Oxidation flow reactors (OFRs)
or environmental chambers can be used to estimate
secondary aerosol formation potential of different emission sources.
Emissions from anthropogenic sources, such as vehicles, often vary on short
timescales. For example, to identify the vehicle driving conditions that
lead to high potential secondary aerosol emissions, rapid oxidation of
exhaust is needed. However, the residence times in environmental chambers
and in most oxidation flow reactors are too long to study these transient
effects (
Aerosol particles in the atmosphere affect climate, health and visibility. To reduce these impacts, the sources of aerosol particles have to be resolved. One large but uncertain source of atmospheric aerosol particles is secondary organic aerosol (SOA) formation, which takes place in the atmosphere when particle mass forms as a result of atmospheric oxidation of organic precursor gases. Because the emission of precursor gases and the formation of secondary aerosol mass occur separately, the estimation of SOA sources and their magnitudes is difficult.
The total amount of atmospheric SOA is typically estimated using laboratory
data of SOA yields (
An alternative and more direct method for characterizing SOA sources was introduced by Kang et al. (2007). Instead of measuring precursor gases and estimating the amount of potential SOA based on their yields, the SOA formation potential of a single emission source can be measured by oxidizing the emitted sample and measuring the secondary aerosol mass produced. This method reduces the uncertainty of the SOA emission magnitude, since unknown precursors as well as those whose measurement is difficult are taken into account.
Using this in situ method, the emission oxidation and SOA formation process can be characterized using large environmental chambers, such as the one Platt et al. (2013) used when they measured the SOA potential of a gasoline vehicle. Another alternative is to use an oxidation flow reactor (OFR), in which the sample is oxidized in a similar manner but with higher oxidant concentrations than in large environmental chambers. Such a setup was first introduced by Kang et al. (2007), who also introduced their own oxidation flow reactor, the potential aerosol mass (PAM) chamber, hereafter referred to as PAM. The setup has been used, for example, to estimate the SOA formation potential of in-use vehicle emissions by sampling air from a highway tunnel (Tkacik et al., 2014), to measure the SOA formation from urban ambient air (Ortega et al., 2016) and to measure the SOA formation from ambient pine-forest air (Palm et al., 2016). All these applications show the value of the direct measurement of SOA potential, since the model results either over- or underestimated the SOA formation.
The use of an oxidation flow reactor instead of a large environmental
chamber provides multiple advantages: short residence time, higher degree of
oxidation and portability
(Bruns et al., 2015).
The short residence time allows for high-time-resolution measurements of
constantly changing situations; for example, the effect of different test
parameters on SOA formation can be studied in a shorter time than with
environmental chambers. It is also possible to measure SOA formation of a
changing emission source in real time because of the short residence time.
For example, Karjalainen et al. (2016)
measured the time-resolved SOA formation potential of a gasoline vehicle during
a transient driving cycle using a PAM reactor. They observed that the
secondary aerosol formation potential is highly dependent on the driving
conditions. However, the PAM reactor is not ideal for rapidly changing
emission sources such as vehicular emissions, since the residence time
(
In this work, we introduce and present a characterization of a new oxidation flow reactor, the TUT Secondary Aerosol Reactor (TSAR). TSAR is better suited to measuring the real-time secondary aerosol formation potential of rapidly changing emission sources than the state-of-the-art oxidation flow reactors, due to its improved flow conditions and shorter residence time. In the following sections, we characterize TSAR by describing its particle losses, oxidant exposure, residence time distribution and laboratory studies on sulfuric acid yield as well as toluene SOA yield and properties, including a comparison between PAM and TSAR. In addition, we present measurements of the secondary aerosol formation of gasoline vehicle emissions during a transient driving cycle. We show that the fast response of TSAR gives valuable information on the effect of the driving condition on secondary aerosol formation potential.
TSAR layout. The residence time chamber (1), the expansion tube (2), the oxidation reactor (3) and the adjustable outlet (4).
Because of the high oxidant concentrations, high UV light intensity at non-tropospheric wavelengths and limited time for condensation, atmospheric implications cannot be directly drawn from flow reactor measurements. However, there are no methods to measure the absolute secondary aerosol formation potential, because the environmental chambers also have their drawbacks (e.g., limited oxidant exposure and inability to measure time-resolved secondary aerosol potential; Bruns et al., 2015). Despite these artifacts, there is a need for the estimation of secondary aerosol formation from different emission sources. Thus, the flow reactor results also provide useful information, as long as a proper error analysis is made. In this work, we address the flow reactor related artifacts of TSAR by modeling the vapor losses caused by photolysis and the short residence time.
