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Article

Fuel Consumption, Emissions of Air Pollutants and Opportunities for Reducing CO2 Emissions from Linear Sources in the Model Rural Municipality

by
Anita Konieczna
1,
Kamil Roman
2,* and
Witold Rzodkiewicz
3
1
Institute of Technology and Life Sciences—National Research Institute, Falenty, 3 Hrabska Avenue, 05-090 Raszyn, Poland
2
Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, 166 Nowoursynowska St., 02-787 Warsaw, Poland
3
Central Office of Measures, Electricity and Radiation Department, 2 Elektoralna St., 00-139 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Energies 2023, 16(14), 5553; https://doi.org/10.3390/en16145553
Submission received: 7 July 2023 / Revised: 17 July 2023 / Accepted: 19 July 2023 / Published: 22 July 2023
(This article belongs to the Section A: Sustainable Energy)
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Abstract

:
The study estimates the amount of emissions resulting from linear sources. There were calculations for a model rural municipality, composed of national, provincial, country, and municipal roads that run through the area. In this study, the following categories of vehicles were assumed to travel along this route: motorcycles, passenger cars, light trucks (vans), heavy trucks without trailers, trucks with trailers, and buses. The analysis used data on the average volume of traffic (SDR) on selected roads on the territory of the sample municipality, based on the frequency of participation in traffic by each mode of transportation on selected road sections. To estimate the emission rates of each pollutant, for each vehicle category separately, the calculations were made based on the emission factor rates for each type of fuel. According to the adopted methodology and based on the adopted assumptions scenarios, pollutants’ emissions were estimated. The implementation of the scenarios offered for reducing CO2 emissions has been proposed, and it is estimated that, depending on the variant adopted, the reductions will be between 13 and 21% in variant I, between 3 and 8% in variant II, and between 18 and 34% in combining these variants. The variant with a reduction in private car transportation in favor of bicycle transportation in combination with public transportation showed the most favorable effects on the environment.

