ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-12097-2017Classifying aerosol type using in situ surface spectral aerosol optical propertiesSchmeisserLaurenlauren.schmeisser@gmail.comhttps://orcid.org/0000-0003-2009-7834AndrewsElisabethhttps://orcid.org/0000-0002-9394-024XOgrenJohn A.https://orcid.org/0000-0002-7895-9583SheridanPatrickJeffersonAnneSharmaSangeetaKimJeong EunShermanJames P.SorribasMarhttps://orcid.org/0000-0003-2131-9021KalapovIvoArsovTodorAngelovChristoMayol-BraceroOlga L.https://orcid.org/0000-0001-8760-0743LabuschagneCasperhttps://orcid.org/0000-0002-7125-0029KimSang-WooHofferAndrásLinNeng-HueiChiaHao-PingBerginMichaelSunJunyingLiuPengWuHaoNational Oceanic and Atmospheric Administration, Earth Systems
Research Laboratory, Boulder, CO, USAUniversity of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USAEnvironment and Climate Change Canada, Science and Technology Branch,
Ontario, CanadaEnvironmental Meteorology Research Division, National Institute of
Meteorological Sciences, Seoul, KoreaDepartment of Physics and Astronomy, Appalachian State University, Boone, NC, USAAtmospheric Sounding Station, El Arenosillo, Atmospheric Research and
Instrumentation Branch, INTA, 21130, Mazagón, Huelva, SpainInstitute for Nuclear Research and Nuclear Energy of the Bulgarian
Academy of Sciences, Sofia, BulgariaUniversity of Puerto Rico, Department of Environmental Science, San
Juan, PR, USASouth African Weather Service, Stellenbosch, South AfricaUnit for Environmental Sciences and Management, North-West
University, Potchefstroom Campus, South AfricaSchool of Earth and Environmental Sciences, Seoul National University, Seoul 08826, KoreaMTA-PE Air Chemistry Research Group, Veszprém, P.O. Box 158,
8201, HungaryNational Central University, Department of Atmospheric Sciences,
Chung-LI, Taoyuan City, TaiwanDuke University, Department of Civil & Environmental Engineering,
Durham, NC, USAState Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, ChinaChina GAW Baseline Observatory, Qinghai Meteorological Bureau, Xining
810001, Chinanow at: University of Washington, Department of
Atmospheric Sciences, Seattle, WA, USALauren Schmeisser (lauren.schmeisser@gmail.com)12October20171719120971212017January20174April20178August201711August2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf
Knowledge of aerosol size and composition is important for
determining radiative forcing effects of aerosols, identifying aerosol
sources and improving aerosol satellite retrieval algorithms. The ability to
extrapolate aerosol size and composition, or type, from intensive aerosol
optical properties can help expand the current knowledge of spatiotemporal
variability in aerosol type globally, particularly where chemical composition
measurements do not exist concurrently with optical property measurements.
This study uses medians of the scattering Ångström exponent (SAE),
absorption Ångström exponent (AAE) and single scattering albedo (SSA)
from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol
type using previously published aerosol classification schemes.
Three methods are implemented to obtain a best estimate of dominant aerosol
type at each station using aerosol optical properties. The first method
plots station medians into an AAE vs. SAE plot space, so that a unique
combination of intensive properties corresponds with an aerosol type. The
second typing method expands on the first by introducing a multivariate
cluster analysis, which aims to group stations with similar optical
characteristics and thus similar dominant aerosol type. The third and final
classification method pairs 3-day backward air mass trajectories with median
aerosol optical properties to explore the relationship between trajectory
origin (proxy for likely aerosol type) and aerosol intensive parameters,
while allowing for multiple dominant aerosol types at each station.
The three aerosol classification methods have some common, and thus robust,
results. In general, estimating dominant aerosol type using optical
properties is best suited for site locations with a stable and homogenous
aerosol population, particularly continental polluted (carbonaceous
aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and
continental dust/biomass sites (dust and carbonaceous aerosol); however,
current classification schemes perform poorly when predicting dominant
aerosol type at remote marine and Arctic sites and at stations with more
complex locations and topography where variable aerosol populations are not
well represented by median optical properties. Although the aerosol
classification methods presented here provide new ways to reduce ambiguity
in typing schemes, there is more work needed to find aerosol typing methods
that are useful for a larger range of geographic locations and aerosol
populations.
Introduction
Although it is well established that aerosol particles affect the radiative
forcing of climate both directly by scattering and absorbing sunlight and
indirectly by influencing cloud formation and precipitation, aerosols still
remain a primary source of uncertainty in assessing the Earth's radiative
budget (Boucher et al., 2013). This uncertainty arises from a large range of
aerosol chemical and physical properties as well as from the high
spatiotemporal variability in aerosol particles. In order to help reduce
this uncertainty and be able to better predict climatic effects of aerosols,
there is a need for long-term global monitoring of aerosols (Hansen et al.,
1996), compiling records not only of aerosol loading but also of aerosol
characteristics and type.
Determination of aerosol type (e.g., black carbon, sea salt, dust),
which is defined by the size and composition of an aerosol, is important in
characterizing the role of aerosols in atmospheric processes and feedbacks,
since different aerosol types have different radiative forcing effects and
atmospheric behavior. Additionally, knowledge of aerosol type helps identify
the aerosol source, which can be useful in implementing controls or policies to
reduce aerosols that negatively influence air quality and public health and
also to better understand atmospheric dynamics and long-range transport.
Constraining aerosol type is also needed for improving aerosol satellite
retrieval algorithms and for validating climate models (Russell et al.,
2014).
Recent studies, discussed below, present classification schemes to infer
aerosol type from intensive optical properties, which are calculated from
ratios of extensive properties and thus not directly dependent on the
aerosol amount. Successful application of this method could allow for access
to aerosol composition information from remote or in situ optical property
measurements that do not otherwise provide an indication of aerosol type.
Aerosol optical property thresholds used to determine aerosol type
in previous studies. Values in parentheses represent standard deviations,
when provided.
StudyMeasurement typeDustFossil fuel burningSea saltBiomass burningBahadur et al. (2012)AERONETAAE440/675nm∼ 2.2 (±0.50) SAE440/675nm < 0.5AAE440/675nm∼ 0.55 (±0.24) SAE440/675nm > 1.2 (referred to asBC/EC/soot)AAE440/675nm∼ 4.55 (±2.01) SAE440/675nm > 1.2 (referred to as OC)Cazorla et al. (2013)AERONET and aircraftcampaignAAE440/675nm > 1.5 SAE440/675nm < 1AAE440/675nm≤ 1 SAE440/675nm > 1.5 (referred to as EC-dominated)AAE440/675nm≥ 1.5 SAE440/675nm > 1.5 (referred to as OC-dominated)Cappa etal. (2016)Surface insituAAE532/660nm > 2 SAE450/550nm < 01 < AAE532/660nm< 1.5 SAE450/550nm > 1 (referred to as BC-dominated)AAE532/660nm > 2 SAE450/550nm > 1.5 (referred to as BrC)Russell etal. (2010)AERONET and aircraftcampaignAAE = 1.5–2.5 EAE = 0.2–1AAE = 0.8–1.5 EAE = 1.5–1.8AAE = 1–1.7 EAE = 1.8–2Clarke etal. (2007)Aircraft campaignAAE470/660nm∼ 1.1 (referred to as pollution)AAE470/660nm∼ 2.1Costabile et al. (2013)Surface insituAAE467/660nm∼ 2 SAE467/660nm < 0.5 SSA530nm > 0.85 (referred to as coarsedust mode, CDM)AAE467/660nm < 1.5 SAE467/660nm∼ 4 SSA530nm < 0.8 (referred to as sootmode, STM)AAE467/660nm > 2 SAE467/660nm < 0.5 SSA530nm > 0.95 (referred to as coarse marine mode, CMM)AAE467/660nm < 2 SAE467/660nm∼ 1-3 SSA530nm < 0.85 (referred to asbiomass burningsmoke mode, BBM)Lee etal. (2012)Surface insituAAE450/700nm∼ 1.2–1.7 SAE450/700nm∼ 0–1.2 (referred to as PD)AAE450/700nm∼ 1–1.5 SAE450/700nm∼ 1.4–1.8 (referred to as P2)AAE450/700nm∼ 0.8–1.4 SAE450/700nm∼ 0.8–1.5 (referred to as P1,higher in OC than P2)Yang etal. (2009)Surface insituAAE370/950nm∼ 1.82 (±0.90) SAE450/700nm∼ 0.59 (±0.41) SSA550nm∼ 0.90.8 (±0.04)AAE370/950nm∼ 1.46 (±0.15) SAE450/700nm∼ 1.39 (±0.20) SSA550nm∼ 0.8(±0.05) (referred to as coal pollution)AAE370/950nm∼ 1.49 (±0.08) SAE450/700nm∼ 1.52 (±0.18) SSA550nm∼ 0.89 (±0.01) Background
Three optical properties that hold information on aerosol type include the scattering Ångström exponent (SAE), absorption Ångström
exponent (AAE) and single scattering albedo (SSA). SAE represents the
wavelength dependence of scattering and varies inversely with particle
size, so that small values of SAE indicate larger aerosol particles (e.g.,
dust and sea salt), and large values of SAE indicate relatively smaller
aerosol particles (Schuster et al., 2006; Bergin et al., 2000, and references
therein). AAE represents the wavelength dependence of absorption and depends
on the composition of absorbing aerosols, such that aerosol materials have a
unique range of AAE values (Russell et al., 2010; Bergstrom et al., 2002,
2007). Black carbon (BC), for example, has a theoretical AAE value of around 1,
while dust aerosol typically has AAE values greater than 2 (Bergstrom et
al., 2002, 2007; Kirchstetter et al., 2004), though AAE of ambient aerosol
will likely evolve with atmospheric processing and depend strongly on
composition (BC-to-OA (organic aerosol) ratio), coating and size (Saleh et
al., 2014; Costabile et al., 2017; Moosmüller et al., 2011). SSA is the
ratio of scattering to extinction (absorption + scattering) and provides
information on aerosol darkness and composition and may determine the net
sign of an aerosol's radiative forcing (Hansen et al., 1997). High SSA
values near 1 indicate low- or non-absorbing “white” aerosols, while low
SSA values (below 0.85) indicate “darker” highly absorbing aerosols, and
thus an SSA value can be used to characterize the aerosol type (Bergstrom et
al., 2002; Russell et al., 2010; Gyawali et al., 2012). Equations for
calculating these properties from extensive optical parameters are found in
Sect. 4. Many studies have used the information inherent in these optical
properties to predict aerosol type; Table 1 provides a review of previous
studies that have utilized intensive optical property thresholds to identify
aerosol type.
The studies listed in Table 1 all take slightly different approaches to show
that intensive aerosol optical properties (SAE, AAE and SSA) can be
utilized to classify aerosol type. Bahadur et al. (2012) determine a scheme
to partition various absorbing aerosol types based on absorbing aerosol
optical depth measurements from numerous AERONET sites that represent a
single absorbing aerosol and test the proposed scheme using California
AERONET sites with mixed aerosols. Cazorla et al. (2013) also make use of
California AERONET sites by combining the measured aerosol optical
properties with in situ aerosol chemical composition measurements from an
aircraft campaign to create a matrix that delineates aerosol type in an AAE
vs. SAE plot space. Eleven AERONET sites from around the globe are used in
the study by Russell et al. (2010) to show that AAE values from full-column
measurements are highly correlated with aerosol type, in general agreement
with the two previously mentioned AERONET aerosol typing schemes that
suggest AAE values near 1 indicate fossil fuel burning aerosol, higher AAE
values indicate absorbing organic carbon (OC)/biomass burning aerosols and
the highest AAE values indicate dust aerosols.
In situ measurements have also been used for aerosol classification schemes.
In situ optical measurements from the INTEX-NA aircraft campaign are used by
Clark et al. (2007) to separate biomass burning from pollution plumes.
Costabile et al. (2013) propose a scheme to classify aerosols based on
absorption and scattering values, using 2 years of in situ urban data from
Rome, Italy, coupled with numerical simulations to create a paradigm linking
key aerosol populations to their unique aerosol optical properties. Six
months of optical property measurements from the in situ monitoring site in
Gosan, South Korea, are used by Lee et al. (2012) and categorized by air mass
type (either pollution or dust) using chemical composition, back
trajectories and meteorological conditions, and SAE and AAE values are
analyzed, yielding results that show dust air masses have the highest AAE
values, with OC-polluted air masses showing the next highest AAE values.