TSAR is an OFR254-type oxidation flow reactor, according to terminology
proposed by Li et al. (2015), which means that OH radicals are produced from the photolysis of the
ozone at 254 nm UV radiation. Its layout is presented in Fig. 1 (see Fig. S3
in the Supplement for a photograph). TSAR consists of a residence time chamber (1 in Fig. 1),
an oxidation reactor (3),
an ozone generator, three mass flow
controllers and an expansion tube (2) that connects the residence time
chamber and oxidation reactor. The residence time chamber is a 50 cm
The TSAR oxidation reactor is a 3.3 L (52 cm
The ozone is generated by an external ozone generator (either model 600 or model 1000, Jelight Company Inc.), which produces ozone from oxygen photolysis by 185 nm UV radiation. The ozone concentration can be adjusted by partially covering the UV lamp (model 600) or by adjusting the flow rate through the generator.
The TSAR outlet is a 10 mm OD stainless steel probe, and its axial position can be adjusted so that the oxidized sample can be measured from any distance from the inlet. From the probe, the sample is led to the measurement devices or to an ejector diluter, which allows the use of multiple instruments while maintaining a constant flow through the oxidation reactor.
The flow conditions inside the TSAR oxidation reactor affect the dynamic
transfer function,
First, three separate CO
Particle losses in the oxidation reactor were measured using dioctyl sebacate (DOS) particles with a mobility diameter from 20 to 100 nm and silver particles from 5 to 30 nm. The DOS particles were generated by atomizing a DOS–isopropanol solution. The silver particles were generated with an evaporation–condensation technique (Harra et al., 2012). In these experiments, the volumetric flow in both the residence time chamber and oxidation reactor was 5 slpm.
A narrow monodisperse particle size distribution, size-selected using a nanometer differential mobility analyzer (nano-DMA; model 3085, TSI Inc.), was injected into TSAR. The particle number concentration was measured with an ultrafine condensation particle counter (UCPC; model 3025, TSI Inc.) before and after the oxidation reactor using the adjustable outlet probe. This procedure was repeated two or three times for each particle size.
The length of the duration of atmospheric oxidation that the oxidation flow
reactor simulates is determined by exposure of the sample to OH radicals. OH
exposure (OH
Because the OH radicals are produced in a reaction between water molecules
and O(
Based on the OH
In an ideal oxidation flow reactor, all the condensable vapors condense onto particle phase and will be measured as potential secondary aerosol mass. However, there are also other pathways than condensation for the vapors in the flow reactor, and some of them are non-tropospheric. First, the intensity of the UV radiation is higher and the wavelength is smaller than those of the UV radiation in the troposphere. This can cause unrealistic photolysis of the precursor vapors and the secondary aerosol formed (Peng et al., 2016). Second, the residence time in the flow reactor is small, and thus the condensable vapors may exit the reactor before condensing onto particle phase. Third, because of high oxidant concentrations, the timescale of condensation can be much higher than the timescale of oxidation, leading to fragmentation of oxidized vapor molecules before they have condensed. This is of concern especially in TSAR, where the short residence time requires higher oxidant concentrations than, for example, the PAM chamber. Fourth, the surface-area-to-volume ratio is high in the flow reactor, and thus the vapor wall losses may be significant (Palm et al., 2016)
Peng et al. (2016) have studied the losses of precursor gases and SOA due to photolysis in flow reactors. In their study, they show that the photolysis rate of SOA in oxidation flow reactors is uncertain because of the lack of knowledge on quantum yields. In any case, the loss of SOA due to photolysis is much smaller in oxidation flow reactors than in the troposphere at equivalent OH exposure. However, the photolytic losses of precursor gases in oxidation flow reactors can be higher than in the troposphere.