1. Introduction

In 2021, the Polish government adopted a new strategic document for the fuel and energy sector, setting the direction for the development of the sector in the next 12 years, following the establishment of the previous strategy. In the field of energy transition, the “Energy Policy of Poland until 2040” provides the basis for programming EU funds in the energy sector, as well as addressing economic needs associated with the COVID-19 pandemic. By implementing a fair energy transition, developing renewable energy sources, improving energy efficiency, and improving air quality, the document aims to achieve a low-carbon economy. As part of the transformation process, RES technologies for heat generation and alternative fuels in transportation should be increased in the long run. This includes electromobility and hydro-mobility [1,2] as well as sustainable solutions for the transport sector development [3]. The transportation sector is estimated to be responsible for around 30% of global energy consumption. Renewable transportation is key to a sustainable energy future, and electric vehicles (EVs) can be a solution that incorporates the synergies between clean transportation and low-carbon electricity [4,5,6].
The International Renewable Energy Agency (IRENA) 1.5 °C scenario [7] shows that low-carbon solutions and efficiency measures can reduce transportation energy consumption from 121 EJ in 2018 to 91 EJ in 2050. Electricity meets approximately 52% of all energy needs, followed by hydrogen and its derivatives (23%), and biofuels (13%). The use of fossil fuels contributes to 11% of the total. Increased awareness of environmental and ecological issues and better urban planning will, at the same time, play a very significant role in reducing transportation demand. To fully understand the problem, a differentiated transition model focusing on systemic change must be analyzed, which generates new research questions and new hypotheses, clarifies the typology of transition paths, and provides practical methodological guidance on how to select cases based on the complexity of this problem. Future research on the transition from traditional to modern energy sources [8,9,10] will also be based on the identification and evaluation of factors influencing consumers’ energy-saving behaviors, as well as their sense of social responsibility when it comes to energy consumption [11].
The quality of the atmosphere is a component of the natural environment that directly impacts health, and through that, the quality of everyday life for people [12]. The bicycle gained significant interest after the pandemic in 2020 due to the restrictions introduced in connection with it. To control the spread of the pandemic, multiple steps have been taken in the process of accelerating the process [13,14]. This effort will be aided by continued investment in bicycle infrastructure in both small towns and cities. This will ensure that cyclists are safe whenever they are on the road or cycling between cities [15,16,17,18]. Pollutants are found in the atmosphere, such as gases and dust. The sources of these pollutants can come from both natural and anthropogenic processes (PEP2040). It is estimated that global temperatures will rise between 1 and 2 degrees Celsius by 2020, and by 2070, the increase will range between 2 and 5 degrees Celsius [19]. There has been a study commissioned by the Intergovernmental Panel on Climate Change—the IPCC—that has come to this conclusion.
This phenomenon, although it is a slow process, is causing a great deal of concern because the rate at which it is growing is increasing at an alarming rate. Among other factors, the occurrence of such a phenomenon is largely attributed to greenhouse gases (GHGs) such as carbon dioxide, methane, and nitrous oxide, which are the main contributors to the phenomenon. They can be emitted by a variety of industrial and transportation activities. Several existing technologies are being improved, and creative solutions are being sought to make them more energy-efficient and environmentally friendly [20,21,22,23,24], as well as economically viable [25,26]. To take advantage of the possibility of reusing waste heat, it may be possible to recover waste heat from the wastewater for heating agricultural land and crops [27].
There are several things that automobile engines produce, including sulfur dioxide and nitrogen oxides, among others. The process of burning fuel leads to the formation of sulfuric acids and nitric acids, which, in turn, result in acid precipitation in the form of acid rain. This is due to the reaction between water vapor and oxygen in the presence of ultraviolet light to create sulfuric acid and nitric acid. It has been shown that they pose a serious threat to almost all ecosystems, especially soil and forest environments [28,29].
Transport accounts for about 27% of all carbon dioxide emissions in OECD (Organization for Economic Cooperation and Development) member countries, with 80% coming from roads. According to the International Energy Agency, in 2020 the transportation sector accounted for 6.9 Gt of CO2 emissions. This was one-fifth of global energy-related CO2 emissions. The transportation sector contributed almost a quarter of global CO2 emissions from energy use in 2019. The sector consumed 104 EJ of energy in 2020, which was 14% less than in 2019, meeting its demand mainly from fossil fuels (95%), biofuels (4%), and electricity (1%). Among all modes of transportation, road transportation alone accounted for more than three-quarters of the sector’s energy use, followed closely by maritime transport (10%), air transport (8%), and rail transportation (2%) [7]. Transport services are experienced by an increasing number of people all over the world, which indicates that a sustainable sector transformation towards zero-carbon is a necessity.
Air quality within an area is determined by the magnitude and spatial distribution of emissions from all sources, taking into account transboundary flows and physicochemical transformations occurring in the atmosphere. Energy consumption and spatial distribution of pollutants have wide variations within regions [30,31]. Forehead et al. (2018) point out that linear emissions are often associated with urbanized areas in large cities, which are typically concentrated in small areas [32]. Attention should also be paid to rural areas, as their ecosystems help to mitigate environmental pressures and natural hazards, proper management of the resources of these areas is key to preserving natural capital, they are natural carbon sinks from the atmosphere [33,34,35].
