California Institute of Technology



Supporting Information: “Characterization of laboratory and real driving emissions of individual Euro 6 light-duty vehicles – fresh particles and secondary aerosol formation”Pauli Simonena, Joni Kalliokoskia, Panu Karjalainena, Topi R?nkk?a, Hilkka Timonenb, Sanna Saarikoskib, Minna Aurelab, Matthew Blossb, Georgios Triantafyllopoulosc, Anastasios Kontsesc, Stavros Amanatidisc, Athanasios Dimaratosc, Zissis Samarasc, Jorma Keskinena, Miikka Dal Masoa, Leonidas Ntziachristosa,caAerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, FinlandbAtmospheric Composition Research, Finnish Meteorological Institute, Helsinki, FinlandcLaboratory of Applied Thermodynamics, Aristotle University of Thessaloniki, Thessaloniki, GreeceContentsLaboratory measurement setupSP-AMS settingsCalculation of volumetric exhaust flow rateTSAR convolutionTSAR photochemical ageTSAR LVOC lossesComparison of TSAR and PAM driving cycle experimentsEuro 6 diesel vehicle aged aerosolParticle size distributionsEvaluating the accuracy of mass determined from ELPIAdditional tablesVehicle specificationsNumber of driving cycles run in laboratory and real-drive experimentsAmbient conditions during real-drive experimentsRegulated emissionsAdditional figuresReal-drive chase measurement setupComparison of calculated and measured background concentrationsEFs of fresh aerosol massFresh particle number EFs normalized to fuel consumedEFs of fresh aerosol mass normalized to fuel consumedLaboratory SOA PFs normalized to fuel consumedLaboratory SOA PFs normalized to CO concentrationAged mass concentration and composition in laboratory cyclesReferences1. Laboratory measurement setupThe laboratory measurement setup is shown in Fig. S1. The sample was first diluted using a porous tube diluter (PTD) followed by chamber with a residence time of 2.8 s to stabilize aerosol. The dilution air temperature was set to 30 °C and the dilution ratio (DR) was controlled with two mass flow controllers (MFC) and adjusted to approximately 12:1. An ejector diluter (DR ~6; Dekati Ltd.) was installed after the first dilution stage to provide enough sample for all the instruments. The CO2 concentrations were measured after each dilution stage during steady driving at 80 km h-1 to determine the actual DRs.Figure S SEQ Figure \* ARABIC 1. Laboratory sampling setup.Humidified air and ozone was mixed with the sample upstream of TSAR, producing an additional dilution of approximately 1.5. An additional ejector diluter (DR ~6) was used for the cold start cycle, so that the DR upstream of TSAR was approximately 100 for the cold-start cycle and 18 for the hot start cycles. The cold-start cycle requires a higher DR than the warm-start cycle to achieve a similar photochemical age in TSAR. This is due to the higher NOx and HC concentrations in the beginning of the cold-start cycle ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/acp-17-11991-2017","ISBN":"1711991201","ISSN":"1680-7324","abstract":"Oxidation flow reactors (OFRs) are increasingly employed in atmospheric chemistry research because of their high efficiency of OH radical production from low-pressure Hg lamp emissions at both 185 and 254 nm (OFR185) or 254 nm only (OFR254). OFRs have been thought to be limited to studying low-NO chemistry (in which peroxy radicals (RO2) react preferentially with HO2) because NO is very rapidly oxidized by the high concentrations of O3, HO2, and OH in OFRs. However, many groups are performing experiments by aging combustion exhaust with high NO levels or adding NO in the hopes of simulating high-NO chemistry (in which RO2 + NO dominates). This work systematically explores the chemistry in OFRs with high initial NO. Using box modeling, we investigate the interconversion of N-containing species and the uncertainties due to kinetic parameters. Simple initial injection of NO in OFR185 can result in more RO2 reacted with NO than with HO2 and minor non-tropospheric photolysis, but only under a very narrow set of conditions (high water mixing ratio, low UV intensity, low external OH reactivity (OHRext), and initial NO concentration (NOin) of tens to hundreds of ppb) that account for a very small fraction of the input parameter space. These conditions are generally far away from experimental conditions of published OFR studies with high initial NO. In particular, studies of aerosol formation from vehicle emissions in OFRs often used OHRext and NOin several orders of magnitude higher. Due to extremely high OHRext and NOin, some studies may have resulted in substantial non-tropospheric photolysis, strong delay to RO2 chemistry due to peroxynitrate formation, VOC reactions with NO3 dominating over those with OH, and faster reactions of OH&ndash;aromatic adducts with NO2 than those with O2, all of which are irrelevant to ambient VOC photooxidation chemistry. Some of the negative effects are the worst for alkene and aromatic precursors. To avoid undesired chemistry, vehicle emissions generally need to be diluted by a factor of &gt;&thinsp;100 before being injected into an OFR. However, sufficiently diluted vehicle emissions generally do not lead to high-NO chemistry in OFRs but are rather dominated by the low-NO RO2 + HO2 pathway. To ensure high-NO conditions without substantial atmospherically irrelevant chemistry in a more controlled fashion, new techniques are needed.","author":[{"dropping-particle":"","family":"Peng","given":"Zhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jimenez","given":"Jose L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Chemistry and Physics","id":"ITEM-1","issue":"19","issued":{"date-parts":[["2017","10","10"]]},"page":"11991-12010","title":"Modeling of the chemistry in oxidation flow reactors with high initial NO","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"(Peng and Jimenez, 2017)","plainTextFormattedCitation":"(Peng and Jimenez, 2017)","previouslyFormattedCitation":"(Peng and Jimenez, 2017)"},"properties":{"noteIndex":0},"schema":""}(Peng and Jimenez, 2017).2. SP-AMS settingsIn an SP-AMS, an aerodynamic lens produces a focused beam of particles in the 30- 800 nm size range, which is subsequently led through a differentially pumped vacuum chamber to a double vaporizer. These comprise a normal tungsten vaporizer at 600 °C to vaporize non-refractive compounds and an intracavity Nd:YAG laser vaporizer (1064 nm) to vaporize refractive compounds (mainly rBC and metals). The vaporized compounds are ionized using electron impact ionization (70 eV) and are led to the time-of flight classifier. V-mode