Long-Range Atmospheric Transport of Polycyclic
Aromatic Hydrocarbons: A Global 3-D Model Analysis
Including Evaluation of Arctic Sources
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Citation
Friedman, Carey L., and Noelle E. Selin. “Long-Range
Atmospheric Transport of Polycyclic Aromatic Hydrocarbons: A
Global 3-D Model Analysis Including Evaluation of Arctic
Sources.” Environmental Science & Technology 46, no. 17
(September 4, 2012): 9501-9510.
As Published
http://dx.doi.org/10.1021/es301904d
Publisher
American Chemical Society (ACS)
Version
Author's final manuscript
Accessed
Wed May 25 22:04:45 EDT 2016
Citable Link
http://hdl.handle.net/1721.1/82191
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Detailed Terms
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Long-range atmospheric transport of polycyclic aromatic hydrocarbons: A global 3-D
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model analysis including evaluation of Arctic sources
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Carey L. Friedman§* and Noelle E. Selin†
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Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA
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*Corresponding author e-mail: clf@mit.edu.
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§
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Massachusetts, 02139 USA
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge,
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†
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Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA
Engineering Systems Division and Department of Earth, Atmospheric, and Planetary Sciences,
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TOC ART
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ABSTRACT
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We use the global 3-D chemical transport model GEOS-Chem to simulate long-range
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atmospheric transport of polycyclic aromatic hydrocarbons (PAHs). To evaluate the model’s
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ability to simulate PAHs with different volatilities, we conduct analyses for phenanthrene (PHE),
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pyrene (PYR), and benzo[a]pyrene (BaP). GEOS-Chem captures observed seasonal trends with
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no statistically significant difference between simulated and measured mean annual
21
concentrations. GEOS-Chem also captures variability in observed concentrations at nonurban
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sites (r = 0.64, 0.72, and 0.74, for PHE, PYR, and BaP). Sensitivity simulations suggest snow/ice
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scavenging is important for gas-phase PAHs, and on-particle oxidation and temperature-
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dependency of gas-particle partitioning have greater effects on transport than irreversible
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partitioning or increased particle concentrations. GEOS-Chem estimates mean atmospheric
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lifetimes of <1 day for all three PAHs. Though corresponding half-lives are lower than the 2-day
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screening criterion for international policy action, we simulate concentrations at the high-Arctic
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station of Spitsbergen within four times observed concentrations with strong correlation (r =
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0.70, 0.68, and 0.70 for PHE, PYR, and BaP). European and Russian emissions combined
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account for ~80% of episodic high-concentration events at Spitsbergen.
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INTRODUCTION
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Polycyclic aromatic hydrocarbons (PAHs) are contaminants of concern because of their
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detrimental health effects. PAHs travel through the atmosphere across national boundaries1 and
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are found in Arctic regions far from sources2, 3 where they dominate invertebrate and fish
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persistent organic pollutant (POP) tissue burdens; indeed, PAH concentrations are at least 100×
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higher than other legacy POPs4. PAHs are regulated internationally as POPs by the United
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Nations Economic Commission for Europe’s (UNECE’s) Convention on Long-Range
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Transboundary Air Pollution (CLRTAP), but there remains uncertainty surrounding pathways by
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which they reach remote regions, especially with respect to gas-particle partitioning and
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oxidation. Here we use the chemical transport model (CTM) GEOS-Chem to investigate the
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influence of uncertain PAH properties on atmospheric transport and source-receptor relationships
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globally.
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Existing PAH models have over- or under-predicted observed concentrations by ~2×
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(e.g., in Europe5) to more than 10× (e.g., at Arctic locations6). Previous investigations of
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PAH/POP atmospheric transport have relied primarily on two model types: multimedia
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screening/assessment tools, and regional CTMs. Multimedia models7-11 focus on pollutant
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chemical properties while the larger environment has fixed characteristics, and are commonly
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used to identify a POP’s potential for environmental persistence or long-range transport12.
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Regional CTMs, by contrast, consider dynamic atmospheric processes in addition to pollutant
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properties and have been used to investigate PAH distribution over Europe5, 13, cross-Pacific
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sources to western US receptors14, sources to the Arctic15, and transboundary outflow16-18.
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Lammel et al.19 used a general circulation model (GCM) to investigate global transport of
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anthracene, fluoranthene, and benzo[a]pyrene (BaP). Their simulations demonstrated that gas-
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particle partitioning has a substantial effect on long-range transport, with a parameterization
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assuming absorption into organic matter and adsorption to black carbon (BC) agreeing best with
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remote observations.
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Our use of GEOS-Chem to simulate PAHs makes several important contributions to
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POPs modeling. We use a finer spatial resolution (4°×5°) than previous global POP models20,
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and thus can conduct a detailed model performance evaluation at multiple sites. The
4
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representation of atmospheric oxidants, partitioning, and deposition at this scale allow us to
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examine in detail their influence on PAH behavior, similar to studies with regional models5, 13
63
but providing a global perspective on transport. Additionally, because the model is driven by
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assimilated meteorology21, we can assess the influence of episodic transport to remote locations.
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Here we describe model development, compare results to observations, and assess the
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importance of oxidation, gas-particle partitioning, and deposition to the global atmospheric
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lifetimes of phenanthrene (PHE), pyrene (PYR), and BaP (three-, four-, and five-benzene ring
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PAHs, respectively). These PAHs were chosen based on variation in expected fraction associated
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with particles (with PHE primarily in the gas phase, BaP primarily in the particulate phase, and
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PYR in both phases), and because of potential toxicity22, 23. We then use the model to investigate
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the effect on global concentrations and particulate fraction of: i) temperature-dependent
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partitioning; ii) non-reversible partitioning; iii) increased particle concentrations; iv) variable
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gas-phase oxidation, and; v) on-particle oxidation. Additionally, we simulate PAHs in the remote
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Arctic and attribute concentrations to different source regions.
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METHODS
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General model description
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We use GEOS-Chem version 8-03-0221 (http://www.geos-chem.org/) to simulate gas
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phase, organic carbon (OC)-bound particulate, and BC-bound particulate PAH global transport
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and chemistry. The model is driven by GEOS-5 assimilated meteorology from the NASA
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Goddard Earth Observing System (GEOS) with 6-hour temporal resolution, 47 vertical levels,
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and 0.5°×0.667° horizontal resolution, re-gridded to 4°×5° for input to the PAH simulation.
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Simulations are conducted for meteorological years 2004-2009, with 2004 used for initialization.
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Emissions
We use the 2004 global atmospheric PAH emission inventory from Zhang and Tao24.
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Total (gas + particulate) annual emissions were 6.0×104 Mg, 2.1×104 Mg, and 4.1×103 Mg for
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PHE, PYR, and BaP, respectively, with no seasonal variation. Biofuel is the dominant source
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(57%) and Chinese emissions predominate (27%, 30%, and 37% of total for PHE, PYR, and
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BaP, respectively). Emissions are re-gridded from 1°×1° to 4°×5° horizontal resolution for
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inclusion in the model. PAHs are emitted as total concentrations and then distributed between the
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gas and particle phase throughout the planetary boundary layer by considering ambient OC/BC
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concentrations. We neglect re-emissions from surfaces, as monitoring data suggests PAH
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concentrations in the ocean surface and atmosphere are unconnected25, and quantification of
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fluxes from has been limited. However, as recent studies suggest PAH re-emission from soils
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may contribute to atmospheric concentrations26, inclusion of secondary sources is a priority for
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further model development.
