The transport sector has been identified as an important source of emissions in many countries in the European Union. In 2019, road transport was the principal source of nitrogen oxides, responsible for 39% of emissions [1]. This problem is significant in cities worldwide [2–4]. Over the last decade, the automotive industry has been undergoing rapid changes, particularly in the electric vehicle sector. Directive 2014/94/EU of 22 October 2014 [5] on deploying alternative fuels infrastructure was crucial to increasing electricity use in the automotive industry.

The increase in demand for electricity in the transportation sector is also associated with an increase in production at power plants operating in the national power systems, especially when the development of heavy-duty electric vehicles fleet is considered, such as electric buses [6] and electric trucks [7]. Studies are underway to assess the feasibility of moving to 100% renewable energy sources (RES) for transportation electrification [8]. Unfortunately, this is not always possible, as shown in [9] using the US as an example. On the other hand, the transition to low-carbon sources of electricity involves the search for other reliable sources of electricity, such as nuclear or gas-fired power plants. For countries such as Poland, which are heavily dependent on coal-fired power generation, a significant change in the energy mix is proposed by 2050 [10–11].

The topic of electromobility development is also connected with the need to model the planned growth of power demand in the daily time horizon. This problem is well-known in the literature. One of the key tasks is to find the distribution of electric vehicle charging according to type of vehicle, charging location and assign it to specific times of the day. A publication [12] states that the increase in power demand resulting from EV charging at night is associated with charging in homes, while during the day, it is associated with charging in public places. It is confirmed in a publication [13] that charging during evening hours in homes is a more significant problem than charging in public places during working hours. Similar findings can also be found in studies conducted by Li et al. [14] and Lin et al. [15]. Slightly different charging profiles will be analyzed for electric trucks [7,16] and electric buses [17]. On the other hand, [18] notes the problem of the load on the distribution network resulting from charging EVs with high-powered charging points, particularly concerning voltage drops and increased load on lines.

The impact of the transformation of the fleet from fossil fuel vehicles to those with an electric motor on the emission of CO₂ is well known [19–23]. Unlike CO₂, the impact of electric vehicles on air pollution is less studied. The impact of traffic emissions on background air pollution is substantial [24–25]. Brady and O'Mahony [26] estimated the reduction in emissions from introducing electric vehicles in Dublin. Depending on the scenario, the concentration reduction ranged from 1 to over 70%, and the improvement of air quality will be gradual with the increase in the market share of electric vehicles. Sparke [27] raised concerns that electric cars relocate the emissions from the car exhaust to the exhaust stack of the power generation plant.

In selected studies, air quality modelling was applied to analyse the impact of EV car fleets on air quality. Thompson et al. [28–29] focused on ozone - the CAMx model used in the analyses showed a reduction in average concentrations in almost the entire domain and an increase in the concentration of this pollutant during night hours and in the most urbanized locations. Razeghi et al. [30] used the air quality model UCI-CIT to assess the impact of different mobile and power generation scenarios on urban air quality in California. The results show a general improvement for 8-h averaged ozone and 24-h averaged particulate matter concentrations in the South Coast Air Basin, with some local increase (up to 6 ppb decrease in 8-h-average ozone and 6 μg/m³ decrease in 24-h-averaged PM2.5). GFDL/AM4 model was used to investigate the impact on US air quality due to replacing internal combustion vehicles with battery-powered electric vehicles (EVs). The most significant changes were detected for maximum concentrations. The number of conventional vehicles replaced by EVs had a more significant effect on O₃ than PM2.5. At the same time, the source of the electricity controlled the PM2.5 emissions [31]. The results of the Salt Lake County test case indicate that high rates of EV adoption could drastically improve air quality during inversion events [32]. Regarding the geographical regions analysed, Requia et al. [33] assessed 4734 studies, selected 123 articles, and found that most studies were conducted in the United States or China. EVs may have a role in reducing air pollution.

