Air Quality Benefits of Ship Electrification: A Modeling Case Study for Saronic Gulf, Greece ^†

Abstract

Maritime transport significantly contributes to air pollution, especially in coastal areas. This study evaluates air quality improvements from replacing conventional ferries with hybrids on the Perama–Paloukia line (Greece). Using WRF-CAMx modeling and bottom-up shipping emissions data for the Perama–Paloukia line (a busy line/high-frequency line) for January and July 2019, we simulated full electrification atmospheric impacts. The results revealed up to 9.2% and 7.9% reductions in NO₂ and PM_2.5 mean monthly levels, respectively, during summer. These findings highlight the benefits of ferry electrification, offering actionable insights for policymakers to reduce pollution, enhance public health, and support sustainable maritime practices in densely populated coastal zones.

1. Introduction

Maritime transport is a significant contributor to air pollution, emitting large amounts of nitrogen oxide (NO_X) and fine particulate matter (PM_2.5) [1,2], which pose serious risks to human health and the environment and contribute to climate change [3,4]. NO_X emissions play a role in ozone formation while PM_2.5 can alter atmospheric radiation balance and cloud formation, contributing to both environmental degradation and adverse health outcomes such as cardiovascular and respiratory diseases [5].

These impacts are particularly severe in coastal and port areas [6,7], where shipping activities are concentrated. The shipping sector accounts for approximately 15% of global anthropogenic NO_X and 5–8% of sulfur oxide (SO_X) emissions and emits nearly 1–2 million tons of particulate matter annually [3]. In response to these impacts, the International Maritime Organization (IMO) and the European Union have adopted stringent emission control strategies, including the implementation of Sulphur Emission Control Areas (SECAs), and energy efficiency regulations such as the Energy Efficiency Design Index (EEDI). Among emerging solutions, ship electrification—particularly on short, high-frequency routes—offers an immediate pathway to reduce in-port and near-coast emissions [8,9]. More particularly, electrification eliminates combustion-related emissions during ship operation, thereby reducing local pollutant levels. Prior studies have shown that switching to cleaner fuels or electrification can reduce NO₂ and PM_2.5 concentrations by up to 30% and 3%, respectively, in port areas, significantly improving air quality and public health [10].

This study assesses the potential air quality benefits of replacing conventional vessels with hybrid ones (conventional combustion engine and a rechargeable battery) along the Perama–Paloukia line in Saronic Gulf (Greece). To simulate the atmospheric impact of replacing conventional ferries with hybrid electric vessels, this study employed the WRF-CAMx regional air quality modeling system. Anthropogenic emissions were obtained from the CAMS-REG European emissions database, incorporating shipping emissions from the STEAM model. Additionally, a bottom-up methodology was applied to estimate local emissions from ferry operations along the Perama–Paloukia line, enabling precise quantification of their contribution to total maritime emissions in the study area.

2. Materials and Methods

2.1. Modeling System

The air quality modeling approach employed in this study integrates the Weather Research and Forecasting (WRF) model [11] for meteorological fields with the Comprehensive Air Quality Model with Extensions (CAMx) [12] for simulating chemical processes. Simulations were conducted over two nested spatial domains: a coarse 18 km resolution domain encompassing Europe and North Africa, and a finer 6 km resolution domain focusing on the Eastern Mediterranean. Modeling was performed for two representative months in 2019—January and July—capturing winter and summer atmospheric conditions, respectively. The year 2019 was selected as the reference period due to the unavailability of more recent CAMS emissions data (e.g., for 2023) and to avoid the atypical COVID-19 years (2020–2022), which were marked by reduced transport activity. Additionally, choosing a pre-2020 period excludes the influence of recent fuel sulfur regulations (i.e., IMO regulations), allowing for an independent assessment of the electrification impact (i.e., without confounding reductions in sulfate levels from low-sulfur fuel policies).

Meteorological boundary conditions were provided by the ERA5 reanalysis dataset. For chemical species, the CAMx model was initialized and constrained at the boundaries using outputs from the global CAMS Integrated Forecasting System (CAMS-IFS).

Natural emissions considered in the simulations included sea salt aerosols, windblown dust, and biogenic volatile organic compounds (BVOCs) from vegetation. These were estimated using the Natural Emissions Model (NEMO) [13], which is driven by WRF meteorology.

