The Emission Balance of Selected Groups of Fuels Used in Households to Generate Pollution in the Małopolskie Voivodeship

Abstract

This article presents an analysis of the relationship between air pollution and the type of fuel used in households in the Małopolskie Voivodeship from 2010 to 2021. For this article, they are marked as HEU-C (coal), HEU-L (liquid), and HEU-O (other). The analysis area was selected due to the constantly occurring problem of air pollution with PM₁₀ and PM_2.5. Using regression, the relationships between energy consumption in households and specific components of air pollution recorded in the Małopolskie region were established. The developed models were used to determine the potential of individual groups of fuels to generate pollution. The primary finding suggests that the derived regression models offer an appropriate predictive framework. Studies show significant reductions in pollutants like BbF, Cd, Pb, and PM_2.5. Increasing the use of HEU-O fuel is key to cleaner air in the Małopolskie Voivodeship. However, HEU-O may emit more NO_x and NO₂ than HEU-C. The selected thematic emphasis differentiates this article from others examining air quality issues within the Małopolskie Voivodeship.

1. Introduction

The quality of the air is currently one of the most important social issues in the modern world. Both society and the natural environment around us are exposed to air pollution. Sources of pollution can be diverse. Many of them directly affect health and affect diversity in ecosystems. Poor air quality is a social problem, harms public health and the environment, and therefore leads to economic losses due to increased healthcare costs and damage to local ecosystems [1,2]. A significant part of Europe’s population lives in cities and is therefore directly exposed to air pollution. According to the World Health Organization (WHO), over 2 million people die prematurely every year due to air pollution, so there is a constant need to improve air quality [2,3,4,5,6]. The latest research indicates that even low concentration levels contribute to the negative impact of air pollution on human health [2,3,7,8,9,10,11,12,13,14]. Suspended dust is particularly dangerous and harmful in terms of both short- and long-term effects. The condition of atmospheric air in a given area is the result of the influence of various local and regional factors [15,16]. The dominant share is the size and density of emission sources of individual air pollutants and regional weather conditions [17]. Key factors also include terrain and inflow emissions, e.g., emissions outside the area where the current air quality is analyzed. The current state of air pollution results from human activity and natural conditions. Working to improve air quality is a multi-year process. Poland faces the task of implementing projects to improve air quality, primarily to reduce air pollution emissions [18,19,20,21,22]. The existing regional policy has resulted in a reduction in emissions from industrial sources. Despite a significant reduction in air pollution over the last few decades, the air quality in the Małopolskie Voivodeship is far from EU standards [23,24]. Since the previous Supreme Audit Office’s audit on air protection issues, Poland’s situation compared to official EU norms has not improved dramatically [25]. Taking into account air pollution of PM₁₀, PM_2.5, and benzo(a)pyrene (B(a)P), Poland is one of the EU countries with the worst air quality [26,27]. The data from the 2017 report of the European Environment Agency (EEA) show, among others, that in 2015, among the 28 EU countries, the most frequent exceedances of daily PM₁₀ concentrations (on a national scale) occurred in Bulgaria, followed by Poland. In turn, in the case of PM_2.5 and B(a)P, the annual concentrations of these substances in 2015 placed Poland first among the most polluted EU countries [28,29]. It is necessary to continue corrective actions in the individual consumer sector, especially in those zones of the Małopolskie Voivodeship where the permissible levels for PM₁₀ and PM_2.5 and the target levels for B(a)P are exceeded. Reports of the European Environment Agency [30,31,32] indicate, among others, that air quality is a very important issue in the sphere of public health, the economy, and the environment [30,31,32]. Recent years indicate that emissions of air pollutants, such as sulfur dioxide, carbon monoxide, benzene, and lead have been significantly reduced in Europe [33,34]. Despite this, suspended dust particles, ozone, reactive nitrogen substances, and some organic compounds still pose a serious threat [34,35]. Household combustion of fuels such as coal and wood has been identified as an important source of direct air emissions of suspended dust and Polycyclic Aromatic Hydrocarbons (PAHs), which are carcinogenic. On a European scale, air pollution from suspended dust and ozone currently has the most harmful impact on human health. Data presented in these reports indicate the estimated population of inhabitants of urban areas exposed to air pollution levels exceeding the values recommended by the EU and WHO [30,31,36,37].