In Poland, the share of electric cars is still very meagre compared to other European countries [34–35]. The analysis of the system of factors currently operating in Poland that are supposed to motivate consumers to buy electric cars was performed by Sendek-Matysiak and Łosiewicz [36]. The difficulties encountered in the development of electromobility in Poland were discussed in many aspects [37–39]. The Government Electromobility Development Plan [40] assumes that one million electric cars will be on Polish roads by 2025. The ecological effect of replacing the conventional car fleet with the electric one was calculated. The total emission reduction in Poland until 2017 was 6325 tons CO₂, 88 tons CO, 17 tons NOx and 3 tons PM [23]. A discussion on potential emission changes in one of the Polish cities due to electrification of public transport was presented [41].

In Poland, road transport causes high nitrogen dioxide concentrations, particularly in urban areas and on the outskirts of cities near significant traffic junctions. Since nitrogen dioxide has documented adverse health effects and the total number of cars in Poland is increasing, decisions to implement clean transportation should be undertaken. In response to the initial government plan for the electrification of road transport, the project "Energy Efficiency through the Development of Electromobility in Poland" was initiated in 2017–2018. The objective of the present paper is to propose theoretical scenarios of electromobility development, to make assumptions on emission changes and to calculate the environmental impact of the electric car fleet in Poland in terms of gaseous pollutants concentrations: NO₂, O₃, SO₂, and CO.

The assessment of the possible increase in the number of electric cars started with an analysis of the structure of the vehicle fleet in Poland and the number of electric car registrations in subsequent quarters in the last few years. While preparing the electromobility scenarios for Poland, we considered the current trends in the number of registered electric cars as well as the assumptions of the national programmes [40]. The project concept is presented Fig. S1.

The electric power demand was calculated using the estimated number of electric vehicles and their spatial distribution. We have also assessed the potential emissions changes for the three scenarios. In the model scenarios, data from the EMEP emission inventory were applied. As a last step the GEM-AQ model was applied for each scenario.

The air quality model GEM-AQ [47] was used as a calculation tool. The model is capable of simulating atmospheric conditions across a wide range of scales, from global to meso-gamma. The model uses an arbitrarily rotated latitude-longitude grid to focus on specific regions. Such formulation eliminates the need to provide boundary conditions for the area of interest, as they are internally generated during the calculations. Calculations using the GEM-AQ model were performed on a variable-resolution grid with a resolution over Poland and neighbouring countries equal to approx-10 km (Fig. S7). The selected configuration of the model grid ensures that the requirements for representing the long-range transport are met.

GEM employs a generalized sigma coordinate system vertically, which transitions from terrain-following sigma surfaces near the ground to pressure surfaces at higher altitudes. For the simulations in this study, the model was set up with 28 hybrid vertical levels, with the model top at 10 hPa.

In the version run for this experiment, the GEM-AQ, the model includes 36 advected and 14 non-advected gas-phase species. The gas-phase chemistry mechanism in this version consists of 50 species, 120 chemical reactions, and 16 photolysis reactions, representing air quality and free tropospheric chemistry. The time evolution of all species is resolved using a mass-conserving implicit time-stepping discretization, with Newton's method used to obtain the solution. The advective transport of chemically active tracers was computed using semi-Lagrangian advection scheme. The vertical transport of trace species due to subgrid-scale turbulence is parameterized through eddy diffusion.

The model incorporates anthropogenic emissions of gaseous and particulate pollutants, categorized by individual SNAP (Standardized Nomenclature for Air Pollutants) sectors. These emissions are distributed within the four lowest model layers (up to 630 meters), using distinct injection height profiles for each SNAP sector. Temporal profiles modulating the annual and diurnal variation of emission fluxes for each SNAP sector were applied. Surface emissions, both anthropogenic and biogenic, served as the lower boundary condition in the vertical diffusion equation.

The anthropogenic emissions data, reported by member states under the LRTAP Convention, were used for Europe. The EMEP's emission base, with a resolution of 0.1° x 0.1° (approx. 10 km), contains emission estimates for 2015. Emissions of non-methane volatile organic compounds (VOCs) were disaggregated into substances and groups of substances necessary for modelling chemical transformations. Outside Europe, the ECLIPSE emissions prepared by IIASA were used (http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5.html).