The basic classification of zones used to assess air quality annually distinguishes the following levels, including acceptable, acceptable plus tolerated, target, and long-term objectives. Three levels can be achieved by the Regulation on the levels of certain substances in the air issued by the Minister of the Environment on 24 August 2012. There is a permissible level, a permissible level plus a tolerance level, and a long-term objective level. There are two types of limit levels (equivalent to Directive 2008/50/EC: limit value). The first is a limit level, which is the level of a substance in the air determined based on scientific knowledge that will be used to reduce or avoid adverse effects on human health or the environment as a whole. This level should be achieved within a given period and should not be exceeded afterward. There is a concept known as target level (equivalent in the Directive to target value) that specifies a certain concentration of a substrate in the air.
To prevent, limit, or find a solution to the adverse effects it can have on human health or the environment, there needs to be a specified time frame that can be achieved. To determine a critical level, we use the scientific knowledge available, and we establish that it is the level of substances in the air which direct adverse effects may occur concerning certain receptors, such as trees, other plants, or natural ecosystems, but not humans. This level of medium-term objective (equivalent to the Directive’s long-term objective) refers to a level of substance in the air that must be maintained in the long run–except where such a level cannot be achieved by applying proportionate measures, to ensure effective protection of human health and the environment in the long run. According to the directive, the margin of tolerance allows the level to be exceeded by a specified percentage of the permissible level. This is under the directive’s conditions.
It has been determined that the air quality assessment for the Mazovian zone in 2020 was carried out according to human health and plant protection criteria, separately for 12 pollutants. Among the pollutants measured were SO2, NO2, CO, C6H6, PM10, PM2.5, O3, As, Cd, Ni, BaP, and Pb. According to the results, the concentrations of pollutants in most of them did not exceed the permissible values, so they were categorized as Class A. However, three of them exceeded the permissible concentration levels, so they were classified as Class C. The results were presented in Table 1 [36].
The amount of pollution in the atmosphere is largely influenced by traffic along transportation routes. Transport industry pollutants are produced when fuels in internal combustion engines are burnt to produce energy, resulting in pollutants’ combustion. These pollutants accumulate in the atmosphere’s ground layers, particularly along transportation routes. The amount of emissions depends on the number and type of vehicles, as well as the type of fuel used to power those vehicles. There is also a relationship between the volume of emissions associated with non-fuel emissions, such as tire wear, brake wear, and road surface abrasion. While emissions from brake abrasion account for a small percentage of non-fuel emissions, secondary (from uplift) emissions of PM 10 from road surfaces can account for up to 60% of all transportation emissions. Road surface quality, pavement quality along the roadside, and road surface maintenance all play a role in how well the road surface performs [37]. However, these emissions are quite small depending on their technical condition and pavement degree.
Several characteristics make linear emission systems interesting, including the high concentration of carbon monoxide, nitrogen oxides, volatile hydrocarbons, and a concentration of pollutants along transportation routes, as well as the irregularity in daily and seasonal emission rates due to variable traffic volumes. To determine the magnitude of these emissions, it is critical to consider the condition and performance of the roadway, the engine design and technical condition of the vehicles, the engine operating conditions, and the type of fuel used. Traffic emissions are directly influenced by the type of fuel employed in vehicle engines and the amount of fuel emitted. In addition, the condition and characteristics of the roadway, the design and technical state of the engine, the operating conditions of the engine, and the liquidity of traffic. Even though not every one of these factors is influenced by the municipality, it is nevertheless possible to improve the quality of road surfaces. This is done by building traffic circles and creating bypass roads so that it is less congested. The vehicle will undoubtedly increase traffic flow, which in turn will result in lower fuel consumption and, thus, a reduction of greenhouse gas emissions, but it will also, perhaps more importantly, improve road safety, which is of great importance from a social perspective [38].
Fuel consumption and transport gas emissions in urban areas have been studied a lot in studies that examine the issue. The problem is indeed very critical, not only in terms of atmospheric pollution and climate change but also in terms of human health [10,13,32]. Rural municipalities pay much less attention to this issue than their urban counterparts. These areas are often connected by transport links, even those that are international. In the short run, car transportation is more convenient, but this inconvenience can also be reduced through better organization and infrastructure. The purpose of this paper is to present the benefits related to the scenarios that have been proposed.
A successful rural municipality requires close coordination between local authorities, residents, and businesses to reduce fuel consumption, air pollutants, and CO2 emissions, in addition to the potential for reducing the emissions of greenhouse gases. The model municipality of a rural region can become a leader in the field of ecology, drawing the attention of other rural municipalities and motivating them to take similar measures to protect the environment and reduce the impact on our climate as a result of their efforts. The innovation and novelty of the article can be found in its proposal for a computational methodology to be used as a comprehensive organizational tool for the reduction of emissions in linear traffic. To carry out the study in the model municipality, we collected and analyzed the actual traffic which was taking place on the roads of the municipality studied in terms of the presence of vehicles with internal combustion engines. This work has presented several measures that if taken, would result in greater efficiency in the transportation of goods, as well as a reduction in pollution from the combustion of fuels due to the implementation of more efficient and environmentally friendly transportation solutions. By promoting non-fossil fuel modes of transportation, such as bicycles, especially for short distances, and by using environmentally friendly technologies, we will all play our part in reducing the emissions of greenhouse gases into the atmosphere. By focusing on these activities, the work will contribute to the development of a sustainable transport system and make it evident how important these activities are to the reduction of carbon dioxide emissions.

