Inhalation of atmospheric particles is linked to human diseases.
Reactive oxygen species (ROS) present in these atmospheric aerosols may play
an important role. However, the ROS content in aerosols and their formation
pathways are still largely unknown. Here, we have developed an online and
offline ROS analyzer using a 2
Aerosol particles have negative effects on human health (Pope and Dockery,
2006), with an estimated 3 % of cardiopulmonary and 5 % of lung
cancer deaths attributable to particulate matter (PM) globally (WHO, 2013).
One of the important pathways leading to deleterious impacts on health is
believed to be induced oxidative stress by the generation of reactive oxygen
species (ROS), through the interaction of particulate matter with the human
lung (Donaldson et al., 2002). Reactive oxygen species denote
chemically reactive molecules containing oxygen (e.g., radicals,
oxygen ions and peroxides including the OH radical, the O
Currently, many acellular assays exist for the determination of ROS
quantities in particles, including dithiothreitol (DTT) (Fang et al., 2015)
and 2
In this work, we developed and characterized a highly sensitive ROS analyzer
which can be used either online or offline. The removing efficiency of
interfering oxidizing trace gases of O
An overview of the online ROS analyzer. OF-UPW refers to oxygen-free ultra-pure water. The same setup without the aerosol collector was used for the offline analysis (shown in Fig. S2).
In our experiments, ROS were measured using a DCFH assay, which is commonly used for examining ROS generation at a cellular level but has also been used for determining the oxidation potential of PM as an acellular assay (Fuller et al., 2014; King and Weber, 2013; Perrone et al., 2016; Sauvain et al., 2013; Venkatachari et al., 2005, 2007). In this assay, the presence of oxidizing species is assessed from the rapid oxidation of DCFH to the fluorescent compound dichlorofluorescein (DCF) in the presence of horseradish peroxidase (HRP). The chemical reaction mechanism is shown in Fig. S1 in the Supplement.
A schematic of the online aerosol ROS analyzer is shown in Fig. 1. The analyzer is composed of three components: the aerosol collector, the reaction chamber and the fluorescence analyzer. The same setup without the aerosol collector was used for offline analysis (Fig. S2).
Particles were collected at a flow rate of
In most studies using the DCFH assay, aerosol samples were extracted either
in a DCFH–HRP (King and Weber, 2013) or an HRP solution (Fuller et al.,
2014). We tested the auto-oxidation of the working solution containing both
HRP and DCFH. By mixing only OF-UPW with the HRP–DCFH working solution, the
signal, which is actually the background, increased with a rate of
0.9 % h
The aerosol aqueous extract collected from the aerosol collector was sampled
by a peristaltic pump through a “TRACE TRAP bubble trap” debubbler (TRACE Analytics GmbH,
Germany), which effectively removed gas bubbles in the sample liquid without
introducing a large dead liquid volume and signal broadening. At the same
time, the two reagent solutions DCFH and HRP were drawn by another
peristaltic pump and mixed to form the WS.
The aerosol aqueous extract was then mixed with the WS and pumped through a
reaction coil consisting of polyetheretherketone (PEEK) tubing (9.8 m length, 1.6 mm OD,
1.0 mm ID, Kinesis GmbH) in an air-ventilated temperature controlled housing
held at 37
The instrument was also used for offline analysis of filters (Fig. S2). In
general, we extracted a filter punch of 14 mm
Often filters are extracted in an ultrasonic bath. However, recent studies
suggest that sonication of pure water with dissolved air may create hydroxyl
radicals due to the high temperature and pressure created by the collapse of
bubbles formed by cavitation, which then form H
The stability of the WS is an important factor. Since HRP can catalyze the
reaction of DCFH with dissolved oxygen in the phosphate buffer (Berglund et
al., 2002; Huang et al., 2016; Rota et al., 1999a, b), the phosphate buffer
solution (PBS, 1 M, Sigma-Aldrich, USA)
was degassed with 99.999 % N
For the DCFH reagent, 2
For the HRP reagent, horseradish peroxidase (0.44 mg, HRP, type II,
Sigma-Aldrich, USA) was dissolved in PBS (35.6 mL) to generate a stock
solution of 2 units mL
WS-A and WS-B were then degassed for 20 min and only mixed together
during the analysis at a The pH of the WS was maintained constant at 7.2, which resulted in a
stable background. HRP and DCFH were prepared separately and mixed together only right before
the combination with the sample solution. This reduced auto-oxidation and
decreased the instrument background signal. Both working solutions were stored at
The instrument was calibrated with known concentrations of H
For the online operation mode, H
The instrument background of the online operation mode was always higher than that of the offline operation mode, which may be due to the uptake of oxygen in the mist chamber in the online system.