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Gas-particle partitioning
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An OC absorption model, in which a compound’s octanol – air partition coefficient (KOA)
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describes its sorption into the particle organic fraction27, 28, is often used to model PAH/POP gas-
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particle partitioning3, 29. PAHs have also been shown to strongly adhere to particulate BC30. We
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implement a dual OC absorption and BC adsorption model30 using both KOA31 and a BC – air
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partition coefficient (KBC)32 to describe PAH partitioning in/onto OC and BC aerosols,
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respectively. We incorporate KOA temperature dependence into the default model according the
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van’t Hoff relationship (see Supporting Information (SI) for details).
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The temperature dependency of KOA is well established, but that of KBC as a surface
108
process is less certain, particularly as we use an empirical KBC considering BC volume rather
109
than surface area. Therefore, in our standard model, KBC does not vary with temperature.
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However, as the temperature dependence of PAH surface adsorption to soot has been shown to
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follow the van’t Hoff relationship33, we conduct sensitivity analyses where KBC varies according
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to the van’t Hoff equation (Equation S1 and Table S1).
113
Monthly mean hydrophobic OC and BC concentrations for all years are from GEOS-
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Chem aerosol simulations for 2008 using emissions from Bond et al.34 scaled following Wang et
115
al.35 (see SI for details). GEOS-Chem assumes 50% of OC and 80% of BC emissions are
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hydrophobic with lifetimes of 1.2 days before conversion to hydrophilic OC and BC36. Empirical
117
and modeling observations suggest this conversion rate should vary regionally and may be too
118
fast37, 38. Therefore, we also conduct an increased aerosol sensitivity analysis with 2× OC and BC
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concentrations.
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Oxidation
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We incorporate PAH loss through reaction with hydroxyl radical (OH). We use an
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empirically-derived rate constant (kOH) for PHE39, with sensitivity simulations conducted using a
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kOH calculated with the U.S. EPA’s AOPWIN software40. PYR and BaP kOHs are calculated with
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AOPWIN. Standard simulations have temperature-independent kOHs, but a PHE sensitivity
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analysis was conducted with temperature dependency (see SI text and Table S1).
127
The importance of on-particle BaP oxidation by ozone (O3), the PAH for which O3
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reaction rate constants (kO3) have been most widely determined, was tested with three
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parameterizations (see SI for details): i) kO3 for BaP on soot particles from Pöschl et al.41; ii) kO3
7
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for BaP dissolved in octanol from Kahan et al.42, and; iii) kO3 for BaP on wet azaelic acid
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aerosols from Kwamena et al.43. On-particle oxidation schemes were tested for BC-phase BaP
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only (for consistency with the Pöschl scheme which only applies to soot) and were not included
133
in standard simulations.
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Deposition
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GEOS-Chem wet deposition includes rainout and washout from large-scale and
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convective precipitation and scavenging in convective updrafts44 and is compatible with GEOS-
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Chem version 9-02-01. Wet deposition is applied to both gas and particulate PAHs. Gas-phase
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PAHs are scavenged into liquid water according to the temperature-dependent air-water partition
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coefficient (KAW31) and retained at 100% efficiency above 268 K and 0% otherwise. However, as
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cold-temperature scavenging is likely an important process45, 46, we also investigate the addition
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of gas-phase PHE scavenging by ice (i.e., precipitation ≤ 268 K) using a PHE snow scavenging
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ratio from Wania et al.45. Particle-phase PAHs are scavenged as hydrophobic OC and BC
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aerosols44 in the default model, with a hydrophilic OC and BC scavenging efficiency tested in a
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sensitivity analysis.
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PAH dry deposition velocities are calculated following a resistance-in-series scheme
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from Wesely et al.47 with improvements by Wang et al.48. We adjust this scheme by scaling
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cuticular resistances with KOA to account for lipophilic uptake of gas-phase PAHs in waxy leaf
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cuticles49, 50. Uptake of particulate PAHs into plant material is not considered, as uptake of
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gaseous PAHs is the dominant pathway51.
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Source-receptor relationships
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We assess the model’s ability to reproduce episodic transport to remote sites by
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simulating daily concentrations for 2005-2009 at the Spitsbergen, Norway EMEP Arctic
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monitoring station (80N, 12E). We investigate the contribution of various source regions by
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removing emissions from source regions designated by the CLRTAP (Europe: 10W–50E, 25N–
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65N; North America: 125W–60W, 15N–55N; East Asia: 95E–160E, 15N–50N; South Asia:
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50E–95E, 5N–35N) and re-running the 2007 time series. Though not a CLRTAP source region,
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we also investigate the impact of Russian emissions (50E–180E, 50N–75N) on Spitsbergen.
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RESULTS AND DISCUSSION
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Annual mean concentrations
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Figure 1 shows global simulated annual mean total (gas + particulate) PHE, PYR, and
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BaP concentrations for 2005-2009 compared with observations. All observations are from land-
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based northern hemisphere sites and were collected using high-volume air samplers. Table 1
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shows mean annual total concentrations observed and simulated at each location. Also shown are
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total observed and simulated means from all sites, from nonurban locations only, and from Arctic
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locations only.
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The GEOS-Chem simulation successfully reproduces the five-year mean concentrations
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overall and captures variability among nonurban sites. Differences between mean observed and
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simulated concentrations from all sites (n=19 for PHE and PYR and n=20 for BaP after
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averaging observations within the same grid box) are not statistically significant at α=0.05
173
(p=0.10, 0.17, and 0.41 for PHE, PYR, and BaP, respectively), though high variability in
174
observed concentrations contributes to this indifference. When only nonurban sites are
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considered (n=15 for PHE and PYR, n=16 for BaP), discrepancies between observed and
9
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simulated concentrations decrease for all three compounds, but because variance also decreases,
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differences become statistically significant for PYR and BaP (Table 1; p=0.27, 0.04, and <0.01
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for PHE, PYR, and BaP, respectively). However, there is a significant correlation (r) between
179
simulated and measured concentrations at nonurban sites (0.64 (PHE), 0.72 (PYR), and 0.74
180
(BaP), with p<0.01 for all three; Figure S1). Mean simulated Arctic concentrations generally
181
match observations well, though too few observations are available for statistical analyses.
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We compared mean annual concentrations to ship-based measurements from the
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Atlantic52, 53 (data not shown). Simulated concentrations were generally >10× lower than
184
measured, likely because of (i) omission of secondary emissions from oceans, (ii) interference in
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cruise-based measurements from ship stack vapors, which can lead to strong overestimates25, or
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(iii) lack of seasonality in emissions.