2. Materials and Methods

2.1. The Traffic Measurement

For the model municipality to estimate and analyze linear emissions within its boundaries, it is imperative to conduct traffic measurement studies. Based on the measurements taken at individual points located on the roads within the model municipality, the study was conducted based on the results of traffic measurements made at those points. Individual measurement points should be chosen along the roads within the defined municipality to measure. Under the adopted assumptions and in line with the methodology used for calculating carbon dioxide emissions from this source, the tests should be carried out taking into account the adopted assumptions. This analysis was performed according to the accepted methodology used for linear source emissions estimation.
The first step is to select various positions along roads within the model municipality to place measurement points. The selection process should consider various kinds of roads, traffic density, and key points in the road network. These points should include intersections or areas with high traffic volumes, as part of the selection process. To perform the measurement, it is necessary to install appropriate traffic sensors. Traffic monitoring devices can include loops built into the road surface or cameras that monitor traffic flow. Using these sensors, data can be collected about the number of vehicles, their speed, travel time, and other related data relevant to traffic management. Following the installation of traffic sensors at selected locations, the data collection process begins to gather results. During the monitoring process, the sensors record traffic for some time, and then the information obtained from those recordings is stored on a computer.
To gather valuable information about traffic volume, vehicle speeds, types of vehicles, as well as other valuable aspects related to the acquisition of the collected data, the data should be subjected to technical processing. Analysis of the data is carried out to obtain information that will allow us to characterize traffic at individual measurement points. Taking into account the traffic measurement results obtained, an estimate of emissions from this source can be derived based on the collected data. Various methods exist for calculating emissions, but one of the most popular ones is the model used for estimating vehicle emissions. Song and Cha (2022) say to calculate emissions from fuel combustion in engines, an estimation method is employed, in which the amount of emissions emitted depends on the type of vehicle, the type of fuel, the engine operating load, and other factors [39]. The emissions of all roads and all vehicles within the study area are then added to obtain the total emission rate for the study area. In our study this was the area of a model rural municipality.