The instrument can be easily disassembled and rebuilt to be used in both
laboratory and field campaigns. The instrument is not yet fully automatized.
The following manual operations are required: (1) calibration; (2) replacing
the hydrophilic and hydrophobic filters in the aerosol collector and the
denuder every 2–3 days during ambient measurements – while in laboratory
experiments, we exchanged the denuder for each laboratory experiment
(
Fluorescence responses to
In order to assess the performance of the ROS analyzer several tests were
performed, including the following:
The influence of the reaction time and the instrument detection limit,
repeatability and reproducibility (Sect. 3.1.1 and 3.1.2). Response of
the DCFH assay to selected components with expected capability to act as
reactive oxygen species (Sect. 3.1.2 and 3.3.2). Assessment of the interference from selected abundant gas-phase and PM
constituents (Sect. 3.2 and 3.3) on the ROS signals. Verification of the instrument performance using genuine aerosol
samples. Measurement of the ROS content in ambient aerosols was performed
offline using filter samples collected in Milan (Italy), San Vittore
(Switzerland) and Bern (Switzerland) and online using the developed ROS
analyzer in Bern (Switzerland) (Sect. 3.1.3, 3.4.1 and 3.4.2). These samples
include total suspended particulate matter (TSP), PM
The reaction time between the WS and the aerosol sample is an important
parameter. Here, reaction times of 11 and 22 min were investigated by using
different reaction tube lengths in the reaction chamber and followed by
measurement of the fluorescence intensity resulting from the reaction of
H
Calibration curves of H
Under normal instrument operation conditions, an instrument limit of
detection (LOD) of 2 nmol m
We assessed the instrument performance based on three repeated calibrations
with 0, 30, 50, 100 and 150 nM H
The instrument reproducibility was assessed based on the variation in the
instrument sensitivity (in V nM
Model organic peroxides used in this study.
While the characterization tests discussed above were carried out using the
offline mode, we obtained similar results when the instrument was used in the
online mode. Figure 3 shows that a similar linear relationship was obtained
between the instrument response and the H
We also tested the response of the instrument to components expected to
exhibit the capability to act as reactive oxygen species, including peracetic
acid (PAA;
Response curves of the selected compounds with an expected capability to act
as reactive oxygen species compared to H
In order to evaluate the performance of the ROS analyzer in the field, two
sets of experiments were conducted. In the first set, the instrument was
operated in the offline mode using filter samples collected at two different
sites: (a) a site influenced by traffic emissions in Milan (northern Italy),
where quartz filters were sampled during October 2013 (Perrone et al., 2016);
and (b) a rural site in San Vittore (southern Switzerland in an Alpine
valley) influenced by biomass burning, where samples were collected during
January 2013 (Daellenbach et al., 2017; Zotter et al., 2014). More details on
the analysis of the samples can be found in the cited references. The samples
from both sites were stored in the freezer at
ROS content vs. dissolved particle mass concentration. Blue symbols
represent PM
Evolution of the concentrations of OA mass and ROS during an online
wood combustion smog chamber aging experiment.
