

and chemical composition (i.e., m(λ)).

Figure 2 investigates the link between chemical, micro-

physical, and optical properties. AAE is plotted against

PM1 major chemical components (OA, BC, nitrate, sul-

fates, and ammonium PM1 mass fractions, fx). Grey mark-

ers show the longest available time series for AAE and

fx, while colored markers show the data points for which

the droplet-mode aerosol scores (marker color) and the

dmed(S) (markers size) were available (see Sect. 3). The BrC

aerosol population (identified by AAE > 2.3) shows higher

fNO3

and fNH4

and lower fBC values coupled to larger

dmed(S) and high droplet-mode aerosol scores. Relevant av-

erage values are as follows (mean ± standard deviation):

AAE = 3.2 ± 0.9, fNO3= 0.38 ± 0.05, fBC = 0.01 ± 0.01,

fOA = 0.35 ± 0.04, fSO4= 0.1 ± 0.02, fNH4= 0.14 ± 0.01,

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320

F. Costabile et al.: Brown carbon in the Po Valley atmosphere

and SSA530 = 0.98 ± 0.01 (and σa467 = 7.6 ± 3.33 Mm−1,

σs467 = 312 ± 64 Mm−1, and SAE = 0.5 ± 0.4). The relation

between AAE and SSA will be further analyzed in Fig. 5 (in

Fig. S4 of the Supplement we show the relation between the

droplet-mode aerosol and SSA).

Both Fig. 2 and Table 1 show no direct correlation be-

tween AAE and fOA. This may be explained by the fact

that AAE correlates with larger particles (larger dmed(S)) of

the droplet mode (larger PC3 scores), while the fOA cor-

relates with smaller particles (lower dmed(S)) from residen-

tial heating emissions (larger PC2 scores). There is instead a

significant correlation between AAE and the ratio of OA to

BC (r = 0.78, p < 0.001), a variable indicating either com-

bustion characteristics (higher for biofuels than for fossil

fuel combustion) or aerosol ageing (lower for fresh aerosols)

(Saleh et al., 2014; Bond et al., 2013). The increase of the

AAE with the decrease of the BC-to-OA ratios is illustrated

in Fig. 3. Light-grey markers show the longest available time

series for AAE and BC-to-OA ratios, while colored markers

correspond to data points for which the droplet-mode aerosol

scores and fNO3 were available. The relation observed be-

tween AAE and BC-to-OA is an average aerosol property

(i.e., for both BC and OA) as suggested by the parametriza-

tion of AAE as a function of the BC-to-OA ratio (inner panel

of Fig. 3) consistent with Lu et al. (2015) (see Sect. 4.3).

The grey line (referring to all the data points) shows the

parametrization for the bulk aerosol. The black line (refer-

ring to the subset of all the data with PC3 scores > 0) shows

the parametrization for BrC. We note a larger dependence of

AAE on the BC-to-OA ratio for BrC (r = −0.86, p < 0.001)

– as opposed to the bulk aerosol (r = −0.7, p < 0.001).

However, this can be due to the fact that at low BC-to-OA

ratios OA has more weight in dictating AAE.

In Fig. 3 AAE plateaus at values higher than 1 for large

BC-to-OA ratios. We acknowledge that BC and AAE derived

from PSAP measurements are affected by uncertainty related

to the assumption employed to convert raw PSAP measure-

ments to absorption coefficients, which is about ±40 % based

on literature studies (Lack et al., 2008; Nakayama et al.,

2010; Lack and Langridge, 2013; Bond et al., 2013; Back-

man et al., 2014). This uncertainty could in principle recon-

cile our plateau AAE values higher than 1 with the common

assumption that AAE tends to 1 when the aerosol is domi-

nated by BC. Nevertheless, we modeled AAE as a function

of aerosol chemical and microphysical properties (Fig. 1).

Such simulation clearly shows that AAE close to 1 can be ob-

tained for aerosol population dominated by fresh fossil fuel

combustion emissions, with diameter centered below 20 nm.

Conversely the size distribution of aerosol population inves-

tigated in this study is centered around 80–300 nm (Fig. 1).

In addition, source apportionment studies performed in the

Po valley show that carbonaceous aerosol, both in urban and

rural sites, is strongly affected by wood-burning emissions

(Gilardoni et al., 2011; Larsen et al., 2012; Gilardoni et al.,

2016). It follows that, even considering the possible bias due

Figure 3. Dependence of AAE on BC-to-OA ratios. Grey markers

show the longest available time series for AAE at 467–660 nm and

BC-to-OA ratios, while marker color and size indicate data points

for which the droplet-mode aerosol scores and fNO3 were avail-

able (Sect. 3). Median values (grey squares) and relevant data un-

certainty are indicated at the upper, mean, and lower AAE bins.

