A multimodel evaluation of the potential impact of shipping on particle species in the Mediterranean Sea
Abstract. Shipping contributes significantly to air pollutant emissions and atmospheric particulate matter (PM) concentrations. At the same time worldwide maritime transport volumes are expected to continue to rise in the future. The Mediterranean Sea is a major short-sea shipping route within Europe, as well as the main shipping route between Europe and East Asia. As a result, it is a heavily trafficked shipping area, and air quality monitoring stations in numerous cities along the Mediterranean coast have detected high levels of air pollutants originating from shipping emissions.
The current study is a part of the EU Horizon 2020 project SCIPPER (Shipping contribution to Inland Pollution - Push for the Enforcement of Regulations) which intends to investigate how existing restrictions on shipping-related emissions to the atmosphere ensure compliance with legislation. To demonstrate the impact of ships on relatively large scales, the potential shipping impacts on various air pollutants can be simulated with chemistry transport models.
To determine formation, transport, chemical transformation and fate of PM2.5 in the Mediterranean Sea in 2015, five different regional chemistry transport models (CAMx – Comprehensive Air Quality Model with Extensions, CHIMERE, CMAQ – Community Multiscale Air Quality model, EMEP – European Monitoring and Evaluation Programme model, LOTOS-EUROS) were applied. Furthermore, PM2.5 precursors (NH3, SO2, HNO3) and inorganic particle species (SO42−, NH4+, NO3−) were studied, as they are important for explaining differences among the models. STEAM version 3.3.0 was used to compute shipping emissions, and the CAMS-REG v2.2.1 dataset was used to calculate land-based emissions for an area encompassing the Mediterranean Sea at a resolution of 12 × 12 km2 (or 0.1° × 0.1°). For additional input, like meteorological fields and boundary conditions, all models utilized their regular configuration. The zero-out approach was used to quantify the potential impact of ship emissions on PM2.5 concentrations. The model results were compared to observed background data from monitoring sites.
Four of the five models underestimated the actual measured PM2.5 concentrations. These underestimations are linked to model-specific mechanisms or underpredictions of particle precursors. The potential impact of ships on the PM2.5 concentration is between 15 % and 25 % at the main shipping routes. Regarding particle species, SO42− is main contributor to the absolute ship-related PM2.5 and also to total PM2.5 concentrations. In the ship-related PM2.5, a higher share of inorganic particle species can be found when compared to the total PM2.5. The seasonal variabilities in particle species show that NO3− is higher in winter and spring, while the NH4+ concentrations displayed no clear seasonal pattern in any models. In most cases with high concentrations of both NH4+ and NO3−, lower SO42− concentrations are simulated. Differences among the simulated particle species distributions might be traced back to the aerosol size distribution and how models distribute among the coarse and fine mode (PM2.5 and PM10). The seasonality of wet deposition follows the seasonality of the precipitation, displaying that precipitation predominates the wet deposition.
Lea Fink et al.
Status: open (until 21 Jun 2023)
- RC1: 'Comment on egusphere-2023-406', Anonymous Referee #2, 28 May 2023 reply
Lea Fink et al.
Lea Fink et al.
Viewed (geographical distribution)
The manuscript provides a relevant analysis of the overall shipping emissions impact on the Mediterranean basin and on the coastal areas that are characterized by significant population density. Moreover, the proposed analysis completes the previous paper focused on gas pollutants already published by the same group of authors on ACP (Fink et al., 2023).
The Authors specify that part of the set-up of the compared models is heterogeneous (including meteorology, boundary conditions and dust and sea salt treatment). Nevertheless, in different part of the manuscript these features should be better discussed even because the presented material (e.g. sea salt concentration in Figure S1) clearly shows the impact of the modelling of PM component not directly tied to anthropogenic emissions. Some of the different features as e.g. boundary conditions and biogenic emissions should be better described to let the reader understand if the used dataset are derived from 2015 larger scale model simulations, climatological datasets or observations.
Some comments discussing if and how the general underestimation of PM2.5 concentration provided by the models can affect the evaluation of the shipping contribution would complete the proposed discussion.
It could be of general interest if the presented analysis of PM composition could be compared with data derived from measurements in small island potentially impacted by shipping emissions and long range transport (e.g. https://acp.copernicus.org/articles/19/11123/2019/).
Page 2, line 39
“…how models distribute among the coarse…” probably refer to the distribution of emissions.
Page 4, line 115
The S before “At” should be probably cancelled.
Page 5, line 139-141
The sentence includes a repetition of the portion “used for all CTMs”
A reference to Table 1 could be added here.
It should be clarified if BCs are derived from model results, climatologies or 2015 specific data. The description is rather clear for CMAQ and LOTOS, referring to CAMS products, not for the other models.
The description provided for EMEP BCs is not clear. Does the provided sentence mean the EMEP provides BCs based on observations, model results or both? Are those data specific to year 2015?
Concerning biogenic emissions, are MEGAN emissions calculated from 2015 meteorology or do they refer to different periods? Is it possible to provide a specification better than “calculated online” for EMEP and LOTOS?
The dust emissions description too should be improved.
2.1.2 Wet Deposition Mechanisms
Page 9, line 215-217
These sentences contain repetitions and can be merged.
2.2.2 Shipping Emissions
Page 10, line 246
Do the Authors mean that both the mentioned lower levels are characterised by the same depth of 42m?
2.3 Observational Data, Statistical Analysis and Analysis of Model Results
Page 11, line 266
Fink et al., 2013 should probably be Fink et al., 2023
3.1 PM2.5 Model Performance
Page 12, line 278
It should be reminded that CMAQ has no dust contribution.
3.2 PM2.5 Spatial Distribution
Page 15, line 317-318
Can this behaviour be caused by the sea salt contribution? Is it tied to wind speed distribution?
Is it the particle chemistry that causes the largest differences or dust & sea salt have a major role as it could be guessed from the presented comparison?
Did the Authors investigate the surface wind speed and its treatment impact? The sea salt scheme itself seems similar those implemented in the other models.
3.4.2 Wet Deposition
Page 30, line 491-492
What is the possible reason of this peculiar behaviour of CHIMERE only?
Page 32, line 518-527
In should be reminded in the discussion that the analysis and comparison of PM mass results are affected by the relevant differences in dust treatment, sea salt modelling and from the used boundary conditions (including themselves dust and salt issues).
Page 34, line 585-592
Which kind of boundary conditions are used in particular for dust and sea salt? i.e. model driven, climatological, etc.
This discussion should be moved at the beginning of the section because it affects the PM mass and not only the size distribution of particles.
Page 37, line 675-679
It could be considered for discussion the general suggestion to perform analysis of test cases with controlled and shared BCs, sea salt and dust emissions (e.g. externally provided), that could enable a more consistent
investigation of model results.