Simulation of the effects of low volatility organic compounds on aerosol number

10 PMCAMx-UF, a three-dimensional chemical transport model focusing on the simulation of the 11 ultrafine particle size distribution and composition has been extended with the addition of reactions 12 of chemical aging of semi-volatile anthropogenic organic vapors, the emissions and chemical 13 aging by intermediate volatile organic compounds (IVOCs) and the production of extremely low 14 volatility organic compounds (ELVOCs) by monoterpenes. The model is applied in Europe to 15 quantify the effect of these processes on particle number concentrations. The model predictions 16 are evaluated against both ground measurements collected during the PEGASOS 2012 summer 17 campaign across many stations in Europe and airborne observations by a Zeppelin measuring 18 above Po-Valley, Italy. PMCAMx-UF reproduces the ground level daily average concentrations 19 of particles with diameter larger than 100 nm (N100) with normalized mean error (NME) of 45% 20 and normalized mean bias (NMB) close to 10%. For the same simulation, PMCAMx-UF tends to 21 overestimate the concentration of particles larger with diameter than 10 nm (N10) with a daily NMB 22 of 23% and a daily NME of 63%. The model was able to reproduce more than 75% of the N10 and 23 N100 airborne observations (Zeppelin) within a factor of 2. 24 According to theThe results of PMCAMx-UF predictions,showed that Tthe ELVOC production 25 by monoterpenes is predicted to leads to surprisingly small changes of the average number 26 concentrations over Europe. The total number concentration decreased due to the ELVOC 27 formation by 0.2%, the N10 decreaseds by 1.1%, while N50 (particles with diameter larger than 50 28 nm) increased by 3% and N100 by 4% due to this new secondary organic aerosol (SOA) source. 29 This small change is due to the nonlinearity of the system with increases predicted in some areas 30


40
Two major processes are responsible for the introduction of new particles in the 41 atmosphere: direct emission from numerous sources and nucleation from low-volatility vapors.
which the SOA components were modeled as semi-volatile first-generation products of the 124 oxidation of VOCs. The model predictions were compared against size distribution measurements 125 from 16 stations in Europe during a photochemically active period. Including SOA condensation 126 on ultrafines in PMCAMx-UF improved its ability to reproduce the N10 and N100 concentration at 127 ground level. The inclusion of SOA decreased the daily normalized mean bias (NMB) of N10 from 128 85% to 75% and the daily NMB of N100 from 40% to 20%. However, the results suggested that 129 there is a need for additional improvements. 130 The primary goal of this study is to examine the role of IVOCs and ELVOCs on particle  For the simulation of aerosol microphysics, PMCAMx-UF uses the updated version of 153 DMANx which simulates the processes of coagulation, condensation/evaporation and nucleation (Patoulias et al., 2015) with the two-moment aerosol sectional (TOMAS) algorithm (Adams and 155 Seinfeld, 2002;Jung et al., 2006). A key feature of TOMAS is its ability to track two independent 156 moments of the aerosol size distribution for each size bin: the aerosol number and mass 157 concentration. 158 The aerosol size distribution is discretized into 41 sections covering the diameter range from 159 approximately 0.8 nm to 10 μm. The lowest boundary is at 3.75 × 10 −25 kg of dry aerosol mass per 160 particle. Each successive boundary has twice the mass of the previous one. The particle 161 components modeled include sulfate, ammonium, nitrate, sodium, chloride, crustal material, 162 water, elemental carbon, primary organic aerosol (POA) and eight surrogate SOA components. 163 In this work, the nucleation rate is calculated using a scaled ternary parameterization based is employed if the NH3 concentration is below a threshold value of 0.01 ppt. 167 Coagulation of particles in the atmosphere is an important sink of aerosol number but is 168 also a mechanism by which freshly nucleated particles grow to larger sizes. Following Adams and 169 Seinfeld (2002), TOMAS assumes that the aerosol particles coagulate via Brownian diffusion and 170 that the effects of coagulation from gravitational settling and turbulence on coagulation a are 171 negligible. The calculation of the coagulation coefficients is based on the wet diameters of the 172 particles. These wet diameters are calculated following the approach of Gaydos et al. (2005). For 173 small particles (<100 nm), we use the expression of Dahneke et al. (1983) to correct for non-174 continuum effects. The coagulation algorithm uses an adaptive time step. The time step is limited 175 so that the aerosol number or mass concentration in any size category does not increase by more 176 than an order of magnitude or decrease by more than 25% in each step.

183
Condensation of gas-phase species to existing aerosol particles is an important source of 184 aerosol mass and a means by which small particles grow to CCN sizes. Sulfuric acid is assumed to be in pseudo-steady state in DMANxPMCAMx-UF. This pseudo steady-state approximation 186 (PSSA) for sulfuric acid proposed by Pierce and Adams (2009)  and computationally efficient. Condensation of ammonia wasis simulated following the approach 190 described by Jung et al. (2006). Ammonia condensation on the ultrafine particles ends when sulfate 191 is fully neutralized to ammonium sulfate.

192
NSemi-volatile nitric acid and hydrochloric acids in DMAN partition to particles (as nitrate 193 and chloride, respectively) in the accumulation mode range in PMCAMx-UF assuming that the  The PMCAMx-UF model assumes that organics and inorganics are in different phases, but 202 in the same particles. Therefore, the condensation of one affects the size distribution of the particles   (Table S1). The first scheme 248 (case 1 or base case) includes (i) the aging of SOA components from anthropogenic sources, using   The measurement of organic carbon and therefore the estimated OA using filters is  for N50, and 820 cm −3 for N100 during the simulated period.  before sunrise (Fig. 2a). The average measured N10 at all heights was 6,000 cm -3 , while the 412 predicted concentration was equal to 4,700 cm -3 .

