The new submodel SVOC for the Modular Earth Submodel System (MESSy) was developed and applied within the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model to simulate the atmospheric cycling and air–surface exchange processes of semivolatile organic pollutants. Our focus is on four polycyclic aromatic hydrocarbons (PAHs) of largely varying properties. Some new features in input and physics parameterizations of tracers were tested: emission seasonality, the size discretization of particulate-phase tracers, the application of poly-parameter linear free-energy relationships in gas–particle partitioning, and re-volatilization from land and sea surfaces. The results indicate that the predicted global distribution of the 3-ring PAH phenanthrene is sensitive to the seasonality of its emissions, followed by the effects of considering re-volatilization from surfaces. The predicted distributions of the 4-ring PAHs fluoranthene and pyrene and the 5-ring PAH benzo(a)pyrene are found to be sensitive to the combinations of factors with their synergistic effects being stronger than the direct effects of the individual factors. The model was validated against observations of PAH concentrations and aerosol particulate mass fraction. The annual mean concentrations are simulated to the right order of magnitude for most cases and the model well captures the species and regional variations. However, large underestimation is found over the ocean. It is found that the particulate mass fraction of the benzo(a)pyrene is well simulated, whereas those of other species are lower than observed.
The atmospheric cycling of semivolatile organic compounds (SOCs) is particularly complex because of partitioning across phases and air–surface exchange processes, including multihopping
Global and regional distribution and transport of SOCs has been studied using multimedia fate (box) models and chemistry transport models (CTMs)
The sensitivity of distributions to specific processes of SOC cycling and related input parameters has been the focus of CTM-based studies
This study presents the new multicompartment module (submodel) SVOC for the Modular Earth Submodel System
The global model applied in this study is the ECHAM5/MESSy Atmospheric Chemistry Climate model (EMAC), a three-dimensional Eulerian model for the simulations of meteorological variables, gases, aerosols, clouds, and other climate-related parameters. EMAC combines the general circulation model ECHAM5 (here version 5.3.02)
Summary of MESSy process submodels used in this study.
The new MESSy submodel SVOC for simulating the fate and cycling of SOCs in the global environment is presented. Processes involved in the submodel include gas–particle partitioning, volatilization from the surface, dry and wet depositions, and chemical and biotic degradations. These processes are connected to other MESSy submodels. For example, deposition of gas-phase SOCs are calculated by the submodels SCAV and DDEP, aerosol microphysics by GMXe, gas-phase chemistry mechanisms by MECCA, and ocean–air flux exchange by AIRSEA. Figure
Overview of EMAC-SVOC model structure, the cycling processes in SVOC submodel, and its interaction with other MESSy submodels. SMIL (submodel interface layer) and SMCL (submodel core layer) are components of MESSy coding standard, see
The parameterizations of aerosol microphysical processes for SOCs such as gas-to-particle partitioning and dry and wet deposition depend on the way the particulate phase is represented in the model. Here, there are two approaches employed in the submodel to represent the particulate-phase SOC: (1) it is assumed as a bulk species or (2) the particle sizes are resolved into
Gas–particle partitioning is assumed to take place when SOC is in equilibrium between the gas and particulate phases. The concentration of the species that is bound to particles (
In a model configuration using size-resolved particles (viz. the
It is noted that this approach may not hold the constraint of mass consistency and is thus subject to further corrections. For the current study, the effects from this problem are expected to be minimal, given the fact that PAHs in the particulate phase are mainly distributed in the accumulation mode
For
The In the study, the ppLFER scheme is incorporated into SVOC in which it defines The ppLFER scheme calculates the sorptive partition coefficient for every aerosol system, as summarized in Table S1 in the Supplement. Each coefficient requires information on system parameters (
Soil For soil volatilization, two parameterization schemes are implemented in the SVOC submodel: the Jury scheme Once In the Smit scheme, an empirical relation was established between Vegetation For compounds with Snow and glaciers The parameterization of substance loss by volatilization from snow pack follows Volatilization rate ( Ocean In the study, the ocean is represented as a surface mixed layer of a depth varying spatially and in time without lateral transports. The mixed layer depths were obtained from Sorption of SOCs in water to suspended particulate matter (colloidal or sinking detritus) is neglected. Therefore, SOC concentration in surface seawater, and hence volatilization from sea surface, is overestimated, in particular for very lipophilic (