Developing air quality legislation is an important aspect of environmental protection. The first attempts at regulation in this area took place in the 1960s. The United States was a pioneer in shaping air quality regulations, establishing the Environmental Protection Agency (EPA) in 1970 [30,31]. In Europe, initiatives to regulate air quality have become increasingly important as problems with smog and air pollution have become more prominent. The European Union developed the Ambient Air Quality Directive, which was adopted in 2008 and contains provisions for monitoring and assessing air quality in the Member States [9,32]. The World Health Organization (WHO) presented new air quality guidelines in 2021. These guidelines established more stringent air quality standards, including limits for major air pollutants such as PM_2.5, PM₁₀, and nitrogen oxide (NO₂).

Directive 2008/50/EC of the European Parliament of 21 May 2008 [23] is an important legal instrument of the European Union that aims to protect human health and the natural environment by establishing air quality standards and limiting emissions of harmful substances. This directive was adopted in response to the need to address ambient air pollution, which constitutes a significant threat to public health and the environment [23].

According to the assumptions of the directive, the main goal is to ensure high air quality that does not threaten human health or the natural environment. To this end, the directive sets air quality standards for various substances, such as sulfur dioxide, nitrogen oxides, PM₁₀ and PM_2.5, benzene, lead (Pb), and many others. It also introduces limits on the concentrations of these substances that should not be exceeded to ensure the protection of public health.

Directive 2008/50/EC [23] requires Member States to monitor air quality in their territory and report the results to the European Commission. This monitoring includes tracking the concentrations of harmful substances in the air and making this information publicly available. Where air quality standards are exceeded, Member States are obliged to develop action plans to improve air quality, which may include measures such as reducing pollutant emissions, promoting alternative energy sources, or encouraging the use of public transport.

In light of the above assumptions, Directive 2008/50/EC [23] is an important regulatory instrument aimed at improving air quality in the European Union. Its introduction has a significant impact on the actions taken by Member States in the field of environmental protection and public health, and its effective implementation may contribute to reducing the negative effects of atmospheric air pollution on society and the natural environment.

Research on air quality in Poland has a long history and is an important element of monitoring the state of the environment [28,38,39]. Currently, by applicable European Union law, the results of air quality measurements are used to assess the air condition, and the collected data are transferred to the European Commission and made available to a wide range of stakeholders [23]. Over the last ten years, the emission of measured pollutants has significantly decreased, but the problem of air pollution remains relevant [24,28]. The most current problem is the level of suspended dust: although lower than a dozen or so years ago, it remains high.

In the process of integration with the European Union, Poland adapted the monitoring system to the EU requirements in the Environmental Protection Law (EPL) and the implementing regulations [23]. Air quality measurements in Poland are carried out as part of the State Environmental Monitoring (PMŚ), and the responsibility for its implementation rests with the Environmental Protection Inspection.

With the entry into force of the “Act on the State Inspection of Environmental Protection” in 1991, a two-instance institution for control and examination of the state of the environment was established [28]. The rules for conducting inspections were unified in all voivodeships and uniform measurement methodologies were developed and implemented. During that time, the laboratory potential of provincial inspectorates was developed, enabling research on the state of the environment and reliable control of compliance with ecological law. Rules for the operation of the State Environmental Monitoring were also developed and a program of activities aimed at counteracting extraordinary environmental threats was undertaken. A system for controlling the cross-border movement of waste was also created [24].

The basic legal provision regulating air quality issues in Poland is the Act—Environmental Protection Law [40]. Air quality is determined by the content of pollutants, e.g., specific substances (gaseous or solid) that are present in the air in quantities higher than those required by the standards contained in applicable regulations. The most common air pollutants in Poland are sulfur and nitrogen compounds, carbon dioxide, and fine dust. Every year in Poland, air quality is assessed in terms of its pollution with 12 substances: sulfur dioxide, nitrogen dioxide, carbon monoxide, benzene, and ozone; suspended dust PM₁₀ and PM_2.5; and pollutants determined in PM₁₀ dust: lead, arsenic, cadmium, nickel, and benzo(a)pyrene.