2.2. Assumptions for Analysis

In the EU, 30% of road trips do not exceed 3 km, and 50% do not exceed 5 km in distance. The following variants I and II have been considered, with two options for minimizing air pollution in each variant Ia, Ib, IIa, and IIb:
  • Variant I—replacement of trips up to 3 km made by private transport (passenger car) with bicycle transport.
  • Variant Ia—replacing 30% of car trips with bicycle transportation.
  • Variant Ib—replacing 30% + 10% (caused by the COVID pandemic) of car trips with bicycle transportation.
  • Variant II—reducing commuter to work, reducing private transportation (minibuses),
  • Variant IIa—10% of residents will convert private transportation to public transportation.
  • Variant IIb—30% of residents will replace private transportation with public transportation.
The analysis was also conducted using combinations of variants IaIIa, IaIIb, IbIIa, and IbIIb, respectively. It was assumed that based on the assumptions, there would be an average of 1.4 passengers per car, 4.5 passengers per minibus, and 25 passengers per city bus. This was depending on assumptions that were made. Calculations were based on the average daily traffic volume on county and municipal roads. To mitigate the effects of the change, it was assumed that county and municipal road users would be affected by the change. In most cases, short-distance trips are conducted on these roads because they are the most convenient. It was decided that the reduction of emissions was to be calculated by subtracting 30% of trips driven by passenger cars on county and municipal roads and replacing them with bicycle transportation in this variant, from the amount of emissions in the base year to calculate reductions of emissions. In our study, one of the limitations is that we assumed that a certain number of trips could be made by bicycle in theory. The number of bicycle trips taken in the study area cannot be automatically recorded due to the absence of such a facility.

2.3. Counting Method

The model municipality was assumed to have roads of the following categories within it, with each road having a different category based on its role in the Polish road network from the Journal of Laws of 2013 (Journal of Law of 21 March 1985, item 260 on public roads) amended [40]. National roads, provincial roads, county roads, and municipal roads were all included in the model municipality. It was assumed that the total length of public roads in the analyzed municipality is approximately 210 km, based on the number of national roads, provincial roads, county roads, and municipal roads. These roads include a length of approximately 7 km for national roads, a length of 23 km for provincial roads, a length of 35 km for county roads, and a length of 85 km for municipal roads. To compile this inventory, the following categories of vehicles were considered: motorcycles, passenger cars, and light trucks (vans); in the category of heavy trucks, cars without and with trailers, and buses were considered separately, as well. The traffic volume on the national and provincial roads in the model municipality (SDR) is presented in Table 2 [41].
It was determined by the Regulation of the Minister of the Environment that for the conversion of unit emission factors, the conversion factor specified in that regulation was used to calculate the amounts payable as well as the extent and amount of environmental use and data contained on these lists [42]. Based on this document, it can be assumed that a liter of diesel fuel has a density of 0.84 kg per liter and that a liter of gasoline has a density of 0.65 kg per liter when converting a liter of fuel. For a certain level of fuel consumption, the E(i) emissions of gases/pollutants according to the Tier 3 method can be calculated using the following factors as presented in EMEP EEA 2009 [8], using emission factors of g/kg ON as assumed. The emission factors are presented in Table 3.
In the estimation of air pollution emissions from the operation of tractors used in agriculture, the data from Municipal Offices that have been provided from the re-registration of the number of subsidies for the purchase of ON for the use of agricultural machinery. The level of subsidies per 1 hectare of agricultural land per year is expressed by the number of subsidies per 1 hectare. The values of unit emission factors from agricultural tractor ON combustion were used to calculate emissions. The emission factors from diesel consumption by agricultural tractors in Poland are presented in Table 4 [8].
The above table was used to compare emissions of different types of pollutants by different categories of vehicles, and in this particular case, farm tractors, as well as vehicles registered in the same category. The farm tractors category shows various values of emitted pollutants. In the case of carbon dioxide (CO2) emissions, large amounts of the gas are emitted at a rate of 3170 g per kilogram of fuel. There is a lower rate of methane (CH4) emissions per kilogram of fuel, while nitrogen oxides (N2O) are emitted at a lower rate of 0.19 g per kilogram of fuel, both of which are lower than that of methane (CH4). Furthermore, a large number of other pollutants are emitted, including carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NOx), particulate matter (PM), and sulfur dioxide (SO2).