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Seasonal variation
We compare seasonal variation in simulated and observed total PAH concentrations at all
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nonurban mid-latitude (NUML) and Arctic sites from Table 1 to assess the influence of natural
191
seasonal processes (Figures 2 and 3, and Figures S2 and S3). For all three PAHs, mean
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observed concentrations over NUML and Arctic sites are significantly higher in winter than in
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summer at most locations (p < 0.005)15, 54, 55, reflecting the influence of oxidative loss, gas-
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particle partitioning, and seasonal variation in emissions. GEOS-Chem captures this seasonal
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variability (p <<0.001) despite using a constant emission rate.
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At NUML sites (Figure 2), GEOS-Chem simulates monthly mean PHE and PYR within
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one standard deviation of measured means, but overestimates BaP. GEOS-Chem systematically
10
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overestimates winter concentrations for all three PAHs. We explore the influence of oxidation on
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this overestimate below.
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Arctic concentrations exhibit stronger seasonal variation than NUML sites (Figure 3),
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reflecting either increased seasonal variation in oxidation or transport, or the effect of springtime
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Arctic haze56. Wintertime concentrations are ~3, 6, and 8× higher than summer in observations.
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GEOS-Chem overestimates winter PHE concentrations by ~4×, and underestimates summer
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concentrations. The model exhibits a smaller overestimate of Arctic winter PYR, and no bias in
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BaP, suggesting it does not capture a gas-phase natural process or underestimates the particulate
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fraction (fP, equal to the sum of OC- and BC-phase PAH divided by the sum of the gas-, OC-,
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and BC-phases, discussed further below), as the bias decreases with increasing particle
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partitioning. To test whether snow/ice scavenging is responsible for the discrepancy, we
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conducted a simulation including wet scavenging of gas-phase PHE at temperatures below 268
210
K. Results for Arctic stations (Figure 3a) suggest cold-temperature scavenging can reduce
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winter simulated gas-phase concentrations by up to 30%, but will not account for the full
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disagreement. Daly and Wania46 suggested scavenging by the snow pack on land may contribute
213
to reduced atmospheric concentrations; this could further reduce Arctic wintertime PHE. A wide
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range of observed particle-phase snow scavenging ratios46 suggests snow could also have a non-
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negligible effect on BaP concentrations.
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In contrast to NUML and Arctic sites, Great Lakes (USA) and urban locations show a
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maximum in summer, particularly for PHE, which GEOS-Chem does not capture (Figure S4).
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The summer maximum may be due to volatilization of higher vapor pressure PAHs from
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polluted land/water surfaces during warmer temperatures, a process not included in our
11
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simulation. Given that the model captures observed NUML seasonal trends without including
221
emissions variability, the trends are likely due primarily to natural processes.
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Global budget
Figure S5 shows mean annual global budgets of gas-, OC-, and BC-phase PHE, PYR,
225
and BaP in GEOS-Chem for 2005-2009, and Table S2 shows simulated lifetimes with respect to
226
different removal processes, the overall lifetime of each PAH in each phase, and total (gas +
227
particulate) lifetimes. Overall lifetimes are 4, 3, and 6 hrs and the mean fPs are 0.002, 0.02, and
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0.93, for PHE, PYR, and BaP respectively. Overall lifetimes are shorter than those predicted by
229
other modeling studies, especially for BaP, which is almost completely in the particle phase; e.g.,
230
Lammel et al.19 report a BaP lifetime of 48 hours.
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We find that as PAH molecular weight (and fP) increases, overall lifetimes stay fairly
232
consistent. Similar lifetimes result from three concurrent processes: (i) decreasing gas-phase
233
lifetimes balancing an increasing fP; (ii) low variability between OC and BC deposition lifetimes
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for different PAHs; and, (iii) exchange between the gas and OC/BC phases dominating over
235
deposition with consistent net exchange across PAHs (Figure S5). Gas-phase lifetimes with
236
respect to oxidation are ~4, 3, and 0.4 hrs for PHE, PYR, and BaP respectively. The PHE
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oxidation lifetime is in the same range as that found by Halsall et al.3 for summertime transport
238
from the UK to the Arctic. Despite the same kOHs, BaP oxidation lifetime is ~10× shorter than
239
that of PYR. Spatial and temporal differences in gas phase prevalence account for this: BaP
240
exists as a gas only in warmer regions and seasons where OH concentrations tend to be higher.
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Gas-phase lifetimes with respect to wet and dry deposition decrease with increasing PAH
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molecular weight, owing to decreasing KAWs and increasing KOAs, respectively. OC- and BC-
12
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phase lifetimes do not vary substantially between PAHs, so increases in the particulate phase
244
result in greater deposition. Despite a shorter lifetime and a similar gas-particle partitioning
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parameterization, GEOS-Chem predicts higher BaP concentrations in remote regions than the
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global model of Lammel et al.19 Greater BaP emissions and potentially longer aerosol lifetimes
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are likely causes of the discrepancy.
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Gas-particle partitioning
To evaluate the effect of gas-particle partitioning on global concentrations, we compare
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monthly mean simulated PAH fPs to those from the Integrated Atmospheric Deposition Network
252
(IADN; Table 1), and conduct sensitivity simulations using different partitioning
253
parameterizations (Figure 4). On average, our standard model simulates observed fP for BaP
254
well (0.93 annual mean, compared with 0.90 in measurements), but underpredicts observed fP for
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PHE and PYR by ~10×. The IADN, covering sites near the U.S. Great Lakes, provides the most
256
consistent fP data, but comparisons should be interpreted with caution because (i) data from 2005
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onward have not yet undergone quality assurance/control procedures (M. Venier, Indiana
258
University, personal communication), and (ii) half of IADN’s sites are urban, and gas-particle
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partitioning may depend strongly on the distribution upon emission. Though fP has been
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measured at high latitude stations, we do not consider them here because long sampling periods
261
with high flow rates can cause biases toward the gas phase (H. Hung, Environment Canada,
262
personal communication).
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We examine three hypotheses for low simulated PHE and PYR fP and conduct sensitivity
264
simulations to test the effect of including these processes on both fP and total concentrations.
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First, partitioning between gas and particles is likely not 100% reversible: Galarneau et al.57 and
13
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Arp et al.58 observed fPs orders of magnitude higher than predicted and attributed them to a
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fraction of particulate PAH that is analytically extractable but non-exchangeable with the gas
268
phase. Second, simulated OC and BC concentrations could be too low, or the hydrophobic to
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hydrophilic folding time could be too fast37, 38. Third, sorption to BC is likely temperature-
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dependent33, 59 .
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To test the effect of irreversible partitioning, we assume 30% of BC-associated PAH
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becomes trapped within the particle volume while 70% is available for surficial reversible
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partitioning (given BC’s high surface area41). This results in a small increase in the mean fP
274
(Figure 4), not enough to match measured fP, a minor increase in the total concentrations
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(Figure 2c), and negligible increases in total atmospheric burdens.
