The identification of different sources of the
carbonaceous aerosol (organics and black carbon) was investigated at a
mountain forest site located in central Germany from September to October
2010 to characterize incoming air masses during the Hill Cap Cloud
Thuringia 2010 (HCCT-2010) experiment. The near-PM
Air masses with the strongest marine influence, based on back trajectory
analysis, corresponded with a low particle mass concentration
(6.4–7.5
Atmospheric aerosol particles affect global climate, through direct and indirect radiative forcing (IPCC, 2013), human health (Lelieveld et al., 2015; Burnett et al., 2014; Pope et al., 2011) and the ecosystems (Bohlmann et al., 2005; Jickells et al., 2005). The chemical composition of atmospheric particles at a specific sampling place (e.g., rural, urban, or marine environment) not only depends on the local environment and sources but is also influenced by the history of the particles reaching the sampling site. During transport, so-called aging processes not only modify the chemical composition of the particles but also affect their physical properties (e.g., size distribution, volatility, hygroscopicity, cloud condensation nuclei (CCN) activity, and optical properties; Donahue et al., 2014; Farmer et al., 2015; Moise et al., 2015). As a consequence, aerosol particles at a specific location result from a complex mixture of different sources combined with complex processing.
Carbonaceous aerosol particles are a dominant fraction of total particle mass and are made of a large number of chemical species, which can be divided into organic aerosol (OA) and black carbon (BC; e.g., Cabada et al., 2002). One of the most significant aerosol particle components influenced by atmospheric aging processes is the OA fraction, which can represent up to 90 % of the fine aerosol particle mass (e.g., Zhang et al., 2007). To better understand the origins of OA, source apportionment analysis is commonly applied to distinguish primary organic sources (e.g., related to fossil fuel, biomass, or coal combustion) from secondary organic aerosol (SOA) sources, based on either online measurements (e.g., Zhang et al., 2011; Canonaco et al., 2013), offline chemical analysis (e.g., van Pinxteren et al., 2016; Srivastava et al., 2018), or a combination of both (Srivastava et al., 2019). Black carbon is associated with primary emissions from the combustion processes of either anthropogenic (car, household heating, and industry) or biogenic (e.g., wildfires) origins. In contrast to OA, the identification of the different sources of BC is sparse, and only the recent development of an aethalometer model approach now allows us to distinguish equivalent BC (eBC) related to traffic emissions from wood combustion eBC (e.g., Sandradewi et al., 2008; Laborde et al., 2013; Zhu et al., 2018; Martinsson et al., 2017; Liakakou et al., 2020). Not only local sources drive the aerosol particle chemical composition; long-range transport, influenced by air mass origin, also plays an important role in local number size distribution and aerosol particle chemical composition (e.g., van Pinxteren et al., 2016, 2019; Waked et al., 2018). Therefore, not all the eBC mass concentration has to be linked to local sources, and a significant fraction can be attributed to long-range transport (e.g., Healy et al., 2012; van Pinxteren et al., 2019).
The present work investigates the aerosol particle chemical composition and
the different sources of carbonaceous particles reaching a site close to the
village of Goldlauter in the Thuringian forest in central Germany. The
measurements took place during September–October 2010 as part of the Hill Cap Cloud Thuringia 2010 (HCCT-2010) experiment, which aimed to investigate
the impact of cloud processing on aerosol physico-chemical properties. The
Goldlauter site served as upwind site for studying air masses before they entered hill cap clouds at the Schmücke mountain. The present study focuses on an in-depth characterization at this site, while companion papers have related upwind site data to the other experiment sites (see, e.g., Harris et al., 2013, and
For the Hill Cap Cloud Thuringia 2010 (HCCT-2010) experiment, the same
places were used as for the FEBUKO/MODMEP experiments in 2001–2002 (Herrmann et al., 2005). This work is focused on measurements performed at the upwind site (10
A large setup of online and offline instruments was deployed during HCCT-2010, covering both gas and particle phases. Online instruments were positioned in two nearby laboratory containers and were operated continuously during the entire campaign. On the other hand, offline sampling systems were applied only during specific intensive observation periods (IOPs) associated with the following two different conditions: (1) SW wind direction and the presence of cloud at the Schmücke mountain site for the full cloud events (FCEs) and (2) SW or northeasterly (NE) wind direction and no clouds or fog at any site for the non-cloud events (NCEs). A detailed overview of these events is given by Tilgner et al. (2014).