was used for classification, which is a single-reflection configuration with resolving power up to ~2000 at m/z 200. A multi-channel plate (MCP) was used as a detector. The time resolution of AMS measurements was 7 s during driving cycles and 34 s during steady driving. Igor 6.37 with SQUIRREL1.62A and PIKA1.22A analysis packages were used for the data analysis.3. Calculation of volumetric exhaust flow rateThe volumetric flow of the exhaust was calculated by eq. (S1):volumetric flow=RTP×massflowMavg,(S1)where R is the ideal gas constant, T and P are the ambient temperature and pressure, respectively, Mavg is the weighted average of the molar masses of the exhaust components (using the measured CO2, CO, NO, NO2 and O2 concentrations, assuming a H2O concentration equal to the CO2 concentration, and assuming that all unmeasured gas consists of nitrogen). The mass flow in the equation is the sum of intake airflow and fuel consumption. The intake airflow is obtained directly from the OBD data of the diesel vehicle, and for the gasoline vehicles it is calculated by eq. (S2):airflow=PiV2 RTi×Mair×f,(S2)where Pi and Ti are the intake pressure and temperature, respectively (from OBD), V is the engine displacement, Mair is the molar mass of air molecules and f is the engine speed (revolutions per second). The fuel consumption (fc) is calculated by using the air-fuel equivalence ratio (λ):fc=airflowλ×AFRstoich,(S3)where AFRstoich is the fuel-specific stoichiometric air-fuel ratio (14.7 assumed for gasoline and 14.6 for diesel). The air-fuel equivalence ratio is obtained directly from the diesel vehicle OBD data, and for the gasoline vehicles, it is calculated by Brettschneider equation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Brettschneider","given":"Johannes","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bosch Technische Berichte","id":"ITEM-1","issue":"4","issued":{"date-parts":[["1979"]]},"page":"177-186","title":"Berechnung des Luftverhaeltnisses λ von Luft-Kraftstoff-Gemsichen und des Einflusses on Me?fehlern auf λ","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"(Brettschneider, 1979)","manualFormatting":"(Brettschneider, 1979","plainTextFormattedCitation":"(Brettschneider, 1979)","previouslyFormattedCitation":"(Brettschneider, 1979)"},"properties":{"noteIndex":0},"schema":""}(Brettschneider, 1979 as cited by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.4271/970514","abstract":"A confusing number of equations have been developed and published for calculating the air/fuel ratio of an operating engine from the composition of its exhaust gasses. These methods make varying use of the information available from the gas concentration measurements, but they all are based on the same chemistry of combustion. The method described here is a single algorithm that duplicates the results of all the well known published equations and can adapt to different measurement circumstances, such as when an oxygen measurement is not available or if the gas sampling point is moved to after the catalyst. Data are presented to demonstrate the equivalence of the algorithm and equation evaluations.","author":[{"dropping-particle":"","family":"Silvis","given":"William M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"SAE Technical Paper Series","id":"ITEM-1","issue":"412","issued":{"date-parts":[["2010"]]},"title":"An Algorithm for Calculating the Air/Fuel Ratio from Exhaust Emissions","type":"article-journal","volume":"1"},"uris":[""]}],"mendeley":{"formattedCitation":"(Silvis, 2010)","manualFormatting":"Silvis, 2010)","plainTextFormattedCitation":"(Silvis, 2010)","previouslyFormattedCitation":"(Silvis, 2010)"},"properties":{"noteIndex":0},"schema":""}Silvis, 2010), neglecting the effect of ambient humidity:λ=CO2+CO2+O2+NO2+HCV4×3.53.5+COCO2-OCV2×CO2+CO1+HCV4-OCV2×CO2+CO+[HC],(S4)where [CO2], [CO], [O2], [NO] and [HC] are the mixing ratios (%) of carbon dioxide, carbon monoxide, oxygen, nitric oxide and hydrocarbons, respectively. HCV is the atomic ratio of hydrogen to carbon in the fuel (1.8 assumed), OCV is the atomic ratio of oxygen to carbon in the fuel (0.016 assumed). Since there was no hydrocarbon measurement in the PEMS, we assume [HC] = 0 in the calculation. As a sensitivity test, if we use the maximum [HC] observed in the laboratory during warm-start cycles (1360 ppm), the average fuel consumption would be 0.1 % higher.4. TSAR convolutionBecause of the TSAR residence time distribution (RTD), there is delay and broadening of the emission peaks in the ELPI data downstream of TSAR when compared to the upstream data. Thus, the upstream data has to be corrected for the TSAR RTD when comparing fresh emissions and aged mass (e.g. in Fig. 3). Simonen et al. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/amt-10-1519-2017","ISBN":"1015192017","ISSN":"18678548","abstract":"<p>Oxidation flow reactors 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. Here, we present a new oxidation flow reactor, TSAR (TUT Secondary Aerosol Reactor), which has a short residence time and near-laminar flow conditions. This allows studying e.g. the effect of vehicle driving conditions on secondary aerosol formation potential of the exhaust. We show that the flow pattern in TSAR is nearly laminar and particle losses are negligible. The secondary organic aerosol (SOA) produced in TSAR has a similar mass spectrum as the SOA produced in the state-of-the-art reactor, PAM (Potential Aerosol Mass). Both reactors produce the same amount of mass, but the TSAR has a higher time-resolution. We also show that the TSAR is capable of measuring secondary aerosol formation potential of a vehicle during a transient driving cycle, and that the fast response of the TSAR reveals how different driving conditions affect the amount of formed secondary aerosol. Thus, the TSAR can be used to study rapidly changing emission sources, especially the vehicular emissions during transient driving.