Cappa et al. (2016) utilized surface in situ measurements from the CARES
field campaign in California to categorize aerosol they observed and to
suggest some modifications to the Cazorla et al. (2013) aerosol
classification scheme. Finally, Yang et al. (2009) used the distinct SSA,
AAE and SAE values of different air plumes in the EAST-AIRE campaign to
identify absorption contributions from desert dust, biomass burning,
industrial plumes and clean air in Beijing, China. It is worth mentioning
that some studies take into account the spectral dependence of SSA in
aerosol classification schemes (Li et al., 2015; Russell et al., 2010). This
parameter was calculated for the monitoring stations in this study but was
not useful in classifying aerosol type compared to the other optical
properties discussed; therefore, the spectral dependence of SSA is not
discussed here.
Care must be taken in comparing thresholds from all aforementioned studies,
as differences are likely between column-average, ambient AERONET
measurements and low-RH, surface in situ measurements. Furthermore,
different wavelength pairs are used to calculate AAE and SAE depending on
the study. In general, however, all studies suggest similar typing
thresholds. Most previous works agree that AAE values of around 1 represent BC
and/or fossil fuel burning aerosols and higher AAE values indicate
light-absorbing OC (a.k.a. brown carbon; BrC) and/or dust and that high SAE
values are associated with small anthropogenic aerosols (e.g., BC, sulfates or nitrates) and low SAE values are associated with large aerosols like sea
salt and dust.
This paper aims to assess the applicability of previous typing
methods/schemes to data from 24 in situ monitoring sites within the NOAA/ESRL
Federated Aerosol Monitoring Network and to explore how typing schemes may
be improved based on methods using cluster analyses and air mass back
trajectories. The following questions are addressed:
Are the
relationships between SAE and AAE data from 24 stations in the NOAA/ESRL
Federated Aerosol Monitoring Network consistent with relationships used to identify dominant
aerosol type using aerosol classification schemes previously reported in the
literature?
Can multivariate cluster analyses on aerosol properties be
used to reduce both the ambiguity in inferring the likely dominant aerosol
type from median aerosol optical properties and the uncertainty in aerosol
type optical property thresholds?
How can back trajectory clusters and
subsequent information on air mass source help elucidate multiple aerosol
types at individual sites?
The literature on classifying aerosols has been largely dominated by
the analysis of ground-based remote sensing or satellite data (Cazorla et al.,
2013; Russell et al., 2010, 2014; Omar et al., 2005; Giles
et al., 2012; Bergstrom et al., 2007, 2010; Bahadur et
al., 2012; Dubovik, 2002), with fewer analyses done using surface in situ
aerosol optical property measurements (Cappa et al., 2016; Costabile et al.,
2013; Yang et al., 2009; Lee et al., 2012). The analyses in this paper
utilize ground-based in situ spectral optical data that afford a unique
insight into long-term, quality-assured point observations. Furthermore,
since the in situ data sets used in this study are not restricted by aerosol optical depth
(AOD) thresholds as are AERONET data sets, they offer a more thorough look at
regions with relatively clean air.
Unlike most previous studies, this study looks at long-term records of
aerosol optical properties and does so at a wide range of geographic
locations, including mountaintop, desert, continental and coastal sites. Not
only does the study offer a wide range of aerosol types to be analyzed in an
individual geographic location but provides analysis of the same aerosol
type in different geographic locations.
Monitoring site locations and descriptions. Stations in
bold indicate stations that are part of the ARM Mobile Facility (AMF)
program and are temporary measurement sites.
Station abbreviationStation locationLatitude longitude altitude (m a.s.l.)Absorption instrument1Measurement datesSite classificationSite description (and references)ALTAlert, Canada+82.45-62.52 210PSAP-3W2012–2013ArcticRemote Arctic site, situated away from major anthropogenic and industrial areas, and the most northerly site in the network (Sharma et al., 2002)AMYAnmyeondo,South Korea+36.54+126.33 45CLAP-3W2012–2013Polluted marinePolluted marine site that receives both continental and marine air masses, located on Anmyeon Island off the coast of South Korea (Park et al., 2010)APPBoone, North Carolina, USA+36.2-81.7 1100PSAP-3W2012–2013Continental pollutedSemirural continental site, located in the Appalachian Mountains, a region high in biogenically derived aerosol (Sherman et al., 2015)ARNEl Arenosillo, Spain+37.10-6.73 41CLAP-3W2012-MAY-15 to 2013Marine pollutedLocated near the Atlantic Ocean and Huelva City. Site is located in protected coastal area of Doñana National Park and experiences episodes of desert dust and pollution (Toledano et al., 2007)BEOBEO-Moussala, Bulgaria+42.18+23.59 2925CLAP-3W2012-JUN-03 to 2013Continental polluted, mountaintopThe Basic Environmental Observatory (BEO) sits atop Moussala Peak, the tallest point on the Balkan Peninsula. Given the site's altitude, it is considered to be in the free troposphere and more or less unperturbed by regional pollution sources (Angelov et al., 2011).BNDBondville, Illinois, USA+40.05-88.37 230CLAP-3W2012–2013Continental pollutedAnthropogenically influenced rural site located in Champaign County, Illinois, USA, near soy and corn farms south of Bondville (Delene and Ogren, 2002; Sherman et al., 2015)BRWBarrow, Alaska, USA+71.32-156.6 11CLAP-3W2012–2013ArcticCoastal Arctic site 3 km from Arctic Ocean, located north of the Arctic Circle near the small town of Barrow. Though the site is remote, drilling activities nearby may influence aerosol populations (Bodhaine, 1995).CPRCape SanJuan, Puerto Rico+18.48-66.13 17CLAP-3W2012-MAR-30 to 2013Marine pollutedMarine site, located on the northeast edge of the Caribbean island of Puerto Rico on Las Cabezas de San Juan nature reserve. Prone to African desert dust episodes (Allan et al., 2008).CPTCape Point, South Africa-34.35 +18.49 230PSAP-3W2010–20112Marine cleanMarine site, located on the southwest tip of South Africa. Site is influenced by remote marine air and polluted and/or dusty continental air (Brunke et al., 2004).FKBHeselbach, Germany+48.54+8.40 511PSAP-3W2007-MAR-23 to 2007-DEC-31Continental pollutedContinental site in the Black Forest region of Germany surrounded by coniferous trees. The site is in the agricultural Murg valley, and experiences heavy precipitation and influence from anthropogenic industrial activities (Jefferson, 2010).GRWGraciosa Island, Azores, Portugal+39.09-28.03 15.24PSAP-3W2009-APR-18 to 2010-DEC-31Marine cleanMarine site located on the remote Azores Islands surrounded by the Atlantic Ocean. Site may be influenced at times by local pollution and African desert dust episodes (Jefferson, 2010)GSNGosan, Jeju Island, South Korea+33.28+126.17 72CLAP-3W2012–2013Marine pollutedCoastal site located on the western edge of Jeju Island and prone to influence from marine aerosols, anthropogenic pollution and long-range Asian desert dust (Kim et al., 2005)KPSK-puszta, Hungary+46.96+19.58 125CLAP-3W2012–2013Continental pollutedContinental site located in the Hungarian Great Plain, 70 km southeast of Budapest. Measures regional background air, and although it is situated as remotely as possible, is still influenced by biomass burning aerosol from home heating in the winter (Ion et al., 2005).
Continued.
Station abbreviationStation locationLatitude longitude altitude (m a.s.l.)Absorption instrument1Measurement datesSite classificationSite description (and references)LLNLulin, Taiwan+23.47+120.87 2862CLAP-3W2012–2013Continental polluted, mountaintopHigh-altitude site influenced by air masses from polluted biomass and industrial continental Asian sources, as well as clean marine regions (Wai et al., 2008)MLOMauna Loa, Hawaii, USA+19.54-155.58 3397CLAP-3W PSAP-3W2012–2013Marine polluted, mountaintopHigh-altitude site on the northern side of the Mauna Loa volcano on the Big Island of Hawaii. Distinct diurnal patterns in upslope/downslope air flow, with minimal influence from regional aerosol sources (Bodhaine, 1995).NIMNiamey, Niger+13.48+2.18 205PSAP-3W2005-DEC-01 to 2006-DEC-31Continental dust/biomassContinental site susceptible to biomass burning and African desert dust, prone to high heat and heavy rains in the monsoon season (Liu and Li, 2014)PGHNainital, India+29.36+79.46 1951CLAP-3W2011-JUN-09 to 2012-MAR-27Continental dust/biomassContinental site located in the Ganges Valley in the remote foothills of the Himalayas. Biomass burning, dust and growth in nearby industrial activities sporadically influence the site (Liu and Li, 2014; Kotamarthi, 2013).PVCCape Cod, Mas-sachusetts, USA+42.07-70.20 1CLAP-3W2012-JUL-16 to 2013-JUN-24Marine pollutedMarine site on a peninsula of Massachusetts reaching into the Atlantic Ocean. Site is also near major urban areas, including Boston, Massachusetts and Providence, Rhode Island, and is thus influenced by both polluted and clean air masses (Titos et al., 2014).PYEPt. Reyes,California, USA+38.09-122.96 5PSAP-3W2005-MAR-21 to 2005-SEP-15Marine cleanMarine site on the California coast north of San Francisco. Air masses from the west are strictly maritime, while air masses from the north, south and east are influenced by continental pollution (Berkowitz et al., 2005).SGPSouthern Great Plains, Oklahoma, USA+36.61-97.49 315CLAP-3W2012–2013Continental pollutedRural continental site located near wheat fields and cattle pastures southeast of Lamont, Oklahoma. There are no large urban areas nearby, but point sources, like power plants and oil operations, influence the site occasionally (Delene and Ogren, 2002; Sherman et al., 2015).SPLStorm Peak, Colorado, USA+40.45-106.73 3220CLAP-3W2012–2013Continental polluted, mountaintopHigh-altitude forested site in the Rocky Mountains of northwestern Colorado. Located near the town of Steamboat Springs and agricultural Yampa Valley, though the station frequently measures uncontaminated free troposphere (Borys and Wetzel, 1997).SUMSummit, Greenland+72.58-38.48 3238CLAP-3W2012–2013Arctic, mountaintopArctic station atop the Greenland Ice Sheet. Remote and clean, with occasional influence from long-range biomass and industrial pollution (Hagler et al., 2007).THDTrinidad Head, California, USA+41.05-124.15 107CLAP-3W2012–2013Marine cleanMarine site on the northern California coast, with Pacific Ocean to the west and redwood forests to the east. Though maritime airflow is predominant, some anthropogenic influences from other airflows is observed (Oltmans et al., 2008).WLGMt Waliguan, China+36.28+100.90 3816PSAP-3W2012–2013Continental dust/biomass, mountaintopHigh-altitude station located on the dry, arid Tibetan Plateau in China. The site experiences clean or dusty air masses coming in from the west and anthropogenically influenced and polluted air masses coming from the east (Kivekäs et al., 2009; Che et al., 2011).
1 All scattering instruments are TSI nephelometers.
2 Cape Point (CPT) had data loss issues in the 2012–2013 time period, so the
period 2010–2011 was used instead.
Map of 24 in situ monitoring stations within the NOAA/ESRL
Federated Aerosol Monitoring Network that were utilized in this study. Locations are
labeled with each site's three-letter station abbreviation.
Site descriptions
This study investigates aerosol populations at 24 monitoring stations in the
NOAA/ESRL Federated Aerosol Monitoring Network. Sites were selected for the
study based on the availability of data – each site had to meet the following
criteria: (1) aerosol optical data available at three wavelengths and (2) long-term (> 6 months) continuous measurement records of
scattering and absorption coefficients during the 2-year time period
2012–2013, unless otherwise noted (see Table 2 for time range for each
site). The ARM Mobile Facility (AMF; part of the US Department of
Energy's ARM Climate Research Facility) deployments, indicated in bold in
Table 2, are typically 1- to 2-year deployments. Most of the AMF
measurement times do not overlap with the 2012–2013 analysis period but
should nevertheless be comparable to other sites and are included as a
means of broadening the range of geographic locations for the analysis. One
advantage of this study is the wide diversity of location types and observed
aerosol loadings (which span over 3 orders of magnitude). This study
includes sites in both the Northern and Southern Hemispheres, ranging in
altitude from sea level to 3800 m above sea level (a.s.l.), with various
climate regimes including marine, continental and Arctic. The sites
experience different levels of anthropogenic influence ranging from clean
remote sites to very polluted urban sites. The 24 stations are described in
Table 2, and Fig. 1 shows a map of the stations.
Table 2 presents monitoring site location, latitude, longitude, altitude,
scattering and absorption instruments, date range of data utilized, site
classification, and site description for 24 monitoring stations in the
NOAA/ESRL Federated Aerosol Monitoring Network. Bolded station names in the
table indicate sites where the short-term AMF was deployed.