Dry deposition is simulated using deposition velocities. For gas-phase SOCs, the velocities are calculated by the DDEP submodel
Wet deposition is applied to both gas and particulate SOCs. The gaseous fraction is scavenged into cloud and rain droplets according to diffusion limitation, Henry's law equilibrium, and accommodation coefficient, and this process is parameterized and solved empirically in the SCAV submodel
The atmospheric degradation of SOCs in the gas phase as well as within aerosol particles are explicitly treated in SVOC. The gas-phase chemical mechanism is calculated within the MECCA submodel
Most models do not consider oxidation rate of particulate-phase SOCs as experimental aerosols studied in laboratory cover only a small part of relevant atmospheric aerosols. For PAHs, such as benzo(a)pyrene, which stays mostly in the particulate phase, the degradation is more efficient by surface reactions with
The reaction rate coefficient for particulate-phase phenanthrene with
Biotic and abiotic processes in surface compartments contribute to the degradation of chemicals and are strongly dependent on local environmental conditions, e.g., nutrient contents, water, temperature, PH, and light. In SVOC, these factors are not explicitly quantified. The degradation is alternatively described as following a first-order decay law (Eq.
The model simulations were performed for four PAH species: phenanthrene (PHE), pyrene (PYR), fluoranthene (FLT), and benzo(a)pyrene (BaP). To simulate the fate and environmental distribution of these species, the model requires some physicochemical properties as summarized in Table S5 of the Supplement. These include equilibrium partition coefficients and their related energies of phase transfer. The characteristics from PHE to BaP are indicated by decreasing volatility (as molar mass increases), increasing
As model input, several emission datasets were employed in the study. Emission estimates for PAHs were obtained from the annual mean inventory of
Emissions of aerosol species such as organic carbon (OC), black carbon (BC), mineral dust (DU), and sea salt (SS) were included. For BC and OC, the Representative Concentration Pathway (RCP) 6.0 emission scenario of the IPCC (Intergovernmental Panel on Climate Change)
DU and SS emissions were computed online by the ONEMIS submodel
Emissions of other gases including volatile organic species (
Locations of monitoring stations used in the study. The initial letter of each station ID refers to the individual monitoring network (E: EMEP and AMAP, D: DEFRA, I: IADN, M: MONET-Africa)
The observation data used for model performance evaluation were collected from several surface monitoring networks: the European Monitoring and Evaluation Programme (EMEP)
The study also compared simulated concentrations in the marine atmosphere to two ship cruise measurement campaigns: (1) on a west to east transect across the tropical Atlantic Ocean
The model was run on a spectral T42 grid in the horizontal (approximately 2.8
Factor separation analysis
The factor separation technique is described in the Supplement Sect. SIV including the equations used to compute the model sensitivity to four factors. A total of 16 (or 2
List of experiments performed for the factor separation analysis to study sensitivity to temporal variation in emission and process parameterizations (particulate-phase representation, gas–particle partitioning scheme, and volatilization); L+L: Lohmann–Lammel; PpLFER: poly-parameter linear free-energy relationships.
The analysis of the factor separation results is given below. For each factor, the analysis includes the assessment of direct effects (
Direct and interaction effects on seasonal-mean near-surface PAH concentrations of
We studied the relative effects in five climate zones (Arctic, northern midlatitudes, the tropics, southern midlatitudes, and Antarctica) The global distributions of the relative effects are presented in Figs. S6–S13, whereas Figs. S14–S21 present the relative interaction effects from the individual combination of factors. In the following, we do not look to interpret concentration responses to each interaction term. The reasons for this are that (1) accounting for all such interactions is complicated given the number of factors and (2) higher-order interactions (combinations of more than two factors) are hard to physically interpret.
We further investigate the factor effects on model performance by comparing the predicted seasonal mean near-surface concentrations from 16 experiments against observation data in the Arctic and northern midlatitudes (Supplement Sect. SVII).