</p>","author":[{"dropping-particle":"","family":"Simonen","given":"Pauli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saukko","given":"Erkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Panu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timonen","given":"Hilkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloss","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aakko-Saksa","given":"P?ivi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"R?nkk?","given":"Topi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keskinen","given":"Jorma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dal Maso","given":"Miikka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Measurement Techniques","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017"]]},"page":"1519-1537","title":"A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"(Simonen et al., 2017)","manualFormatting":"(2017)","plainTextFormattedCitation":"(Simonen et al., 2017)","previouslyFormattedCitation":"(Simonen et al., 2017)"},"properties":{"noteIndex":0},"schema":""}(2017) show that in TSAR oxidation reactor, the flow profile is almost laminar. Here, we assume that the flow is laminar also in TSAR residence time chamber and the expansion tube. Thus, to simulate the residence time of TSAR, the upstream data is convolved with the RTD of a laminar flow according to eqs. (1) and (19) in Simonen et al. when necessary.The convolution is applied for the volumetric flow of the exhaust when calculating the EFs of the aged mass both in laboratory and in real-drive experiments. In real-drive experiments, the tailpipe and ambient CO2 concentrations are convolved to obtain DR for the ELPI downstream of TSAR. In addition, in Fig. 3, the fresh aerosol mass concentration is convolved to allow better comparison between the fresh and the aged mass, but the fresh aerosol mass EFs are always calculated from the unconvolved data.5. TSAR photochemical ageThe OH exposure of the exhaust in the TSAR is modeled with a differential equation solver and a set of chemical reactions. The model is a modified version of a PAM_chem_v8 made by William Brune (available at: ). The model and the fitting parameters are described in Simonen et al. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/amt-10-1519-2017","ISBN":"1015192017","ISSN":"18678548","abstract":"<p>Oxidation flow reactors 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. Here, we present a new oxidation flow reactor, TSAR (TUT Secondary Aerosol Reactor), which has a short residence time and near-laminar flow conditions. This allows studying e.g. the effect of vehicle driving conditions on secondary aerosol formation potential of the exhaust. We show that the flow pattern in TSAR is nearly laminar and particle losses are negligible. The secondary organic aerosol (SOA) produced in TSAR has a similar mass spectrum as the SOA produced in the state-of-the-art reactor, PAM (Potential Aerosol Mass). Both reactors produce the same amount of mass, but the TSAR has a higher time-resolution. We also show that the TSAR is capable of measuring secondary aerosol formation potential of a vehicle during a transient driving cycle, and that the fast response of the TSAR reveals how different driving conditions affect the amount of formed secondary aerosol. Thus, the TSAR can be used to study rapidly changing emission sources, especially the vehicular emissions during transient driving.</p>","author":[{"dropping-particle":"","family":"Simonen","given":"Pauli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saukko","given":"Erkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Panu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timonen","given":"Hilkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloss","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aakko-Saksa","given":"P?ivi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"R?nkk?","given":"Topi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keskinen","given":"Jorma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dal Maso","given":"Miikka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Measurement Techniques","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017"]]},"page":"1519-1537","title":"A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"(Simonen et al., 2017)","manualFormatting":"(2017)","plainTextFormattedCitation":"(Simonen et al., 2017)","previouslyFormattedCitation":"(Simonen et al., 2017)"},"properties":{"noteIndex":0},"schema":""}(2017).The model inputs were the measured relative humidity and temperature, the ozone concentration of the sample prior to TSAR, NOx and CO measured from CVS and the OH reactivity (OHR) of the hydrocarbons (HC) measured from CVS. The OHR is approximated based on the VOC profile of Euro 5 gasoline vehicle reported by Karjalainen et al. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/acp-16-8559-2016","ISSN":"1680-7324","abstract":"Changes in vehicle emission reduction technologies significantly affect traffic-related emissions in urban areas. In many densely populated areas the amount of traffic is increasing, keeping the emission level high or even increasing. To understand the health effects of traffic-related emissions, both primary (direct) particulate emission and secondary particle formation (from gaseous precursors in the exhaust emissions) need to be characterized. In this study, we used a comprehensive set of measurements to characterize both primary and secondary particulate emissions of a Euro 5 level gasoline passenger car. Our aerosol particle study covers the whole process chain in emission formation, from the tailpipe to the atmosphere, and also takes into account differences in driving patterns. We observed that, in mass terms, the amount of secondary particles was 13 times higher than the amount of primary particles. The formation, composition, number and mass of secondary particles was significantly affected by driving patterns and engine conditions. The highest gaseous and particulate emissions were observed at the beginning of the test cycle when the performance of the engine and the catalyst was below optimal. The key parameter for secondary particle formation was the amount of gaseous hydrocarbons in primary emissions; however, also the primary particle population had an influence.","author":[{"dropping-particle":"","family":"Karjalainen","given":"Panu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timonen","given":"Hilkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saukko","given":"Erkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kuuluvainen","given":"Heino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saarikoski","given":"Sanna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aakko-Saksa","given":"P?ivi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Murtonen","given":"Timo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloss","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dal Maso","given":"Miikka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Simonen","given":"Pauli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ahlberg","given":"Erik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Svenningsson","given":"Birgitta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brune","given":"William