Sites are categorized based on the site's geography and surrounding land
use. Arctic sites are at latitudes greater than 70∘ N. Continental polluted
sites have influence from urban and industrial pollution. Continental
dust/biomass sites are generally more rural with influence from desert dust
and/or biomass burning. Marine clean sites are in remote coastal locations,
have little influence from pollution sources (except perhaps from long-range
transport events) and see an abundance of marine aerosols. Marine polluted
sites are also in coastal locations and may measure pollution aerosols (from
continental air masses) or marine aerosols (from oceanic air masses) or some
combination thereof, depending on the wind direction. Mountaintop
classifications indicate sites that are higher than 2800 m in elevation;
these high-altitude monitoring stations sample both free-troposphere air and
air masses transported from lower elevations due to upslope/downslope flow.
Site classification is inherently subjective and not always clear-cut. We acknowledge that sites could be considered to have more than one
classification and have multiple aerosol types. However, the classifications
were designated based on “best fit” to the site characteristics and are
intended to be representative of the dominant aerosol type at each site.
Data and instruments
The data sets used for the analysis are comprised of in situ scattering and
absorption coefficients (σsp and σap,
respectively), which are quality assured and used to calculate additional
parameters (AAE, SAE and SSA) as described in Eqs. (1)–(3). One-hour averaged data are used for the assessment of aerosol classification schemes
and the multivariate cluster analysis. However, we use 6 h averaged
optical properties for the back trajectory analysis, since back trajectories
are run at 6 h intervals. Data sets from NOAA and collaborators are
publically available from the World Data Center for Aerosols
(http://ebas.nilu.no/), with the exception of WLG data, while the AMF
data sets are publically available from Department of Energy (DOE) (http://www.arm.gov/).
Scattering coefficients were obtained with a TSI 3563 integrating
nephelometer (TSI Inc.) at all sites, operating at wavelength channels 450,
500 and 700 nm. Absorption coefficients were measured by either a
three-wavelength particle soot absorption photometer (PSAP, Radiance Research),
or a three-wavelength continuous light absorption photometer (CLAP, NOAA). The
PSAP instruments operate at wavelengths 467, 530 and 660 nm, and CLAP
instruments operate at wavelengths 467, 528 and 652 nm. In either case, the
σap values are corrected to 450, 550 and 700 nm (using AAE) so
as to match the wavelengths of the σsp measurements.
Table 2 indicates which instruments operate at each station. At MLO and BND,
data from both the PSAP and CLAP were utilized, since at both stations the
PSAP was replaced with a CLAP in the middle of the study period. An analysis
of concurrent PSAP and CLAP measurements shows that the two instruments
produce comparable measurements, and thus combining or directly comparing
data from both instruments is not expected to affect results (Ogren et al.,
2010).
To ensure data sets are comparable across monitoring stations, all data are
quality controlled. In order to minimize aerosol hygroscopic effects,
measurements at all stations (except SUM and SPL) are made at a reduced
relative humidity (RH < 40 %) by heating the inlet air or by
diluting with filtered, dry air. The inlets at most sites are either gently
heated (heating does not exceed 40 ∘C) with a stack heater or a
small heater by the impactor and are only utilized if the relative humidity
exceeds 40 %. Although heating the sampling inlet can cause loss of
organic and volatile aerosol material, which can alter the aerosol spectral
optical properties, this is not expected to substantially impact results
here. Studies show that the number of volatile components removed at
40 ∘C (by a thermal denuder) is less than 10 % (Mendes et al.,
2016; Huffman et al., 2009). For this particular study, we do not have the
data necessary to evaluate the extent to which aerosol optical properties
are affected by the heating, but evidence from other studies suggests the
effect is likely small.
Monitoring station buildings are also temperature controlled, and inlet
stacks have protective caps and screens to prevent interference from
precipitation, insects or debris. All aerosol scattering coefficient
measurements from the TSI nephelometers are corrected for angular
non-idealities using corrections from Anderson and Ogren (1998). After the
corrections, scattering coefficients measured by the nephelometer have an
uncertainty of 9.3 % for the 10 µm size cut, based on the analysis by
Sherman et al. (2015). The Sherman et al. (2015) calculations represent
median continental conditions and might change at sites with cleaner or
more polluted conditions. Aerosol absorption coefficient measurements from
PSAP and CLAP instruments are adjusted for flow rate, spot size and aerosol
scattering, using the correction from Bond et al. (1999) and further
adjusted for wavelength based on corrections from Ogren (2010). After
corrections, absorption coefficients measured by the PSAP or CLAP have an
uncertainty of ∼ 20 % (Sherman et al., 2015). Finally, all
data are passed through a quality-assurance–quality-control editing process
in which measurement records are screened for atypical aerosol parameters
(see Delene and Ogren, 2002, and Sheridan et al., 2016, for detailed
descriptions of quality assurance and quality control procedures). Points
that appear anomalous due to local pollution sources (nonrepresentative of
regional aerosol), instrument error or excessive noise are not included in
this analysis.
The measured scattering and absorption coefficients are extensive aerosol
properties because they depend on the amount of aerosol present (Ogren,
1995; Delene and Ogren, 2002). Intensive aerosol optical properties are
calculated from ratios of the extensive properties. The aerosol intensive
properties, including AAE, SAE and SSA, are of
primary interest to this study since they contain information on aerosol
size or composition and are calculated as indicated in the following
equations:
AAEλ1/λ2=-logσap,λ1σap,λ2logλ1λ2,SAEλ1/λ2=-logσsp,λ1σsp,λ2logλ1λ2,SSAλ1=σsp,λ1σsp,λ1+σap,λ1,
where σap,λ1 represents absorption coefficient at
wavelength λ1 and σap,λ2 represents
absorption coefficient at wavelength λ2. Similarly, σsp, λ1 and σsp, λ2 represent
scattering coefficients at wavelengths λ1 and λ2, respectively. Unless otherwise indicated, all data presented here
refer to the green wavelength channel (550 nm) for SSA, absorption and
scattering coefficient values or the blue/red wavelength pair (450 nm/700 nm) for the SAE and AAE values. CLAP and PSAP wavelengths were adjusted to
match the nephelometer wavelengths to compute the intensive variables.
Only aerosol measurements where σsp > 1
and σap > 0.5 Mm-1 are included in the
analyses. Data below these values are less reliable due to instrument noise
at low aerosol loading, thus the constraints are meant to act as noise
thresholds. This inherently adds bias to the data, as monitoring sites with
consistently low absorption and scattering coefficients may end up with
limited data points after the thresholds are applied, leaving measurement
records with higher loadings that may not be fully representative of typical
aerosol populations at the site. This constraint has the greatest effect on
clean sites like ALT, BRW and SUM (which measure Arctic air), BEO and MLO
(which sometimes measure free-tropospheric air), and CPR, CPT, PVC, PYE and
THD (which sometimes measure clean marine air). The constraints push the
extensive scattering and absorption values higher. More details on the effect
of the thresholds on the analysis of clean stations can be found in Table S5
in the Supplement.
There are some differences in monitoring station data that may affect the
results of the following analyses and are noted here. SUM utilizes a 2.5 µm size cut, while all
other stations use a size cut of 1 and 10 µm, but
only the 10 µm data are used in this study. This size cut
discrepancy will bias SUM data towards higher SAE values than would be found
with a larger size cut. Since ARM station data records are typically less
than 1 year in length, while all other station data are 2 years in length,
any site-specific seasonal variations may not be captured in the ARM data
records. Furthermore, ARM measurement times and CPT times typically do not
overlap with the baseline study period of 2012–2013, so any extreme events
specific to those years are not reflected in the CPT (data only from years
2010–2011) or ARM (FKB, GRW, NIM, PGH, PVC, PYE) sites measurements.
Data analysis methods
The aerosol classification analysis presented here proceeds in three steps.
Application and assessment of previous aerosol typing schemes:
presenting station intensive optical property medians in an AAE vs. SAE plot
space modeled closely on Cappa et al. (2016) in order to link a
combination of AAE and SAE values to aerosol type.
Multivariate cluster
analysis: performing a multivariate cluster analysis to group stations with
like optical properties to better infer a common aerosol type.
Back
trajectory analysis: combining back trajectories and the land type over
which they traveled with aerosol optical properties to better understand the
relationship between trajectory origin (proxy for likely aerosol type) and
aerosol intensive properties, while allowing more than one dominant aerosol
type at each station. The methods for these analysis techniques are
described in detail here.
Methods for application and assessment of previous aerosol typing
schemes
Like many previous studies (Cappa et al., 2016; Cazorla et al., 2013;
Costabile et al., 2013; Yang et al., 2009; Russell et al., 2010; Lee et al.,
2012; Bahadur et al., 2012), an AAE vs. SAE plot space is used here to
visualize relationships between aerosol optical properties and likely
aerosol type. Since SAE indicates aerosol size and AAE holds information on
aerosol composition and size (Costabile et al., 2017), a unique combination
of the two, and thus where that combination falls within the AAE vs. SAE
plot space, suggests a particular aerosol type. Many previous studies use
chemical composition data (Costabile et al., 2013; Lee et al., 2012; Cazorla
et al., 2013) or numerical simulations (Costabile et al., 2013) to validate
the proposed aerosol classification scheme; however, since neither of those
methods are available for this study, thresholds from previous studies are
used here to infer likely dominant aerosol type, and results are assessed
based on knowledge of the site. For the first iteration of the analysis,
long-term optical property medians from multiple stations are presented in
one plot space for a comparative overview of inferred dominant aerosol type
at many sites. A variation in the Cappa et al. (2016) classification matrix
is used here. The version used here omits “large black particles” from the
lower left plot space designation, as this does not correspond to data
presented here.
It should be noted that the Cappa et al. (2016) and Cazorla et al. (2013)
matrices are very similar. Both designate high SAE and high AAE values as
BrC or mixed BC–BrC (though Cazorla et al., 2013, refers to BrC as OC). Both
designate low SAE values and high AAE values as dust or dust mixed with BC
and BrC, and both suggest that an AAE value of around 1, accompanied by higher SAE
values indicates aerosol populations dominated by BC. Three main differences
between the matrices can be identified. The Cappa et al. (2016) matrix makes
more specific designations of aerosol mixtures (e.g., adds “mixed
dust, BC, BrC” and “large-particle–BC mix”). The Cappa et al. (2016) matrix
also replaces the Cazorla et al. (2013) matrix designation of “large coated
particles” with “large-particle–low-absorption mix or large black
particles”. Finally, the Cappa et al. (2016) matrix replaces the Cazorla et al. (2013) matrix designation of “EC” with “small-particle–low-absorption
mix”. We chose to primarily use the Cappa et al. (2016) matrix since it is
based on in situ data (Cazorla et al., 2013, is based on AERONET data) and
since the aerosol designations seemed to align most closely with our data. Results
are presented in Sect. 6.1.
Methods for multivariate cluster analysis
In order to infer a more accurate representation of aerosol type using
intensive optical properties as an indication of aerosol size/composition
and extensive optical properties as an indication of loading, a multivariate
clustering analysis is performed to build on the first classification
method. A cluster analysis is the process of statistical grouping that
yields “clusters” with similar characteristics. A few other studies also
implement multidimensional clustering as a means of solidifying aerosol
property thresholds for different aerosol types (Russell et al., 2010; Omar
et al., 2005; Levy et al., 2007). In this study, a cluster analysis is used
to determine groups of stations with similar aerosol type based on aerosol
optical properties. The clusters are then plotted in a 3-D parameter space
(AAE vs. SAE vs. log(σsp)) as a means of visualizing any
spatial patterns that emerge.
The k means clustering algorithm was run using medians of four aerosol
optical property parameters – SAE, AAE, SSA and the log of the scattering
coefficient (log(σsp)) – from hourly averaged records at
each monitoring station. The scattering coefficient, σsp, is
an indication of aerosol loading and is implemented here as an additional
parameter to improve the inference regarding aerosol types. The log of
σsp (in Mm-1) is used rather than the raw
σsp median in order to make the scattering coefficient
values more comparable with the magnitude of the optical property values, so
the clustering is not dominated by one parameter. While the magnitude of
loading (σsp) alone does not correspond to a specific
aerosol type (for example, high loadings can be observed for dust, pollution
or biomass burning events), it may act as a secondary indicator of aerosol
conditions (i.e., frequency of aerosol type occurrence, loading) and source
contributions, so it is included in the clustering analysis.
To run the clustering algorithm, a number of clusters k is selected.