Purpose of Research

The conducted literature review allows us to conclude that most articles related to the topic of pollution focus on establishing correlations between demographic and economic indicators along with air pollution. A significant portion of them concentrate on time series analysis, examining changes in pollutant concentrations over specific periods. Few articles focus on the issue of the types of fuels being burned [41,42,43,44,45]. This is surprising because the majority of air pollution originates from inefficient combustion of various energy fuels. The dominant share of articles is related to CO₂ emissions into the atmosphere [42,46]. One can infer that most articles are based on models or develop models that link pollution in various ways to demographic or health indicators, or directly to demographic data [30,31,32,33,34,43,45,47]. The problem of particularly polluted zones is highly complex. A conducted case study revealed that around the world, specific meso- or micro-regions could be identified that were especially burdened by air pollution [43]. Caring for sustainable development does not only involve national efforts to protect the air or the environment but must also consider particular regions that are more susceptible to the accumulation of pollutants. On a European scale, the Małopolskie voivodeship is one of the most polluted regions, with pollution comparable to certain areas in Romania, Kazakhstan, India, or China. A certain number of articles focus on selected fuel groups in the context of their combustion and the subsequent impact on the atmosphere [41,42,45,46,48]. For this reason, a research topic was undertaken, focusing on the relational analysis of specific air pollutants versus household energy consumption. Unlike other articles of this type, this article is entirely dedicated to a major emission sector in the Małopolskie Voivodeship, which is individual users. Most articles collectively address the relationship between general energy consumption and pollution [42,43,46,48]. According to the authors of this study, this is an oversimplification because industrial development is subject to statutory regulations, while the private sector is being reformed very slowly and inadequately. Additionally, the private-user sector is the worst possible emitter of pollutants because it is decentralized, which directly impacts the pollution levels.

Before the actual commencement of analytical work, a literature study was carried out on the topic of air pollution in the Małopolskie Voivodeship. The articles on this research topic encompass a broad spectrum of contexts. Air pollution has been contextualized alongside various issues such as economic development [49,50,51], public health [52,53], the economy [50,54,55], and energy consumption [56,57,58,59]. These articles can be categorized into statistical [60,61] and comparative studies [62,63], analytical studies involving time predictions [64], and more comprehensive studies providing a broader perspective on the problem [50,52,59,65,66]. However, most of the literature consists of purely statistical [60,67,68] or nationwide studies [51,61,62]. Numerous scientific studies have addressed the issue of air quality in Poland. Scholars have conducted analyses of air quality trends over different periods and geographical areas [49,69], while also examining the health effects of pollution. Of particular concern are rising levels of PM₁₀ and PM_2.5, which pose an escalating challenge due to their increasing presence in the atmosphere [51,64,65,67,70,71,72,73,74,75]. Despite overall improvements in air quality across Europe, including Poland, ongoing research suggests that urban air pollution continues to have a notable impact on population health [52,53]. The studies scientifically reveal a rising interest in household emissions, observing the potential of dispersed emissions. They place significant importance on household energy efficiency and optimizing domestic energy use [49,50,61,70,76]. However, there is a lack of research comparing the level of pollution with energy consumption in households, especially concerning specific types of fuels over a longer period. The mentioned lack of research is particularly important for the Małopolskie Voivodeship, which, compared to other voivodeships, faces a constant problem of air pollution. Households constitute a specific energy flow system [33]. On the one hand, energy is supplied in the form of electricity and gaseous fuel; on the other hand, this energy is produced from available solid and liquid fuels. At the same time, households constitute a group of dispersed emitters that are still in the process of modernizing heating installations [25,36,39,50,53,54,55,57,59,72,73,74,75,77]. Based on data from the Chief Inspectorate of Environmental Protection for the years 2010–2017, the Supreme Audit Office prepared a summary report on air pollution in Poland [28,30]. It showed that the most common reasons for exceeding the permissible levels of PM₁₀ and B(a)P were emissions related to individual heating of buildings. The report showed that air quality depended on the region. Southern Poland had the highest levels of pollution [25,39]. In 2017, the Małopolskie Voivodeship had the largest number of days who exceeded the air standards (Figure 1). From an analytical point of view, the relationship between air pollution concentrations and household energy consumption (HEU) is very important. Most forecasts indicate that the demand for energy in Poland will increase, which, given the increase in energy prices from ecological sources such as electricity, may become a factor causing an increase in the use of solid fuels. In 2017, there were radical changes in the share of energy fuels used in the individual consumer sector [28].