Anthropogenic emissions for carbon monoxide (CO), ammonia (NH₃), NO_x, SO₂, Non-methane Volatile Organic Compounds (NMVOCs), PM10 and PM_2.5 were obtained from the CAMS-REGv6.1 inventory [14], which provides spatially and sectoral resolved data across Europe while shipping emissions, both international and domestic, are derived from the Ship Traffic Emission Assessment Model (STEAM) [15]. The aforementioned emissions database was spatially, chemically (for NMVOCs and PM) and temporally (on a monthly, weekly and hourly basis) analyzed over the study domains using split factors provided by the CAMS-REG database. The monthly profiles for the shipping sector were based on the CAMS-GLOB-SHIPv2.1 database for the year 2019.

WRF-NEMO-CAMx air quality modeling system has been widely applied and validated in previous research for both regional-scale and urban-scale air quality assessments [16].

2.2. Shipping Emissions on Perama–Paloukia Line

A bottom-up methodology was applied to estimate the local emissions from shipping on the Perama–Paloukia line, allowing for an accurate quantification of their contribution to the total shipping emissions in the study area. It was based on the Flexible Emission Inventory for GREece and the Greater Athens Area (FEI-GREGAA) [17], which includes detailed vessel activity data, including ship types, engine characteristics, operational patterns, and trip frequencies. The methodology for emissions calculation was based on the EMEP/EEA Guidebook (Tier 3). Moreover, the use of updated load-dependent emission factors was investigated in order to study their impact on emissions calculations.

The percentage contribution of shipping emissions from the Perama–Paloukia line to the total maritime (both domestic and international) air pollutant and particulate emissions within a corresponding 6 km grid cell, as derived from the CAMS-REG database (see Figure 1), has been estimated. It should be noted that this grid cell also partially includes the commercial port of Piraeus, which further contributes to shipping emissions. Therefore, the estimated share attributed to the ferry line may be conservative. Thus, shipping emissions from the Perama–Paloukia ferry route accounted for approximately 30% of total NO_X emissions and 45% of total PM_2.5 emissions as reported in the CAMS-REG-AP shipping sector. These local emissions were set to zero in the modeling scenario to simulate the effects of full electrification along the route.

2.3. Simulation Scenarios

WRF-NEMO-CAMx modeling runs were carried out for two emission scenarios: (1) a baseline scenario (BCASE), which included all emission sources, and (2) an electrification scenario (ECASE), in which shipping emissions from the Perama–Paloukia ferry line were removed to simulate the effects of full electrification. The simulated concentration differences in absolute and percentage values on a daily and monthly basis between the base scenario (BCASE) and the electrification scenario (ECASE) are presented in the following section.

3. Results and Discussion

In July 2019, mean monthly NO₂ and PM_2.5 concentrations decreased by up to −9.2% and −7.9%, respectively. As for the winter study period, reductions were relatively more modest, with NO_X levels dropping by −8.8% and PM_2.5 by −2.3% on average.

Figure 2 presents the daily percentage difference in PM_2.5 levels on an indicative day in July 2019. For instance, on July 7, at the grid cell positioned along the Perama–Paloukia route, PM_2.5 decreased by approximately –12%. On absolute values, the corresponding daily reductions reach up to around 2 μg/m³.

Also, on 7 July, the electrification scenario led to the maximum daily NO₂ reduction within the entire model domain reaching 8.3% (approximately 6 µg/m³), observed at the 6 × 6 km² grid cell covering the Perama–Paloukia route. In fact, peak daily decreases during summer can approach 15% for both NO₂ and PM_2.5.

The aforementioned improvements were found on the ferry corridor, yet also influenced nearby parts of the Saronic Gulf and the adjacent densely populated coastal areas (Figure 2).

4. Conclusions

This study demonstrates that electrifying a high-frequency route such as the Perama–Paloukia delivers tangible air quality improvements in the local area. By comparing baseline and electrification scenarios using the WRF–CAMx model, notable reductions in NO₂ and PM_2.5 levels were observed, especially during the summer, while winter gains remained modest but meaningful. These improvements were concentrated along the ferry route which extended across the Saronic Gulf and into nearby coastal zones, demonstrating that notable air quality improvements are achievable through ship electrification and providing valuable insights for policymakers aiming to improve air quality in coastal regions. Given that the ferry accounts for a significant share of local shipping emission, electrification of this route emerges as a powerful and effective measure to enhance regional air quality. Finally, as part of future work, the ambient profiles of aerosol and greenhouse gas emissions along the Perama-Paloukia ferry line and in the surrounding area will be assessed through mobile monitoring campaigns. These measurements will aim to map spatial concentration patterns and validate the model results.