Choosing the k initial seed points is inherently subjective – in this
analysis, k needs to be small enough such that the number of stations that
fall into each cluster makes for a meaningful grouping and large enough
such that a distinction between station groups is apparent. The algorithm
then takes k initial seed points at random and iteratively assigns each
point to the nearest cluster centroid taking into account the clustering
properties. The next iteration chooses k new seed points and repeats the
process until the algorithm converges. In this study, six clusters are
selected, creating six unique groups each with similar SAE, AAE, SSA and
log(σsp) characteristics. Each monitoring station was assigned
to one of the six clusters produced from the algorithm, and the groupings
were used to further analyze aerosol type and conditions. Results are
presented in Sect. 6.2.
Number of hourly data points, plus median values and lower and upper
quartiles for scattering Ångström exponent and absorption Ångström exponent, single scattering albedo, scattering coefficient
(σsp), absorption coefficient (σap) and inferred
aerosol type at each monitoring station. All data are filtered by thresholds
σsp > 1 Mm-1 and σap > 0.5 Mm-1.
The NOAA Air Resources Laboratory Hybrid Single Particle Lagrangian
Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003) was utilized
to produce 3-day air mass back trajectories at 6 h intervals for the
entirety of the measurement period at each station. A cluster analysis was
performed in HYSPLIT on the back trajectories from individual stations in
order to group air masses of similar speed, direction and altitude. A
thorough description of the HYSPLIT cluster analysis methodology can be
found in Kelly et al. (2013). The number of back trajectory clusters differs
by station, since the selection of cluster numbers is dependent on the
individual data set and is somewhat subjective. For this study, and in
adherence with typical clustering methodology, a plot of total spatial
variance versus number of clusters was used to determine the cluster number; the
cluster number point just before the total spatial variances increases
dramatically is the number of clusters used for analysis at that site. From
the cluster analysis, each 6 h (00:00, 06:00, 12:00, 18:00 UTC) trajectory was assigned
a cluster number and paired with 6 h averaged aerosol optical property
data from the monitoring station for which the back trajectories were
produced. For example, the back trajectory at 06:00 UTC was paired with aerosol
optical property data averaged over 03:00–09:00 UTC. The paired optical
property data were then plotted in the AAE vs. SAE plot space and color-coded based on back trajectory cluster number, individually for each site.
The method described assumes that clustered back trajectories may carry similar
aerosol type(s) that may be unique compared to aerosol found in another back
trajectory clusters; this allows for temporal variation in aerosols at a site
that is dependent on the geography from which the air masses arrived at the
station. Results are presented in Sect. 6.3.
ResultsApplication and assessment of previous aerosol typing schemes
The median and interquartile spread of SAE, AAE, SSA, scattering coefficient
and absorption coefficient values at each site are presented in Table 3.
Additionally, Table 3 indicates the aerosol type as determined by the
variation in the Cappa et al. (2016) matrix overlaid on the plot of optical
property medians in Fig. 2b (“aerosol type before clustering”), as well as
the aerosol type determined from a clustering analysis (“aerosol type after
clustering”), as described in the next section. Descriptions of the aerosol
types can be found in Cazorla et al. (2013) and Cappa et al. (2016).
AAE vs. SAE medians plotted for 24 in situ monitoring stations in
the NOAA/ESRL Federated Aerosol Monitoring Network. (a) Bars represent interquartile values,
and points are color-coded by median SSA value at the station; (b) points
are color-coded by station location type, and the plot is overlaid with the aerosol
classification matrix from Cappa et al. (2016).
Median AAE and SAE values for each station are shown in Fig. 2a along with
bars that represent the interquartile spread (25th to 75th
percentiles) of the data. Points are shaded by median SSA value at that
station. Medians are used in order to minimize influence from outliers.
There are no strong spatial patterns visible in SSA shading within the AAE
vs. SAE plot space in Fig. 2a. Stations with high median SAE (smaller
particles) tend to have slightly lower median SSA values (darker particles)
than those with low median SAE and vice versa. However, there are
exceptions to this tendency, with NIM having a low median SAE value and
relatively low median SSA and PVC having a high median SAE value and
relatively high median SSA. Previous studies established that SSA and the
wavelength dependence of SSA can be used to signify aerosol type (Yang et
al., 2009; Russell et al., 2010). A three-dimensional plot space helps
visualize the relationships amongst SAE, AAE and SSA. This will be further
explored in the next section.
Figure 2a shows the wide variance of intensive properties at any one site,
with values spanning beyond the optical property signatures of a single
aerosol type. For example, CPR has interquartile AAE values ranging from
1.16 to 2.65, a spread that encompasses multiple potential aerosol
compositions, as outlined by the thresholds in Table 2 and by the
classification matrix in Fig. 2b. Interquartile ranges conservatively
bound the intensive properties and thus represent the dominant aerosol type
at each monitoring site. Some, if not all, of the sites could have multiple
aerosol types that are not well represented by the medians illustrated in
Fig. 2, as discussed in the next section.
Figure 2b shows the same optical property medians that are plotted in Fig. 2a. Station points are colored by station location type (as listed in
Table 2), with the aerosol classification matrix from Cappa et al. (2016)
overlaid on the plot space. Optical properties from the 24 NOAA/ESRL
Federated Aerosol Monitoring Network stations were evaluated with multiple existing published
aerosol classification schemes; however, given the clear visualization and
complete characterization of the parameter space afforded by the Cappa et al. (2016) matrix, that is the only scheme used for a visual comparison in
this study. The station location type provides the reader guidance on what
aerosol types might be expected at the site.
There is a natural clustering of all continental polluted sites on the right-hand side of the plot in Fig. 2b, in the section Cappa et al. (2016)
designated as BC dominated. Median AAE > 1 at these sites is
consistent with other studies (Russell et al., 2010; Lee et al., 2012; Yang
et al., 2009; Cazorla et al., 2013). Furthermore, both remote/clean marine
(e.g., GRW, PYE, THD) sites and dust-influenced sites (e.g., NIM) tend to
fall on the left-hand side of the plot with low SAE values, indicative of
sea salt, highly processed and coated particles, or dust (Cappa et al.,
2016; Cazorla et al., 2013; Lee et al., 2012; Clarke et al., 2007; Yang et
al., 2009). The largest median AAE values are observed at NIM and CPR, both
of which experience Saharan dust events. NIM is located at the southern edge
of the Saharan desert. Dust transport to CPR is predominantly from the
African Sahel region (Prospero et al., 2014). Although ARN experiences
Saharan dust events (Toledano et al., 2007), these events are not frequent
enough to substantially influence the median in situ aerosol optical
properties. The high AAE values at sites influenced by dust agree with the
findings of Russell et al. (2010), Lee et al. (2012) and Yang et al. (2009), which identified dust aerosol as having the largest AAE values of
observed aerosol types. These sites also fit in well with the Cappa et al. (2016) and Cazorla et al. (2013) matrices. Aerosol types assigned to the
marine THD, ARN, GRW, PYE, CPT, CPR and PVC sites by the Cappa et al. (2016) and Cazorla et al. (2013) aerosol classification schemes exhibit high
variance in their properties, indicating a diverse influence of aerosol. For
example, the high SAE values at PVC show the strong influence of transport
from the nearby urban centers of Boston and Providence as well as pollution from
summer traffic on Cape Cod, which dominate the effect of marine aerosol on
the site's median SAE value (Titos et al., 2014).
Median AAE, SAE, SSA and log(σsp) values (along with
corresponding interquartile spread) for each cluster resulting from the
cluster analysis.
Cluster no.AAESAESSAlog(σsp)Sites included in clusterAerosol type according to Cappa et al. (2016) matrixCluster commonality/site descriptions11.04 (0.99, 1.37)1.40 (1.27, 1.69)0.93 (0.92, 0.93)2.27 (2.23, 2.34)ALT, BRW,MLO, SPL,SUMSmall particles, low absorption + BC dominatedRemote Arctic or mountaintop with long-range transport aerosol or occasional local influence21.22 (1.21, 1.22)1.54 (1.53, 1.55)0.93 (0.92, 0.93)4.44 (4.29, 4.57)AMY, GSNBC dominatedHeavily polluted South Korean coastal sites31.20 (1.11, 1.31)1.94 (1.80, 2.06)0.92 (0.89, 0.92)3.26 (3.18,3.48)APP, ARN,BEO, BND,FKB, KPS,LLN, PVC,SGPBC dominatedPrimarily continental sites experiencing urban or biomass burning aerosol41.34 (1.19, 1.50)0.53 (0.43, 0.64)0.92 (0.92, 0.93)4.67 (4.59, 4.76)NIM, PGHLarge-particle–BC mixContinental sites experiencing heavy dust loading and biomass burning aerosol52.000.280.973.56CPRMixed dust, BC, BrCCoastal site experiencing occasional dust, biomass burning or pollution61.12 (0.62, 1.37)0.96 (0.67, 0.98)0.95 (0.94, 0.97)3.43 (3.07, 3.69)CPT, GRW,PYE, THD,WLGLarge-particle–BC mixCoastal or remote sites experiencing occasional sea salt, dust, biomass burning or pollution aerosol
Figure 2 illustrates that the Cappa et al. (2016) aerosol classification
scheme agrees with the expected dominant aerosol type at continental
polluted (BC-dominated), marine polluted (BC-dominated mixed with sea salt) and continental dust/biomass sites (mixed dust, BC, BrC). On the other hand,
the classification scheme assigns dominant aerosol types at remote marine
sites and Arctic sites that differ from what would be expected at these
sites, given their location and proximity to aerosol sources. Marine clean
sites in this analysis (CPR, CPT, GRW, PYE, THD) have a wide spread of AAE
values, and although they are all situated on the left side of the plots in
Fig. 2, due to a common low SAE value among the sites, they are not
clustered along the AAE axis. All stations in the plot with median SAE
values less than or equal to 1.1 are classified as either continental
dust/biomass or marine clean, but those classifications cannot be
distinguished in the Cazorla et al. (2013) matrix or the modified Cappa et al. (2016) matrix. An improved matrix may include dust, marine aerosol,
large coated particles and/or highly processed (aged) particles as possible
aerosol types for SAE values less than 1.1. Figure 2a shows that marine
clean sites exhibit much higher SSA values than the continental dust/biomass
sites with similarly low SAE values, which suggests that the addition of
more optical parameters, including SSA, into the clustering analysis could
yield more optimized aerosol classification results. Consequently, in the
next section, results from a multivariate cluster analysis are used to help
reduce ambiguity in aerosol classification and further hone potential
aerosol type identification.
Three-dimensional parameter space of SAE vs. AAE vs. log of scattering
coefficient, σsp (Mm-1). Station points are colored by
cluster number resulting from the clustering analysis and sized by median
SSA value.
Multivariate cluster analysis
Figure 3 shows median optical property values, plotted in a 3-D AAE vs. SAE
vs. log(σsp) parameter space. Station points are color-coded by
cluster number and sized by SSA median values. Not only does the 3-D
parameter space provide a robust visualization of the clustering results, but it
also provides further insight into an aerosol population than the AAE vs. SAE
parameter space used previously, since information on loading and SSA are
also visible.
Table 4 shows median AAE, SAE, SSA and log(σsp) values along
with interquartile values for each cluster, plus aerosol type and condition
(where applicable) based on cluster optical property medians, thresholds
from previous literature and previous knowledge of station characteristics
at the sites within each cluster.
In the 3-D plot seen in Fig. 3, stations that fall
within the same cluster number are also located near each other in the
three-dimensional parameter space, making for an effective visualization of
the relationship between aerosol population and optical properties.
Furthermore, stations in each cluster generally share similar site
characteristics and expected aerosol type. Discussion of results for each
individual cluster is available in the Supplement, while more
general results are discussed here.
The clusters presented in Fig. 3 generally group together sites that are
expected to have similar aerosols, and the expected aerosol
characterizations generally agree with the aerosol type inferred with the
aerosol classification schemes. The method does particularly well with
identifying aerosol type at stations with a more or less stable, homogeneous
aerosol population, including continental stations sampling BC-dominated
aerosol (i.e., clusters 2 and 3), as well as the continental stations
sampling high loads of dust aerosol (i.e., Cluster 4). The method also does
a fair job at identifying remote Arctic or mountaintop sites (i.e., Cluster
1) that sample large processed particles (due to aging during transport) and
occasional instances of local pollution. These methods do not do as well at
identifying the dominant aerosol type at stations with a more complex location and
topography, where variable aerosol populations that depend on wind direction
and/or occasional extreme aerosol events are not well characterized by
median optical properties within the parameter space.
