cedrene SOA formed in the flow reactor under dry conditions was measured in real time using a Xevo TQS triple quadrupole mass spectrometer (Waters) equipped with a com- mercial DART ion source (IonSense, DART SVP with Vapur ® Interface). As DART-MS is a surface-sensitive technique, to ensure that the bulk of particles can be effectively probed, theaerosol stream exiting the flow reactor, in which the gas phase species
the particles were analyzed with a thermal desorption atmospheric pressure ioniza- tion aerosol mass spectrometer (TD-API-AMS). Comparing the smog chamber SOA compositionand non processed nebulized aqueous extracts with this technique re- vealed that sampling, extraction and/or nebulization did not significantly impact the chemical compositionof SOA formed from isoprene andα-pinene, whereas it affected
smaller than the sensitivity ofthe H-TDMA. (2) Other fac- tors, other than the bulk O : C, can control the water uptake of SOA. Recently, Alfarra et al. (2014) reported a positive correlation between hygroscopicity of particles and their de- gree of oxidation for SOA produced fromthe photooxidation ofα-pinene, β-caryophyllene, linalool and myrcene, but not for limonene SOA. They suggested that other factors such as solubility, surface tension, molecular weight, density and particle phase are likely to be playing important roles in con- trolling GF values. We observed that the O : C ofthe parti- cle surface was lower than the bulk O : C and remained con- stant over time (see Sect. 3.4.1). This structure would affect the heterogeneous chemistry ofthe particle and avoid the water absorption on particles. Broekhuizen et al. (2004) ob- served that the oxidation of oleic acid by low concentration of ozone, shown to produce oxygenated products (Moise and Rudich, 2002), did not increase the particle CCN activity. Katrib et al. (2004) demonstrated that hydrophobic products were formed at the surface of oleic acid particles after ozone exposure that prevented water adsorption on particles. McIn- tire et al. (2010) exposed particles formed fromozonolysisof surface-bound alkenes to ozone and observed formation of polar groups buried inside a hydrophobic shell. This is also consistent with Moussa et al. (2009) who observed that the uptake of water does not increase after the oxidation of surface-bound alkenes. A core–shell structure could explain the low O : C ofthe surface α-pinene–O 3 SOA obtained in
However, the number of species and reactions from a detailed gas phase chemical mechanism for SOA formation are much larger than those commonly used within 3- dimensional Eulerian regional air quality models for the purpose of predicting ozone or particulate matter concentrations. The detailed mechanisms represent an extremely large computational burden in memory and CPU time in this context, hence the need
The extracts obtained fromthe filters were directly injected into the ion trap mass spectrometer without separation using a syringe pump at 240 µlpm, to obtain a molar mass distribu- tion. This allows a quick visual comparison to be made be- tween SOA of different degrees of ageing. Figure 3 shows the negative ion m/z distribution ofthe lower and higher initial concentration samples at 2 h and 6 h. Prior to the ex- periments presented here, 2 α-pinene experiments were car- ried out under identical conditions (30 ppbV VOC) but on consecutive days to test variability. Filter samples were col- lected at the same point in each experiment andthe sam- ples were analysed using LC-MS. Eight α-pinene SOA peaks were chosen (including cis-pinonic acid and 3-methyl-1,2,3- butanetricarboxylic acid – MBTCA) andthe peak areas were determined. The ratio of peak areas between the two sam- ples was calculated and gave an average value of 1.17. This indicates that any variability seen in the mass spectra for the β-caryophyllene analysis is a result ofaerosol ageing and not a product of experiments being conducted on different days. It is clear from Fig. 3 that photochemical ageing changes the SOA composition. After two hours of ageing the SOA composition is relatively simple and is dominated by peaks from m/z 230–340. As the photochemical ageing proceeds, the condensed phase mass spectra become increasingly com- plex. This can be seen over the range m/z 200–290, where rather than comprising single peaks 14–16 m/z apart, as was seen at t = 2 h, the mass spectra at t = 6 h starts to fill up at every odd mass. There is also a change in the molar mass distribution with time, with an increase in smaller molecules
Fig. 6. Comparisons of refractive indices of thermodenuded and undenuded SOA studied in previous studies (Kim et al., 2010, 2012). Ranges of refractive index for each SOA result from changing chemical composition as the particles are growing or aging; error bars are ±0.15 for Kim et al. (2010, 2012) studies. Barkey et al. (2007) has ±0.15, Lang-Yona et al. (2010) has ±0.05 and Nakayama et al. (2010) has ±0.04 uncertainties, respectively. Uncertainties of Schnaiter et al. (2005) study is not available. Horizontal lines indicate SOA or organicaerosol refractive indeces assumed by different current aerosol/climate models. Black symbols repre- sent literature values of SOA generated using same hydrocarbon and oxidation chemistries denoted in the figure. The phenol data is not published and generated at HC/NO x = 35–41.