Henry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hillamo","given":"Risto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keskinen","given":"Jorma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"R?nkk?","given":"Topi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Chemistry and Physics","id":"ITEM-1","issue":"13","issued":{"date-parts":[["2016","7","14"]]},"page":"8559-8570","title":"Time-resolved characterization of primary particle emissions and secondary particle formation from a modern gasoline passenger car","type":"article-journal","volume":"16"},"uris":[""]}],"mendeley":{"formattedCitation":"(Karjalainen et al., 2016)","manualFormatting":"(2016)","plainTextFormattedCitation":"(Karjalainen et al., 2016)","previouslyFormattedCitation":"(Karjalainen et al., 2016)"},"properties":{"noteIndex":0},"schema":""}(2016), so that the OHR:HC ratio in the beginning of the cold-start cycle (first 390 seconds) is 64.33 s-1 ppm-1, in the middle of the cycle (391-780 s) 18.23 s-1 ppm-1 and for the rest of the cycle 9.82 s-1 ppm-1. For the hot-start cycles and steady driving at 80 km h-1, the ratio of 9.82 s-1 ppm-1 is assumed throughout the cycle. All the gaseous data is convolved with TSAR residence time chamber and the expansion tube RTDs before modeling, and the acquired OH exposure is convolved with the TSAR oxidation reactor RTD. It is assumed that all NO in the exhaust is converted to NO2 before TSAR oxidation reactor due to ozonolysis in the residence time chamber.The time series of the OH exposure (as equivalent days in the atmosphere, assuming ambient OH concentration of 1.5×106 cm-3) for each driving cycle is presented in Fig S2. The average OH exposures for the driving cycles and steady driving are shown in Fig 4.Figure S SEQ Figure \* ARABIC 2. OH exposures (as equivalent atmospheric days) during the driving cycles.6. TSAR LVOC lossesThe low volatile organic compounds (LVOCs) formed by the photo-oxidation in TSAR may produce SOA by condensing into particle phase. However, there are also non-atmospheric losses for the LVOCs in TSAR: they may condense on walls, fragment into more volatile compounds before condensing or exit the reactor before condensing ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/acp-16-2943-2016","ISBN":"1629432016","ISSN":"16807324","abstract":"Ambient air was oxidized by OH radicals in an oxidation flow reactor (OFR) located in a montane pine forest during the BEACHON-RoMBAS campaign to study biogenic secondary organic aerosol (SOA) formation and aging. High OH concentrations and short residence times allowed for semi-continuous cycling through a large range of OH exposures ranging from hours to weeks of equivalent (eq.) atmospheric aging. A simple model is derived and used to account for the relative time scales of condensation of low volatility organic compounds (LVOCs) onto particles, condensational loss to the walls, and further reaction to produce volatile, non-condensing fragmentation products. More SOA production was observed in the OFR at nighttime (average 4 μg m-3 when LVOC fate corrected) compared to daytime (average 1 μg m-3 when LVOC fate corrected), with maximum formation observed at 0.4–1.5 eq. days of photochemical aging. SOA formation followed a similar diurnal pattern to monoterpenes, sesquiterpenes, and toluene + p-cymene concentrations, including a substantial increase just after sunrise at 07:00 LT. Higher photochemical aging (> 10 eq. days) led to a decrease in new SOA formation and a loss of preexisting OA due to heterogeneous oxidation followed by fragmentation and volatilization. When comparing two different commonly used methods of OH production in OFRs (OFR185 and OFR254), similar amounts of SOA formation were observed. We recommend the OFR185 mode for future forest studies. Concurrent gas-phase measurements of air after OH oxidation illustrate the decay of primary VOCs, production of small oxidized organic compounds, and net production at lower ages followed by net consumption of terpenoid oxidation products as photochemical age increased. New particle formation was observed in the reactor after oxidation, especially during times when precursor gas concentrations and SOA formation were largest. Approximately 6 times more SOA was formed in the reactor from OH oxidation than could be explained by the VOCs measured in ambient air. Several recently-developed instruments quantified ambient semi- and intermediate-volatility organic compounds (S/IVOCs) that were not detected by a PTR-TOF-MS. An SOA yield of 24–80 % from those compounds can explain the observed SOA, suggesting that these typically unmeasured S/IVOCs play a substantial role in ambient SOA formation. Our results allow ruling out condensation sticking coefficients much lower than 1. Our measurements help clar…","author":[{"dropping-particle":"","family":"Palm","given":"Brett B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Campuzano-Jost","given":"Pedro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ortega","given":"Amber M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Day","given":"Douglas A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kaser","given":"Lisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jud","given":"Werner","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karl","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hansel","given":"Armin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hunter","given":"James F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cross","given":"Eben S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kroll","given":"Jesse H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peng","given":"Zhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brune","given":"William H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jimenez","given":"Jose L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Chemistry and Physics","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2016"]]},"page":"2943-2970","title":"In situ secondary organic aerosol formation from ambient pine forest air using an oxidation flow reactor","type":"article-journal","volume":"16"},"uris":[""]}],"mendeley":{"formattedCitation":"(Palm et al., 2016)","plainTextFormattedCitation":"(Palm et al., 2016)","previouslyFormattedCitation":"(Palm et al., 2016)"},"properties":{"noteIndex":0},"schema":""}(Palm et al., 2016). Due to these losses, the SOA formation potential derived from a TSAR measurement is underestimated. Here, we estimate the effect of non-atmospheric losses by the method described by Palm et al. using the same assumptions on LVOC diffusion coefficient, molar mass and reaction rate with OH as Palm et al., and the same rate coefficient for wall loss as Simonen et al. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/amt-10-1519-2017","ISBN":"1015192017","ISSN":"18678548","abstract":"<p>Oxidation flow reactors 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. Here, we present a new oxidation flow reactor, TSAR (TUT Secondary Aerosol Reactor), which has a short residence time and near-laminar flow conditions. This allows studying e.g. the effect of vehicle driving conditions on secondary aerosol formation potential of the exhaust. We show that the flow pattern in TSAR is nearly laminar and particle losses are negligible. The secondary organic aerosol (SOA) produced in TSAR has a similar mass spectrum as the SOA produced in the state-of-the-art reactor, PAM (Potential Aerosol Mass). Both reactors produce the same amount of mass, but the TSAR has a higher time-resolution. We also show that the TSAR is capable of measuring secondary aerosol formation potential of a vehicle during a transient driving cycle, and that the fast response of the TSAR reveals how different driving conditions affect the amount of formed secondary aerosol. Thus, the TSAR can be used to study rapidly changing emission sources, especially the vehicular emissions during transient driving.</p>","author":[{"dropping-particle":"","family":"Simonen","given":"Pauli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saukko","given":"Erkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Panu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timonen","given":"Hilkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloss","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aakko-Saksa","given":"P?ivi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"R?nkk?","given":"Topi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keskinen","given":"Jorma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dal Maso","given":"Miikka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Measurement Techniques","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017"]]},"page":"1519-1537","title":"A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"(Simonen et al., 2017)","manualFormatting":"(2017)","plainTextFormattedCitation":"(Simonen et al., 2017)","previouslyFormattedCitation":"(Simonen et al., 2017)"},"properties":{"noteIndex":0},"schema":""}(2017). Mass accommodation coefficient of unity is assumed.The effect of the potential LVOC loss on SOA PFs is shown as error bars in Fig. 4. However, the effect is not necessarily that high, because the LVOC correction was not applied for the background aged mass. For this correction, we would need to know the fraction of organic aerosol in the background mass, but due to the small particle size it is not available from the AMS data. If majority of the background aerosol mass is organic, the LVOC correction would lead to a higher background than what was actually measured and thus the SOA production factor corrected with LVOC loss could be even lower than the uncorrected one.7. Comparison of TSAR and PAM driving cycle experimentsAs shown by Simonen et al. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/amt-10-1519-2017","ISBN":"1015192017","ISSN":"18678548","abstract":"<p>Oxidation flow reactors 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. Here, we present a new oxidation flow reactor, TSAR (TUT Secondary Aerosol Reactor), which has a short residence time and near-laminar flow conditions. This allows studying e.g. the effect of vehicle driving conditions on secondary aerosol formation potential of the exhaust. We show that the flow pattern in TSAR is nearly laminar and particle losses are negligible. The secondary organic aerosol (SOA) produced in TSAR has a similar mass spectrum as the SOA produced in the state-of-the-art reactor, PAM (Potential Aerosol Mass). Both reactors produce the same amount of mass, but the TSAR has a higher time-resolution. We also show that the TSAR is capable of measuring secondary aerosol formation potential of a vehicle during a transient driving cycle, and that the fast response of the TSAR reveals how different driving conditions affect the amount of formed secondary aerosol. Thus, the TSAR can be used to study rapidly changing emission sources, especially the vehicular emissions during transient driving.</p>","author":[{"dropping-particle":"","family":"Simonen","given":"Pauli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saukko","given":"Erkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Panu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timonen","given":"Hilkka","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloss","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aakko-Saksa","given":"P?ivi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"R?nkk?","given":"Topi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keskinen","given":"Jorma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dal Maso","given":"Miikka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Measurement Techniques","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017"]]},"page":"1519-1537","title":"A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"(Simonen et al., 2017)","manualFormatting":"(2017)","plainTextFormattedCitation":"(Simonen et al., 2017)","previouslyFormattedCitation":"(Simonen et al., 2017)"},"properties":{"noteIndex":0},"schema":""}(2017), TSAR suits better for studying the secondary aerosol formation potential of rapidly changing emission sources than PAM reactor because of the faster response time. The same conclusion can be drawn from this study by comparing the cold-start data shown in Fig. 3 to other cold-start experiments utilizing a PAM reactor (Karjalainen et al., 2016; Pieber et al., 2018; Timonen et al., 2017). In Fig. 3a, the SOA formation peak from the cold-start levels off to a stable level after ~200 s, whereas in the publications using PAM reactor this takes ~400-1000 s. The fast response time is important when analyzing which driving phases of the test cycle produce most SOA. In addition, when using partial flow sampling (as in this study), the measured concentrations have to be multiplied by the time-dependent exhaust volumetric flow (eq. 2). Thus, any delay in the sampling causes error to the EF calculation, and the faster the response, the smaller