Data indicated by dark grey “o” show case study values illustrated

in Fig. 4. Inner panel: best fit lines with relevant equation and

Pearson’s correlation coefficients (r,p) to all the data measured

(grey line) and droplet-mode BrC data (black line) (as indicated by

droplet-mode aerosol score > 0).

to the experimental uncertainty, the AAE plateauing at values

higher than 1 for large BC-to-OA ratios is still is in agree-

ment with model results and source apportionment data.

Taken together, these findings prove that BrC in the ob-

served ambient aerosol shows AAE467−660 = 3.2 ± 0.9 with

k(530) = 0.017 ± 0.001 and occurs in particles in the droplet-

mode size range, enriched in ammonium nitrate and poor in

BC, with a strong dependance on OA-to-BC ratios, SSA530

being 0.98 ± 0.01. In particular, when the bulk aerosol is

dominated by the BrC droplet-mode particles, the AAE is

greater than 2.3 ± 1 (median ± uncertainty), and BC-to-OA

ratios are lower than 0.05 ± 0.03.

4.2 A case study

We present here a case study (Fig. 4) to show the main micro-

physical and chemical features of the BrC aerosol observed.

On the case study day (i.e., 1 February 2013) the rela-

tive humidity was high (97.5 ± 0.4 %, against a mean value

for the winter campaign of 82 ± 14%, and a maximum of

98%), temperature averaged 2.8 ± 0.0 ◦C (campaign mean

value = 3.5 ± 2.8 ◦C), and aerosol liquid water content was

above 200 µg m−3 (Gilardoni et al., 2016). Absorption and

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321

Figure 4. A case study illustrating BrC major features. Case study time period (1.5 h) is from 17:30 to 19:00 UTC on 1 February 2013 (local

time at the Po Valley sampling site in this period is UTC + 1 h). Case study values are compared with mean values over the whole field

experiment. Panels illustrate (a) particle volume size distribution (dV/ dlog10dm, based on electrical mobility particle diameter dm) during

the case study and relevant mean values; (b) particle mass size distribution (dM/ dlog10dva, based on vacuum aerodynamic diameter, dva)

during the case study; (c) relevant mean values; (d) aerosol vertical profiles in the atmosphere during the entire case study day (time–height

cross section of the range corrected signal, RCS = ln(S × R2), from an LD40 ceilometer); (e) particle number size distributions during the

entire case study day.

scattering coefficients at 530 nm (Fig. S1 of the Supplement)

ranged from 5 to 10 Mm−1 (with larger AAE) and from 300

to 400 Mm−1 (with lower SAE), respectively, with SSA530 =

0.98 ± 0.01. The number concentration of 2–10 µm particles

(Fig. 4e) had a peak at approx. 04:00 UTC (we interpret this

as particle growth by water vapor) and then decreased until

09:00 UTC. After this, the number concentration of 0.3–1 µm

particles increased. This increase occurred just after the part

of the day (from 11:00 to 14:00) when the strong signal in the

aerosol vertical profile at the ground (the darker red layer in

Fig. 4d) is observed to dissipate (we interpret this as droplet

evaporation). These processes are consistent with the forma-

tion of the droplet-mode aerosol (John, 1990; Meng and Se-

infield, 1994; Seinfeld and Pandis, 2006) explaining the in-

crease of the droplet-mode aerosol scores (PC3) observed in

the afternoon. The case study was selected during this period

(1.5 h from 17:30 to 19:00), and relevant data are shown in

Figs. 2, 3, 5, and S1 of the Supplement (in this figure, the

case study is the first day of the winter field campaign).

During the case study (i.e., from 17:30 to 19:00 on

1 February 2013) we observed peculiar data. The AAE

was significantly higher (3.4–5.3) than the median value

(2.0 ± 0.5 during both field campaigns and 2.1 ± 0.6 in the

winter). The volume size distribution (Fig. 4a) was narrow

and monomodal, centered on the droplet mode (dm from

450 to 700 nm). Relevant mass size distributions of the main

constituents of nr-PM1 (NO−

3

, OA, and NH+

4

) were cen-

tered around 700nm of the vacuum aerodynamic diame-

ter (dva), corresponding to about 500nm in mobility diam-

eter (for spherical particles in the continuum regime with

ρp = 1.4 g cm−3; Seinfeld and Pandis, 2006). In addition, the

OA mass below dva = 300nm was significantly lower than

that of the droplet mode, especially when compared to the

average field results (Fig. 4c). It is important to note that the

collection efficiency of the HR-ToF-AMS is 50 % for 600 nm

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322

F. Costabile et al.: Brown carbon in the Po Valley atmosphere

Figure 5. Absorption Ångström exponent at 467–660nm (AAE)