Figure
In general,
The total interactions between
The direct effects of the modal scheme (
As is the case for the direct effects, the interaction contributions are peculiar to individual species (Fig.
Figure
The effects from
The direct effects of re-volatilization (
For the studied species of mid semivolatility, PYR and FLT, a positive signal is apparent over the high and middle latitudes during local summer in contrast to a negative signal during local winter (Fig.
The interactions generally point toward positive effects for the high-to-medium volatility species (Fig.
Model performance using the sophisticated realization of the four features (factors), i.e., Seasonal emission
Seasonal mean total (gas
Statistics comparison of model simulation and observations of total (gas
Firstly, the comparison to land monitoring stations is as follows.
Additional findings are discussed in the Supplement Sect. SIX related to the comparison between EMAC model results and those from other global PAH modeling studies.
Secondly, the comparison to ship cruise measurements is as follows. Measurements of PHE, PYR, and FLT concentrations over the Atlantic Ocean were taken during a cruise in July 2009
As reported in
The model tendency to underestimate the marine air concentrations may likely be due to several factors. (a) The grid resolution is not sufficient to reproduce fine-scale processes at the grid points close to shipping tracks; (b) high uncertainties associated with the air–sea gas exchange parameterizations still exist, most notably in the estimation of gas transfer velocity; (c) the global inventory
Simulated concentrations of PHE, PYR, and FLT (
Simulated BaP concentrations (
Measurements of particulate mass fraction (
Statistics comparison of model simulation and observations of particulate mass fraction (
Figure
Seasonal mean particulate mass fraction (
The submodel SVOC has been developed and operated within the EMAC model for the application to global distribution and environmental fate of SOCs. In this first development, the focus was set on the predictions of four PAH species: phenanthrene (PHE), pyrene (PYR), fluoranthene (FLT), and benzo(a)pyrene (BaP). Multicompartmental fate and air–surface exchange processes were included in SVOC. Some novel features in PAH modeling were tested, including seasonality in emissions, the modal scheme for particulate-phase tracer representation, the ppLFER scheme for gas–particle partitioning, and re-volatilization from surfaces. The results indicate that using seasonal emission compensates for model biases in the predictions of more volatile species (PHE), whereas the effects of the modal and ppLFER schemes are of less significance. Re-volatilization increases the near-ground concentrations in air, which is found most significant for species of mid semivolatility (PYR and FLT). The attribution of model response to individual features (factors) is blurred by the nonlinear interactions between two or more factors. The effects of these interactions are found to both reinforce (positive feedback) and suppress (hence negative feedback) the effects of the individual factors.
For near-surface concentrations, model bias varies by region and/or species, being negative (positive) in the Arctic within typically a factor of 2–13 (6 % to a factor of 2) for PHE and BaP (PYR and FLT); positive in the northern midlatitudes for PHE, PYR, and FLT by up to a factor of 3; negative in the tropics (by a factor of 2–3); and largely over ocean up to 3 orders of magnitude. The model adequately reproduces the seasonal variation of the particulate mass fraction (
Moreover, the implicit assumption of instantaneous gas–particle equilibrium for SVOC may cause both over- and underestimates of
The SVOC submodel presented here is based on the Modular Earth Submodel System (MESSy) version 2.50 and the global atmospheric model ECHAM version 5.3.02. MESSy is continuously developed and applied by a consortium of institutions. The usage of MESSy and access to the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can be a member of the MESSy Consortium by signing the MESSy Memorandum of Understanding. More information can be found at
The supplement related to this article is available online at:
MO and GL conceived the study and designed the experiments. MO developed the SVOC submodel with input from all co-authors. MO performed model simulations and data analyses. MO and GL discussed the results. MO wrote the article with contributions from all co-authors.
The authors declare that they have no conflict of interest.
This study was supported by the Max Planck Institute for Chemistry. We thank the MESSy community and MESSy submodel developers for providing technical support. The model simulation was performed at the Max Planck Computing and Data Facility (MPCDF), Garching.
The article processing charges for this open-access publication were covered by the Max Planck Society.
This paper was edited by Havala Pye and reviewed by two anonymous referees.