The photolytic loss is significant if the photolysis rate is high relative
to reaction rate with OH radicals. We define relative photolytic loss as
follows:
According to the modeling results by
Peng et al. (2016), the
relative photolytic loss of studied precursor gases is less than 60 % in
most cases in OFR254, even at “riskier” conditions ([H
We study the fate of condensable vapors (other than photolysis) in TSAR
using a similar approach to
Palm et al. (2016). We start with a low-volatility organic compound (LVOC, saturation vapor
concentration
At some point, the reaction between LVOCs and OH radicals leads to
fragmentation and produces high-volatility compounds which cannot condense
onto particle phase. Palm et al. (2016) assumed that the fifth oxidation
reaction produces fragmented compounds. In addition, the heterogeneous OH
reaction on the particle surface may result in fragmentation
(Kroll et al., 2009). Thus, assuming that the
molecule fragments into two parts, we get
We tested the model validity by oxidizing SO
The sulfuric acid yield was measured by injecting humidified air, ozone and
SO
In addition to sulfuric acid, the measured particles also contain water. The
sulfuric acid mass was calculated from Eq. (15)
(Lambe
et al., 2011):
The theoretical (maximum) sulfuric acid mass was calculated by multiplying
the loss of SO
A key application of TSAR is to estimate the amount of secondary aerosol
mass formed from engine exhaust emissions, which in turn contains a complex
mixture of organic and inorganic gases. Therefore, the SO
The organic precursor gas in this experiment was toluene, because it is
present in engine exhaust gas
(Peng
et al., 2012; Wang et al., 2013). In addition, toluene is globally one of
the most emitted anthropogenic SOA precursors
(Kanakidou et al., 2005).
Gas-phase toluene was produced using a permeation oven with a toluene
permeation tube (KIN-TEK Analytical, Inc.), and its output rate
(
The gas-phase toluene was mixed with ozone and humidified air before it was fed to the TSAR residence time chamber. After the residence time chamber, 5 slpm of the sample was introduced into the TSAR oxidation reactor and 5 slpm to PAM. A four-way valve was installed after the reactors, so that the instruments were sampling from one reactor while the sample from the other reactor was drawn to the vacuum line through a mass flow controller.
PAM was used in OFR185 mode
(Li et al., 2015), and thus
the external ozone generator was switched off when the instruments were
sampling from PAM. PAM was operated in OFR185 mode instead of OFR254
mode because the OFR185 mode is used in previous engine exhaust studies
(Karjalainen
et al., 2016; Timonen et al., 2016; Tkacik et al., 2014). Similar results
from the two reactors would then indicate that TSAR operating in OFR254
mode could be used in similar applications as PAM in OFR185 mode. The
OH
Toluene injection cycles.
The particle size distribution downstream of TSAR and PAM was measured with an SMPS (model 3081 DMA and model 3775 CPC, TSI Inc.) and also with an engine exhaust particle sizer (EEPS; TSI Inc.; Johnson et al., 2004) in some experiments. The EEPS sample had to be diluted with a mass flow controller to keep the total flow rate through the chambers at 5 slpm. Aerosol chemical composition and size distribution were measured with an SP-AMS (soot particle–aerosol mass spectrometer; Onasch et al., 2012). In addition, the ozone concentration (model 205, 2B Technologies) and relative humidity (Hygroclip SC05, Rotronic AG) were measured.
Two different toluene experiments were run: steady-state and pulse experiments. In the steady-state experiments, a constant concentration of toluene was continuously injected into the reactors. Based on these experiments, the toluene SOA yield was determined for both reactors.
The pulse experiments were performed to study the reactors' behavior during rapid changes of toluene concentration. In these experiments, toluene was injected through a three-way solenoid valve to either the reactors or to the excess line. Three different pulse experiments were performed: a single 10 s pulse and two different cycles with several pulses (cycle 1 and cycle 2). In cycle 1, three toluene pulses were injected with intervals of 10 and 15 s, whereas cycle 2 had intervals of 40 and 50 s. The cycles are described in detail in Table 1.
In both cycles, the total toluene injection time was 25 s; therefore, the total amount of injected toluene was equal. EEPS was used to measure the particle number distribution of produced SOA at a time resolution of 1 s. For the pulse experiments, the flow rate through each reactor was 5 slpm. Since PAM is approximately 4 times bigger than TSAR in volume, a 10 slpm flow rate was also used for PAM to compare the reactors at more similar mean-plug-flow residence times. In this case, TSAR was bypassed to keep the total flow at 10 slpm.