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To test the influence of increased OC and BC, we double OC and BC concentrations
277
globally. This increases fP only slightly (Figure 4), and has a negligible effect on NUML
278
concentrations for PHE and PYR (data not shown). BaP NUML concentrations increase on
279
average by 1.1× (Figure 2c), amplifying their positive bias, and Arctic BaP concentrations
280
increase by 1.5× (Figure 3c). Increasing OC and BC concentrations does not substantially affect
281
the overall lifetime or atmospheric burdens of PHE or PYR, but increases BaP’s lifetime by ~1
282
hour and global burden by 1.1×.
283
To test the effect of temperature-dependent BC partitioning, we include a KBC that
284
follows the van’t Hoff equation. This increases fP for all compounds such that PHE and PYR
285
simulated fP are now within the range of observed fP (Figure 4). PHE and PYR fP increase by an
286
order of magnitude but BaP increases by only 1.1×. Making KBC temperature-dependent does not
287
affect total NUML concentrations of PHE (data not shown), but slightly increases those of PYR
288
(by 1.1×) and BaP (by 1.3×; Figure 2b and 2c), increasing their positive bias. It also increases
14
289
PYR and BaP concentrations in the Arctic (by 1.1× and 4.9×, respectively; Figure 3b and 3c),
290
and the atmospheric burden of each (by 1.1× and 1.3×, respectively). We conclude that
291
temperature-dependent BC partitioning is the most likely explanation for the fP underprediction;
292
this suggests BC temperature-dependent partitioning may be reasonably approximated by
293
considering BC volume rather than surface area.
294
295
Oxidation
296
We test several different oxidation schemes for individual PAHs: (i) temperature
297
dependence for PHE; (ii) the magnitude of kOH for PHE; and (iii) on-particle oxidation for BaP.
298
Including temperature dependence in kOH for PHE does not affect mean NUML
299
concentrations (data not shown), consistent with the near-zero activation energy for PHE39.
300
Using the AOPWIN PHE kOH, which is ~half our default kOH, increases average seasonal
301
concentrations and the atmospheric burden (both by 1.2×), with a stronger effect in winter
302
(Figure 2a). The AOPWIN kOH also increases mean Arctic concentrations (by 1.5×; Figure 3a),
303
decreasing measurement-model agreement. Results suggest kOHs may be underestimated and/or
304
other oxidants play a non-negligible role in gas-phase PAH removal. Both kOH sensitivity
305
analyses affect PHE fP by less than 0.1% (data not shown).
306
We test the three different parameterizations for reaction of O3 with on-particle BaP
307
described in the Methods/SI. The effect on total BaP concentrations depends strongly on the O3
308
reaction scheme used. Experimental studies have shown that PAHs can be rapidly oxidized by
309
O3 at the particle surface41-43 but models often omit this process. Matthias et al.5 included O3
310
oxidation of BaP in European regional modeling experiments and found that it decreased
311
simulated concentrations by 5×, bringing simulated and measured concentrations closer. When
15
312
we apply the Pöschl scheme, NUML and Arctic BaP concentrations are reduced >30× and the
313
total atmospheric burden by 18×, causing large underestimates of observed concentrations
314
(Figures 2c and 3c). The Kahan scheme does not substantially reduce concentrations for either
315
the NUML stations or Arctic stations, and reduces the atmospheric burden by only 10%. At
316
NUML sites, the Kwamena scheme reduces average concentrations by 5× (Figure 2c),
317
improving the match to observations, similar to Matthias et al.5, who also used the Kwamena
318
scheme. The total atmospheric burden is also reduced by 5×. In the Arctic, however,
319
concentrations are reduced by 11×, weakening the match to observations and reducing seasonal
320
variation. In general, the Kwamena scheme brings observed and simulated concentrations closest
321
together, and has little effect on the BaP fP, which is already well-simulated. We conclude that
322
on-particle oxidation has a substantial effect on BaP concentrations, and that a rate intermediate
323
to the Kahan and Kwamena schemes best matches existing data constraints. There remains
324
considerable uncertainty in on-particle oxidation rates, which depend on particle content and
325
size, relative humidity, and ambient temperature.
326
327
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Deposition
Deposition flux measurements are available at few sites, and all are in northern Europe
329
(n=3, Table 1). We compare mean simulated seasonal and annual combined wet and dry
330
deposition fluxes to observed (ng m-2 day-1; Figures S6–S8). Similar to other models5, we
331
overestimate deposition at these sites by ~5.6×, with better agreement in the winter for PHE and
332
PYR. Some of this difference may be explained by overprediction of concentrations at those
333
sites, which are all in the same region, or by not accounting for emissions seasonality.
334
Additionally, we may underestimate oxidative losses as sensitivity analyses indicated. The
16
335
limited number of sites, however, provide few constraints; additional deposition measurements
336
would improve our ability to constrain the relative magnitudes of emission, deposition, and
337
oxidation.
338
339
340
Source-receptor relationships for remote regions
To assess the model’s ability to reproduce episodic transport to remote sites, we simulate
341
daily concentrations for 2005-2009 at Spitsbergen, Norway, the Arctic station with the shortest
342
measurement integration time. For 2005 and 2007-2009, correlation ranges are r=0.53-0.76
343
(PHE), 0.40-0.68 (PYR), and 0.40-0.70 (BaP). Figure 5 shows results for 2007. The model
344
captures wintertime variability for all three compounds and reproduces several episodic transport
345
events. While summer concentrations appear underestimated, simulated values are below the
346
quantification limit (W. Aas, European Monitoring and Evaluation Programme, personal
347
communication) and should be interpreted with caution. The model overestimates winter PHE
348
concentrations by ~4×, is nearly 1:1 with measured PYR, and underestimates BaP by ~2×. All
349
other years show similar biases (Figures S9-S12), except 2006, during which anomalously high
350
PYR and BaP concentrations were measured in mid-May; the model does not capture these,
351
which are likely due to local sources, regional fires60, or interannual variability in emissions. In
352
general, however, it is unlikely Spitsbergen concentrations have a strong local signal, given the
353
remoteness of the station.61 We also conduct Spitsbergen simulations at a finer spatial resolution
354
(2°×2.5°) to investigate effects on Arctic transport (Figures S11 and S12); higher concentrations
355
suggest coarser resolutions may average PAH plumes.
356
We assess the contribution of different regions to Spitsbergen PAH concentrations by
357
removing emissions from source regions designated by the CLRTAP and re-running the 2007
17
358
time series. European emissions contributed the most (51%, 47%, and 70% for PHE, PYR, and
359
BaP, respectively) followed by Russian (24%, 29%, and 13%) and North American (15%, 13%,
360
and 9%). East and South Asian emissions combined contributed 1% (PYR) to 8% (BaP). Most
361
episodic high concentration events can be attributed to European sources (Figure 5).
362
Though we overestimate mean Spitsbergen BaP by ~2×, this bias compares well to
363
previous efforts to simulate BaP at the same station. Sehili and Lammel6 modeled the
364
contribution of European and Russian BaP emissions for the years 1994-2004 with a GCM and
365
predicted concentrations as much as 100× less than observed, potentially due to low BaP
366
emissions. Lammel et al.19 considered global BaP emissions using the same GCM to simulate
367
concentrations at Spitsbergen; this improved agreement with mean concentrations, but winter
368
concentrations were still ~100× lower than observed.