against: (a) OA mass fraction, (b) OA-to-BC ratios, and (c) BC-to-

OA ratios. Grey markers show the longest available time series for

AAE and fOA, while colored markers show data points for which

the SSA at 530 nm and dmedS were available (Sect. 3). Best fit lines

to data taken from (i) Shinozuka et al. (2009) (at different SSA bins,

i.e., 0.98–1.00, 0.96–0.98, 0.90–0.92, from top to bottom) and Rus-

sell et al. (2010) are indicated in panel (a) (thin and thick black lines,

respectively); (ii) Saleh et al. (2014) and Lu et al. (2015) are indi-

cated in panel (c) (black thick and thin lines, respectively); (iii) this

work is indicated in panel (c) (grey line, as in Fig. 3). Data indicated

by dark grey “o” show case study values illustrated in Fig. 4.

particles and decreases for larger sizes (Liu et al., 2007).

This explains the difference between the size distributions

in Fig. 4a and b at larger sizes.

At the light of source apportionment study performed on

organic aerosol (Gilardoni et al., 2016) we explain the in-

crease of AAE during the case study period with the forma-

tion of secondary organic aerosol in the aqueous phase asso-

ciated with aerosol particles. The analysis of microphysical

properties reported in this study confirms that the aqueous

secondary organic aerosol formation adds mass to the atmo-

spheric aerosol in the droplet-mode range. This case study

both illustrates and confirms general features observed for

BrC during the whole field campaign.

4.3 Discussion in comparison with previous works

In this section we discuss our findings and explore their con-

sistency with the literature.

The analysis of chemical and microphysical properties

shows that BrC associated with the formation of secondary

aerosol has a narrow monomodal size distribution centered

around the droplet mode (400–700nm) in the entire PM10

size range. This result agrees with the observations reported

by Lin et al. (2010), showing that 80% of the mass of at-

mospheric humic-like substances, a light-absorbing organic

aerosol component, was found in the droplet mode. The

correspondence between BrC and the droplet-mode aerosol

points to the important role that aqueous reactions within

aerosol particles can play in the formation of light-absorbing

organic aerosol (Ervens et al., 2011; Laskin et al., 2015).

The study of optical and chemical properties indicates that

in our case the larger AAE values associated with BrC de-

pend on the organic fraction in a different way from that in

literature (Shinozuka et al., 2009; Russell et al., 2010; Arola

et al., 2011). In Fig. 5a we compare our measurements col-

lected at the urban background site of Bologna with the trend

previously found based on airborne and sun photometers ob-

servations (Shinozuka et al., 2009; Russell et al., 2010). AAE

is plotted versus the mass fraction of organic aerosol (fOA),

the marker color being SSA530 as in Fig. 7 in Shinozuka et

al. (2009). We show the best fit lines (thin black lines) identi-

fied by Shinozuka et al. (2009) and corresponding to different

SSA bins, and the best fit reported by Russell et al. (2010)

(thick black line). Note that the larger AAE values in our

study (associated with BrC in the droplet mode) correspond

to the fit line in Shinozuka et al. (2009) at SSA = 0.98–1 but

were associated with lower scattering coefficients in those

previous studies. There is consistency between our study and

those reported previously. However, our data show that in-

creasing AAE is accompanied by increasing the OA normal-

ized to BC (Fig. 5b, r = 0.78, p < 0.001) rather than by in-

creasing OA (Fig. 5a). This comparison adds to the literature

that for the ambient aerosol in the lower troposphere AAE

correlates with OA-to-BC ratios far more than with the or-

ganic fraction.

We found that BrC corresponds to BC-to-OA ratios be-

low 0.05 ± 0.03. Figure 5c shows the dependence of AAE on

the BC-to-OA ratio as parametrized by Saleh et al. (2014)

and Lu et al. (2015) for biomass-burning emissions and pri-

mary organic aerosol emissions. The best fit line to measure-

ments performed in this study (grey line) is similar to the

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F. Costabile et al.: Brown carbon in the Po Valley atmosphere

323

fitting lines reported previously (thicker and thinner black

lines, showing respectively data from Saleh et al., 2014 and

Lu et al., 2015). While we cannot compare absolute values

because we compare the AAE of the bulk aerosol (this study)

to the AAE of OA only (Saleh et al., 2014; Lu et al., 2015), it

is evident that patterns are similar. This comparison extends

the dependence of AAE on BC-to-OA observed in chamber

experiments (solely for fresh biomass-burning primary OA

and not for fossil fuel OA) by Saleh et al. (2014) and Lu

et al. (2015) to ambient aerosol dominated by wood-burning

emissions (Gilardoni et al., 2016).