3. Results

The daily emissions of pollutants from transport activities in the municipality were calculated using the adopted methodology, in conjunction with the assumptions contained therein, based on the assumptions that were made in the methodology. To estimate the daily transportation emissions for a municipality, a particular methodology needs to be applied that involves several steps and takes into account a variety of factors, such as the type of road, the type of vehicle, the traffic volume, the average fuel consumption and the CO2 emission factors for individual vehicles. There is no doubt that the methodology may differ from place to place, depending on the factors, including local conditions, data availability, and the approach taken. The calculations of CO2 emissions are based on assumptions about fuel consumption and emission factors, which can be adjusted based on specific local conditions and current data, to obtain accurate estimates. The results of calculations of daily CO2 emissions are presented in Table 5 [43].
The majority of CO2 emissions on national roads are generated by passenger cars, with 16,189 kg per day released. As a result, trucks emit almost half as much carbon dioxide as cars—15,348 kg of CO2 per day. The amount of CO2 emitted by public transportation in a day is only 417.20 kg. There are approximately 124,085 kg of carbon dioxide produced per day by the use of passenger cars on provincial roads, resulting in most of the emissions. The amount of CO2 produced by county and municipal roads is 48,078 kg and 110,086 kg in a day, respectively. According to the estimates, the highest bus emissions were found on municipal roads and totaled 4924 kg of CO2 per day. To estimate the emissions of other pollutants from petroleum combustion using the fuel emission factors adopted by the Fuels Institute, it was necessary to take into account the type of transportation mode and the type of fuel used. The estimated daily emissions of CO, NMLZO, NOx, and PM as a result of means of transportation from the model municipality, where they were compared to actual results are summarized in Table 6.
According to the analysis, passenger cars are the largest culprit when it comes to the emissions of CO and NMLZO, on each of the different types of roads. In the analyzed area of the model municipality, trucks caused the most emissions of NOx and PM, followed by cars. The model municipality is based on the data provided by the federal government, and as a result, the daily atmospheric emissions of various pollutants have been estimated based on the data. These pollutants include CO (carbon monoxide), NMLZOs (non-methane volatile organic compounds), NOx (nitrogen oxides), and PM (particulate matter). In the combustion of fossil fuels, several pollutants are released as a by-product, which are released as a result of vehicle emissions. The main source of carbon monoxide (CO) is incomplete combustion of carbon-containing fuels, which is caused by incomplete combustion of gaseous fossil fuels. This article discusses the major sources of non-methane volatile organic compounds (NMLZOs), including vehicle exhausts, and their potential contribution to the formation of ground-level ozone and smog at ground level. Several types of nitrogen oxides are produced when nitrogen and oxygen react with each other at high temperatures, mainly because of combustion engines found in vehicles, which are a major source of nitrogen oxides. Particulate matter (PM) refers to tiny particles suspended in the air, which can be emitted directly from vehicle exhaust or generated through secondary processes. The estimation of daily atmospheric emissions of CO, NMLZO, NOx, and PM from means of transportation in the model municipality is presented in Figure 1.
There is a table below that lists the emissions from diesel combustion by tractors that are used in agricultural operations in the municipality analyzed. The emissions from diesel combustion by agricultural tractors in the model municipality are presented in Table 7.
It was necessary to subtract the amount of emissions that occurred in the base year from the emission reductions that could be achieved under each variant to calculate the emission reductions. There was a comparison of the amounts of CO2 emission reductions that could be achieved in each of the variants compared with those in the base year. The reductions in CO2 emissions from linear sources to base year 2010 emissions for each variant based on the reductions in CO2 emissions from linear sources are presented in Table 8.
According to the calculations, the scenario that combines reducing private transportation in favor of bicycles and public transportation would have the most significant environmental impact. These results presented in this paper are based on measurements made by the authors on a certain set of N elements for which measurements were taken. The dataset that has been analyzed includes a total of eight weighted elements (N), which are used during the analysis. Taking the sum of all values in the analyzed dataset, as well as dividing it by the number of elements, we calculate a mean value of 7.84 × 104. The mean value is a measure that indicates the average value for all values in the data set we have analyzed. The dataset contains a value of 1.16 × 104 which is the smallest in the dataset. This implies that among the elements recorded in the dataset, the lowest value is 1.16 × 104. The highest value in the dataset is 14.39 × 104. This is the highest value among the analyzed elements. Standard deviation is a measure of the dispersion of the data around the mean. In this case, it is 4.34 × 104. The higher the standard deviation, the greater the variability or spread of values in the data set. Based on the data provided, we can conclude that the analyzed dataset consists of eight weighted elements, the average value of which is 7.84 × 104. The lowest recorded value is 1.16 × 104, and the highest value is 14.39 × 104. The analyzed dataset also shows a significant standard deviation, which indicates that there is some variation in the values. As a result of this, it would be possible to reduce CO2 emissions by 34%. This would mean that the amount of CO2 that is emitted per day would decrease by 14.39 × 104 kg. The values of CO2 emissions from linear sources in every variant based on the amount of energy used are presented in Figure 2.