Ozone was created from oxygen gas via corona discharge using an ozone generator (Azcozon) andthe concentration was measured with an ozone monitor (Dasibi, 1008-PC). Ozone concentrations in the reactor ranged from 300 ppb to 1 ppm. The ozone injected into the reactor was allowed to equilibrate for approximately 15 min before terpene injec- tion. The chamber was considered to be well mixed when the ozone concentration was stable for several minutes. Dur- ing some ofthe experiments, 0.5 mL of 2-butanol (Sigma- Aldrich, 99.5%) was injected into the smog chamber via a gas dispersion tube in order to scavenge hydroxyl radicals produced during terpene ozonolysis (Chew and Atkinson, 1996; Keywood et al, 2004). Two types of monoterpene pre- cursors were used in these studies. The first was α-pinene (Sigma-Aldrich, 99+%) andthe second involved a mixture of four monoterpenes: β-pinene (Sigma-Aldrich, 99+%), limonene (Fluka, 99+%), 3-carene (Sigma-Aldrich, 99%), andα-pinene (Sigma-Aldrich, 99%). These monoterpenes are estimated to make up the majority of global monoterpene emissions (Griffin at al., 1999). The monoterpene mixture was made before each experiment to avoid potential reac- tions.
cent study of Ehn et al. (2014) showing the formation of first generation products as the rate-limiting step. There was an apparent positive offset on the hydrocarbon consumed for α-pinene and β-pinene, and barely an offset for limonene, since the reaction prod- ucts needed to reach their saturation concentration to condense on the particle phase. For limonene, within the time resolution of our measurement they reached the satu-
ratios are slightly lower than those reported by Shilling et al. (2009), but well within the experimental uncertainty. When all α-pinene is consumed andthe SOA growth has leveled out (∼58 µg m − 3 ), O/C and H/C ratios and fragment mass spectrum (not shown here) remain unchanged. As discussed by Ng et al. (2006), the first oxidation step in theozonolysisofα-pinene (a hydrocarbon with a single double bond) is most likely
A variety of factors may contribute to the absolute numer- ical values of these yields differing from yields reported in the literature. For example, vapor-phase wall losses were not accounted for (Zhang et al., 2014), andthe chamber size, mixing, and conditioning of walls differ from other studies. Since these experiments were conducted without seed parti- cles, rather than having a constant particle distribution for va- pors to condense onto, size distributions emerged as freshly nucleated particles that proceeded through full growth curves until they exceeded the range ofthe SEMS and eventually were removed through the constant outflow ofthe chamber. This combination ofgrowthand dilution led to an oscilla- tory behavior of periodic full growth curves as the conden- sational sink was changing, thus preventing a true steady state from ever being achieved. The yield curves shown in Fig. 4 highlight a single growth curve for each experiment, but these yields may be more indicative of kinetically limited growth than thermodynamic partitioning, causing them to differ from other studies. Additionally, and perhaps most im- portantly, the chemistry itself (including both first-generation oxidation and peroxy radical fate) is expected to differ sub- stantially in these mixed oxidant conditions compared to sin- gle oxidant studies in the literature. With all of those factors in mind, the precision reflected in the error ranges in Fig. 4 gives us confidence that the relative yield comparisons be- tween individual experiments in this study are robust.