369
370
371
Recommendations for policy and measurement constraints
GEOS-Chem simulates mean global concentrations that are not statistically different
372
from measured, and captures variability in nonurban observations. PAHs have shorter lifetimes
373
in GEOS-Chem than in other models3, 19 with little variation between different PAHs.
374
Discrepancies likely arise from differences in model resolutions and averaging across seasons.
375
Additionally, emissions from wildfires may contribute to longer PAH lifetimes than accounted
376
for here, since smoke plumes can rise into the free troposphere where constituents are less
377
susceptible to deposition.62, 63 The uncertainty associated with wildfire plumes is likely less than
378
that from lack of seasonality in all emissions sources, however, given that total PAH
379
concentrations vary orders of magnitude between seasons15, 64.
18
380
Lifetimes presented here are >10× less than the threshold for inclusion as a POP under
381
the CLRTAP protocol65. Successful simulation of Arctic concentrations, however, suggests
382
model results have a role in hemispheric policy discussions, particularly with respect to
383
prioritizing emissions reductions. Use of the model for more localized policy analysis would
384
benefit from greater spatial and temporal resolution of PAH processes.
385
The model holds promise for investigating the transport of other semivolatile POPs with
386
similar behaviors, especially when evaluating response of transport to variable particle
387
concentrations, temperature, and oxidizing species. However, there remain substantial
388
uncertainties in physicochemical properties that could have significant impacts on results.
389
Though we find strong dependence of PAH long-range transport on the temperature sensitivity
390
of partitioning and on-particle oxidation, enthalpies of phase exchange and reaction rate
391
constants that govern these processes have not been extensively defined. Combined with
392
international policy interest in aerosols, this suggests the need for improved characterization of
393
these properties. Our modeling highlights areas where land-atmosphere exchange and snow
394
scavenging in the atmosphere and by the snowpack play important roles, but few data exist to
395
constrain efficiencies of these processes. Additionally, overestimates of deposition suggest a
396
need for alternative sinks in the model and/or additional observational constraints. Finally,
397
measurement networks in Asia, the southern hemisphere, and the Arctic are needed to improve
398
our ability to evaluate PAH fate globally.
399
400
401
402
ACKNOWLEDGMENTS
This research was supported by the MIT James H. Ferry Fund for Innovation in Research
Education, the MIT Leading Technology and Policy Initiative, and the U.S. National Science
19
403
Foundation Atmospheric Chemistry program (grant #1053648). We thank MIT undergraduates
404
Abigail Koss and Anthony Longboat, the MIT John Reed Undergraduate Research Opportunity
405
fund, QiaoQiao Wang (Harvard University) for aerosol simulations, and Marta Venier and
406
Ronald Hites (Indiana University) and Hayley Hung and the Northern Contaminants Program
407
(Environment Canada) for observational data.
408
409
Supporting Information Available
410
Supporting information includes details related to physicochemical constants, oxidation,
411
temperature dependence of gas-particle partitioning, deposition, global budgets, urban
412
concentrations, and Spitsbergen simulations for years 2005, 2006, 2008, and 2009. This material
413
is available free of charge via the Internet at http://pubs.acs.org.
414
415
20
415
FIGURES
416
417
Figure 1. Average PHE (a), PYR (b), and BaP (c) total (gas + particulate) simulated
418
concentrations in surface air from 2005-2009 (background). Land-based observations from Table
419
1 are shown with circles. Observations from long-term monitoring stations are inter-annual
420
means for the years shown in Table 1.
21
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422
Figure 2. Nonurban mid-latitude mean total concentration (gas + particle) seasonal variation
423
from sites/years listed in Table 1 (observed; solid black line) and for simulated years 2005-2009
424
(simulated; dotted black line) for (a) PHE, (b) PYR, and (c) BaP. Error bars are ± one standard
425
deviation of monthly means across sites. Colored lines represent results from sensitivity
426
analyses.
22
427
428
429
Figure 3. Arctic mean total concentration (gas + particle) seasonal variation from sites/years
430
listed in Table 1 (observed) and for simulated years 2005-2009 (simulated) for (a) PHE, (b)
431
PYR, and (c) BaP. Error bars are ± one standard deviation of monthly means across sites.
432
Colored lines represent results from sensitivity analyses.
23
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434
Figure 4. Mean particulate fraction measured (solid black line) at IADN sites over years 2004-
435
2008 and mean simulated particulate fraction (dotted black line) at same sites for years 2005-
436
2009. Error bars represent one standard deviation of monthly mean particulate fraction across
437
sites. Colored lines represent results from sensitivity analyses.
24
438
439
440
Figure 5. 2007 simulated and measured total a) PHE, b) PYR, and c) BaP at Spitsbergen,
441
Norway. PAHs were measured over three-day periods weekly. Also shown are simulated
442
concentrations when European and Russian emissions are removed from the simulation.
25
443
TABLES
Mean total concentration (ng m-3)
Lat
Long
82
-62
Observation
years
2004-2008
80
12
2004-2009
2004-2007
2007-2008
2004-2008
2008-2009
2004-2008
2007-2008
2006-2008
2004-2008
2007-2008
2004-2008
2007-2008
2004-2008
2004-2008
2004-2006
2004-2008
2004-2008
2004-2008
2007-2008
2007-2008
2007-2008
2007-2008
Location Name
Alert, Canada
Spitsbergen/Zeppelinfjell,
Norway
68
24
Pallas/Matorova, FinlandΩ
60
26
Lahemaa, Estonia
60
17
Aspvreten, Sweden Ω
58
8
Birkenes, Norway
57
12
Rao, Sweden Ω
55
8
Westerland, Germany
54
13
Zingst, Germany
54
-1
High Muffles, Great Britain
51
11
Schmucke, Germany
50
15
Kosetice, Czech Republic
48
8
Schauinsland, Germany
47
-88
Eagle Harbor, MI, USA†
45
-86
Sleeping Bear Dunes, MI, USA†
43
-5
Niembro, Spain
43
-79
Sturgeon Point, NY, USA†
42
-88
Chicago, IL, USA†§
41
-82
Cleveland, OH, USA†§
41
117
Gubeikou, China†§
40
116
Beijing, China†§
40
115