4. Discussion

According to the European Economic and Social Committee [44], the Rural Energy and Digital Transformation Strategy has received very little attention and support over the course of the past few years. To improve the energy efficiency of rural areas in the EU, it is essential to develop a long-term vision, with a special focus on the most vulnerable, energy-poor rural areas. To achieve a case-sensitive energy transition, citizens, local authorities, and SMEs can be encouraged to form renewable energy communities and citizen energy communities, where they voluntarily cooperate to achieve social and economic benefits. This is the official journal of the European Union, which published the report on the development of renewable energy communities and citizen energy communities.
Rural areas can be classified into areas within which it is possible to commute to work in the city (within a 60-km radius) and whose development is integrated with the city, regions that are not part of the urban labor market but which are the origin and destination of flows of goods and environmental services and other types of economic activity, and remote regions where the local economy is heavily dependent on exporting the output of primary activities (agricultural products) outside the region. These diverse rural areas face a wide variety of challenges in implementing the energy transition, indicating the importance of a just transition to achieve the desired goals.
Emissions of air pollutants, including GHG emissions, and carbon dioxide (CO2), are a significant problem that must be looked at and ways must be found to reduce them. In the case of the model rural municipality, there are many potential sources of emissions of this gas, among them those that can be classified as linear sources, that is, those along road routes. Motor vehicles powered by fossil fuels generate CO2 emissions when they are burned. Reduction of CO2 emissions can be achieved through various measures. One method is to promote public transportation, by encouraging residents to move using buses, rail, or bicycle.
Transport and mobility of people and goods are crucial to today’s economy [45]. Many rural areas are physically isolated, characterized by low economic diversity, and low population density. A big problem in implementing the energy transition in such regions is people living alone with little social interaction. Investment in bicycle infrastructure: building bicycle lanes and promoting the use of bicycles as a mode of transportation can also contribute to energy and climate policy goals [46]. According to Philips et al. (2022), the maximum CO2 reduction capacity per person per year in England is estimated to be 750 kg of CO2. The value of the index was highest for rural residents and residents living on the borders of rural and urban areas [47]. Berjisian and Bigazzi (2019) show that, on average, each additional electric bicycle will reduce vehicle kilometers traveled by 2000 per year, yielding a net reduction in CO2 emissions of 460 kg per year [48]. The European Committee of the Regions, in the EU Cycling Action Plan [41], emphasizes the need to push for greater accessibility of public transport stops for pedestrians and cyclists, the creation of safe, universally accessible rober parking at interchange points, and service-oriented, bike-sharing, as part of territorial programming instruments [49]. Raposo and Silva (2022) conducted a study in Portugal that found that the implementation of such measures can potentially prevent 36 tons of greenhouse gas emissions and reduce energy consumption by 451 GWh annually as a result of these measures [50].
One of the recommendations is to double (from the current 7–8% share of cycling to 15%) cycling in EU member states over the next ten years. In the long term (2030/2040/2050), the level of investment funding should be estimated and analyses should be carried out taking into account a cross-cutting approach to cycling, taking into account areas such as the economy, environment, climate, energy efficiency, transport, education, and health [44]. Electrification of transportation, using electric or hybrid vehicles, can significantly reduce traffic-related CO2 emissions. IRENA’s 1.5 °C Scenario, presented in the World Energy Transitions Outlook, shows a pathway to achieve the 1.5 °C target by 2050, identifying electrification and efficiency as key drivers of the transition, supported by renewable energy, clean hydrogen, and sustainable biomass.
An important role can be played by the implementation of carpooling and ride-sharing systems, allowing several people traveling the same route to share cars. Even a simple measure such as reducing the maximum speed limit on roads can help reduce CO2 emissions. At lower speeds, cars burn less fuel and emit fewer pollutants. In addition, the model rural municipality may also have other sources of CO2 emissions from linear sources, such as agriculture, the energy sector (e.g., power plants), or small and medium-sized businesses. Increasing the share of renewable energy in electricity generation can reduce CO2 emissions from the power sector. In addition, efficient waste management, including energy recovery from organic waste or recycling, can help reduce waste-related CO2 emissions. Upgrading infrastructure, investing in energy efficiency, and modernizing industrial installations for important activities will help to improve energy efficiency and mitigate the effects of climate change [51]. Education and public awareness among residents of the model rural municipality is one element of this complex and very important problem. The implementation of all of these measures will assist in the achievement of the “European Green Deal” goals set by the European Commission [52].

5. Conclusions

The reduction of CO2 emissions from linear sources within a model rural municipality requires several measures, which must be taken on both an individual as well as a societal level to achieve success. As a consequence of these linear emissions, which can be attributed most to urban centers and areas along major transportation routes, their effects are more acute and feel more acute within urban centers, and they are not insignificant in rural areas where transportation routes frequently pass through. There is a major effect of the transportation system on the air quality, including in rural areas, the state of which is heavily influenced by it. The areas of rural municipalities in many cases experience heavy traffic congestion caused by vehicle transits and coaches transporting tourists through the areas. The effects of transport on municipalities—both urban and rural ones—are influenced not only by the volume of traffic taking place on the roads but also by the fluidity of the traffic and the organization of the traffic, as well as by the quality of the roads and the condition of the vehicles. Several measures must be taken both at the individual and social levels to reduce the amount of CO2 emitted from linear sources within a model rural municipality. Providing reliable research results to residents of rural municipalities is one of the most critical components in the process of convincing them of the benefits of environmental protection. The implementation of the proposed solutions, depending on the adopted scenario, will result in reductions in CO2 emissions of between 13 and 21% for variant I, from 3 to 8% for variant II, and from 18 to 34% for combinations of these variants. It is also important to note that reducing emissions will also have a positive impact on health and the economy. The challenge of climate change can easily be addressed by planning sustainable development, investing in renewable energy sources, promoting energy efficiency, and educating the public about protecting the environment. The development of pedestrian and bicycle traffic will likely result in a reduction in safety hazards that may arise from the joint use of roadways by everyone, especially those who are “vulnerable traffic participants” (pedestrians and cyclists) who are forced to use the roadways as a result of the lack of sidewalks and bicycle lanes. The problem in rural municipalities is that they have limited access to public transportation, low frequencies of service, and poorly developed internal connections, as they are obliged to make use of roads of supra-local importance to get around.

Author Contributions

Conceptualization, A.K., K.R. and W.R.; methodology, A.K. and K.R.; software, A.K. and K.R.; validation, A.K. and K.R.; formal analysis, A.K. and K.R.; investigation, A.K. and K.R.; resources, A.K. and K.R.; data curation, A.K. and K.R.; writing—original draft preparation, A.K. and K.R.; writing—review and editing, A.K. and K.R.; visualization, A.K., K.R. and W.R.; supervision, A.K. and K.R.; project administration, A.K. and K.R.; funding acquisition, A.K., K.R. and W.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

MDPI Research Data Policies.