The modelling was carried out for one year for the base scenario for 2015 and each scenario (conservative, moderate, aggressive) for the subsequent horizons 2020, 2025, and 2030. In total, calculations were performed for ten scenarios. The simulations were carried out using meteorological data for the year 2015.

The analysis of the impact of electromobility on air quality was based on annual differences in the pollutants’ concentrations in the lowest model layer, representing near-surface pollution. Based on the modelling results, a map of concentration differences between the baseline state and the emission scenarios was prepared for Poland. The delta was calculated for each grid square as an averaged value for the entire year as shown in Eq. (1).

Positive values of the defined measure indicate an increase in concentrations of a specific pollutant in a given scenario. In contrast, negative values indicate a decrease compared with the baseline scenario for 2015. The maps of delta concentrations were calculated for analysed compounds NO₂, O₃, SO₂, and CO for each of the nine scenarios.

To better interpret the results expressed as the relative difference in concentrations, the analysis of emission for two activity sectors is essential for emission modulations in our study.

Fig. S8 shows the trend in the share of NOx emissions from the power generation and transport sectors in the total emissions in each scenario. The share of NOx emissions from the power generation sector (SNAP1) increased in each scenario from 30.6% in 2015 to 35.4% in the aggressive scenario in 2030. At the same time, emissions from the transport sector (SNAP7) show a slight downward trend: a decrease from 30.8% in the current state in 2015 to 27.8% in the aggressive scenario in 2030. The combined emissions from the SNAP1 and SNAP7 sectors show a slight upward trend of about 0.6–0.7% on average over 5 years, with a smaller increase in the first period of 2015–2020.

NO₂ concentrations in 2020 showed a very similar spatial distribution of the delta concentration in all scenarios (Fig. 3). The differences between the baseline run and the scenarios were minor - mostly below 1% and, in the central part of Poland, in the 1 – 5% range. For 2025, the spatial pattern was similar for all scenarios. The concentration increases were found near the largest power plants in the 5–10% range due to the higher energy demand. The NO₂ concentrations were reduced in northern Poland only in the aggressive scenario. 2030 showed the most significant differences between the EV scenarios. The area where annual average concentrations increased by more than 5% was the highest in the conservative scenario. Around power plants, the increase in concentrations exceeds 10%. In moderate and aggressive scenarios, the area with reduced concentrations is growing in northern Poland.

Fig. S9 shows the trend in the share of SOx emissions from the power generation and transport sectors in the total emissions in each scenario. The share of SOx emissions from the power generation sector (SNAP1) was over 50%. It grew from 56.1% in the current state in 2015 to 60.4% in the aggressive scenario in 2030. Emissions from the transport sector (SNAP7) represent only a small share of the total emissions. The trend decreased from 0.17% in 2015 through 0.05% in 2020 to 0.04% in the aggressive scenario in 2030. The upward trend in SOx emissions is associated with rising emissions in the power generation sector, and changes in the transport sector are negligible.

As a consequence of the rise of total SOx emissions, SO₂ concentrations raised over Poland. For SO₂ in 2020, in the northern and western parts of the country, the delta did not exceed 1%, and the remaining area varied from 1 to 5% (Fig. 4 - second panel). In 2025, the area where the model showed a 2–5% increase in concentrations of SO₂ was bigger. Near the largest power plants, the increase was 5–10% in the moderate and aggressive scenarios exceeded 10%. In 2030, almost over the entire area of Poland, the annual concentrations increased by more than 1% and in the central part of the country by 5–10%. Close to the largest power plants, the concentrations increase exceeds 10%. The area of increased concentrations is the most extensive in the aggressive scenario.

Fig. S10 shows the trend in the share of CO emissions from the power generation and transport sectors in the total emissions in each scenario. The share of CO emissions from the power generation sector (SNAP1) was small. It increased from 2.04% in 2015 to 2.6% in the aggressive scenario in 2030. At the same time, emissions from the transport sector (SNAP7) show a slight downward trend: they decrease from 23.4% in the current state in 2015 to 20.9% in the aggressive scenario in 2030. The combined emissions from the SNAP1 and SNAP7 sectors showed a negligible downward trend from 23.5% to 23.4%.