The second set of experiments was performed at the PSI smog chamber.
Beechwood logs were combusted in a residential wood burner (Avant, 2009,
Attika), following the procedure described in Bruns et al. (2016, 2017). The
resulting emissions were sampled from the chimney through a heated line
(473 K), diluted by a factor of
The evolution of ROS measured by the online method is shown in Fig. 5 for one
exemplary smog chamber aging experiment. Injection of the wood combustion
emissions led to a primary organic aerosol (POA) concentration of 25
To investigate the influence of aging on ROS formation, SOA and secondary ROS
(ROS formed during aging) were calculated by subtracting POA and primary ROS
from the total OA and total ROS measured during lights on (Fig. 5b),
respectively. Here the POA and primary ROS calculation was based on the
assumption that they were not further oxidized after lights on and the wall
loss rate was the same as for the inert tracer black carbon (BC). The content
of ROS in SOA (represented by ROS/SOA) was in the range of
0.4–1.26 nmol
Effects of the potential interferences in the gas and aerosol phase on the DCFH signal.
We tested the potential interference of trace gases and aerosol components on
the DCFH signal. In principle, at the applied sample flow rate, 99 % of
the trace gases should get removed by the denuder. Specifically, we assessed
the removal efficiency of the denuder with respect to the most abundant
oxidizing trace gases O
Previous measurements of filters from Milan showed a clear correlation of ROS
with the particulate SO
Results show that the signals generated by injecting (NH
The relative fluorescence intensity during Fe
Transition metals may induce a response through redox cycling. Iron is one of
the most abundant transition metals in the aerosol (Valko et al., 2005;
Dall'Osto et al., 2016). However, potential iron-catalyzed ROS formation in
an oxygen-rich environment has not yet been examined using a DCFH assay. In
order to investigate the effect of metals on the ROS signal we conducted two
experiments: (1) the analysis of the H
In the first set of experiments (shown in Fig. 6) the signal of
H
Comparison of the filter extract (fe)–H
We then investigated whether the complex matrix of ambient particles, which
also include different forms of iron together with other metals, has an
influence on ROS measurements. For this second set of experiments, ambient
filter samples from a rural site in San Vittore (Switzerland) collected in
January 2013 and an urban site located in Bern (Switzerland) collected in
November 2014 were extracted and cross tested with H
A direct intercomparison of online in situ and offline filter sample measurements of the ROS content from different emission sources was performed. These aerosol samples included fresh and aged aerosols from wood combustion emissions from a smog chamber, as well as ambient aerosols collected in Bern (Switzerland).
The smog chamber experiments and the online performance were described in
Sect. 3.1.3. In addition to the online measurements, the particles from the
chamber were collected on quartz filters (47 mm, Pall Corporation) at a flow
rate of 26 L min
Ambient measurements were performed at an urban site located at the Institute
of Anatomy of the University of Bern. A stainless steel cyclone
(URG-2000-30ET, URG Corporation) was operated at a constant flow rate of
The ROS concentrations measured by the online and offline method from the wood combustion experiments and ambient air in Bern are compared in Fig. 8. We did not observe a systematic difference between ROS concentrations on filters taken before and after the VACES compared with the online measurements. The ROS concentrations measured offline are on average 31 % lower than the online data in the Bern ambient measurements and on average 67 and 61 % lower than the online data for primary and secondary wood combustion samples, respectively. For the ambient measurements in Bern, a small number of measurements show agreement between the two methods indicating no ROS decay. A more detailed analysis is given in the following section to further explain the discrepancies of offline and online measurements.