XiaoLongmen, China†§
39
116
Donghe, China†§
Mean from all locations ± 1 sd
Mean from nonurban locations ± 1 sd
Mean from Arctic locations ± 1 sd
Ref
PHE Observed
PHE Simulated
PYR Observed
PYR Simulated
BaP Observed
BaP Simulated
1
0.065
0.138
0.025
0.016
0.002
0.000
2
0.063
0.260
0.018
0.035
0.003
0.002
2
2
2
2
2
2
2
2
2
2
2
3
3
2
3
3
3
4
4
4
4
0.405
NA
1.261
0.767
1.085
2.455
2.802
5.282
3.306
5.630
1.468
0.403
0.600
0.024
4.787
28.037
28.611
68.825
92.125
10.500
217.825
12.9 ± 28.0
1.71 ± 1.83
0.688
2.115
2.279
2.279
3.083
3.057
1.328
5.614
5.347
4.688
0.138
0.609
0.651
0.762
3.606
2.240
3.606
2.869
21.353
21.353
21.353
3.31 ± 4.67
2.19 ± 1.84
0.118
0.394
0.483
0.483
0.600
0.646
0.255
1.142
1.187
1.010
0.016
0.099
0.104
0.158
0.761
0.416
0.761
0.816
6.637
6.637
6.637
0.81± 1.46
0.45 ± 0.40
0.020
0.133
0.056
0.024
0.062
0.081
0.108
0.055
0.094
0.305
0.042
0.011
0.033
0.031
0.095
0.408
0.319
3.425
4.150
0.167
9.075
0.48± 1.20
0.07 ± 0.07
0.014
0.156
0.135
0.167
0.167
0.264
0.293
0.106
0.474
0.547
0.423
0.032
0.032
0.059
0.239
0.130
0.239
0.436
2.975
2.975
2.975
0.34 ± 0.65
0.18 ± 0.18
0.178 ± 0.197
0.362 ± 0.289
0.079
NA
0.238
0.093
0.249
0.535
0.365
0.443
0.424
1.226
0.207
0.047
0.098
0.178
0.499
3.024
2.911
18.475
14.625
0.750
35.525
2.34 ± 5.46
0.28 ± 0.31
0.040 ±
0.034
0.057 ± 0.054
0.008 ± 0.010
0.006 ± 0.008
444
Table 1. Mean (± one standard deviation) total (gas + particulate) concentrations observed and simulated at measurement locations
445
(2005-2009). †Gas-particle ratios provided by reference. §Site considered urban and/or highly impacted by local sources. Sites > 66°N
446
are considered Arctic. ΩMean seasonal total deposition (wet + dry) provided by reference. Observations from sites shaded with the
447
same color occurred within the same GEOS-Chem grid box and were averaged. References: (1) Northern Contaminants Program and
26
448
Environment Canada; (2) EMEP; (3) IADN; (4) Wang et al. (2011)66. Data from reference 3 was provided prior to a routine QA/QC
449
procedure.
27
450
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473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
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32
Long-range atmospheric transport of polycyclic aromatic hydrocarbons: A global
3-D model analysis including evaluation of Arctic sources
SUPPORTING INFORMATION
Carey L. Friedman§* and Noelle E. Selin†
Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA
*Corresponding author e-mail: clf@mit.edu
§
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge,
Massachusetts, 02139 USA
†
Engineering Systems Division and Department of Earth, Atmospheric, and Planetary
Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA
Contents
Parameterization of temperature dependent gas-particle partitioning
S2
OC and BC aerosol concentrations
S2
Gas phase OH oxidation schemes
S2-S3
On-particle O3 oxidation schemes
S3
Table S1: Physicochemical constants used in model for PHE, PYR, and BaP
S4
Table S2: Overall lifetimes and lifetimes due to different processes for PHE, PYR, and
BaP
S4
Figure S1: Simulated vs. measured PHE, PYR, and BaP scatter plot of data from main
text Table 1
S5
Figure S2: Geometric seasonal mean nonurban mid-latitude concentrations
S6
Figure S3: Geometric seasonal mean Arctic concentrations
S7
Figure S4: Simulated vs. measured PHE, PYR, and BaP at urban locations
S8
Figure S5: GEOS-Chem global budget for PHE, PYR, and BaP
S9
Figure S6: Seasonal wet and dry deposition of PHE, PYR, and BaP
S10
Figure S7: Annual wet and dry deposition of PHE, PYR, and BaP
S11
Figure S8: Simulated vs. measured BaP with simulations conducted using both
hydrophobic and hydrophilic aerosol scavenging efficiencies
S12
Figure S9: Simulated vs. measured PHE, PYR, and BaP at Spitsbergen, NO (2005) S13
Figure S10: Simulated vs. measured PHE, PYR, and BaP at Spitsbergen, NO (2006) S14
Figure S11: Simulated (both 4°×5° and 2°×2.5° spatial resolution) vs. measured PHE,
PYR, and BaP at Spitsbergen, NO (2008)
S15
Figure S12: Simulated (both 4°×5° and 2°×2.5° spatial resolution) vs. measured PHE,
PYR, and BaP at Spitsbergen, NO (2009)
S16
Literature cited
S17
S1
Temperature dependence of gas-particle partitioning
We incorporate KOA temperature dependence into the default model according the van’t
Hoff relationship:
#
KOA (T2 ) = KOA (T1 )" e
$ OA H % 1 1 (
' # *
R & T2 T1 )
(Eq. S1)
where T1 is 298 K and T2 is ambient atmospheric temperature (K), R is the ideal gas
!
constant (J mol-1 K-1), and ΔOAH is the enthalpy of phase change from air to octanol (J
mol-1), estimated from the enthalpy of phase change from the pure liquid state to the gas
phase1. Values of KOA and ΔOAH are provided below in Table S1.
OC and BC aerosol concentrations
Monthly mean OC and BC aerosol concentrations were simulated with GEOS-Chem
separately from PAHs for the year 2008. Monthly mean OC and BC concentrations were
then used as input to all years of the default PAH simulation. Therefore, there was no
interannual variability in OC/BC. Minimum monthly OC concentrations ranged from 0
ng C m-3 (Feb., Sep., Oct.) to 4.2E-12 ng C m-3 (March), while maximum concentrations
ranged from 1.2E+4 ng C m-3 (Nov.) to 1.7E+5 ng C m-3 (June). Minimum BC
concentrations ranged from 0 ng C m-3 (June) to 4.4E-12 ng C m-3 (August) and
maximum concentrations ranged from 4.0E+3 ng C m-3 (Dec.) to 3.8E+4 ng C m-3 (June).
OH oxidation
Standard simulations have a temperature-independent kOH, but a temperature-dependent
kOH sensitivity analysis was conducted for PHE, with kOH determined by the Arrhenius
expression:
S2
# "E &
kOH = A exp% a (
$ RT '
(Eq. S2)
where the pre-exponential factor (A) and the activation energy (Ea) are from Brubaker
!
and Hites2. Empirically determined
A and Ea are unavailable for PYR and BaP.
On-particle O3 oxidation schemes
Pöschl reaction scheme: According to Pöschl et al. (2001)3, the reaction of soot
particulate BaP with ozone (O3) will proceed at rate k (s-1):
k = kmax (KO3 )[O3 ]/(1+ KO3 [O3 ])
(Eq. S3)
where kmax is the maximum pseudo-first-order BaP decay rate coefficient in the limit of
!
high O3 concentrations (s-1); KO3 is the Langmuir adsorption equilibrium constant for O3
(cm3), and [O3] is the ambient ozone concentration (molec/cm3). Pöschl et al. determined
that for oxidation of BaP on spark discharge soot particles at 296 K and 1 atm, kmax =
0.015 ± 0.001 s-1 and KO3 = (2.8 ± 0.2) × 10-13 cm3.