An advantage to the incorporation of log(σsp) into the
clustering algorithm and the 3-D parameter space plot is that it allows for a
more complete picture of aerosol type and conditions at the station. For
example, even though the Cappa et al. (2016) aerosol typing scheme assigns a
BC-dominated aerosol to both clusters 1 (remote Arctic and mountaintop
stations: ALT, BRW, SUM, MLO, SPL) and 2 (heavily polluted urban coastal
sites: AMY, GSN), Fig. 3 shows that these clusters are clearly different,
given that Cluster 1 exhibits much lower aerosol loading than Cluster 2. The
stations in these clusters are indistinguishable within just the AAE vs. SAE
2-D parameter space. Using σsp in the analysis gives further
insight into the frequency of occurrence and loading of the inferred aerosol
(stations in Cluster 1 measure less BC-dominated aerosol than stations in
Cluster 2).
There are a few weaknesses to the approaches used thus far in typing
aerosols using median optical properties and clustering to reduce ambiguity
in the aerosol classification. First, knowledge of station location alone
cannot accurately determine the type of aerosols found there (Omar et al.,
2005). For example, long-range transport or extreme events may result in
aerosols being sampled that are not generally representative of the local
geographic region. Second, using a climatological mean or median value of an
optical property like SAE or AAE can be misleading in the case of two or
more differing aerosols being present at different times over the measurement
period. For example, a median SAE value of 1 for a site that measures sea
salt (low SAE near 0) over half the measurement period and pollution aerosol
(high SAE near 2) over the other half of the measurement period, does not
provide any real information about the aerosol population, since neither
aerosol type has an SAE value of 1. In order to address these concerns, an
additional analysis using air mass back trajectories is performed as a means
of exploring the spread in optical property data at each site. This analysis
also allows for multiple aerosol types to be present at any one location.
Back trajectory analysis
The preceding results are derived from the application of aerosol typing schemes
to median optical properties at multiple stations, a method that depends on
the assumption that each site has only a single dominant aerosol type. Many
of the sites in this analysis, however, are likely to have a heterogeneous
aerosol population with various aerosol types. Backward air mass
trajectories are incorporated into the analysis here as a means of both (1) allowing for the consideration of multiple dominant aerosol types at one
station and (2) allowing for attribution of a likely aerosol source, which can
help confirm the practicality of using optical properties to infer aerosol
type.
Case studies
Due to a need for brevity, the back trajectory analyses for all 24 stations
cannot be presented, so we selected four monitoring stations to present
here: Mt Waliguan, China (WLG); Cape Cod, Massachusetts, USA (PVC); Niamey,
Niger (NIM); and Heselbach, Germany (FKB). The four sites presented here were
chosen to represent cases both where back trajectories helped identify
aerosol types and where back trajectories did not elucidate information
beyond the initial aerosol classification analysis using median optical
properties. Shown for each of the four stations (Figs. 4–7) are a map of mean
back trajectory paths for each cluster, a plot of trajectory height vs.
backward time (color-coded by trajectory cluster number), and a plot of AAE
vs. SAE properties for 6 h averaged optical property data, color-coded by
paired trajectory cluster number and overlaid by the median optical property
values of each cluster in the largest color-coded point. If a station's
dominant aerosol type differs with air mass origin, these plots can
elucidate a station's various aerosol types.
Back trajectory map, back trajectory height (in kilometers above
ground level) vs. time and AAE vs. SAE plot space for Mt Waliguan, China, station WLG, all color-coded by back trajectory cluster number. The
percentage of air mass back trajectories corresponding to each cluster are
also shown next to the mean trajectories.
Mt Waliguan, China
The back trajectories at WLG were grouped into four clusters in
HYSPLIT, as shown in Fig. 4. Cluster 1 contains ∼ 33 % of
the site's back trajectories and has origins to the west of the station near
northern Pakistan and traveling through western China; Cluster 2 contains
∼ 30 % of the site's back trajectories and has origins (on
average) to the west of the station in rural China; Cluster 3 contains
∼ 33 % of the site's back trajectories and has origins very
near the site itself and slightly to the east; and Cluster 4 contains
∼ 3 % of the site's back trajectories and has origins to the
far northwest of the station, traveling to the station at high altitudes
from rural Russia. AAE values are similar for each trajectory cluster,
though SAE values vary. Furthermore, the median aerosol optical property
values from each of the trajectory clusters are unique, suggesting a variety
of aerosol types using thresholds from previous literature (Cazorla et al.,
2013; Costabile et al., 2013). The optical properties from the aerosols in
back trajectory Cluster 1 (from deserts in northern Pakistan and western
China) imply a dust mixture. Lower SAE values mean the aerosols from this
trajectory cluster are larger, and AAE values near and above 1.5 likely mean a dust and/or carbonaceous aerosol mixture (Cazorla et al., 2013). These
results support those of Che et al. (2011) and Kivekäs et al. (2009),
which cite deserts as aerosol sources from western wind sectors at WLG.
clusters 1 and 2 are most similar in terms of median SAE and AAE values,
though the map shows that Cluster 1 trajectories traveled farther in the
3-day period, and thus had faster wind speeds. Cluster 2 and Cluster 3 have
mean trajectory paths that are relatively short and are thus associated with
low wind speeds. This means that these clusters are likely to be more
influenced by local aerosol sources. The optical properties of the aerosols
from back trajectory Cluster 3 coming from the east suggest BC, given the AAE
value near 1. This is in agreement with findings of Kivekäs et al. (2009) that show that increased particle concentrations from the east of the WLG
station indicated anthropogenic pollution. Cluster 4 looks quite different
than the other trajectories and has median optical properties indicative of
dust (Cazorla et al., 2013), which makes sense given the trajectory
cluster's origin to the northwest of the site (Che et al., 2011; Kivekäs
et al., 2009).
Back trajectory map, back trajectory height (in kilometers above
ground level) vs. time and AAE vs. SAE plot space for Niamey, Niger, station
NIM, all color-coded by back trajectory cluster number.
Niamey, Niger
The back trajectories at NIM were grouped into three clusters in
HYSPLIT, as shown in Fig. 5. Cluster 1 contains slightly over half
(∼ 53 %) of the back trajectories, with air mass
trajectories reaching the site (on average) from the south/southwest, and
traveling at a relatively low altitude over populated regions. Cluster 1
differs from clusters 2 and 3 in that it has a lower median AAE value and a higher median SAE value. Given the optical properties of the trajectory
cluster 1, along with the knowledge of anthropogenic activities in the
source region, the likely dominant aerosol during those trajectories is a
biomass-burning–soot-aerosol mixture (e.g., Osborne et al., 2008; MacFarlane
et al., 2009). Clusters 2 and 3 constitute slightly less than half
(∼ 46 %) of the back trajectories at NIM and originate (on
average) from the north and northeast of the site. In Fig. 5, the median
optical property values of clusters 2 and 3 are nearly indistinguishable.
For these two clusters, the small SAE values and AAE values above
∼ 1.5 suggest dust mixtures (Cazorla et al., 2013; Lee et al.,
2012; Yang et al., 2009). Previous observations by Osborne et al. (2008)
noted dust during northerly flow due to the proximity of the Sahara desert
to the north/northeast of the site, as did MacFarlane et al. (2009). NIM
provides a good example of trajectory analysis elucidating two dominant
aerosol types that were obscured when only the climatological medians of AAE
and SAE values were evaluated. However, it should be noted that local
sources and meteorological conditions also have a large influence on aerosol
at the site, in addition to trajectory sources.
Back trajectory map, back trajectory height (in kilometers above
ground level) vs. time and AAE vs. SAE plot space for Cape Cod,
Massachusetts, USA, station PVC, all color-coded by back trajectory cluster
number.
Cape Cod, Massachusetts, USA
Back trajectories at PVC were clustered into three groups in HYSPLIT,
as shown in Fig. 6. Cluster 1 contains almost
half (∼ 49 %) of the trajectories and originates (on
average) to the south and southeast of the Cape Cod site along the heavily
populated eastern US seaboard. Cluster 2 contains ∼ 43 %
of the trajectories and (on average) travels to the monitoring station from
the northwest over eastern Canada. Cluster 3 contains only ∼ 8 % of trajectories and comes to the station from over the North Atlantic.
Cluster 3 is distinct from clusters 1 and 2 with the lowest SAE value
(largest particles) and, given its source region, suggests at least partial
marine sea salt aerosols. The classification matrix suggests Cluster 3 is
large particles mixed with BC. Clusters 1 and 2, on the other hand, with
continental source regions and optical properties indicative of elemental
and organic carbon suggest anthropogenic aerosols. Due to its proximity to
both the ocean and large cities like Boston, it is unsurprising that the
site measures both marine and urban aerosols, depending on the wind
direction. The pairing of back trajectory analysis with optical property
classification gives a more detailed picture of the multiple aerosol
populations at PVC, in accord with other aerosol research done at the site
(Titos et al., 2014). Since the back trajectories from over the Atlantic
make up such a small portion of the air masses that arrive at PVC, this
could explain why this station clusters with continental polluted stations
instead of marine polluted stations in the first cluster analysis of this
study in Sect. 5.1.
Back trajectory map, back trajectory height (in kilometers above
ground level) vs. time and AAE vs. SAE plot space for the Black Forest,
Germany, station FKB, all color-coded by back trajectory cluster number.
Heselbach, Black Forest, Germany
Back trajectories at FKB group into two clusters (Fig. 7), each containing
approximately half of the back trajectories. Back trajectories associated
with Cluster 1 typically originated from the northwest over the North
Atlantic and are associated with higher wind speeds and longer-distance
transport than those in Cluster 2. Cluster 2 tended to travel shorter
distances in reaching the site, with mean back trajectories originating from
the east of the station in southern Germany, as shown in Fig. 7. Despite the
very different geographical origins of the two air mass clusters and very
different wind speeds (on average), both trajectory groups have similar
median optical property signatures and suggest a BC-dominated aerosol type
(Cazorla et al., 2013; Yang et al., 2012; Costabile et al., 2013; Lee et al.,
2009). The similarity in aerosol properties between the two trajectory
clusters arriving at FKB suggests that FKB measures aerosols that are
regionally representative aerosols of western Europe. Previous analysis of
FKB data shows that the site is dominated by anthropogenic aerosol
(Jefferson, 2010). Due to the homogeneity of the aerosol population at the
FKB site, back trajectory analysis does not provide any additional
information useful for aerosol typing.
All stations
A broader understanding of the link between back trajectory clusters, aerosol
optical property measurements and aerosol type can be gained by collectively
analyzing all trajectory clusters at all stations rather than looking at
stations individually. Here, each trajectory cluster from every site is
classified based on where the trajectory originated and the geography over
which the air mass traveled; then trajectory clusters from all stations are
plotted in one AAE vs. SAE plot space. The classifications include
continental Arctic, continental dust, continental dust/polluted, continental
polluted, polluted marine and remote marine. A trajectory cluster is
classified as continental Arctic if it passes over land north of
60∘ N latitude, continental dust if it passes over remote desert,
continental dust/polluted if it passes over populated desert regions with
anthropogenic influence, continental polluted if it passes over populated
land, polluted marine if it passes over populated coastal regions with
anthropogenic influence, and remote marine if it passes over clean,
unpopulated ocean regions. Table S6 details classifications of each
trajectory cluster at all stations. There is unavoidable subjectivity in this
classification method, for a few reasons. For one, some trajectories travel
over geography that falls into one or more of the classifications chosen for
the analysis. In these cases, other factors, such as underlying geography and
typical site aerosol populations, were considered to make the more nuanced
classifications. Back trajectory analysis of aerosol type are needed to
account for air mass dispersion, aerosol wet and dry deposition, cloud
processing, and additional sources added at low altitudes and locally. A good
example of this is long-range transport of African dust over the Atlantic
Ocean. A 3-day back trajectory may not be sufficient to identify long-range
dust transport from the African continent. Here, the delineation of dust from
marine aerosol is ambiguous. More information on the aerosol composition and
hygroscopicity is needed for more conclusive aerosol identification. The
authors acknowledge this weakness of the methodology and its inherent
uncertainty and subjectivity.
Median values of optical properties from each trajectory cluster at all sites
are presented in Fig. 8. There are some clear spatial patterns that emerge
when visualizing the trajectory cluster classifications and the median
optical properties in the AAE vs. SAE plot space. The majority of continental
polluted trajectory clusters group tightly in the area of the plot that would
be classified as BC dominated by the Cappa et al. (2016) matrix. This is
similar to earlier findings in this paper where continental polluted sites
were aggregated at higher SAE (smaller size) and at AAE values in the range
of ∼ 1–1.5. Trajectories classified as polluted marine show a similar
range of AAE values as the continental polluted trajectories, though with
lower SAE values, indicative of large sea salt mixed with organic carbon.