Several modules have been developed to predict SOA for- mation in atmosphere and are used in chemical transport models. The Odum/Griffin et al. (1999a) and Carnegie Mel- lon University/Sonoma Technology Inc. modules (Strader et al., 1999a, b) represent SOA absorptive partitioning into a mixture of primary andsecondary particulate organic com- pounds, with some differences in the formulation ofthe ab- sorption process, the selection of SOA species, and their pre- cursors. Empirical representations based on measured lab- oratory AMF are used for condensable organic products in both these modules. The Atmospheric and Environmental Research (AER) module simulates SOA absorption into or- ganic and aqueous particulate phases, and a representation based on an explicit gas-phasemechanism is used in the module developed by Pun et al. (2002). Pun et al. (2003) showed that these modules predicted SOA concentrations that can vary by a factor of 10 or more.
The results of chamber investigations of SOA formation fromα- and β-pinene ozonolysis have commonly been inter- preted or modelled in terms of absorptive gas-aerosol parti- tioning of semivolatile oxidation products. This has involved either the assignment of empirically-derived yields and parti- tioning coefficients to two notional products (e.g. Hoffmann et al., 1997; Yu et al., 1999a; Cocker et al., 2001), or the con- sideration ofthe partitioning of a series of known products, with partitioning coefficients based on estimated properties (Kamens et al., 1999) or on experimentally measured con- centrations in the gaseous and condensed phases (Yu et al., 1999a; Cocker et al., 2001). In the present paper, the de- velopment, optimization and application of a highly detailed chemical description of SOA formation fromtheozonolysisofα- and β-pinene is described. The methodology is based on the near-explicit Master Chemical Mechanism version 3 (MCM v3) to describe the gas phase chemistry, coupled with a representation of gas-to-aerosol transfer of ca. 200 semivolatile and involatile oxygenated products. The sim- ulated major components of SOA are compared with those reported in a number of experimental studies, andthe im- pacts of different experimental conditions on SOA yields are explored.
Abstract. Particles consisting ofsecondaryorganic material (SOM) are abundant in the atmosphere. To predict the role of these particles in climate, visibility and atmospheric chem- istry, information on particle phase state (i.e., single liquid, two liquids and solid) is needed. This paper focuses on thephase state of SOM particles free of inorganic salts produced by theozonolysisofα-pinene. Phase transitions were investi- gated in the laboratory using optical microscopy and theoreti- cally using a thermodynamic model at 290 K and for relative humidities ranging from < 0.5 to 100 %. In the laboratory studies, a single phase was observed from 0 to 95 % relative humidity (RH) while two liquid phases were observed above 95 % RH. For increasing RH, themechanismof liquid–liquid phase separation (LLPS) was spinodal decomposition. The RH range over which two liquid phases were observed did not depend on the direction of RH change. In the modeling studies, the SOM took up very little water and was a sin- gle organic-rich phase at low RH values. At high RH, the SOM underwent LLPS to form an organic-rich phaseand a water-rich phase, consistent with the laboratory studies. The presence of LLPS at high RH values can have consequences for the cloud condensation nuclei (CCN) activity of SOM
2.2.2 Condensation oforganic vapors to chamber walls To determine P wall , we assume that condensable vapors are not lost to the clean Teflon walls, but instead condense onto particles that have been deposited to the walls. This is a com- plex process, so here we will limit our analysis to constrain- ing the problem with two limiting cases. Case 1 assumes that the condensable products only partition to the suspended par- ticles, so there is no wall condensation and P wall = 0. Previ- ous wall-loss treatments that only account for particle deposi- tion onto the chamber walls are consistent with this assump- tion. Case 2 assumes that condensation to the particles on the walls is not slowed by any additional mass-transfer resis- tances, so the particles on the walls are in equilibrium with theorganic vapors and behave exactly as if they were sus- pended. There is no obvious reason why particles deposited to the chamber walls should completely lose contact with the vapors in the chamber, a conclusion supported by the precur- sor spiking experiments of Weitkamp et al. (2007). However, we cannot rule out a mass-transfer limitation to the walls, and a conservative approach is to treat these two limiting cases, while noting that the true behavior ofthe system may be somewhere in between. Mass transfer oforganic vapors to the walls (P wall ) in case 2 scales with the particle mass frac- tion on the walls. Therefore, the limiting cases diverge and uncertainty in the observed SOA production increases during an experiment.