In the case of CO, minor changes were detected in all scenarios - from −0.2 to 0.4% (Fig. 5). In 2020, all scenarios showed a slight decrease in concentrations in the northern part of Poland and an increase in the remaining area. The highest delta concentrations were computed in the aggressive scenario. For 2025 and 2030, the area with decreased concentrations was wider. Slight increases were limited to the vicinity of the largest power plants.

Ozone production in lower troposphere is produced in a chain of photochemical and chemical reaction involving nitrogen oxides and volatile organic compounds. A ratio between these two precursors defines chemical regime and ozone production rate [47].

Fig. S11 shows the trend in the share of non-methane volatile organic compounds (NMVOC) emissions from the power generation and transport sectors in the total emissions in each scenario. The share of NMVOC emissions from the power generation sector (SNAP1) slightly increased in the individual scenarios from 4.04% in 2015 to 7.2% in the aggressive scenario in 2030. In the aggressive scenario, the contribution of the emissions from the transport sector (SNAP7) showed a downward trend - from 22.07% in 2015 to 15.19% in 2020 and 14.38% in 2030. The reduction in the share of emissions from transportation in 2015–2020 is due to assumptions about the gasoline and diesel car fleets and the implementation of EURO standard. The combined emissions from the SNAP1 and SNAP7 sectors show a downward trend: they decrease from 26.1% to 21.6%. The sum of emissions from the SNAP1 and SNAP7 sectors decreased from 26.1% to 21.6%. Changes in NOx emissions were discussed above.

In the case of ozone (Fig. 6), the concentrations generally decreased up to 2% across the country. For 2020, the concentrations increase by 5% occurred only in the areas of power plant sites. A similar spatial pattern was obtained for all scenarios. For 2025, a reduction in concentrations was about 2%. The areas with the concertation increase around power plant sites were slightly larger. They ranged from 5 to 15%, depending on the scenario. At the same time, there are regions where the reduction in concentration is higher. Such an effect was related to the perturbations of the chemical regime in these areas near power plants, resulting from the modulation of ozone precursor emissions. In 2030, in the vicinity of the largest power plants, concentrations increased by more than 15%. They decreased by more than 2% over the rest of the country.

Compared with other European countries in Poland, the number of electric cars registered is relatively low – around 0,2% [42]. At the end of 2023, approximately 52,100 battery electric vehicles (BEVs) were registered, according to the Polish New Mobility Society (PSNM). While this number saw a slight increase to nearly 60,000 by the end of May 2024, the overall electric vehicle count in Poland, including hybrid cars, was just over 115,000. Considering these reports, Poland follows conservative scenario. The electricity consumption for road transport in 2021 in Poland was ~50 GWh

It was estimated that the average electricity consumption of passenger electric vehicles is 30 kWh/km as in [49] and 1,3 kWh/km for electric buses, which is slightly higher than in [50–51].

Depending on the sector of activity and the analysed pollutant, the contribution of emissions from the energy production and road transport sectors were comparable for both sectors (NOx), much higher in the energy production sector (SOx) or higher in the transport sector (CO, VOC). Therefore, depending on the proportion of emissions from the two analysed sectors, which are crucial for the development of electromobility in Poland, the trend related to changes in the total emissions is not the same for all pollutants and may be upward or downward. It is in agreement with calculations by Zheng et al. [52], which showed that the battery electric vehicles may reduce the volatile organic compounds (VOCs) and nitrogen oxides (NOx) emissions by 98% and 34%, respectively, but have comparable or slightly higher sulphur dioxide (SO₂) emissions. In the case of NO₂, the largest increases occurred around power plants (>10%). Concentrations decreased up to −1.5% only in northern Poland for the scenario with the largest number of electric vehicles. In the case of SO₂, the background concentrations were increased by 1 – 5%, especially at power plant sites (>10%). In the case of CO, a very small reduction over most of the country was achieved in all scenarios (up to −0.25%). Ozone background concentrations mainly decreased compared to 2015 across the country by −2%. The increase of over 15% in concentrations occurred locally. In 2030, the differences between scenarios will be relatively small for SO₂ over most of the country and for NO₂ in the central and southern parts. The biggest differences between the three years that were analysed were obtained for alternative scenarios.