Comparison of online and offline measured ROS concentrations in the
city of Bern in winter and during wood combustion smog chamber experiments
(Ex
Short-lived and long-lived ROS fractions and parameters from the
different experiments (Ex
As ROS decay with time, we investigated the evolution of the particle-bound ROS
over time by measuring ROS from filter samples taken during
additional biomass combustion laboratory experiments. The temperature of the
filter samples was maintained at
A pellet boiler was operated under two different conditions: high excess of
combustion air (
The measured ROS concentrations in SOA from the different wood combustion
experiments exhibit a clear decrease with increasing filter storage duration
(Fig. 9). In addition, this decay seems to follow a double exponential
function. This indicates the presence of a short-lived fraction
The results show that the two ROS fractions have highly different reactivity.
The final modeling yields
The model considers ROS to be composed of two components with different decay
rates. However, we do expect that the OA contains the spectrum of ROS with a
wide range of reactivities. The model is thus a simplification of the ROS in
the aerosol. Another simplification is that the decay rates of these two ROS
components are considered to be the same between experiments. This may
explain the reasons behind the high uncertainties in determining the rates,
but does not have a significant effect on the determination of the
contributions of the two fractions,
Measured and modeled ROS decays in SOA from wood combustion
emissions with increasing sample storage duration for six experiments
(Ex
To understand the variability in the contributions of the long-lived and
unstable ROS fractions of different experiments, the long-lived fraction of
ROS was compared with various wood combustion parameters. No correlation was
found with
Estimations of ROS lifetimes were done previously. ROS measured in oxidized
oleic acid particles were separated into short- and long-lived species with a
half-life of a few minutes and hours to days, respectively (Fuller et al.,
2014). Chen et al. (2011) determined a ROS half-life of 6.5 h in oxidized
organic aerosols. Krapf et al. (2016) showed that more than 60 % of
peroxides contained in SOA from
To compare the ROS online measurement with immediate offline measurements,
2,6-dimethoxyphenol was used as a precursor and aged in the PAM chamber. SOA
was then sampled on a Teflon filter (47 mm Fluoropore membrane,
3.0
As a summary of the ROS decay behavior in aerosols from Bern ambient and wood
combustion experiments, a normalized frequency distribution of the ROS decay
percentage of different sources is plotted in Fig. S6. The decay percentage
of ROS was calculated as follows:
In this study, a modified online and offline ROS analyzer was presented and
characterized. The major improvements compared to previous studies (Fuller et
al., 2014; Huang et al., 2016; Wang et al., 2011; King et al., 2013) to
optimize the analysis were as follows: (1) degassing of the water and PBS to
prepare the working solutions; (2) separation of DCFH and peroxidase working
solutions, which were then mixed just before reaction with the sample
solution; and (3) no ultrasonic filter extraction for offline analysis. All
these efforts resulted in an instrument LOD of 2 nmol m
As shown with model organic compounds only peracetic acid was quantitatively
measured, while large organic peroxides or those with bulky functional groups
(i.e.,
Both online and offline measurements with the analyzer were performed in
field and laboratory experiments. ROS concentrations from offline field
measurements showed a linear relationship with increasing ambient particle
concentrations. Smog chamber aging experiments of wood combustion emissions
revealed a high initial ROS content in SOA, which then strongly decreased
with OH exposure. Generally, ROS decayed with increasing filter storage
duration. Due to the degradation of the highly reactive ROS fraction, the
offline method generally underestimates the ROS concentration on average by
60
Data related to this article are available online at
The authors declare that they have no conflict of interest.
This study was financially supported by the Swiss National Science Foundation (NRP 70 “Energy Turnaround”) and the China Scholarship Council (CSC) under grant agreement no. 201007040040. The research leading to these results also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 290605 (PSI-FELLOW) and from the Competence Center Environment and Sustainability (CCES; project OPTIWARES). The authors thank Maria Grazia Perrone and Manuel Krapf for providing the ambient filters, Mao Xiao for the helpful discussions, and René Richter and Günther Wehrle for their competent technical advice, as well as Samuel Brown, Ilaria Gavarini, Laure-Estelle Cassagnes and Deepika Bhattu for their support in the lab. Edited by: Francis Pope Reviewed by: two anonymous referees