of direct influence of emissions from New York and Boston, but little evidence of impact from power plant emissions (De Gouw et al., 2005). The measured organic aerosols showed a correlation pattern that suggested that they were formed mainly through sec- ondary photochemical production and were from anthropogenic sources. However, De Gouw et al. (2005) found that the amount of measured SOA was significantly greater
Individual points are data from individual SEAC 4 RS flight days (8 August–10 September), av- eraged on the GEOS-Chem grid. OMI data are for SEAC 4 RS flight days and coincident with the flight tracks. GEOS-Chem is sampled for the corresponding locations and times. Results from our simulation with aqueous-phase isoprene SOA chemistry are shown in red, and re- sults from a simulation with the Pye et al. (2010) semivolatile reversible partitioning scheme are shown in blue. Aerosol concentrations are per m 3 at standard conditions of temperature and pressure (STP: 273 K; 1 atm), denoted s m −3 . Reduced major axis (RMA) regressions are
Each sampled filter was cut into small pieces and its organic content extracted in a flask by refluxing dichloromethane for 24 h. The extract was filtrated andthe solvent concentrated to volumes of approximately 4 mL. The resulting solvent extract was transferred to vials, evaporated until dryness using a stream of nitrogen. In order to separate individual classes oforganic compounds, the vials were successively washed using 5 solvents of increasing polarity and transferred to the top of a 30×0.7 cm column containing 1.5 g of silica gel (activated at 150 °C for 3 h), to fractionate the total organic extract by flash chromatography. Nitrogen pressure was used in order to obtain a flow of 1.4 mL min -1 at the bottom ofthe column.
(Sjogren et al., 2007b), or a thermodynamic effect caused by persistent chemical in- teractions. The distinction between these three effects is important because organic films may not necessarily affect the water absorption of atmospheric particles at sub- saturation because of longer time scales for RH changes compared to just a few sec- onds applied in the traditional H-TDMA technique (Rader and McMurry, 1986; McMurry
time-of-flight mass spectrometry. The time resolution ofthe deployed AMS was 10 min; details regarding the sampling protocol, AMS data analysis, applied corrections (collection efficiency, relative ionization efficiencies, etc.) can be found elsewhere (Freutel et al., 2013; Crippa et al., 2013a). Here theorganic mass spectral time series with unit mass reso- lution is used as input for the positive matrix factorization (PMF) source apportionment model (see Sect. 2.2), together with the corresponding time series of measurement uncer- tainties (Allan et al., 2003). For the purposes of PMF, the AMS uncertainty matrix accounts for electronic noise (which corresponds to a minimum random error in the number of ions detected during the sampling period), ion-to-ion vari- ability at the detector and ion counting statistics, where the probability that a single molecule is ionized and detected is approximated as a Poisson distribution (Allan et al., 2003). In order to perform PMF, 268 AMS ions were considered (m/z range up to 300) and error pretreatment procedures were applied according to Ulbrich et al. (2009), as discussed below. Low signal-to-noise m/z values (SNR < 0.2) were removed, whereas “weak” variables (0.2 < SNR < 2) were downweighted by a factor of 2. In the AMS data analysis pro- cedure, certain organic peaks are not directly measured but rather calculated as a fraction oftheorganic signal at m/z 44 (Allan et al., 2004). The errors for these m/z were adjusted to prevent overweighting ofthe m/z 44 signal, following the method of Ulbrich et al. (2009); of these m/z 44-dependent peaks, m/z 19 and 20 were simply removed due to their neg- ligible masses.
sulfate ratio increases. For t<0 theaerosol is entirely inorganic seed and its evolu- tion is entirely due to wall losses. The modeled inorganic suspended aerosol mass (black dotted line) calculated from Eq. (8) thus tracks the total suspended mass C sus OA (t) for t<0. When the UV-lights are turned on, initiating toluene oxidation, the measured mass deviates fromthe modeled inorganic mass, consistent with net condensation of