the error.8. Euro 6 diesel vehicle aged aerosolThe aged aerosol from laboratory driving cycles was not determined for the same diesel vehicle that was studied in real-drive conditions. Instead, we provide laboratory measurements of another Euro 6 light-duty diesel vehicle (model year 2015) for a comparison with the gasoline vehicles. This vehicle was equipped with an 88 kW engine (1560 cm3 displacement) and DOC+SCR+DPF aftertreatment. The measurement principle was similar as for the gasoline vehicles in the sense that the aged aerosol was measured by oxidizing the diluted exhaust in TSAR. However, in the diesel measurements, there was no simultaneous measurement of fresh exhaust, so we cannot determine which portion of the aged mass was of secondary origin. The exhaust was diluted using a Fine Particle Sampler (FPS; Dekati Ltd.), consisting of a porous tube diluter (PTD) and an ejector diluter. The total dilution ratio of this combination was 20:1, and the PTD dilution air was heated to 200 °C, as opposed to the primary dilution ratio of 12:1 at 30 °C used for the gasoline vehicles. The average OH exposures in these experiments were 5.3, 8.3 and 3.9 equivalent atmospheric days in cold NEDC, hot NEDC and hot WLTC, respectively.The aged aerosol mass concentrations of the diesel vehicle during three driving cycles are shown in Fig. S3. Generally, the mass concentration of the aged exhaust is significantly lower than that of the gasoline vehicles, but high mass concentrations are occasionally observed. At the end of the cold NEDC, the DOC outlet temperature rises up to 700 °C simultaneously with the increase in the aged mass, indicating a DPF regeneration. Thus, the aerosol mass observed here does not represent a typical continuous operation of the vehicle. In the hot NEDC, there is no regeneration event since the catalyst temperature stays below 400 °C throughout the cycle, and the aged mass is close to detection limit. The same applies for the hot WLTC, but at the very end of the cycle, the aged mass is at same level as with the gasoline vehicles and consists mainly of ammonium nitrate. Overall, the SOA formed from the diesel vehicle exhaust is negligible compared to the gasoline vehicle exhaust, even if we assume that all the aged OA is of secondary origin. The effect of DPF regeneration on total OA emission or SOA formation potential of the diesel vehicle depends on the regeneration interval, which was not studied here.Figure S SEQ Figure \* ARABIC 3. Aged aerosol mass, vehicle speed and DOC outlet temperature of a Euro 6 diesel vehicle during cold NEDC (a), hot NEDC (b) and hot WLTC (c), calculated to raw exhaust. The mass concentration calculated from EEPS particle number size distribution is shown with a red line and the mass concentration from SP-AMS is presented as a superposition of the chemical species.9. Particle size distributionsFigure S4 shows the average particle number and mass size distributions measured in the engine laboratory during hot NEDC 780-850 s after start (i.e., the average distributions during the acceleration from 0 to 70 km h-1; see Fig. 3) measured by the ELPIs. The mass size distributions were calculated by assuming spherical particles of unit density.Figure S SEQ Figure \* ARABIC 4. The average fresh aerosol particle number size distribution (a) and mass size distribution (b) during acceleration from 0 to 70 km h-1, and the mass size distribution of the aged aerosol during the same acceleration (c). The distributions were measured by ELPI and are calculated to raw exhaust.The time resolved particle number size distributions, calculated to raw exhaust, are shown for both vehicles and driving cycles in Figs S5-S8. The difference between the GDI and PFI fresh particle size distributions is mostly similar as in Fig. S4: the GDI emits more particles in the accumulation mode, whereas the PFI usually has higher concentrations in the nucleation mode. Some exceptions are seen during accelerations in the WLTC (Fig S7), where the PFI emits occasionally higher concentrations of particles in the accumulation mode than the GDI. This is reflected also in fresh aerosol mass concentrations in Fig 3e.Figure S SEQ Figure \* ARABIC 5. The fresh aerosol particle number size distributions measured by ELPI for the PFI during hot NEDC (a), for the GDI during hot NEDC (b) and for the GDI during cold NEDC (c).Figure S SEQ Figure \* ARABIC 6. The aged aerosol particle number size distributions measured by ELPI for the PFI during hot NEDC (a), for the GDI during hot NEDC (b) and for the GDI during cold NEDC (c).Figure S SEQ Figure \* ARABIC 7. The fresh aerosol particle number size distributions measured by ELPI for the PFI during hot WLTC (a) and for the GDI during hot WLTC (b).Figure S SEQ Figure \* ARABIC 8. The aged aerosol particle number size distributions measured by ELPI for the PFI during hot WLTC (a) and for the GDI during hot WLTC (b).10. Evaluating the accuracy of mass determined from ELPITo evaluate the accuracy of ELPI mass measurements, we compared the fresh mass EFs measured by ELPI to those measured gravimetrically (Fig S9a). For the gravimetric measurements, the particles were collected to filter from the CVS during the driving cycles. Both methods give EFs of same order of magnitude, but in all cases the gravimetric method results in higher EFs. However, the gravimetric particle mass should not be considered as the true mass because of the artifacts related to filter collection, such as gas adsorption and lack of sensitivity for low mass loadings ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.jaerosci.2013.09.003","ISBN":"0021-8502","ISSN":"00218502","abstract":"Particulate emissions from motor vehicles have received increased attention over the past two decades owing to associations observed between ambient particulate matter (PM) levels and health effects. This has led to numerous changes in emissions regulations worldwide, including more stringent standards, the broadening of these to include non-road engines, and the adoption of new metrics. These changes have created a demand for new instruments that are capable of real time measurement, enhanced sensitivity, and on-board vehicle operation. In response, researchers and instrument manufacturers have developed an array of new and improved instruments and sampling methods. It is generally recognized that the exhaust aerosol concentration measured depends on both the sampling technique and the instrument used. Hence, many of the new instruments are complementary and offer merits in measuring a variety of particulate emissions attributes. However, selecting the best instrument for each application is not a straightforward task; it requires on one hand a clear measurement objective and, on the other, an understanding of the characteristics of the instrument employed.This paper reviews how vehicle exhaust particulate emission measurements have evolved over the years. The focus is on current and newly evolving instrumentation, including gravimetric filter measurement, chemical analysis of filters, light extinction, scattering and absorption instruments, and instruments based on the electrical detection of exhaust aerosols. Correlations between the various instruments are examined in the context of steadily more stringent exhaust emissions standards. The review concludes with a discussion of future instrument and sampling requirements for the changing nature of exhaust aerosols from current and future vehicles. ? 