The trace gases O
Additionally, a Monitor for AeRosol and Gases in Ambient Air (MARGA 1S ADI
208; Metrohm AG, Switzerland; Rumsey et al., 2014; Twigg et al., 2015; Stieger et al., 2017 ) connected to a Teflon-coated PM
The online physico-chemical characterization of the ambient aerosol
particles was performed using a large set of instruments, including a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, later referred to as AMS; Aerodyne Research, Inc.; DeCarlo et al., 2006), a dual mobility particle size spectrometer (TROPOS-type T-MPSS; Birmili et al., 1999), a multi-angle absorption photometer (MAAP; model 5012, Thermo Fisher Scientific; Petzold and Schönlinner, 2004), and a three-wavelength nephelometer (model 3563; TSI Incorporated; Heintzenberg et al., 2006). All of these instruments were located in the same laboratory container
and connected to the same sampling inlet consisting of a PM
The AMS data were processed with SQUIRREL, version 1.52L, and PIKA,
version 1.13B (downloaded from
Source apportionment was performed on the high-resolution organic mass
spectra data set using the multi-linear engine (ME-2) model developed by Paatero (1999) and using the source finder tool (Sofi4.9; Canonaco et al., 2013) developed at the Paul Scherrer Institute (PSI, Switzerland). Prior to analysis, the high-resolution organic mass spectra matrix was prepared according to the recommendations of Ulbrich et al. (2009). Isotope ions, which are calculated as a constant fraction of the parent ion, were removed. A minimum counting error was applied, and ions with a signal-to-noise (SNR) ratio between 0.2
Parallel to the online measurements, a five-stage Berner low-pressure
impactor (LPI 80/0.05/2.9; Hauke–MP GmbH, Austria; Berner and Lurzer, 1980) was used to collect PM size segregated, during the IOPs, using a humidity-controlled inlet (RH
The 96 h back trajectories were used to determine the influence of the air
mass origin of aerosol. The trajectories were calculated for every hour from
13 September 2010 until 24 October 2010 for the altitude of 500 m above model ground with the NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT 4) model (
Data analysis will first focus on the overall aerosol particle chemical composition and mass closure. The second part will discuss the source apportionment of both organic aerosol and eBC. Finally, the third section will investigate the influence of the air mass origins on aerosol particle chemical composition and size distribution.
Aerosol particle chemical composition (mass concentration and mass fraction),
as measured by AMS and MAAP, and the particle number size distribution
over the entire time period are shown in Fig. 1. On average, the near-PM
Time series of the ambient temperature
Parallel to the online measurements, the Berner impactor provides size-resolved chemical composition up to 10
Size distribution of OC, EC, and major water-soluble ions from Berner impactor measurements for the different full cloud events (FCE) and non-cloud events (NCE).
According to the large contribution of the PM
Influence of marine air masses on nitrate size distribution.
Additionally, some specific periods were also found in which the nitrate
mass concentration measured by the MARGA appears to be higher and not
related to the one measured by the AMS (Fig. 3). This clearly indicates a larger
contribution of super-
AMS, MARGA, and MAAP measurements provide complementary information on the
aerosol particle chemical composition. Therefore, eBC from MAAP, organics
from AMS, and inorganic ions from MARGA were combined to provide a
comprehensive picture of the ambient PM
Estimation of the PM
An investigation of the organic aerosol source apportionment highlights the
presence of five different factors which were identified based on their
individual time series, mass spectra, diurnal variability, and comparison
with external measurements (Fig. 5). A detailed description of the different
steps of the analysis and the identification of the different factors
is given in the Supplement (Sect. SI-5). Briefly, in a first step, a non-constrained model was run, and, in a second step, a series of partly constrained runs were investigated in order to better distinguish the different primary organic factors. The selected final solution results in a partially constrained model with two primary organic factors, namely hydrocarbon-like organic aerosol (HOA) and biomass burning organic aerosol
(BBOA). HOA was constrained using the mass spectra reported by Mohr et
al. (2012) in Barcelona (Spain) and is available on the AMS mass spectra
database (
Overview of the ME-2 results.
HOA is commonly considered a surrogate for fossil fuel combustion emissions and is especially related to traffic emissions. The HOA mass spectrum is
characterized by a larger contribution of hydrocarbon-like ions
(C
Temperature dependency of the identified factors' contribution to
OA
The BBOA factor (
Wood combustion used for residential, household heating dominates the local anthropogenic emissions in the surrounding area of the sampling place. This is in agreement with the reported BBOA contribution of 20 % for a similar place in Germany in winter (Poulain et al., 2011). The predominance of biomass burning emissions compared to liquid fuel is also supported by the benzene to toluene ratio value during the IOPs (mean 1.1, min 0.47, max 2.65), which is comparable to the ratio reported by Gaeggeler et al. (2008) for a similar location in Switzerland.