A SO₂ and O₃ background concentration increase was also found for Taiwan [54]. Projected emission changes in this study, related to the use of EV cars, would reduce the annual mean surface concentrations of CO, VOCs, NOx and PM2.5 by about 260 ppb, 11.3 ppb, 3.3 ppb and 2.1 μg/m³, respectively, and increase SO₂ by 0.1 ppb. More significant reductions tend to occur during high pollution events. Using clean energy sources may lead to further reduction of pollutants’ concentrations. Surface O₃ concentrations would increase by up to 7 ppb across Taiwan and increase by 2 ppb in the centre of metropolitan Taipei.

In addition to the simulations for the baseline scenarios, alternative calculations were also performed, which did not consider the anticipated steady growth in energy demand associated with Poland's electrification. In these simulations, the only factor influencing emissions from power plants is the development of the electric car fleet and the related electricity demand. In contrast, the emissions reduction from the transport sector remained the same as in the scenarios presented above. The simulation was performed only for the aggressive scenario.

The delta of NO₂ concentrations compared to the baseline scenario decreased significantly, and the concentration reduction was observed in most parts of the country (Fig. 7 - panel one). The remaining part of Poland is characterized mainly by very small changes, ranging from 0 to 1%. A 1 to 5% difference was observed only in the vicinity of power plants.

In the case of sulphur dioxide, a significant reduction in the concentration delta values was observed by 2025. In 2030, a more significant change (5–10%) can be observed, but only in the vicinity of power plants. The area where the changes are in the 1–5% (Fig. 7 - second panel), described in the previous section, is absent.

Changes in carbon monoxide concentrations in the alternative scenario are minor or negative in most parts of the country. Only in the vicinity of the Belchatow power plant, a slight increase in CO concentrations is visible (Fig. 7 - third panel).

Ozone concentrations increased in the alternative scenario compared to the baseline scenario, enlarged areas with higher positive delta for this compound. This was due to the change in the chemical regime of O₃ precursors (Fig. 7 - fourth panel). Simulations were carried out simultaneously with the calculations assumed on the project and presented above for the 2030 horizon, in which renewable sources were assumed to fully cover the increased energy demand. In this case, a slight decrease in background concentrations at the national level was achieved for all pollutants, indicating the right direction of broadly defined activities in infrastructure preparation to support the promotion of electromobility in Poland.

It should be noted that the resolution chosen for the simulations to address the national scale allows for studying changes in the background concentrations as well as trends and differences between scenarios. Although, in higher resolution in the city centres, the changes due to electromobility may be greater, we would face the challenge of making assumptions about the distribution of electric cars in the city, including considerations on low-emission zones in different scenarios, which might increase the uncertainty. Consequently, the differences in concentrations for Poland are smaller than reported for some individual cities in Europe may be bigger. An example could be a study for Torino where Rizza et al. [53] calculated with the ADMS model that the 5% share of electric vehicles would reduce NO₂ concentrations by 52%. Smaller electromobility impact was reported by Soret et al. [55]. In this case, WRF-ARW/HERMESv2/CMAQ system was used to conduct high-resolution simulations for Barcelona and Madrid. In both cities, nitrogen oxide concentrations were reduced by 8 to 17%, NO₂ concentrations - by 8 to 16%, while ozone concentrations increased by about 6% compared to the baseline scenario (without electric vehicles). The authors indicated that only a large share of electric vehicles (26–40%) in the entire fleet would significantly change air quality.

Considering larger regions, our NO₂ concertation reduction is similar to the results obtained for theoretical scenarios developed for California based on the AERMOD model [56].