The SOA yield (
The ability of TSAR to produce secondary aerosol mass from engine exhaust
emissions was evaluated by sampling the exhaust of a Euro 5 GDI light-duty
vehicle during a transient driving cycle (New European Driving Cycle, NEDC)
run on a chassis dynamometer. The official cycle begins with a cold engine
start but, in this study, the NEDC was run with a warm engine, and this is
hereafter called a warm NEDC. Prior to the warm NEDC, the vehicle was run at 80 km h
The sampling setup of vehicle exhaust experiments is shown in Fig. S2. The
engine exhaust was sampled from the tailpipe using a porous tube diluter
(PTD) followed by a short cylindrical residence time chamber with a residence
time of 2.9 s. The dilution air temperature was 30
The particle size distributions were measured with EEPS, an electrical
low-pressure impactor (ELPI
The amount of secondary aerosol mass produced in TSAR was determined by subtracting the primary mass from the mass measured when using TSAR. Primary aerosol was measured with the same setup by operating TSAR with UV lamps and the ozone generator turned off. The primary emission was measured during two warm NEDCs.
Measured and modeled CO
In this setup, the sample flow from the tailpipe is constant regardless of the exhaust mass flow. To determine the emission factors, the measured concentrations are multiplied with the corresponding exhaust mass flow.
The evolution of a CO
Figure 2 shows both the measured pulse after the reactor and the modeled pulse calculated according to Eq. (1) using the theoretical transfer function and the measured input concentration.
Modeled and measured residence time distributions with and without secondary excess flow and UV lights. The shaded area shows the standard deviation of three pulses.
As seen in Fig. 2, the measured pulse is somewhat broader than the modeled one. There are some possible reasons for this discrepancy: first, the flow inside the reactor is probably not totally laminar because of the expansion in diameter between the residence time chamber and the oxidation reactor and because of the abrupt diameter change at the end of the reactor; second, the pulse becomes broader in the sampling lines, which is not taken into account here.
In Fig. 2, UV lamps are turned off and the secondary excess flow is on.
Because both of these affect the flow, the residence time distribution was
measured for different combinations of these parameters, and the results are
shown in Fig. 3. In all cases, the total flow rate through the oxidation
reactor was 5 slpm. Because the incoming pulse is not an ideal Dirac delta
function, the residence time distribution cannot be calculated with Eq. (8).
Instead, the residence time distribution is the measured concentration,
The residence time distributions show that the flow in the TSAR oxidation
reactor is near-laminar. Thus, the mean residence time of the sample in the
reactor can be calculated with Eq. (22) (Fogler, 2006):
The particle transmission efficiency in the TSAR oxidation reactor. The dashed line shows the theoretical transmission efficiency when the diffusion losses are taken into account. Error bars show the standard deviation.
The particle transmission efficiency as a function of particle mobility diameter is presented in Fig. 4, as well as the theoretical diffusive losses of particles in a tube with laminar flow (Brockmann, 2011). The markers indicate the particle material, and the error bars denote the standard deviation between separate experiments.
Figure 4 shows that the measured transmission efficiency agrees well with the theoretical efficiency, as expected, and thus the losses are less than 10 % when the particle mobility diameter is larger than 5 nm. Therefore, the results in the next sections are not corrected with this efficiency curve because the particle losses are negligible.
According to Lambe et al. (2011), the transmission efficiency of particles is significantly lower in PAM: less than 70 % for particles smaller than 100 nm. Since the flow in TSAR is near-laminar, it is not surprising that the measurements agree with the theory. In PAM, the residence time distribution is broad, allowing more time for the particles to diffuse onto walls (and possibly to coagulate or evaporate), resulting in a non-ideal transmission efficiency.
The OH exposure as a function of O3 concentration after TSAR.
Figure 5 shows that the OH exposure in the TSAR oxidation reactor is sensitive
to ozone concentration at low concentrations but levels off to a
near-constant value when the concentration is higher than 25 ppm. The
OH
The measurement results could be reproduced in the photochemical model using
the photon flux, first-order OH radical wall loss and first-order ozone wall
loss as free parameters. The best fit values are
Based on the modeling results in Sect. 3.3, the flux of 254 nm photons in
TSAR is
The ratio of 254 nm photon flux to OH exposure as a function of OH
exposure
Modeled losses of low-volatility organic compounds as a function of
residence time in TSAR in the case of ambient air
As shown by
Peng et al. (2015), the OH
To study the dependence of LVOC fate on residence time and the condensational
sink, we define two cases: the oxidation of ambient air (low condensational
sink) and diluted vehicle exhaust (high condensational sink). The
condensational sink depends on particle number concentration and size and
also on the accommodation coefficient (
In the case of oxidation of ambient air, the timescale for condensation on
aerosol
The vapor losses in TSAR for the ambient case as a function of residence
time were modeled using the method described in Sect. 2.5.2. Two values of
the mass accommodation coefficients were used (
The vehicle exhaust case is based on the measurements in Sect. 3.6. The mass
concentration and
The fraction of the sulfuric acid mass condensed on aerosol phase as a function of measured sulfuric acid mass.