Overall, the sum of the primary OA (POA
The SV-OOA was identified according to the relative similarity of its time
series with nitrate (
Finally, the two OOAs referred to as low oxidized oxygenated organic aerosol
(LO-OOA) and more oxidized oxygenated organic aerosol (MO-OOA) were identified during an early stage of the source apportionment analysis, as
discussed in the Supplement (Sect. SI-5). They present two distinct time series and mass spectra, indicating two different sources rather than an artificial splitting by the model (Fig. 5). Both are characterized by a high contribution of mass
The two OOAs are the two most important contributors to the total OA fraction (
Properties of the different air mass clusters.
In contrast, MO-OOA does not show a pronounced temperature dependency, but
it strongly correlates with eBC (
As mentioned before, eBC correlated with three different organic factors
(HOA, BBOA, and MO-OOA) identified during source apportionment analysis.
Taken together, the sum of these factors correlates strongly with eBC
(
Contribution of the different organic factors to the eBC mass
concentration. The scatter plots present the correlation between the sum of
the OA factors and the measured eBC
A very good correlation between measured and modeled eBC was obtained
(Fig. 7b), and modeled eBC explained 96 % of the measured one. Based
on this approach, long-range transport particles associated with MO-OOA are
the largest source of eBC during the measurement period, contributing to
half of it (52 %), while eBC associated with local emissions of HOA and
BBOA represents 35 % and 13 %, respectively. Considering only local
eBC sources, fossil fuel combustion dominates the eBC fraction (73 % for
eBC
Using single-particle mass spectrometer measurements, Healy et al. (2012)
reported that size distribution of EC can also directly be used to apportion
soot sources. A local EC source was related to particles with a vacuum
aerodynamic diameter (
Overview of the EC size distribution measured by the five-stage
Berner impactor. Colors correspond to the following EC classifications: red is local, and blue is regional/transport. The scatter plot on the bottom
right shows the comparison between the local soot fractions estimated using
the two different approaches, namely the Berner impactor (
A total of six clusters was obtained based on 96 h backward air mass
trajectories (Fig. 9), and they are characterized in Table 1 by their
residence time index (RTI) over different types of ground before reaching
the sampling place, based on the approach described in van Pinxteren et al. (2010) and their meteorological conditions. Clusters C1 (west) and C2 (northwest) correspond to two different types of marine-influenced air masses with RTI
Cluster results of the 96 h backward air mass trajectories
calculated for the entire sampling period.
The aerosol particle chemical composition, number size distribution (PNSD),
and trace gases were averaged according to the different air mass clusters
and are presented in Fig. 10 and summarized in the Supplement (Table SI-4). The highest particle mass concentrations were observed for clusters with the strongest continental influence (i.e., C3, southwest – 11.5
Overview of the chemical composition and the PNSD for each
cluster (1–6). The left column shows the mean estimated PM
Increasing the RTI value of the air masses over continental areas leads to
an increase in the carbonaceous fraction in both absolute and relative mass
concentrations. The highest mass concentrations of OA and eBC are associated
with C4, south (5.4 and 1.0
In the frame of the HCCT-2010 campaign, a detailed description of the
aerosol particle chemical composition reaching the site of Goldlauter was
made by combining continuous online measurements (AMS, MARGA, MAAP, and
MPSS) with offline impactor samples performed during specific IOPs. Merging
online results from the AMS, MAAP, and MARGA together provides an hourly,
time-resolved chemical picture of the ambient PM
All data are available from the corresponding author upon request.
The supplement related to this article is available online at:
LP, FB, GS, KM, DvP, ZW, and YI collected the data, and LP performed data analysis on the AMS. FB, GS, KM, DvP, ZW, and YI contributed to the evaluation of the offline chemical analysis data set, and WB evaluated the MPSS data set. All coauthors participated in the interpretation of the results. LP led the writing of the paper, with contributions from all authors.
The authors declare that they have no conflict of interest.
This article is part of the special issue “HCCT-2010: a complex ground-based experiment on aerosol-cloud interaction”. It is not associated with a conference.
This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. He 3086/15-1).The publication of this article was funded by the Open Access Fund of the Leibniz Association.
This paper was edited by Lea Hildebrandt Ruiz and reviewed by two anonymous referees.