2013 Elsevier Ltd.","author":[{"dropping-particle":"","family":"Giechaskiel","given":"Barouch","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maricq","given":"Matti","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ntziachristos","given":"Leonidas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dardiotis","given":"Christos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Xiaoliang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Axmann","given":"Harald","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bergmann","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schindler","given":"Wolfgang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Aerosol Science","id":"ITEM-1","issued":{"date-parts":[["2014","1"]]},"page":"48-86","publisher":"Elsevier","title":"Review of motor vehicle particulate emissions sampling and measurement: From smoke and filter mass to particle number","type":"article-journal","volume":"67"},"uris":[""]}],"mendeley":{"formattedCitation":"(Giechaskiel et al., 2014)","plainTextFormattedCitation":"(Giechaskiel et al., 2014)","previouslyFormattedCitation":"(Giechaskiel et al., 2014)"},"properties":{"noteIndex":0},"schema":""}(Giechaskiel et al., 2014). Comparison of the fresh aerosol mass with EEPS gives a better correlation (Fig S9b). Figure S SEQ Figure \* ARABIC 9. Comparison between gravimetric mass EF and ELPI-determined mass EF (a), comparison between the mass EFs determined from ELPI and EEPS measurements for the fresh aerosol (b), and for the aged aerosol (c). Assumption of unit density is used when determining the aerosol mass from particle size distributions.For the aged mass, we do not have any gravimetric measurement of particle mass, and the ELPI-determined aerosol mass is approximately three times higher than that determined from EEPS size distributions (Fig S9c). This discrepancy may partly depend on the assumption of unit density. The inversion from ELPI currents to mass concentration is rather unsensitive to the effective density ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/02786826.2015.1117568","ISSN":"15217388","abstract":"This study compared number and mass concentrations obtained using two reference devices, a scanning mobility particle sizer (SMPS) and a tapered element oscillating microbalance (TEOM), with those calculated from raw electrical low-pressure impactor (ELPI) data. ELPI data post-treatment was performed assuming a mobility-dependent effective density and three constant densities: an average effective density, the raw material density, and the standard density (i.e., 1?g/cm3). For the mass concentration, whatever the density considered, the ELPI-determined value was close to the reference. For the number concentration, results indicate good agreement between SMPS and ELPI number concentrations when considering effective density and, to a lesser extent, average effective density. In contrast, with the raw material density or standard density, large uncertainties in number concentration measurements were produced. A good estimation of number concentration was obtained based on ELPI data when assuming a standard...","author":[{"dropping-particle":"","family":"Charvet","given":"Augustin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bau","given":"Sébastien","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bémer","given":"Denis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thomas","given":"Dominique","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Aerosol Science and Technology","id":"ITEM-1","issue":"12","issued":{"date-parts":[["2015"]]},"page":"1263-1270","title":"On the importance of density in ELPI data post-treatment","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"(Charvet et al., 2015)","plainTextFormattedCitation":"(Charvet et al., 2015)","previouslyFormattedCitation":"(Charvet et al., 2015)"},"properties":{"noteIndex":0},"schema":""}(Charvet et al., 2015), whereas the mass determined from the particle mobility size distribution measured by EEPS is directly proportional to the effective density. The aged aerosol effective density is likely higher than 1 g cm-3 because the SOA effective density from aromatics is higher than that ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5194/acp-7-3909-2007","ISSN":"1680-7324","author":[{"dropping-particle":"","family":"Ng","given":"N L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kroll","given":"J H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chan","given":"A W H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chhabra","given":"P S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flagan","given":"R C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seinfeld","given":"J H","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Atmospheric Chemistry and Physics","id":"ITEM-1","issue":"14","issued":{"date-parts":[["2007","7","24"]]},"page":"3909-3922","title":"Secondary organic aerosol formation from m-xylene, toluene, and benzene","type":"article-journal","volume":"7"},"uris":[""]}],"mendeley":{"formattedCitation":"(Ng et al., 2007)","plainTextFormattedCitation":"(Ng et al., 2007)"},"properties":{"noteIndex":0},"schema":""}(Ng et al., 2007), and ammonium nitrate, which is present in the aged aerosol (Fig S16), has density of approximately 1.7 g cm-3. However, this does not fully explain the difference in mass concentrations determined from these two instruments, and it is possible that the aged aerosol mass is slightly overestimated.11. Additional tablesTable S SEQ Table \* ARABIC 1. Vehicle specifications.Vehicle 1 (“PFI”)Vehicle 2 (“GDI”)Vehicle 3 (“Diesel”)ChassisHatchbackHatchbackStation