413
PMCAMx-UF reproduced well the N100 concentration at all heights (Fig 2b). The model 414 also reproduced 80% of the 3-min N100 Zeppelin measurements within a factor of 2. The measured 415 average N100 at all heights was 1,500 cm -3 and the average predicted by PMCAMx-UF was 1,800 416 cm -3 . The ability of the revised model to reproduce reasonably well the high-time resolution (3-417 minute) Zeppelin measurements at multiple altitudes and locations is encouraging.

418
The predictions of PMCAMx-UF for the aerosol mass concentration were compared to the 419 Zeppelin PM1 composition mmeasurements obtained by an AMS (each 3 minutes, 9 flights; ~1300 420 points data points). The average vertical profiles of organics, sulfate, ammonium, and nitrate are 421 shown in Fig. S2. Overall, the model performance aloft was quite similar with that at the ground 422 level. For example, for the 9 Zeppelin flights the OA normalized mean bias was -4% and the 423 normalized mean error equal to 40% ( of the OA (Fig. 3). In these areas a combination of high terpene emissions and high photochemical 431 reaction rates existed during the simulated period. The highest relative predicted increase of OA (1) 436 where, x, is 10, 50, 100 nm or zero (total number). Rather surprisingly, the average fractional  The formation of ELVOCs resulted in a predicted decrease of Ntot by 20% (300-600 cm -3 ) 445 in parts of the Nordic countries and by 5% in central Europe (Fig. 4). The decreases are predicted  Figure S4 shows Tthe spatial variability of the fractional change in the number 466 concentration of N1-10 ((reflecting nucleation rates); up to 60%), sulfuric acid concentration(30%), 467 condensational sink (CS) and coagulation sink due to the ELVOCs is depicted in Figure S4. This  The ELVOC addition played a minor role on the overall performance of PMCAMx-UF. The

491
NMB for N10 decreased (in absolute terms) by 1%, it increased by 5% for N100 due to the addition 492 of the ELVOCs in the simulation ( drops from -72% to -49% (Table 3). The corresponding normalized mean errors changed by 1-2%.

496
These small changes in the performance metrics are consistent with the small overall changes 497 caused by the ELVOC addition.

498
The small change in the OA mass concentration due to the addition of the ELVOCs has a 499 modest impact on the performance of PMCAMx-UF for OA (Table S56 and S67). For example, 500 the PM1 OA bias improves from -6% to 2% while the PM2.5 OA bias increases from 15% to 20%.

501
The changes in normalized error are 1% or less. Sea, but also in large areas over central and eastern Europe (Fig. 6). The high SOA-iv levels over 508 the Mediterranean are due to the oxidation of IVOCs emitted from large wildfires that occurred 509 during the simulation period. The corresponding SOA-iv is 10-25% of the total OA over 510 continental Europe and even higher (about 60%) over parts of the marine atmosphere.

511
The average fractional increase of Nx, due to emission and aging of IVOCs is calculated 512 as: where, x, is 10, 50, 100 nm or zero (total number).

515
According to PMCAMx-UF the addition of the emissions of IVOCs and their aging 516 reactions lead to a reduction of Ntot by 5-10% and N10 by 5% (Fig. 7) for continental Europe. On 517 the other hand, this addition of IVOCs leads to an increase of N50 by 5% and N100 by 5-10% mainly 518 in central Europe and the Mediterranean Sea (Fig. 7). The corresponding changes of the number 519 concentrations for the various size ranges N1-10, N10-50 and N50-100 are summarized in Fig. 8. The 520 predicted N1-10 decreases approximately 15-20% for most of Europe except for the Scandinavian 521 peninsula due to the IVOCs. N10-50 decreases 10-15% mainly in southern Europe and N50-100 522 changes less than ±5% or ±100 cm -3 in the simulated domain.

523
The atmospheric oxidation of the emitted IVOCs produces semi-volatile organic 524 compounds, which condense preferentially on particles in the accumulation mode and not so much on the smallest particles due to the Kelvin effect. This results in an increase of both the 526 condensation and coagulation sinks, which then lead to a decrease of the nucleation rate but also 527 on the coagulation rate of the smaller with the larger particles.

528
The effect of the addition of the IVOCs on the performance of PMCAMx-UF is modest and 529 mixed. The NMB for N10 increased by 4% (from 23% to 27%) and decreased by 5% for N100 (from 530 10% to 5%) ( Table S75). The corresponding NME for both N10 and N100 changed slightly from -18% when IVOCs were neglected to 2% when IVOCs were included (Table S8). The NME 538 indecreased a little (from 385% to 35%) with the IVOC addition. The performance against the OA 539 measurements in the other European sites became a little worse when IVOCs were included in the 540 model (Table S9). The small underprediction (NMB=-8%) in OA became a larger overprediction 541 (NMB=20%) and the NME increased from 50% to 62%. These results are characteristic of the 542 uncertainties in primary OA emissions but also SOA production from the various VOCs and 543 IVOCs emitted by anthropogenic and biogenic sources.  increase the aerosol mass and surface area as they mostly condense on the accumulation mode.

572
Therefore, they increase the condensation sink, decreasing the sulfuric acid supersaturation and 573 the corresponding nucleation rate. They also increase the coagulation sink and thus accelerate the 574 removal of all nanoparticles.

575
Locally the effects of the ELVOC production could be higher. For example, it is estimated 576 that the ELVOC productions leads to a decrease of the total particle concentration Ntot by 20% in