5 Summary and conclusions

We investigated optical–chemical–microphysical properties

of BrC in the urban ambient atmosphere. In situ ground

measurements of chemical (HR-ToF-AMS), optical (3λ

nephelometer and PSAP), and microphysical (SMPS and

APS) aerosol properties were carried out in the Po Valley

(Bologna), together with ancillary observations. BrC was

identified and characterized by linking the wavelength de-

pendence of light absorption (as indicated by the AAE) in

the visible region to key aerosol types with known size dis-

tributions, and to major PM1 chemical components (BC, OA,

nitrate, ammonium, and sulfate). BrC measurements were

interpreted through numerical simulations (Mie theory) of

AAE(dp,λ,m) resolved by particle size (dp) and wavelength

(λ) dependent complex refractive index (m(λ) = n(λ) −ik(λ))

in the visible region.

We found the following:

1. AAE increases with increasing the (optically relevant)

aerosol size. Larger AAEs (3.2 ± 0.9, with values up

to 5.5) occur when the bulk aerosol size distribution is

dominated by the droplet mode, i.e., the large accumu-

lation mode originating from the aerosol processing in

the aqueous phase. These values identify BrC.

2. Specific m(λ) values are necessary in addition to a

proper particle size range to match the high AAE

measured for BrC. These m(λ) values are theo-

retically expected to be k(467) = 0.026 ± 0.001,

k(530) = 0.017 ± 0.001, k(660) = 0.014 ± 0.001, and

n467 = 1.47 ± 0.01 (SSA530 = 0.98 ± 0.01), consistent

with literature m(λ) values for BrC in the ambient

atmosphere.

3. AAE increases with increasing the OA-to-BC ratio,

rather than with increasing fOA, the larger AAEs (and

thus BrC) corresponding to larger ammonium nitrate

(fNO3= 0.38 ± 0.05, fNH4= 0.14 ± 0.01) and lower

BC (fBC = 0.01 ± 0.01).

In the paper by Gilardoni et al. (2016) we investigated or-

ganic aerosol source and identified SOA formation from pro-

cessing of biomass-burning emissions in the aqueous phase.

We than discussed the climate implication of this aqSOA for-

mation at the light of its optical properties, including AAE. In

the present paper we investigated the particle size distribution

and spectral optical properties of brown carbon in the ambi-

ent aerosol and related these to major aerosol chemical com-

ponents. By combining the analysis of microphysical and op-

tical properties reported here with the source apportionment

study by Gilardoni et al. (2016), we demonstrated that the aq-

SOA formation adds mass to the atmospheric aerosol in the

droplet-mode range.

When exploring consistency of these findings with the lit-

erature, our study

i. provides experimental evidence that the size distribu-

tion of BrC associated with the formation of secondary

aerosol is dominated by the droplet mode, consistent

with recent findings pointing to the role of aqueous re-

actions within aerosol particles in the formation of BrC;

ii. shows that in the lower troposphere AAE increases with

increasing OA-to-BC ratios rather than with increasing

OA, contributing to sky radiometer retrieval techniques

(e.g., AERONET);

iii. extends to the ambient aerosol dominated by wood-

burning emissions the dependence of AAE on the BC-

to-OA ratio previously observed in combustion chamber

experiments.

These findings are expected to bear important implica-

tions for atmospheric modeling studies and remote sensing

observations. Both BrC number size distribution and the de-

pendence of AAE on the BC-to-OA ratio can be relevant to

parametrize and investigate BrC in the ambient atmosphere.

Findings can be used to infer preliminary chemical informa-

tion from optical information, as optical techniques are in-

creasingly used to characterize aerosol properties.

6 Data availability

All the data presented in this paper are available upon re-

quest. Please contact the corresponding author (Francesca

Costabile, f.costabile@isac.cnr.it) for further information.

The Supplement related to this article is available online

at doi:10.5194/acp-17-313-2017-supplement.

Acknowledgements. This work was realized in the framework of

the SUPERSITO Project, financed by the Emilia-Romagna Region

(under the DR 1971/13). The work was partly accomplished in the

framework of the DIAPASON (“Desert-dust Impact on Air quality

through model-Predictions and Advanced Sensors ObservatioNs”)

project, funded by the European Commission (LIFE+ 2010

www.atmos-chem-phys.net/17/313/2017/

Atmos. Chem. Phys., 17, 313–326, 2017

324

F. Costabile et al.: Brown carbon in the Po Valley atmosphere

ENV/IT/391).

Edited by: R. Sullivan

Reviewed by: three anonymous referees

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