wagonModel year201520152015FuelGasolineGasolineDieselDisplacement (cm3)99911971968Power (kW)5566140TransmissionmanualautomaticmanualEuro class666AftertreatmentTWCaTWCaDOCb + DPFc + SCRda: Three-way catalystb: Diesel oxidation catalystc: Diesel particulate filterd: Selective catalytic reductionTable S SEQ Table \* ARABIC 2. Number of driving cycles run in laboratory and real-drive experiments.GDIPFIDieselLaboratoryCold NEDC110Hot NEDC220Hot WLTC11080 steady440Real-driveRegular driving28235Slow accelerations443Fast accelerations74280 steady452Table S SEQ Table \* ARABIC 3. Ambient conditions during real-drive experiments (average ± standard deviation).Day 1Day 2Day 3VehicleGDIPFIDieselTemperature (°C)17.7 ± 1.112.8 ± 1.611.8 ± 1.4RH (%)50.7 ± 4.260.3 ± 5.559.9 ± 4.9Pressure (mbar)1008.1 ± 1.01004.8 ± 1.1998.3 ± 1.8Table S SEQ Table \* ARABIC 4. Regulated emissions. The real-drive values are calculated from regular driving cases. The PM emission factors were measured gravimetrically by collecting the particles to a filter from the CVS.NOX(mg km-1)CO(mg km-1)THC(mg km-1)PM(mg km-1)CO2(g km-1)GDI cold NEDC9151200.51129GDI hot NEDC106850.27116GDI hot WLTC1384180.22124GDI real-drive202n/an/a168PFI cold NEDC5330240.24130PFI hot NEDC212550.34117PFI hot WLTC53891030.44114PFI real-drive20320n/an/a172Diesel real-drive14710n/an/a14012. Additional figuresFigure S SEQ Figure \* ARABIC 10. Real-drive chase measurement setup.Figure S SEQ Figure \* ARABIC 11. Comparison between the average background levels estimated by the percentile method described in Section 2.3 and the average background levels measured on-road while not chasing any vehicle. Note that the “true background” and the estimated background are not directly comparable, because the former is a measurement of the background, while the latter is estimated during a chase measurement performed at other time. In other words, the two background values are not determined simultaneously.Figure S SEQ Figure \* ARABIC 12. EFs of fresh aerosol mass from laboratory experiments (a) and real-drive experiments (c), and aged aerosol mass from laboratory experiments (b) and real-drive experiments (d).Figure S SEQ Figure \* ARABIC 13. Fresh particle number emission factors normalized to fuel consumed. Average laboratory EFs of particles larger than 3 nm (a), laboratory EFs of particles larger than 7 nm (b), real-drive EFs of particles larger than 3 nm (c) and real-drive EFs of particles larger than 7 nm (d). Note the different scales for laboratory and real-drive measurements.Figure S SEQ Figure \* ARABIC 14. EFs of fresh aerosol mass (normalized to fuel consumed) from laboratory experiments (a) and real-drive experiments (c), and aged aerosol mass from laboratory experiments (b) and real-drive experiments (d).Figure S SEQ Figure \* ARABIC 15. Laboratory SOA PFs normalized to fuel consumed. The numbers above the bars indicate the average OH exposure during the cycles (as equivalent atmospheric days). Error bars show the uncertainty related to potential losses of low volatility organic compounds in TSAR. *assuming all fresh aerosol is organic.Figure S SEQ Figure \* ARABIC 16. Laboratory SOA PFs normalized to carbon monoxide concentration. The numbers above the bars indicate the average OH exposure during the cycles (as equivalent atmospheric days). Error bars show the uncertainty related to potential losses of low volatility organic compounds in TSAR. *assuming all fresh aerosol is organic.Figure S SEQ Figure \* ARABIC 17. Aged mass concentration and composition in laboratory cycles. The absolute mass concentration is determined from ELPI data, and the proportions of chemical species are from SP-AMS data.11. ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Brettschneider, J., 1979. Berechnung des Luftverhaeltnisses λ von Luft-Kraftstoff-Gemsichen und des Einflusses on Me?fehlern auf λ. Bosch Tech. Berichte 6, 177–186.Charvet, A., Bau, S., Bémer, D., Thomas, D., 2015. On the importance of density in ELPI data post-treatment. Aerosol Sci. Technol. 49, 1263–1270. , B., Maricq, M., Ntziachristos, L., Dardiotis, C., Wang, X., Axmann, H., Bergmann, A., Schindler, W., 2014. Review of motor vehicle particulate emissions sampling and measurement: From smoke and filter mass to particle number. J. Aerosol Sci. 67, 48–86. , P., Timonen, H., Saukko, E., Kuuluvainen, H., Saarikoski, S., Aakko-Saksa, P., Murtonen, T., Bloss, M., Dal Maso, M., Simonen, P., Ahlberg, E., Svenningsson, B., Brune, W.H., Hillamo, R., Keskinen, J., R?nkk?, T., 2016. Time-resolved characterization of primary particle emissions and secondary particle formation from a modern gasoline passenger car. Atmos. Chem. Phys. 16, 8559–8570. , N.L., Kroll, J.H., Chan, A.W.H., Chhabra, P.S., Flagan, R.C., Seinfeld, J.H., 2007. Secondary organic aerosol formation from m-xylene, toluene, and benzene. Atmos. Chem. Phys. 7, 3909–3922. , B.B., Campuzano-Jost, P., Ortega, A.M., Day, D.A., Kaser, L., Jud, W., Karl, T., Hansel, A., Hunter, J.F., Cross, E.S., Kroll, J.H., Peng, Z., Brune, W.H., Jimenez, J.L., 2016. In situ secondary organic aerosol formation from ambient pine forest air using an oxidation flow reactor. Atmos. Chem. Phys. 16, 2943–2970. , Z., Jimenez, J.L., 2017. Modeling of the chemistry in oxidation flow reactors with high initial NO. Atmos. Chem. Phys. 17, 11991–12010. , S.M., Kumar, N.K., Klein, F., Comte, P., Bhattu, D., Dommen, J., Bruns, E.A., Kiluic, D., El Haddad, I., Keller, A., Czerwinski, J., Heeb, N., Baltensperger, U., Slowik, J.G., Prév?t, A.S.H., 2018. Gas-phase composition and secondary organic aerosol formation from standard and particle filter-retrofitted gasoline direct injection vehicles investigated in a batch and flow reactor. Atmos. Chem. Phys. 18, 9929–9954. , W.M., 2010. An Algorithm for Calculating the Air/Fuel Ratio from Exhaust Emissions. SAE Tech. Pap. Ser. 1. , P., Saukko, E., Karjalainen, P., Timonen, H., Bloss, M., Aakko-Saksa, P., R?nkk?, T., Keskinen, J., Dal Maso, M., 2017. A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources. Atmos. Meas. Tech. 10, 1519–1537. , H., Karjalainen, P., Saukko, E., Saarikoski, S., Aakko-Saksa, P., Simonen, P., Murtonen, T., Dal Maso, M., Kuuluvainen, H., Bloss, M., Ahlberg, E., Svenningsson, B., Pagels, J., Brune, W.H., Keskinen, J., Worsnop, D.R., Hillamo, R., R?nkk?, T., 2017. Influence of fuel ethanol content on primary emissions and secondary aerosol formation potential for a modern flex-fuel gasoline vehicle. Atmos. Chem. Phys. 17, 5311–5329. ................
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