The results for the car exhaust case are presented in Fig. 7b. Now, the LVOC
losses in TSAR at typical residence time are 25–80 %, depending on the
value of
In addition to TSAR, we present the estimates for LVOC losses in several other flow reactors, namely MSC, the PAM reactor, and the Caltech Photooxidation Flow Tube reactor (CPOT; Huang et al., 2017) at their typical residence times in Fig. 7. The results differ a little from the TSAR curve because of the different surface-area-to-volume ratios. One must note that the applications of the flow reactors are different; for example, MSC is usually used with a much higher CS than what is modeled here (e.g., Corbin et al., 2015) and, consequently, the losses are smaller than in Fig. 7. Similarly, the main application of TSAR is the engine exhaust measurement, where the CS is usually higher than in ambient air.
The model is tested by comparing the measured and modeled sulfuric acid
losses, and the results are shown in Fig. 8. In the model, the following
values are assumed for the sulfuric acid molecules: molar mass of 98 g mol
The sulfuric acid experiment shows that the model predicts the losses of a non-fragmenting low-volatility compound reasonably well. However, in Fig. S5 we see that it is the fragmentation that causes the highest losses for LVOCs when the residence time is short (< 50 s). The assumption that the five oxidation steps result in fragmentation is artificial but, if we as a sensitivity test assume that the fragmentation does not occur at all, the change in overall loss is small because a higher proportion of the LVOCs will exit the reactor before condensing (Fig. S6). Still, the losses are a little lower in the case of no fragmentation, and thus more studies on fragmentation are needed to verify the assumptions in the model.
The modeled cases inarguably show that there is a tradeoff between residence
time and LVOC losses: the smaller the residence time is, the more losses there are. Thus,
the residence time must be chosen according to the application. If a short
residence time is used and the CS is low, the injection of seed particles in
the sample will reduce the LVOC losses. In the car exhaust case, the CS is
high enough for TSAR if the mass accommodation coefficient of the condensing
vapor is close to unity. For steady-state experiments, we recommend using a
long residence time when there is no need for a fast response. However, even
though the LVOC losses are smallest for long-residence-time reactors
according to the model results, the particle losses are higher (e.g.,
Particle mass size distributions for TSAR- and PAM-generated
toluene SOA, obtained from the SMPS particle number size distribution assuming
spherical particles with a density of 1.45 g cm
The SOA formation studies were conducted as described in Sect. 2.6. Toluene
concentration in the sample entering the reactors was 320 ppb (
The SOA mass formed in the reactors is calculated from the number size
distribution measured by the SMPS, assuming spherical particles with a
density of 1.45 g cm
Toluene SOA yield as a function of OH exposure for both reactors.
Figure 9 shows the SMPS mass distributions of toluene SOA for PAM and TSAR at
OH
Figure 10 shows the toluene SOA yield obtained in steady-state experiments as a
function of OH
The van Krevelen diagram of toluene SOA for both chambers. The color indicates the OH exposure.
When the OH
The PAM yield in Fig. 10 is not corrected for particle losses, because the losses are characterized only for particles that enter PAM at a certain size and do not grow inside PAM by condensation. This is not the case here, because the particles are formed via nucleation inside PAM; thus, it is unknown how long they have spent in the reactor and what the particle size as a function of residence time is. As an estimate, the particle size distribution measured after PAM was corrected with the losses measured for this particular chamber (Karjalainen et al., 2016). With this correction, the PAM yield would increase by 19 % on average.
In addition to yield, the chemical composition of produced SOA was studied.
In Fig. 11, a van Krevelen diagram shows the oxidation state of SOA for both
reactors. In this diagram, the H
Dot product between the organic spectra of PAM- and TSAR-generated SOA.