Trajectory clusters classified as continental dust are best defined by AAE
values greater than 1.4, though they are poorly defined by SAE values due to
the large variance in SAE for those clusters. Continental dust/polluted
trajectory clusters are more or less tightly defined by AAE values between
0.9 and 1.4 and SAE values between 0.5 and 1.2, though it is hard to draw
significant conclusions about this trajectory type, since only three
trajectories meet this classification. Trajectories identified as continental
Arctic are not well defined in this plot space. Both AAE and SAE values of
this trajectory type are variable, though median SSA values for this
trajectory class are more similar and are close to 0.95.
The range of Arctic optical properties most likely stems from the seasonal
transport of European and Siberian continental aerosol to the sites in the
winter and spring, contrasted with sea salt from open water in the summer.
Remote marine trajectories are the least well defined of all the trajectory
cluster classes, with highly variable optical properties. Remote marine
trajectories show AAE values that range anywhere from 0 to 2.2, with SAE
values slightly more defined in a range of -0.4–1.2. Median SSA values
are, however, quite similar within more remote marine trajectories, with high
values near 0.96 indicating a whiter aerosol such as sea salt.
There are some clear outliers within trajectory classification groups that
may be explained by misclassification of trajectories. For example, the
points labeled 1 and 2 in Fig. 8 are back trajectories from CPR, both with
3-day paths that travel only over the Atlantic Ocean. Although the trajectory
classification methodology yielded a class of remote marine for those
specific trajectories (the air masses only traveled over unpopulated ocean
regions for 3 days before reaching the site), previous studies suggest that
these air masses could be heavily influenced by African dust events (Denjean
et al., 2016; Kalashnikova and Kahn, 2008; Reid et al., 2003). If indeed the
dominant aerosol type in these back trajectories was dust, this would fit in
much more neatly with previous dust classification schemes (i.e., Lee et al.,
2012; Clarke et al., 2007; Yang et al., 2009) and the Cappa et al. (2016)
matrix.
AAE vs. SAE medians plotted for all back trajectory clusters from
24 in situ monitoring stations in the NOAA/ESRL Federated Aerosol Monitoring Network. Points
are colored by the trajectory classification, and sized by the median SSA
value of measurements from that trajectory cluster, such that smaller points
indicate low SSA values and larger points indicate high SSA values. The points
labeled 1 and 2 are back trajectories from CPR that are outliers discussed
in the text in Sect. 6.
By classifying back trajectory clusters from all station locations and
including them in the optical property plot space, we get a clearer idea of
what types of trajectories, and thus likely aerosol type, are well defined by
median optical properties, and which are poorly defined by median optical
properties. Continental polluted and marine polluted trajectories have median
optical parameters that are well defined and visually cluster in the plot
space. Continental dust and continental dust/biomass are somewhat well
defined by optical properties in the plot space. Continental Arctic
trajectories appear to be well defined by AAE, with all cluster AAE values of
around 1, though the trajectories are not well defined by SAE, which shows a
larger range. The remote marine trajectory cluster (presumably clean air
masses) is poorly defined by optical properties and thus is not easily
visualized in the plot space.
To our knowledge, few previous studies have classified remote marine aerosol
(only Costabile et al., 2013, classified a coarse marine mode in the suburbs
of Rome, Italy), and no previous studies have classified continental Arctic
aerosols using an aerosol classification matrix. Our findings show that at
these site types, typing schemes that use aerosol optical properties need
more detailed analysis that account for seasonal variability and local
sources. Using aerosol optical parameters to infer aerosol type works well
for certain types of aerosol that fit neatly into matrices like that from
Cappa et al. (2016), including BC-dominated aerosol and dust mixtures. Marine
aerosol, processed aerosol and highly heterogeneous aerosol populations are
much more poorly defined by optical properties and do not fit cleanly in
existing matrices without overlap with different aerosol types.
Discussion
The application of previous aerosol classification schemes to the aerosol
optical property data from stations in the NOAA/ESRL Federated Aerosol Monitoring Network
generally yields a dominant aerosol type that would be expected at that site
location. The classification schemes do particularly well at inferring
aerosol type from optical properties at continental sites that measure BC
mixtures but do not do as well at sites with more complex topography (e.g.,
mountaintop, coastal) that measure a more heterogeneous aerosol population
that changes with wind direction. Including median optical parameters from
multiple stations on one AAE vs. SAE plot allows for a comparison of dominant
aerosol type at many sites, though the use of median optical properties makes
the most sense for sites with a homogenous aerosol population. The single AAE
vs. SAE plot can provide ambiguous results for sites with a heterogeneous
aerosol population.
The two aerosol classification methods (Sect. 6.1 and 6.2) had varying
degrees of success. The first method, a multivariate cluster analysis,
generated groups of monitoring sites with similar AAE, SAE, SSA and
log(σsp) values. The first classification scheme was applied
to median optical properties from all station data within each cluster to
produce a new aerosol type for stations within that cluster. One advantage to
this approach is that the inclusion of log(σsp) in the
clustering analysis, and subsequent visualization of station clusters in the
AAE v. SAE v. log(σsp) 3-D parameter space, provides insight
not only into a cluster's aerosol type. This approach also provides insight
as to how aerosol loading (and thus site conditions) differs between
clusters. Although the AAE and SAE aerosol typing schemes yield similar
inferred aerosol type of BC-dominated aerosol for both remote
Arctic/mountaintop sites and continental sites, the notable difference in
log(σsp) values among these dissimilar stations defines the
separate clusters. An anticipated advantage to the multivariate cluster
analysis was that it would help to reduce ambiguity in the results of aerosol
typing schemes, though this was not the case with every cluster. Rather than
falling more surely within the optical property thresholds of one aerosol
type, the median optical properties of a few clusters still fell on the cusp
of two or more aerosol type thresholds. This left the aerosol type of some
clusters uncertain, particularly for clusters with coastal and/or remote
sites.
The third method (Sect. 6.3), pairing 6 h averaged optical properties with
corresponding back trajectories, provided more detailed insight into the
aerosol population at an individual station. This method allowed for the
typing of multiple aerosols related to different air masses. At stations
where aerosol populations are diverse and varying, such as NIM (dust and
biomass burning), WLG (dust, pollution, free-troposphere long-range transport
aerosol) and PVC (marine aerosol and pollution), the different aerosol types
that were previously obscured using the site's median optical properties were
more apparent when using the trajectory cluster approach. At stations where
aerosol populations are homogeneous (like FKB; regional pollution), no new
information on aerosol type was gained. Consolidating all trajectory clusters
and corresponding classifications into one plot space (Sect. 6.3.2) allowed
us to see a large variety of back trajectory and likely aerosol type and
confirmed previous findings from the paper that some trajectory classes (like
continental polluted and marine polluted) are well defined by a unique range
and combination of optical properties, while other trajectory classes (like
remote marine and continental Arctic) have highly variable ranges and
combinations of SAE, AAE and SSA and are thus less likely to be typed by
aerosol classification schemes using only optical parameters.
The application of varying classification methods gave satisfactory
inferences regarding some aerosol types, in great part due to the quality of
previously developed aerosol classification schemes. Despite the differences
in optical property thresholds presented from each scheme, many of the
schemes' thresholds do have large overlap, making it easy to affirm inferred
aerosol type with multiple schemes. Many typing schemes provided satisfactory
aerosol typing results for fossil fuel burning aerosol, biomass burning
aerosol and dust (Cappa et al., 2016; Cazorla et al., 2013; Lee et al., 2012;
Yang et al., 2009; Bahadur et al., 2012; Russell et al., 2010), though fewer
schemes were available to type large coated particles (Cazorla et al., 2013),
sea salt (Costabile et al., 2013) and mixed aerosol (Cappa et al., 2016;
Cazorla et al., 2013). Perhaps the most useful typing schemes were that of
Cazorla et al. (2013) and Cappa et al. (2016), which provided thresholds for
typing mixed aerosol and large coated particles or a
large-particle–low-absorption mix. The Cazorla et al. (2013) and Cappa et
al. (2016) schemes also delineated the entirety of the AAE vs. SAE plot
space, leaving no combination of optical property values without a category.
It should be mentioned that the success of aerosol classification schemes is
largely dependent on uncertainties in AAE attribution (Cappa et al., 2016).
The scientific community has yet to fully assess AAE as an indicator of
aerosol composition. Although AAE = 1 is often taken within the community
to indicate black carbon, some studies show that this largely depends on
aerosol composition and size, as well as the age of the particle and
atmospheric processing that it endures (Lack and Langridge, 2013; Saleh et
al., 2014; Costabile et al., 2017; Moosmüller et al., 2011). Furthermore,
the accuracy of these aerosol classification methods are only as good as the
extent to which the AAE value is an indication of the aerosol
composition. As the scientific community advances our understanding of AAE and
its relationship to aerosol composition and size, these aerosol
classification schemes should be refined.
A major missing piece of the currently available aerosol classification
methods is the identification and validation of optical property thresholds
to identify sea salt aerosol. To the authors' knowledge, only one study
includes marine aerosol identification; Costabile et al. (2013) provide
values of SSA > 0.95, SAE < 0.5, dSSA = 0–0.05
and AAE > 2 for coarse marine mode aerosol. Many studies ignore
the contribution of sea salt altogether (or do not use data that would have
sea salt aerosol contributions), while other studies do not include sea salt
aerosol in their typing scheme because sea salt has negligible absorption and
thus poorly defined AAE (Russell et al., 2010). The best match with sea salt
aerosol in the Cappa et al. (2016) matrix presented here is likely the
“large particles, low-absorption” classification. Since sea salt aerosols
are dominated by large particles, there is a general consensus that marine
particles are characterized by low SAE values and high SSA values (Russell et
al., 2010; Costabile et al., 2013; Smirnov et al., 2002; Dubovnik et al.,
2002). Of the 24 stations analyzed in this study, sea salt aerosol is
expected at CPT, CPR, GRW, PYE and THD and to a lesser extent at ARN, AMY,
GSN and PVC. With the exception of ARN, AMY, GSN and PVC, which often
measured polluted air masses (see scattering coefficient values for these
four stations in Table 3 and back trajectories for PVC), these coastal
stations have median values of SAE < 1 and SSA > 0.95.
Median values of AAE, however, range from 0.5 to 2.0. Further back trajectory
analysis (not shown here) relating air masses of oceanic origin at these
sites to aerosol optical properties does not show specific patterns in AAE
values for marine aerosols. Although no new marine aerosol typing information
is included here, the authors do encourage consideration of SAE and SSA
thresholds for sea salt to be included in future aerosol classification
analyses. Furthermore, the authors acknowledge that although no sea salt
aerosol types are designated here explicitly at coastal stations, some of the
aerosol types are likely sea salt aerosol mixed (however slightly) with some
absorbing component. Cappa et al. (2016) in some ways account for sea salt
aerosol by changing the categorization in the lower left of the box to
“large-particle–lower-absorption mix” although in the original matrix they
also suggest that this regime could be represented by large black particles.
Although this study generally affirms existing aerosol typing schemes, the
results here are only applicable given certain conditions and for specific
aerosol types. One stipulation of this analysis is that results were
compared to aerosol typing schemes from studies that used optical property
data from in situ surface measurements, aircraft campaigns and AERONET
measurements. There are few studies (e.g., Cappa et al., 2016) that evaluate
the differences that may exist in aerosol typing schemes/thresholds based on
the type of data (in situ vs. remote sensing, column vs. point, dry vs.
ambient measurements) used. The difference in RH between dry (most in situ
surface) and ambient (AERONET) measurements could have some effect on the
determined thresholds. A higher RH would decrease SAE (larger aerosol), SSA
thresholds might shift up (whiter aerosol), scattering coefficients would
get larger, and AAE might change due to coating on absorbing particles.
Future analysis comparing dry and ambient aerosol, as well as surface
measured vs. remotely sensed, typing schemes would be useful for determining
the validity of the comparisons made in this study.
An additional caveat in the parameter clustering analysis and back
trajectory cluster analysis is the presence of externally mixed aerosol with
size-dependent composition that renders the analysis ambiguous for a given
aerosol class. Future work on this would add much needed information to the
subject of aerosol typing from optical properties.
Another limitation to the classification analyses presented here is that
aerosol aging during transport can influence aerosol type. A study by Devi
et al. (2016) shows that prior to atmospheric aging, mobile sources and
biomass burning sources can have relatively high (∼ 1.2–2.0)
AAE values; however, after aging during transport (∼ 1–2 days), the brown carbon signal can go away, reducing the AAE value. There
may be a point when source information from aerosol intensive optical
properties can be lost during transport. In that case, aerosol
classification schemes may no longer be applicable.