The project aimed to assess the impact of Poland's electric car fleet development on air pollution on the country scale. Based on the development scenarios, changes in the emission of pollutants to the atmosphere were estimated, and the gaseous pollutants’ concentrations were calculated.

The analyses were carried out on three horizons - 2020, 2025, and 2030 with 2015 chosen as a base year. Three scenarios were assumed for the electric vehicle fleet development: conservative, moderate, and aggressive. The developed scenarios assume that in 2030 the number of electric cars in Poland will increase from 400,000 (conservative) to about 1 million (aggressive). Assumptions were made regarding the spatial distribution in Poland of the three types of electric vehicles: passenger cars, buses, and trucks. The increase in electricity demand due to the need to supply more electric vehicles is non-linear (calculated based on national energy models). It varies according to the scenario and calculation year, from 7% to 36%. The increase in electricity demand considers the projected constant growth of 1.5%, which is independent of the development of the electric car fleet. Based on the trend analysis, it was assumed that an increase in the number of all vehicles would compensate for the decrease in emissions associated with introducing new engine technologies. The traffic emission level would be approximately constant and close to that reported for 2015. The reduction in transport emissions calculated on this basis is much smaller than the change in emissions from the energy sector and ranges from 0.25 to 3.4%.

Based on the modelling results, the average annual concentrations were calculated for each analysed gaseous pollutant (NO₂, O₃, SO₂, and CO) for individual scenarios and compared against the base scenario calculated for 2015. Delta concentrations of air pollutants in the aggressive scenario were noticeable in the vicinity of the largest power plants and small in the rest of the country.

The alternative scenario, which does not assume the anticipated constant growth in electricity demand, is characterized by the reduction of concentrations background for NO₂ (up to 1.5%) and CO (up to −0.25%), a smaller increase of concentrations (up to 5%) and on average higher ozone background. The increase of the pollutants concentrations in the vicinity of power plants is much smaller than with the assumption of the constant growth in electricity demand.

Overall, the modelling results on a regional scale indicate an increase in background concentrations of sulphur and nitrogen oxides and a slight decrease in the ozone and CO background. The contribution of the power production sector, especially with the assumption of the constant growth of electricity demand, seems to mask the effect of the changes in the car fleet. However, the modelling at the national scale with a resolution of about 10 km does not allow a conclusion on pollution variability in conurbations in zones near the main urban transport routes. The exposure to very high concentrations in city centres and the vicinity of large transport nodes is expected to decrease.

The presented analysis indicates that due to the specific nature of the energy production system in Poland, the impact of introducing electric vehicles will not be significant in terms of concentration levels. However, it is essential to note that, given the proposed new threshold values by the AAQD directive, even small changes in background levels may be significant. Even ambitious and challenging assumptions regarding the increase in the number of electric cars in Poland will not contribute to a significant change in the background pollution on a national scale. The change in the background of gaseous pollutants is minimal, with the most significant changes occurring near large emission sources related to energy production. A minor but clear improvement in air quality could be achieved if all electric cars were powered by energy from renewable sources. The feasibility of such a solution was beyond the scope of this manuscript.

Despite this study's significant achievements, it is important to highlight its methodological limitations. One limitation is the resolution of the computational grid, set at approximately 10 km This resolution was chosen to examine the issue on a national scale, focusing on background pollution rather than detailed urban variations. While this approach helps generalize the spatial distribution of cars and the impact of low-emission zones in cities, it may underestimate pollution differences in densely populated areas.

Another notable limitation is the high uncertainty in future projections, primarily due to Poland's slow adoption of electric vehicles. The low market penetration of electric cars is influenced by their high cost relative to average wages and the lack of solid policy incentives promoting their use.

Moreover, the study focused solely on gaseous pollutants, with the intent of highlighting the potential air quality benefits of electric vehicles. In Poland, the dominant contribution to particulate pollution in the near-surface air comes from the residential and commercial sectors, and it was not expected that changes in the transport sector would significantly alter overall exposure. Moreover, for electric cars, it would be essential to assess non-exhaust emissions, which would require additional assumptions.