To further compare the SOA oxidation state in the reactors, the average
carbon oxidation state (
The average carbon oxidation state (
We also compare the chemical composition of SOA by studying the organic mass spectra. According to Marcolli et al. (2006) and Lambe et al. (2015), a dot product between two normalized mass spectra can be used to determine whether the spectra are similar. The spectra are normalized by dividing each signal by the square root of the sum of the squares of all signals. A dot product of one implies that the spectra are identical and of zero that they are orthogonal.
Toluene SOA here is divided into three categories: low oxidation (
The mass produced from SOA formation of toluene pulses in TSAR at
5 slpm flow rate and in PAM at 5 and 10 slpm flow rates. The shaded area
shows the standard deviation. The figures show the mass formation of a
single pulse
The expected and measured masses produced in pulse experiments. The expected mass is calculated from the mass of injected toluene and its SOA yield.
The TSAR and PAM reactors differ in volume, geometry, flow conditions and residence time. The most significant difference is in the oxidation process: TSAR operates in OFR254 mode and PAM in OFR185 mode. However, the agreement between yields and organic mass spectra of SOA produced in both the TSAR and PAM reactors show that the oxidation products are similar in both reactors, at least in the case of toluene. In OFR254, the sample is first exposed to ozone (before the oxidation reactor) and then to both ozone and OH radicals. If the VOCs in the sample react fast with ozone, the resulting SOA mass might differ between OFR254 and OFR185. This was not the case for toluene, as dark experiments (only ozone and no UV light) did not produce any secondary mass. In other applications, for example when oxidizing biogenic precursors which are highly reactive towards ozone, the results between OFR254 and OFR185 presumably differ, with OFR185 being more realistic as the sample is exposed to ozone and OH simultaneously. However, the main application of TSAR is to measure vehicle emissions, which are more reactive towards OH than ozone (Gentner et al., 2012; Tkacik et al., 2014). The potential of ozone to produce SOA from the emission can be measured by injecting ozone into TSAR with UV lights turned off.
The SOA mass concentrations as a function of time are shown in Fig. 13 for all pulse experiments. The 10 s pulse of toluene results in a sharp peak in mass in TSAR, whereas the PAM reactor produces significantly broader peaks at both used flow rates. Interestingly, the TSAR mass peak is divided into two distinct peaks. We do not know the reason for this phenomenon since the residence time distributions in Sect. 3.1 do not support this kind of behavior. However, the flow conditions in this experiment are not exactly the same as in Sect. 3.1; here, the flow rate in the residence time chamber is only 10 slpm, whereas in Sect. 3.1 it was 50 slpm.
Cycle 1 with three rapid toluene pulses shows the importance of laminar flow and short residence time in TSAR: PAM produces only one broad peak whereas all three pulses can be distinguished in the SOA mass produced by TSAR. In cycle 2, where toluene pulses are injected between longer intervals, the pulses are also separated in the mass produced by PAM.
As the total amount of toluene injected into the reactors is known and the
yield is determined in Sect. 3.5.1, the total mass produced in the reactors
can be predicted with Eq. (23).
In all the experiments, the mass produced in the reactors agrees well with the expected mass. For the 10 s pulse, PAM mass is lower than the expected mass and the TSAR mass; for cycle 1, TSAR produces more mass than expected; and for cycle 2, both reactors produce less mass than predicted. Considering the uncertainties in this experiment, namely the dilution ratio, EEPS inversion and toluene concentration, we conclude there are no significant differences in the total mass the reactors produce, even though the pulse shapes are clearly different. In all the cases, the mass produced in PAM is slightly lower than in TSAR, probably because the particle losses in PAM are higher.
The agreement between the predicted mass and the produced mass suggests that the approach to measure the secondary aerosol formation potential in real time is valid: the narrow residence time distribution of TSAR gives time-resolved information of SOA formation from fast changing precursor concentrations but still produces approximately the same amount of mass as the PAM reactor, where the oxidation process is slower. Based on these results and the LVOC loss estimation in Sect. 3.4.2, this holds when the condensational sink is high enough. When the CS is smaller (e.g., in ambient air measurements), we expect TSAR to produce less mass than PAM due to the higher losses.