There are still many ways in which this analysis can be expanded. The
incorporation of aerosol shape into the typing analysis could be helpful,
particularly in determining the differences between particles with similar
optical properties. Further stratification of the measurement data by
season, time of day, composition or hygroscopicity would elucidate more
about the variability in aerosol type with time. And finally, more analyses
of stations that have concurrent chemistry measurements and aerosol optical
property measurements could help verify existing aerosol classification
schemes (e.g., Cappa et al., 2016; Costabile et al., 2017).
Conclusions
Surface in situ aerosol optical properties obtained at 24 stations in the
NOAA/ESRL Federated Aerosol Monitoring Network were used to classify aerosol type at the site, using
aerosol classification schemes from the literature, cluster analyses, and
general knowledge of station location and characteristics. The monitoring
sites utilized for the analysis offered a diverse range of station locations
and aerosol types, providing a look at fossil fuel burning, biomass burning,
sea salt, dust and regionally mixed aerosols observed at various continental
sites. Plotting station optical property medians in an AAE vs. SAE plot
space, overlaid by the Cappa et al. (2016) classification matrix, for the
most part yielded inferences regarding aerosol types that were to be expected
based on knowledge of the monitoring station location. A handful of stations,
however, yielded unexpected results that appeared uncharacteristic of the
site, which indicated a need for a different visualization or analysis
method. Furthermore, the interquartile values of the optical properties from
each station in an AAE vs. SAE parameter space showed that there is often
large variability in optical properties at any given location, suggesting
that a single dominant aerosol type is not realistic at all stations.
A multivariate cluster analysis was performed as a means of grouping together
monitoring sites with not only similar aerosol type, but similar site
conditions (frequency of aerosol type, loadings, proximity to source,
location, etc.). The multivariate cluster analysis yielded six clusters of
stations with similar median AAE, SAE, SSA and log(σsp)
values. Sites that grouped within the same cluster most often had similar
expected aerosol types that aligned with the aerosol type predicted by the
aerosol typing scheme. Incorporation of the scattering coefficient into the
multivariate cluster analysis improved the inference regarding aerosol type
and conditions (i.e., aerosol loading, source) from optical property
measurements.
In order to further explore the complexity of aerosol populations and allow
for multiple aerosol types at some sites, an additional analysis was
presented using air mass back trajectories. Air mass back trajectories were
clustered based on similar direction, altitude and speed, and these clusters
were paired with optical property data and plotted in the AAE vs. SAE
parameter space. More detailed results from 4 of the 24 stations – WLG,
NIM, PVC and FKB – were discussed in order to show the range of success (or
lack thereof) of this approach. At complex sites like WLG, NIM and PVC,
multiple dominant aerosol types emerged, unique to different clusters of air
mass back trajectories. The classification of numerous aerosol types, along
with the information from the back trajectory clusters on how often those
aerosol types were measured, allowed for a more complete picture of the
heterogeneous aerosol populations at those sites. In the case of FKB, only
one aerosol type is inferred in each of the different trajectory clusters,
suggesting a homogenous aerosol population that is readily predicted by the
simpler analysis of just the median optical properties in the AAE vs. SAE
parameter space.
Combining back trajectory clusters and classifications from all 24 sites
showed that comparing optical characteristics with trajectory
characteristics yields results that further inform aerosol typing schemes.
While all trajectory clusters that were classified as marine polluted or
continental polluted had optical properties that were well defined, other
trajectory clusters classified as continental Arctic or remote marine had
highly variable optical parameters that were not informative in aerosol
typing.
This study has further assessed existing aerosol typing schemes, provided
additional methods that can be implemented to reduce ambiguity in typing
schemes, elucidate aerosol conditions that accompany aerosol type and allow
for the identification of multiple aerosol types at one site. A major
conclusion from the analysis, however, is that there is no combination of
extensive and/or intensive optical properties that allows for a perfect
classification of aerosol types. Prior knowledge of the measurement site can
help inform aerosol classification schemes, but obscurity remains in these
techniques. Furthermore, this paper highlighted the need for further analyses
and suggests specific ideas for future work needed to progress and refine
aerosol typing schemes that infer aerosol type from optical properties:
repeating this analysis with concurrent aerosol chemical and optical
measurements to verify aerosol classification thresholds will be essential to
expand and improve aerosol classification schemes.
Data for AMF sites are available from the DOE/ARM website
(http://www.arm.gov). Data from all other sites (except WLG) are available
from the World Data Center for Aerosols (http://ebas.nilu.no/). WLG data are
available from Junying Sun at CAMS.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-12097-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge the US Department of Energy as part of
the Atmospheric Radiation Measurement (ARM) Climate Research Facility for the
use of data from the southern Great Plains, Oklahoma, USA, as well as data
from the ARM Mobile Facility that was stationed at Heselbach, Germany;
Graciosa Island, Azores, Portugal; Niamey, Niger; Nainital, India; Cape Cod,
Massachusetts, USA; and Point Reyes, California, USA. We would like to thank
Derek Hageman for his extensive technical assistance in obtaining and
archiving the data from all stations in this paper. The aerosol measurements at ALT
are performed by Environment and Climate Change Canada, instrument maintenance and calibration
is performed by Dan Veber, and ALT site maintenance and operation is carried out by operators and
staff of the Canadian Forces Services. The monitoring at
ARN was partially supported by the Spanish Ministry of Science and Technology
(MINECO) through the Project CGL2014-55230-R and the European Union's Horizon
2020 research and innovation programme under grant agreement no. 654109
(ACTRIS2). The authors would like to acknowledge the China Meteorological
Administration for the use of data from Mt Waliguan, China. This work was partly supported
by a grant from National Basic Research Program of China (2014CB441201) and
the National Natural Science Foundation of China (41675129). Sang-Woo Kim was
supported by the KMA R&D program under grant KMIPA 2015-2011. The
measurements at BEO are supported by the EU Project ACTRIS2 H2020-INFRAIA and
the Bulgarian Academy of Sciences. The measurements at LLN are supported by
the Environmental Protection Administration of Taiwan. Edited by: Andreas Petzold Reviewed by: three
anonymous referees
ReferencesAllan, J. D., Baumgardner, D., Raga, G. B., Mayol-Bracero, O. L., Morales-García, F., García-García, F., Montero-Martínez, G.,
Borrmann, S., Schneider, J., Mertes, S., Walter, S., Gysel, M., Dusek, U., Frank, G. P., and Krämer, M.:
Clouds and aerosols in Puerto Rico – a new evaluation, Atmos. Chem. Phys., 8, 1293–1309, 10.5194/acp-8-1293-2008, 2008.
Anderson, T. L. and Ogren, J. A.: Determining aerosol radiative properties
using the TSI 3563 integrating nephelometer, Aerosol Sci. Technol.,
29, 57–69, 1998.
Angelov, C., Angelov, I., Arsov, T., Archangelova, N., Boyukliiski, A.,
Damianova, A., Drenska, M., Georgiev, K., Kalapov, I., and Nishev, A.: BEO
Moussala–A New Facility for Complex Environment Studies, in: Sustainable
Development in Mountain Regions, Springer, 123–139, 2011.
Bahadur, R., Praveen, P. S., Xu, Y., and Ramanathan, V.: Solar absorption by
elemental and brown carbon determined from spectral observations,
P. Natl. Acad. Sci. USA, 109, 17366–17371, 2012.
Bergin, M., Schwartz, S., Halthore, R., Ogren, J., and Hlavka, D.: Comparison
of aerosol optical depth inferred from surface measurements with that
determined by sun photometry for cloud-free conditions at a continental U.
S. site, J. Geophys. Res., 105, 6807–6816, 2000.
Bergstrom, R. W., Russell, P. B., and Hignett, P.: Wavelength dependence of
the absorption of black carbon particles: Predictions and results from the
TARFOX experiment and implications for the aerosol single scattering albedo,
J. Atmos. Sci., 59, 567–577, 2002.Bergstrom, R. W., Pilewskie, P., Russell, P. B., Redemann, J., Bond, T. C., Quinn, P. K., and Sierau, B.:
Spectral absorption properties of atmospheric aerosols, Atmos. Chem. Phys., 7, 5937–5943, 10.5194/acp-7-5937-2007, 2007.Bergstrom, R. W., Schmidt, K. S., Coddington, O., Pilewskie, P., Guan, H., Livingston, J. M., Redemann, J., and
Russell, P. B.: Aerosol spectral absorption in the Mexico City area: results from airborne measurements during
MILAGRO/INTEX B, Atmos. Chem. Phys., 10, 6333–6343, 10.5194/acp-10-6333-2010, 2010.
Berkowitz, C. M., Jobson, B. T. T., Alexander, M. L., Laskin, A., and
Laulainen, N. S.: Aerosol Composition and Morphology during the 2005 Marine
Stratus Radiation Aerosol and Drizzle Study, Publisher is American Geophysical Union Fall Meeting 2005 Abstracts, 2005.
Bodhaine, B. A.: Aerosol absorption measurements at Barrow, Mauna Loa and
the south pole, J. Geophys. Res.-Atmos.,
100, 8967–8975, 1995.
Bond, T. C., Anderson, T. L., and Campbell, D.: Calibration and
intercomparison of filter-based measurements of visible light absorption by
aerosols, Aerosol. Sci. Technol., 30, 582–600, 1999.
Borys, R. D. and Wetzel, M. A.: Storm Peak Laboratory: A research, teaching,
and service facility for the atmospheric sciences, B. Am. Meteor. Soc.,
78, 2115–2123, 1997.
Boucher, O.,
Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P.,
Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K.,
Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change
edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung,
J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Brunke, E., Labuschagne, C., Parker, B., Scheel, H., and Whittlestone, S.:
Baseline air mass selection at Cape Point, South Africa: application of 222
Rn and other filter criteria to CO2, Atmos. Environ., 38, 5693–5702, 2004.Cappa, C. D., Kolesar, K. R., Zhang, X., Atkinson, D. B., Pekour, M. S., Zaveri, R. A., Zelenyuk, A., and Zhang, Q.:
Understanding the optical properties of ambient sub- and supermicron particulate matter: results from the CARES 2010
field study in northern California, Atmos. Chem. Phys., 16, 6511–6535, 10.5194/acp-16-6511-2016, 2016.Cazorla, A., Bahadur, R., Suski, K. J., Cahill, J. F., Chand, D., Schmid, B., Ramanathan, V., and
Prather, K. A.: Relating aerosol absorption due to soot, organic carbon, and dust to emission sources
determined from in-situ chemical measurements, Atmos. Chem. Phys., 13, 9337–9350, 10.5194/acp-13-9337-2013, 2013.
Che, H., Wang, Y., and Sun, J.: Aerosol optical properties at Mt. Waliguan
observatory, China, Atmos. Environ., 45, 6004–6009, 2011.Clarke, A., McNaughton, C., Kapustin, V., Shinozuka, Y., Howell, S., Dibb,
J., Zhou, J., Anderson, B., Brekhovskikh, V., and Turner, H.: Biomass burning
and pollution aerosol over North America: Organic components and their
influence on spectral optical properties and humidification response,
J. Geophys. Res.-Atmos., 112, D12S18, 10.1029/2006JD007777, 2007.Costabile, F., Barnaba, F., Angelini, F., and Gobbi, G. P.: Identification of key aerosol populations through
their size and composition resolved spectral scattering and absorption, Atmos. Chem. Phys., 13, 2455–2470, 10.5194/acp-13-2455-2013, 2013.Costabile, F., Gilardoni, S., Barnaba, F., Di Ianni, A., Di Liberto, L.,
Dionisi, D., Manigrasso, M., Paglione, M., Poluzzi, V., Rinaldi, M.,
Facchini, M. C., and Gobbi, G. P.: Characteristics of brown carbon in the
urban Po Valley atmosphere, Atmos. Chem. Phys., 17, 313–326,
10.5194/acp-17-313-2017, 2017.
Delene, D. J. and Ogren, J. A.: Variability of aerosol optical properties at
four North American surface monitoring sites, J. Atmos. Sci., 59, 1135–1150,
2002.Denjean, C., Cassola, F., Mazzino, A., Triquet, S., Chevaillier, S., Grand, N., Bourrianne, T., Momboisse, G.,
Sellegri, K., Schwarzenbock, A., Freney, E., Mallet, M., and Formenti, P.: Size distribution and optical properties
of mineral dust aerosols transported in the western Mediterranean, Atmos. Chem. Phys., 16, 1081–1104, 10.5194/acp-16-1081-2016, 2016.