Conflicts of Interest

The authors declare no conflict of interest.

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Energies 16 05553 g001 550
Figure 1. The estimation of daily atmospheric emissions of CO, NMLZO, NOx, and PM from means of transport in the model municipality.
Figure 1. The estimation of daily atmospheric emissions of CO, NMLZO, NOx, and PM from means of transport in the model municipality.
Energies 16 05553 g001
Energies 16 05553 g002 550
Figure 2. The value of CO2 emissions from linear sources in each variant.
Figure 2. The value of CO2 emissions from linear sources in each variant.
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Table
Table 1. The resulting zone classes for individual pollutants, obtained in the annual assessment carried out taking into account the criteria established for health protection according to uniform criteria on a national scale, consistent with EU criteria.
Table 1. The resulting zone classes for individual pollutants, obtained in the annual assessment carried out taking into account the criteria established for health protection according to uniform criteria on a national scale, consistent with EU criteria.
Zone NameZone CodeSymbol of the Resultant Class for Individual Pollutants for the Area of the Entire Zone
SO2NO2COC6H6PM10PM2.5O3AsCdNiBaPPb
MazoviaPL1404AAAACCAAAACA
Table
Table 2. Traffic volume on the national and provincial roads in the model municipality (SDR).
Table 2. Traffic volume on the national and provincial roads in the model municipality (SDR).
Vehicle CategoryTraffic Volume on the Roads: SDR, Vehicles/day
NationalProvincialDistrictMunicipalTotal
Motorcycles60323113107603
Cars13,40431,26810,94410,31865,934
Light trucks (vans)18413076107710157009
Trucks without trailer90011153903682773
Trucks with trailers364113624774495929
Buses160339119112730
Total motor vehicles20,00637,48313,12012,36982,978
Table
Table 3. The emission factors.
Table 3. The emission factors.
Type of TransportEmission Factors, g/kg
CONMLZO 1NOxPM
Gasoline-powered passenger cars230.044.034.10.0
Cars with up to 3500 kg total weight, ON18.04.018.86.0
Trucks with more than 3500 kg total weight, ON32.512.553.06.0
1 NMLZO—non-methane volatile organic compounds.
Table
Table 4. Emission factors from diesel consumption by agricultural tractors in Poland.
Table 4. Emission factors from diesel consumption by agricultural tractors in Poland.
Type of ContaminationCO2CH4N2OCONMVOCNOxPMSO2
Vehicle Categoryg/kg
Farm tractors3 1700.190.1646.38525.20.1
Table
Table 5. Estimated daily atmospheric CO2 emissions from means of transport in the model municipality.
Table 5. Estimated daily atmospheric CO2 emissions from means of transport in the model municipality.
Type of RoadVehicle CategoryTraffic Volume, Vehicle/dayThe Average Amount of Fuel Burned, l/100 kmLength
Road Section, km		The Average Amount of Fuel Burned on a Given Road Section, LAverage Emission of CO2, kg CO2/LDaily CO2 Emissions, kg/day
NationalMotorcycles605.0721.002.3749.77
Passenger cars, powered BS10,7238.076004.992.3714,231.83
Passenger cars, powered ON26817.071313.591.491957.25
Light trucks (vans), powered BS36811.07283.512.37671.93
Light trucks (vans), powered ON14739.07927.861.491382.52
Trucks without trailer90030.071890.001.492816.10
Trucks with trailers364133.078410.711.4912,531.96
Buses16025.07280.001.49417.20
ProvincialMotorcycles3235.023371.452.37880.34
Passenger cars, powered BS25,0148.02346,026.502.37109,082.80
Passenger cars, powered ON62547.02310,068.301.4915,001.76
Light trucks (vans), powered BS61511.0231556.462.373688.80
Light trucks (vans), powered ON24619.0235093.861.497589.85
Trucks without trailer111530.0237693.501.4911,463.32
Trucks with trailers136233.02310,337.581.4915,402.99
Buses33925.0231949.251.492904.38
District
(35% of provincial SDRs)		Motorcycles1135.035197.842.37468.87
Passenger cars, powered BS70048.03519,611.292.3746,478.76
Passenger cars, powered ON4387.0351072.491.491598.01
Light trucks (vans), powered BS4311.035165.802.37392.94
Light trucks (vans), powered ON6899.0352170.431.493233.93
Trucks without trailer39030.0354097.631.496105.46
Trucks with trailers47733.0355505.891.498203.77
Buses11935.0351453.461.492165.66
Municipal
(33% of provincial SDRs)		Motorcycles1075.085453.012.371073.63
Passenger cars, powered BS66048.08544,905.852.37106,426.87
Passenger cars, powered ON4137.0852455.791.493659.13
Light trucks (vans), powered BS4111.085379.642.37899.75
Light trucks (vans), powered ON6509.0854969.831.497405.05
Trucks without trailer36830.0859382.731.4913,980.26
Trucks with trailers44933.084.412,518.361.4918,652.36
Buses11235.084.43304.641.494923.91
Table
Table 6. Estimated daily atmospheric emissions of CO, NMLZO, NOx, and PM from means of transport in the model municipality.
Table 6. Estimated daily atmospheric emissions of CO, NMLZO, NOx, and PM from means of transport in the model municipality.
Type of RoadVehicle CategoryTraffic Volume, Vehicle/dayThe Average Amount of Fuel Burned, l/100 kmLength
Road Section, km		The Average Amount of Fuel Burned on a Given Road Section, LEmission, kg/day
CONMLZONOXPM
NationalMotorcycles605.07.021.003.620.690.540.00
Passenger cars, powered BS10,7238.07.06004.991035.86198.16153.580.00
Passenger cars, powered ON26817.07.01313.5919.634.3620.506.54
Light trucks (vans), powered BS36811.07.0283.5148.919.367.250.00
Light trucks (vans), powered ON14739.07.0927.8613.863.0814.484.62
Trucks without trailer90030.07.01890.0050.9819.6183.149.41
Trucks with trailers364133.07.08410.71226.8887.26369.9941.89
Buses16025.07.0280.007.552.9112.321.39
ProvincialMotorcycles3235.023.0371.4564.0812.269.500.00
Passenger cars, powered BS25,0148.023.046,026.507939.571518.871177.130.00
Passenger cars, powered ON62547.023.010,068.30150.4233.43157.1150.14
Light trucks (vans), powered BS61511.023.01556.46268.4951.3639.810.00
Light trucks (vans), powered ON24619.023.05093.8676.1016.9179.4825.37
Trucks without trailer111530.023.07693.50207.5379.82338.4438.31
Trucks with trailers136233.023.010,337.58278.86107.25454.7551.48
Buses33925.023.01949.2552.5820.2285.759.71
District
(35% of provincial SDRs)		Motorcycles1135.035.0197.8434.136.535.060.00
Passenger cars, powered BS70048.035.019,611.293382.95647.17501.560.00
Passenger cars, powered ON4387.035.01072.4916.023.5616.745.34
Light trucks (vans), powered BS4311.035.0165.8028.605.474.240.00
Light trucks (vans), powered ON6899.035.02170.4332.437.2133.8710.81
Trucks without trailer39030.035.04097.63110.5342.51180.2520.41
Trucks with trailers47733.035.05505.89148.5257.12242.2027.42
Buses11935.035.01453.4639.2115.0863.947.24
Municipal
(33% of provincial SDRs)		Motorcycles1075.085.0453.0178.1414.9511.590.00
Passenger cars, powered BS66048.085.044,905.857746.261481.891148.470.00
Passenger cars, powered ON4137.085.02455.7936.698.1538.3212.23
Light trucks (vans), powered BS4111.085.0379.6465.4912.539.710.00
Light trucks (vans), powered ON6509.085.04969.8374.2516.5077.5524.75
Trucks without trailer36830.085.09382.73253.1097.35412.7546.73
Trucks with trailers44933.085.012,607.35340.08130.80554.6062.78
Buses11235.085.03328.1389.7834.53146.4016.57
Table
Table 7. Emissions from diesel combustion by agricultural tractors in the model municipality.
Table 7. Emissions from diesel combustion by agricultural tractors in the model municipality.
Type of ContaminationCO2CH4N2OCONMVOCNOxPMSO2
Vehicle Categorykg/year
Farm tractors67,379.364.043.40984.12170.041105.28110.532.13
Table
Table 8. The reductions in CO2 emissions from linear sources in each variant to the base year 2010 emissions.
Table 8. The reductions in CO2 emissions from linear sources in each variant to the base year 2010 emissions.
VariantEmissions of CO2 in the Base Year 2010Emission Reduction
kg CO2/daykg CO2/day%
Ia4.26 × 1055.60 × 10413
b8.78 × 10421
IIa1.16 × 1043
b3.48 × 1048
Ia, and IIa7.47 × 10418
Ia, and IIb11.20 × 10426
Ib, and IIa10.65 × 10425
Ib, and IIb14.39 × 10434
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Konieczna, A.; Roman, K.; Rzodkiewicz, W. Fuel Consumption, Emissions of Air Pollutants and Opportunities for Reducing CO2 Emissions from Linear Sources in the Model Rural Municipality. Energies 2023, 16, 5553. https://doi.org/10.3390/en16145553

AMA Style

Konieczna A, Roman K, Rzodkiewicz W. Fuel Consumption, Emissions of Air Pollutants and Opportunities for Reducing CO2 Emissions from Linear Sources in the Model Rural Municipality. Energies. 2023; 16(14):5553. https://doi.org/10.3390/en16145553

Chicago/Turabian Style

Konieczna, Anita, Kamil Roman, and Witold Rzodkiewicz. 2023. "Fuel Consumption, Emissions of Air Pollutants and Opportunities for Reducing CO2 Emissions from Linear Sources in the Model Rural Municipality" Energies 16, no. 14: 5553. https://doi.org/10.3390/en16145553

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