When measuring time-resolved secondary aerosol formation during a transient
driving cycle, it is crucial to synchronize real-time aerosol measurements
with vehicle speed data. This is performed by comparing the CO
Time series of the vehicle speed and primary mass concentration
In Sect. 3.3 we showed that the OH
The secondary aerosol mass concentration formed from the GDI exhaust during
a warm NEDC is shown in Fig. 15c. Mass concentration is calculated from
the particle number size distribution measured by EEPS assuming spherical
particles with a density of 1.0 g cm
Figure 15c shows significant differences in secondary aerosol formation
during different driving conditions. The small standard deviation suggests
that the operation of TSAR and the phenomena causing secondary aerosol
formation are highly reproducible. The least secondary aerosol formation
occurs during long steady-state driving, such as at 70 km h
The time-resolved emission factor of secondary aerosol mass in Fig. 15c is
achieved by multiplying the secondary mass concentration by the exhaust mass
flow. Low exhaust mass flow during engine braking cancels out the high mass
concentration peaks. Instead, the peak at the end of the cycle dominates the
emissions of secondary aerosol precursors. The total emission factor over
the cycle is the integral of the time-resolved emission factor over the
cycle length divided by the total distance. For the primary emissions, the
emission factor is 0.1 mg km
The secondary aerosol emission factors for a similar vehicle and driving
cycle reported by Karjalainen et al. (2016)
and Platt et al. (2013) are 4.3 and 12.7 mg km
We also observe a new phenomenon, where engine braking results in high
concentrations of secondary aerosol forming precursors. Every deceleration
(i.e., engine braking) during the warm NEDC produces a peak in secondary mass
concentration. The tail at the beginning of the cycle is also a result of
engine braking, as steady-state driving at 80 km h
Since no aerosol chemical composition measurements were performed, we cannot
specify the amount of organic mass in the formed secondary aerosol;
therefore, we do not present the emission factor for the SOA potential of the
engine exhaust. In addition, the high background mass (i.e., unclean dilution
air) and the lack of real-time OH
In this work, we introduced TSAR, a new short-residence-time oxidation flow reactor for secondary aerosol formation measurements. We studied the performance of the reactor by measuring the sulfuric acid yield, toluene SOA yield and the composition and the secondary aerosol formation potential of light-duty gasoline vehicle exhaust during a transient driving cycle. In addition, we characterized the particle transmission efficiency and the residence time distribution of the reactor and did a modeling study on vapor losses in TSAR
According to the model results, the vapor losses in TSAR are higher than in the reactors with longer residence times. The losses depend strongly on the condensational sink of the sample, which is usually high in exhaust measurements (resulting in lower losses). For applications with the low condensational sink, we recommend a longer residence time than in TSAR or the injection of seed aerosol. When there is no possibility for seed aerosol injection, a tradeoff must be made between fast response and low vapor losses.
The toluene experiments show that both the SOA yield and composition are similar in TSAR SOA and PAM SOA, even though PAM operates in OFR185 mode and TSAR in OFR254 mode. The similarity indicates that TSAR can be used instead of the OFR185 PAM reactor when high time resolution is needed.
The particle losses in TSAR are negligible, and the flow is near-laminar. These properties, together with the short residence time, make TSAR better suited for monitoring the secondary aerosol formation potential of rapidly changing emission sources than the PAM chamber. We demonstrate the importance of this feature by measuring the secondary aerosol formation of car exhaust during a driving cycle. This experiment shows that TSAR is able to differentiate which driving conditions are most significant regarding the secondary aerosol formation potential.
The data of this study are available from the authors upon request.
The authors declare that they have no conflict of interest.
TSAR was designed and built in the “Finnish-Chinese Green ICT R&D&I Living Lab for Energy Efficient, Clean and Safe Environments” project and financially supported by the Finnish Funding Agency for Innovation (Tekes), Ahlstrom Oy, FIAC Invest Oy, Green Net Finland Oy, Kauriala Oy, Lassila & Tikanoja Oyj, Lifa Air Oy, MX Electrix Oy, Pegasor Oy and Sandbox Oy.
The TSAR characterization was conducted in the framework of the HERE project funded by Tekes (the Finnish Funding Agency for Innovation), Agco Power Oy, Dinex Ecocat Oy, Dekati Oy, Neste Oyj, Pegasor Oy and Wärtsilä Finland Oy.
Pauli Simonen acknowledges the Tampere University of Technology Graduate School. Edited by: Y. Iinuma Reviewed by: two anonymous referees