Kahan reaction scheme: Kahan et al. (2006)4 follow the same general reaction scheme,
but fit an observed kO3 to an equation of the form:
kobs =
A " [O3 (g)]
B + [O3 (g)]
(Eq. S4)
and find that for the ozonation of surface BaP dissolved in octanol, A = (5.5 ± 0.2) × 10-3
!
s-1 and B = (2.8 ± 0.4) × 1015 molec/cm3.
Kwamena reaction scheme: Kwamena et al. (2004)5 follow the same equation as Pöschl
et al. and find that for oxidation of BaP on azelaic acid aerosols at 72% relative humidity,
kmax = 0.060 ± 0.018 s-1 and KO3 = (2.8 ± 1.4) × 10-15 cm3
S3
TABLES
Parameter
log KOA
log KBC
ΔOAH (kJ/mol)
ΔBCH (kJ/mol)
kOH
(cm3/molec/s)
A (cm3/s)
Ea (J/mol)
log KAW
ΔAWH (kJ/mol)
ρoct (kg/m3)
ρBC (kg/m3)
Description
Octanol-air partition coefficient
Black carbon-air partition
coefficient
Enthalpy of phase transfer from
gas phase to OC
Enthalpy of phase transfer from
gas phase to BC
Reaction rate constant for
oxidation of gas phase with OH
Pre-exponential factor (Arrhenius
equation)
Activation energy
Air-water partition coefficient
Enthalpy of phase transfer from
water to air
Density of octanol
Density of BC
PHE
7.64
10.0
PYR
8.86
11.0
BaP
11.48
13.9
References
1
2
-74
-87
-110
3
-74
-87
-110
3
2.70 × 10-11,
1.30 × 10-11
14 × 10-12
5.00 ×
10-11
__
5.00 ×
10-11
__
4, 5
-1.6 × 103
-2.76
-47
__
-3.27
-43
__
-4.51
-43
4
1
3
820
1000
4
2
2
Table S1. Physicochemical constants used in model for PHE, PYR, and BaP. References:
(1) Ma et al., 20106; (2) Lohmann and Lammel, 20047; (3) Schwarzenbach et al., 20031;
(4) Brubaker and Hites, 19982, (5) U.S. EPA Episuite software8.
PAH
PHE
PYR
BaP
Phase
Gas
OC
BC
Total
Gas
OC
BC
Total
Gas
OC
BC
Total
Oxidation
0.18
__
__
Lifetime (days):
Wet deposition Dry deposition
45
0.83
11
2.4
8.4
3.0
0.14
__
__
20
14
9.2
0.33
2.4
3.4
0.017
__
__
0.67
3.8
4.2
0.11
1.8
2.3
Overall
0.15
0.30
0.20
0.15
0.10
0.26
0.16
0.11
0.12
0.35
0.23
0.23
Table S2. Lifetimes (days) of gas, OC- and BC-phase PHE, PYR, and BaP against
oxidation and wet and dry deposition, and total PHE, PYR, and BaP lifetimes. The
calculation of overall lifetimes for each phase include loss and addition due to reversible
partitioning (individual lifetimes due to partitioning not shown).
S4
FIGURES
%&'()*+,-$./0$'123$
#!$
#$
!"#$
!"!#$
9:;$
9<=$
>*9$
!"!!#$
!"!!!#$
!"!!!#$
!"!!#$
!"!#$
!"#$
#$
#!$
456,78,-$./0$'123$
Figure S1. Simulated versus observed concentrations (ng m-3) for PHE (blue diamonds),
PYR (red squares), and BaP (green triangles) for all nonurban stations shown in Table 1
in the main text. The one-to-one line is shown in black. The fitted linear equations are y =
0.65x + 1.09 (PHE, n = 15); y = 0.93 + 0.19 (PYR, n = 15); y = 1.78x + 0.07 (BaP, n =
16).
S5
a)
#!$
#$
%&'$()*+,-).$
%&'$+/(,0*1).$2$3-,4*5)-$*6.$&/1)+$57&$
%&'$+/(,0*1).$2$87%9:;$57&$
!"#$
Concentration (ng m-3)
!"!#$
#!$
%<=$()*+,-).$
%<=$+/(,0*1).$
%<=$+/(,0*1).$1)(>)-*1,-)$.)>)6.)61$3?$>*-@@A6/6B$
b)
#$
!"#$
!"!#$
3*%$()*+,-).$
3*%$+/(,0*1).$
3*%$+/(,0*1).$M*L*6$A62>*-@N0)$7D$AO/.*@A6$
3*%$+/(,0*1).$%A+NL0$A62>*-@N0)$7D$AO/.*@A6$
3*%$+/(,0*1).$MP*()6*$A62>*-@N0)$7D$AO/.*@A6$
3*%$+/(,0*1).$1)(>)-*1,-)$.)>)6.)61$3?$>*-@@A6/6B$
3*%$+/(,0*1).$CO$7?Q3?$NA6N)61-*@A6+$
3*%$+/(,0*1).$D!R$6A6-)S)-+/40)$3?$>*-@@A6/6B$
#!$
c)
#$
!"#$
!"!#$
!"!!#$
#$
C$
D$
E$
F$
G$
H$
KA61L$
I$
J$
#!$
##$
#C$
Figure S2. Nonurban mid-latitude geometric mean total concentration (gas + particle)
seasonal variation from sites/years listed in Table 1 (observed; solid black line) and for
simulated years 2005-2009 (modeled; dotted black line) for a) PHE, b) PYR, and c) BaP.
Error bars are ± one geometric standard deviation of monthly means across sites. Colored
lines represent results from sensitivity analyses. Simulated and observed data are
identical to those shown in Figure 2 in the main text.
S6
%&'$()*+,-).$
%&'$+/(,0*1).$2$34%567$84&$
%&'$+/(,0*1).$2$9-,:*8)-$*;.$&/1)+$84&$
%&'$+/(,0*1).$2$9-,:*8)-$*;.$&/1)+$84&$2$<)1$+=*>);?/;?$:)0@<$ABCD$
a)
#!$
#$
!"#$
!"!#$
!"!!#$
Concentration (ng m-3)
!"!!!#$
#$
b)
%EF$()*+,-).$
%EF$+/(,0*1).$
%EF$+/(,0*1).$1)(G)-*1,-)$.)G);.);1$9H$G*-II@;/;?$
!"#$
!"!#$
!"!!#$
!"!!!#$
#$
9*%$()*+,-).$
9*%$+/(,0*1).$
9*%$+/(,0*1).$D*P*;$@;2G*-I=0)$4J$@Q/.*I@;$
9*%$+/(,0*1).$%@+=P0$@;2G*-I=0)$4J$@Q/.*I@;$
9*%$+/(,0*1).$D<*();*$@;2G*-I=0)$4J$@Q/.*I@;$
9*%$+/(,0*1).$1)(G)-*1,-)$.)G);.);1$9H$G*-II@;/;?$
9*G$+/(,0*1).$AQ$4HR9H$=@;=);1-*I@;+$
c)
!"#$
!"!#$
!"!!#$
!"!!!#$
!"!!!!#$
!"!!!!!#$
#$
A$
J$
K$
L$
B$
M$
C$
N$
#!$
##$
#A$
O@;1P$
Figure S3. Arctic geometric mean total concentration (gas + particle) seasonal variation
from sites/years listed in Table 1 (observed) and for simulated years 2005-2009
(modeled) for (a) PHE, (b) PYR, and (c) BaP. Error bars are ± one geometric standard
deviation of monthly means across sites. Colored lines represent results from sensitivity
analyses. Simulated and observed data are identical to those shown in Figure 3 in the
main text.