The share of coal on the market decreased significantly, which increased the share of fuels that were to become an alternative to conventional energy carriers. Biomass and other fuel products constituted a large share of this group. As part of the query, data were obtained on energy use in households about three groups of fuels used. Coal, liquid fuels, and the general category of fuels defined as “other” were selected as the most emissive. In the “coal” group, we included hard coal, brown coal, and coke. In the “liquid” group, we included electric energy, heat from the central heating network, natural gas, liquefied gas (propane–butane), and heating oil. The last group, “other”, consisted of firewood, other types of biomasses, solar energy, and heat pump. The last group was the most interesting for two reasons. The first element was its constantly growing share in the energy balance of households, while the second was its diversity in terms of fuel material [3,7,8,10,51,57,59,73,74].

The topic of energy consumption is a growing issue across Europe as well as in Poland. Southern Poland is particularly affected by air pollution, especially during the winter months. Most of the studies analyzed during the thematic search dealt with the relationship between household energy consumption and air pollution, but they addressed the issue holistically without a deep analysis of specific types of fuel. This article focuses on exploring the relationship between selected types of household energy fuels and air pollution. The selected thematic emphasis differentiates this article from others examining air quality issues within the Małopolskie Voivodeship.

A literature study revealed a significant percentage of publications indicating a direct relationship between household energy consumption and carbon dioxide emissions. Correlational models between household energy consumption in relation to specific fuel groups are present in the literature [41,44]. Energy consumption and production are major factors contributing to air pollution, as confirmed in various publications [41,42,46]. Research conducted within diverse analytical frameworks has confirmed the link between energy consumption and air pollution [43,44,48]. Most articles attempt to connect air pollution with numerous economic or demographic aspects. A considerable portion of the literature analyzes air pollution concerning the natural geographical conditions present in a given area.

A smaller article, based on the achievements of Indian authors, sought to connect household energy consumption with air pollution. The primary justification for the chosen methodology was the characterization of households as dispersed emitters [45]. This article, unlike other thematic publications, focuses directly on most components of particulate matter PM₁₀ individually, which is lacking in the conducted literature study [43,46]. It also considers the correlation of sulfur oxides, nitrogen, and PM_2.5 particulate matter [45,47]. The authors intend to focus solely on energy aspects since, beyond the many factors that may influence the concentration of air pollutants, the burning of fuels in households is a fundamental factor directly affecting air quality in rural areas. Another assumption of the article was the ability to categorize energy sources into groups because the current state of the Polish fuel market is undergoing transformation, often resulting in households utilizing two or more energy sources. This leads to the division proposed in the section related to the definitions of HEU-O and HEU-C groups.

2. Materials and Methods

2.1. Data on Air Pollution

Most air pollution is related to human activities [7,16,38]. Anthropogenic sources can be divided into four basic categories: energy, industrial, communication, and municipal–domestic sectors [9,23,28,29].

Due to how pollution spreads, sources can be divided into point sources (chimneys), linear sources (communication routes, sewage channels), and surface sources (a collection of open reservoirs or a residential district with small chimneys on the roofs). Emissions of the main air pollutants in Europe decreased in the period 2002–2021 [31,33,34,35,36]. Lower emissions resulted in improved air quality in relation to some air pollutants. In turn, in some sectors of the economy, there has been a noticeable increase in emissions of certain air pollutants, by approximately 7% since 2002, as is the case with dust emissions from fuel combustion in businesses, institutions, and households [18].

The mentioned economic sectors are the main dust emitters throughout the European Union. Furthermore, in 2011, eight Member States exceeded one or more of the limits set by EU legislation [23,32]. A significant reduction in emissions occurred in the analyzed period for sulfur dioxide, carbon monoxide, and lead. However, due to the complicated mechanisms occurring between emissions and air quality (which include the height of the emitter, physicochemical transformations, meteorological conditions, topography of the area, etc.), emission reduction is not always reflected in reduced pollutant concentration values, especially for dust and ozone [6].