Devi, J. J., Bergin, M. H., Mckenzie, M., Schauer, J. J., and Weber, R. J.:
Contribution of particulate brown carbon to light absorption in the rural
and urban Southeast US, Atmos. Environ., 136, 95–104, 2016.Draxler, R. R. and Rolph, G.: HYSPLIT (HYbrid Single-Particle Lagrangian
Integrated Trajectory) model access via NOAA ARL READY website,
NOAA Air Resources
Laboratory, Silver Spring, available at: http://www.arl.noaa.gov/ready/hysplit4.html (last access: 30 August 2015), 2003.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M.
D., Tanré, D., and Slutsker, I.: Variability of absorption and optical
properties of key aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590–608, 2002.Giles, D. M., Holben, B. N., Eck, T. F., Sinyuk, A., Smirnov, A., Slutsker,
I., Dickerson, R., Thompson, A., and Schafer, J.: An analysis of AERONET
aerosol absorption properties and classifications representative of aerosol
source regions, J. Geophys. Res.-Atmos.,
117, D17203, 10.129/2012JD018127, 2012.Gyawali, M., Arnott, W. P., Zaveri, R. A., Song, C., Moosmüller, H., Liu, L., Mishchenko, M. I., Chen, L.-W. A., Green, M. C.,
Watson, J. G., and Chow, J. C.: Photoacoustic optical properties at UV, VIS, and near IR wavelengths for laboratory generated
and winter time ambient urban aerosols, Atmos. Chem. Phys., 12, 2587–2601, 10.5194/acp-12-2587-2012, 2012.Hagler, G. S., Bergin, M. H., Smith, E. A., and Dibb, J. E.: A summer time
series of particulate carbon in the air and snow at Summit, Greenland,
J. Geophys. Res.-Atmos., 112, D21309, 10.1029/2007JD008993, 2007.
Hansen, J., Rossow, W., Carlson, B., Lacis, A., Travis, L., Del Genio, A.,
Fung, I., Cairns, B., Mishchenko, M., and Sato, M.: Low-cost long-term
monitoring of global climate forcings and feedbacks, in: Long-Term Climate
Monitoring by the Global Climate Observing System, Springer, 117–141, 1996.
Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate response,
J. Geophys. Res.-Atmos., 102, 6831–6864, 1997.
Huffman, J. A., Docherty, K. S., Mohr, C., Cubison, M. J., Ulbrich, I. M.,
Ziemann, P. J., Onasch, T. B., and Jimenez, J. L.: Chemically-resolved
volatility measurements of organic aerosol from different sources, Environ.
Sci. Technol., 43, 5351–5357, 2009.Ion, A. C., Vermeylen, R., Kourtchev, I., Cafmeyer, J., Chi, X., Gelencsér, A., Maenhaut, W., and Claeys, M.: Polar organic
compounds in rural PM2.5 aerosols from K-puszta, Hungary, during a 2003 summer field campaign: Sources and diel variations,
Atmos. Chem. Phys., 5, 1805–1814, 10.5194/acp-5-1805-2005, 2005.Jefferson, A.: Empirical estimates of CCN from aerosol optical properties at four remote sites,
Atmos. Chem. Phys., 10, 6855–6861, 10.5194/acp-10-6855-2010, 2010.Kalashnikova, O. V. and Kahn, R. A.: Mineral dust plume evolution over the
Atlantic from MISR and MODIS aerosol retrievals, J. Geophys. Res.-Atmos., 113, D24204, 10.1029/2008JD010083, 2008.
Kelly, G., Taubman, B., Perry, L., Sherman, J., Soulé, P., and Sheridan,
P.: Relationships between aerosols and precipitation in the southern
Appalachian Mountains, Int. J. Climatol., 33, 3016–3028, 2013.
Kim, S., Yoon, S., Jefferson, A., Ogren, J. A., Dutton, E. G., Won, J.,
Ghim, Y. S., Lee, B., and Han, J.: Aerosol optical, chemical and physical
properties at Gosan, Korea during Asian dust and pollution episodes in 2001,
Atmos. Environ., 39, 39–50, 2005.Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the
spectral dependence of light absorption by aerosols is affected by organic
carbon, J. Geophys. Res.-Atmos., 109, D21208, 10.1029/2004JD004999,
2004.Kivekäs, N., Sun, J., Zhan, M., Kerminen, V.-M., Hyvärinen, A., Komppula, M., Viisanen, Y., Hong, N., Zhang, Y., Kulmala, M.,
Zhang, X.-C., Deli-Geer, and Lihavainen, H.: Long term particle size distribution measurements at Mount Waliguan, a high-altitude site in
inland China, Atmos. Chem. Phys., 9, 5461–5474, 10.5194/acp-9-5461-2009, 2009.
Kotamarthi, V.: Ganges Valley Aerosol Experiment (GVAX) Final Campaign
Report, 2013.Lack, D. A. and Langridge, J. M.: On the attribution of black and brown carbon light absorption using the
Ångström exponent, Atmos. Chem. Phys., 13, 10535–10543, 10.5194/acp-13-10535-2013, 2013.
Lee, S., Yoon, S., Kim, S., Kim, Y. P., Ghim, Y. S., Kim, J., Kang, C., Kim,
Y. J., Chang, L., and Lee, S.: Spectral dependency of light
scattering/absorption and hygroscopicity of pollution and dust aerosols in
Northeast Asia, Atmos. Environ., 50, 246–254, 2012.Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer aerosol
retrieval over land, J. Geophys. Res.-Atmos., 112, D13210, 10.1029/2006JD007815, 2007.Liu, J. and Li, Z.: Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential
factors and uncertainties, Atmos. Chem. Phys., 14, 471–483, 10.5194/acp-14-471-2014, 2014.McFarlane, S. A., Kassianov, E. I., Barnard, J., Flynn, C., and Ackerman, T.
P.: Surface shortwave aerosol radiative forcing during the Atmospheric
Radiation Measurement Mobile Facility deployment in Niamey, Niger, J. Geophys. Res.-Atmos., 114, D00E06, 10.1029/2008JD010491, 2009.
Mendes, L., Eleftheriadis, K., and Biskos, G.: Performance comparison of two
thermal denuders in volatility tandem DMA measurements, J. Aerosol Sci., 92,
38–52, 2016.Moosmüller, H., Chakrabarty, R. K., Ehlers, K. M., and Arnott, W. P.: Absorption Ångström coefficient, brown
carbon, and aerosols: basic concepts, bulk matter, and spherical particles, Atmos. Chem. Phys., 11, 1217–1225, 10.5194/acp-11-1217-2011, 2011.
Ogren, J. A.: A systematic approach to in situ observations of aerosol properties, in: Aerosol Forcing of Climate,
edited by: Charlson, R. J. and Heintzenberg, J., John Wiley & Sons, Ltd., 215–226, 1995.
Ogren, J. A.: Comment on “Calibration and intercomparison of filter-based
measurements of visible light absorption by aerosols”, Aerosol Sci. Technol., 44, 589–591, 2010.
Oltmans, S. J., Lefohn, A. S., Harris, J. M., and Shadwick, D. S.: Background
ozone levels of air entering the west coast of the US and assessment of
longer-term changes, Atmos. Environ., 42, 6020–6038, 2008.
Omar, A. H., Won, J., Winker, D. M., Yoon, S., Dubovik, O., and McCormick, M.
P.: Development of global aerosol models using cluster analysis of Aerosol
Robotic Network (AERONET) measurements, J. Geophys. Res.-Atmos., 110, 2005.Osborne, S., Johnson, B., Haywood, J., Baran, A., Harrison, M. and
McConnell, C.: Physical and optical properties of mineral dust aerosol
during the Dust and Biomass-burning Experiment, J. Geophys. Res.-Atmos., 113, D00C03, 10.1029/2007JD009551, 2008.
Park, S., Panicker, A., Lee, D., Jung, W., Jang, S., Jang, M., Kim, D., Kim,
Y., and Jeong, H.: Characterization of chemical properties of atmospheric
aerosols over Anmyeon (South Korea), a super site under Global Atmosphere
Watch, J. Atmos. Chem., 67, 71–86, 2010.
Prospero, J. M., Collard, F., Molinié, J., and Jeannot, A.:
Characterizing the annual cycle of African dust transport to the Caribbean
Basin and South America and its impact on the environment and air quality,
Global Biogeochem. Cy., 28, 757–773, 2014.Reid, J. S., Kinney, J. E., Westphal, D. L., Holben, B. N., Welton, E. J.,
Tsay, S., Eleuterio, D. P., Campbell, J. R., Christopher, S. A., and Colarco,
P.: Analysis of measurements of Saharan dust by airborne and ground-based
remote sensing methods during the Puerto Rico Dust Experiment (PRIDE),
J. Geophys. Res.-Atmos., 108, 8586, 10.1029/2002JD002493, 2003.Russell, P. B., Bergstrom, R. W., Shinozuka, Y., Clarke, A. D., DeCarlo, P. F., Jimenez, J. L., Livingston, J. M., Redemann, J.,
Dubovik, O., and Strawa, A.: Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition,
Atmos. Chem. Phys., 10, 1155–1169, 10.5194/acp-10-1155-2010, 2010.
Russell, P. B., Kacenelenbogen, M., Livingston, J. M., Hasekamp, O. P.,
Burton, S. P., Schuster, G. L., Johnson, M. S., Knobelspiesse, K. D.,
Redemann, J., and Ramachandran, S.: A multiparameter aerosol classification
method and its application to retrievals from spaceborne polarimetry,
J. Geophys. Res.-Atmos., 119, 9838–9863, 2014.Saleh, R., Robinson, E. S., Tkacik, D. S., Ahern, A. T., Liu, S., Aiken, A. C.,
Sullivan, R. C., Presto, A. A., Dubey, M., Yokelson, R. J., Donahue, N. M., and
Robinson, A. L.: Brownness of organics in aerosols from biomass burning
linked to their black carbon content, Nat. Geosci., 7, 647–650, 2014.
Schuster, G. L., Dubovik, O., and Holben, B. N.: Angstrom exponent and
bimodal aerosol size distributions, J. Geophys. Res.-Atmos., 111, D07207, 10.1029/2005JD006328, 2006.
Sharma, S., Brook, J., Cachier, H., Chow, J., Gaudenzi, A., and Lu, G.: Light
absorption and thermal measurements of black carbon in different regions of
Canada, J. Geophys. Res.-Atmos., 107, AAC 11-1–AAC 11-11, 2002.
Sheridan, P., Andrews, E., Schmeisser, L., Vasel, B., and Ogren, J.: Aerosol
Measurements at South Pole: Climatology and Impact of Local Contamination,
Aerosol Air Qual. Res., 16, 855–872, 2016.Sherman, J. P., Sheridan, P. J., Ogren, J. A., Andrews, E., Hageman, D., Schmeisser, L., Jefferson, A., and Sharma, S.:
A multi-year study of lower tropospheric aerosol variability and systematic relationships from four North American
regions, Atmos. Chem. Phys., 15, 12487–12517, 10.5194/acp-15-12487-2015, 2015.
Smirnov, A., Holben, B. N., Kaufman, Y. J., Dubovik, O., Eck, T. F.,
Slutsker, I., Pietras, C., and Halthore, R. N.: Optical properties of
atmospheric aerosol in maritime environments, J. Atmos. Sci., 59, 501–523,
2002.Titos, G., Jefferson, A., Sheridan, P. J., Andrews, E., Lyamani, H., Alados-Arboledas, L., and Ogren, J. A.:
Aerosol light-scattering enhancement due to water uptake during the TCAP campaign, Atmos. Chem. Phys.,
14, 7031–7043, 10.5194/acp-14-7031-2014, 2014.
Toledano, C., Cachorro, V., Berjon, A., De Frutos, A., Sorribas, M., De la
Morena, B., and Goloub, P.: Aerosol optical depth and Ångström
exponent climatology at El Arenosillo AERONET site (Huelva, Spain), Q. J. Roy.
Meteor. Soc., 133, 7950–807, 2007.Wai, K. M., Lin, N., Wang, S., and Dokiya, Y.: Rainwater chemistry at a
high-altitude station, Mt. Lulin, Taiwan: Comparison with a background
station, Mt. Fuji, J. Geophys. Res.-Atmos., 113, D06305, 10.1029/2006JD008248, 2008.Yang, M., Howell, S. G., Zhuang, J., and Huebert, B. J.: Attribution of aerosol light absorption to black carbon,
brown carbon, and dust in China – interpretations of atmospheric measurements during EAST-AIRE,
Atmos. Chem. Phys., 9, 2035–2050, 10.5194/acp-9-2035-2009, 2009.