S7
'!"
()*"+,-./0,1"
()*".2+/3-4,1"
a)
&!"
%!"
$!"
#!"
Concentration (ng m-3)
!"
5"
b)
(67"+,-./0,1"
(67".2+/3-4,1"
'"
&"
%"
$"
#"
!"
#8&"
#8$"
c)
@-("+,-./0,1"
#"
@-(".2+/3-4,1"
!89"
!85"
!8&"
!8$"
!"
#"
$"
%"
&"
'"
5"
:"
9"
;"
#!"
##"
#$"
<=>4?"
Figure S4. Mean seasonal total concentrations (ng m-3) of a) PHE, b) PYR, and c) BaP at
two urban stations (also Great Lakes stations): Sturgeon Point, New York, USA, and
Cleveland, Ohio, USA. The figure demonstrates that GEOS-Chem underpredicts
concentrations at urban locations and does not capture the summer-time maximum for
PHE and PYR.
S8
310
820
3700
G7$%"89:;<"='$()*$
0.1H$
0.HH$
+.+$
5.5
13
190
15
35
350
15
62
1200
60000
21000
440
11
87
290
@A4##4BC#$
200
110
76
D'=$5'%B#49BC$
D'=$5'%B#49BC$
24
140
1400
!"#$%&"#'$()*$
+,$
-./$
0.1,$
97
130
830
240
740
3400
234546'5$()*$
50000
15000
3000
E8F$5'%B#49BC$
11000
6400
460
27$%"89:;<"='$()*$
0.00>1$
0.0?1$
0.+/$
0.30
1.6
27
1.4
9.2
55
E8F$5'%B#49BC$
D'=$5'%B#49BC$
E8F$5'%B#49BC$
Figure S5. Global budget of atmospheric PHE (red), PYR (green), and BaP (purple) in
GEOS-Chem. Inventories are in Mg (boxes) and rates are in Mg yr-1 (arrows).
S9
Figure S6. Mean seasonal total deposition (wet and dry combined) of a) PHE, b) PYR,
and c) BaP observed at three northern European stations (solid line; see Table 1 in main
text) and mean modeled total deposition (dotted line) from same sites. Modeled
deposition was determined with a hydrophobic aerosol scavenging rate applied to
particulate PAHs. Error bars are +/- one standard deviation of monthly means across
sites.
S10
Figure S7. Mean annual PHE (a), PYR (b), and BaP (c) total (wet + dry) simulated
concentrations in surface air from 2005-2009 (background). Land-based observations for
deposition from Table 1 are shown with circles. Observations from long-term monitoring
stations are inter-annual means for the years shown in Table 1.
S11
Figure S8. Simulated concentrations of BaP at non-urban mid-latitude locations using
both the default particulate wet deposition scavenging efficiency, i.e., as hydrophobic
aerosols, and scavenging with a hydrophilic aerosol efficiency. Also shown are observed
BaP concentrations. Applying a hydrophilic scavenging efficiency results in a small
decrease in mean atmospheric total BaP concentrations. Changing the particulate
scavenging rate efficiency had no effect on PHE or PYR concentrations. Error bars are
+/- one standard deviation of monthly means across sites.
S12
Figure S9. 2005 simulated and measured total a) PHE, b) PYR, and c) BaP at
Spitsbergen, Norway.
S13
Figure S10. 2006 simulated and measured total a) PHE, b) PYR, and c) BaP at
Spitsbergen, Norway.
S14
Figure S11. 2008 simulated and measured total a) PHE, b) PYR, and c) BaP at
Spitsbergen, Norway. In addition, simulated concentrations at a 2°×2.5° spatial resolution
are shown. Running the model at a finer spatial resolution results in increased plume
concentrations, which are likely due to either a) decreased averaging of PAH plumes
under a finer resolution, or b) decreased averaging of horizontal winds, which can result
in weaker vertical transport and potentially less transport to Arctic regions9. The same
effect is shown for 2009 simulations (below).
S15
Figure S12. 2009 simulated and measured total a) PHE, b) PYR, and c) BaP at
Spitsbergen, Norway. In addition, simulated concentrations at a 2°×2.5° spatial resolution
are shown (see discussion in Figure S11 caption).
S16
Literature Cited
1.
Schwarzenbach, R. P.; Gschwend, P. M.; Imboden, D. M., Environmental
Organic Chemistry. 2nd ed.; John Wiley & Sons: Hoboken, NJ, 2003.
2.
Brubaker, W. W.; Hites, R. A., OH reaction kinetics of polycyclic aromatic
hydrocarbons and polychlorinated dibenzo-p-dioxins and dibenzofurans. J. Phys. Chem.
A 1998, 102, 915-921.
3.
Pöschl, U.; Letzel, T.; Schauer, C.; Niessner, R., Interaction of ozone and water
vapor with spark discharge soot aerosol particles coated with benzo[a]pyrene: O3 and
H2O adsportion, benzo[a]pyrene degradation, and atmospheric implications. J. Phys.
Chem. A 2001, 105, 4029-4041.
4.
Kahan, T. F.; Kwamena, N.-O. A.; Donaldson, D. J., Heterogeneous ozonation
kinetics of polycyclic aromatic hydrocarbons on organic films. Atmos. Environ. 2006, 40,
3448-3459.
5.
Kwamena, N.-O. A.; Thornton, J. A.; Abbatt, J. P. D., Kinetics of surface-bound
Benzo[a]pyrene and ozone on solid organic and salt aerosols. J. Phys. Chem. A 2004,
108, 11626-11634.
6.
Ma, Y.-G.; Lei, Y.; Xiao, H.; Wania, F.; Wang, W.-H., Critical review and
recommended values for the physical-chemical property data of 15 polycyclic aromatic
hydrocarbons at 25 C. J. Chem. Eng. Data 2010, 55, 819-825.
7.
Lohmann, R.; Lammel, G., Adsorptive and absorptive contributions to the gasparticle partitioning of polycyclic aromatic hydrocarbons: State of knowledge and
recommended parametrization for modeling. Environ. Sci. Technol. 2004, 38, 3793-3803.
8.
U.S. EPA, Estimation Programs Interface Suite for Microsoft Windows, v 4.10.
United States Environmental Protection Agency, Washington DC, USA. 2011.
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Wang, Y. X.; McElroy, M. B.; Jacob, D. J.; Yantosca, R. M., A nested grid
formulation for chemical transport over Asia: Applications to CO. J. Geophys. Res. 2004,
109, D22307.
∘
S17