For example, despite significant reductions in ozone precursors in Europe, there was little increase in ozone concentrations (relative to the health target) between 2002 and 2011. A significant proportion of the population in Europe lives in cities where air quality standards are regularly exceeded [6,39]. Dust (PM) and ozone (O₃) are pollutants that are particularly hazardous to health [8,10]. Exposure to high levels of organic pollution, especially PAHs, is also becoming an increasingly important issue in Europe [9,32]. It is worth emphasizing that the systematic development of the Polish economy over the last 20 years, expressed in the growth of the Gross Domestic Product (GDP), has not increased air pollutant emissions [28,29].

This is the result of the increasingly widespread use of pro-ecological technologies in industry, energy, and transport. In the municipal and residential sectors, due to the amount of emissions, programs are being carried out more and more often to replace heating with more ecological ones, but in some places, it is still the most burdensome emission [37,39]. Between 2005 and 2020, there was a downward trend in emissions of all key air pollutants in the EU despite an increase in gross domestic production over the same period [6]. The main source of PM₁₀ and PM_2.5 dust in 2020 was energy consumption in residential, commercial, and institutional buildings. In addition, road transport and the manufacturing and mining industries also had an impact on emissions, while agriculture was an important source of PM₁₀ dust. Since 2005, PM₁₀ and PM_2.5 emissions have decreased by 30% and 32%, respectively [6]. Regular monitoring of air quality allows for the continuous control and assessment of the air condition in a given region [39]. Long-term analysis of these data allows us to determine trends in air pollution levels. The more accurate the information on emissions and immissions of substances in the air, the better we can understand the situation and take appropriate actions to improve air quality. The following subsection describes the actual state of air pollution in 2008–2022 [15]. It presents a series of graphical summaries showing the change in the concentration of suspended dust and individual components of the mentioned pollution. The one of the most disturbing indicators is the graph of the concentration of benzo(a)pyrene (Figure 2b) in suspended dust PM₁₀. The concentrations of heavy metals and nitric oxide were visualized using graphs (Figure 2a,c–e). Additionally, the graph of suspended dust concentrations PM₁₀ (Figure 2g) and PM_2.5 (Figure 2h) deserves the greatest attention. It is particularly dangerous to health, and its concentration exceeds European standards.

The presented graphics clearly show that the concentration of pollutants has been decreasing in the Małopolskie Voivodship over the years. As can be seen from the charts provided, air purification as a result of implementing appropriate actions is a long-term process that, even now, has not achieved sufficient effectiveness in meeting European standards. In the case of suspended dust PM₁₀ and PM_2.5, we are dealing with constant exceedances, especially in winter. Averaged annual data indicated that these standards were constantly exceeded. A similar situation was related to the concentration of benzo(a)pyrene in the atmosphere of the Małopolskie VoivodshipIn the case of heavy metals, a constant decrease and concentration in suspended dust was observed in the years 2008–2019.

Their concentrations did not exceed European standards, which on average for heavy metals are approximately 5 μg/m³. The mentioned decline, which had persisted for years, was disturbed in 2020, where in the case of nickel and arsenic, an increase in concentration values could be observed in a two-year cycle.

These increases did not result in exceeding the standards, but they indicated a certain upward trend, which may soon be an indicator of increases in other heavy metals such as cadmium or arsenic. Their concentrations in PM₁₀ dust did not seem to pose a threat of exceeding the permissible standards in the coming years, but this was a certain indication of changes in pollution generated by local industry.

Observing the charts, it can be concluded that actions to improve air quality were significant but insufficient. The biggest problem in the Małopolskie Voivodship still seemed to be the mismatched support policy related to, among others, the individual boiler replacement program.

2.2. Data on Energy Consumption in Households

This article focused on the problem of emissions related to energy consumption in households. According to the Supreme Audit Office (SAO) [26,27], two factors are responsible for the annual poor state of air quality in the Małopolskie Voivodship. The first one is the heating of households in winter, while the second one is linear transport. According to the above-mentioned SAO report, actions taken at the provincial and local levels have not yet brought the expected results in the form of reducing pollutant emissions.

The Małopolskie Voivodship has dense buildings, which means that emissions are dispersed. This intensifies the process of atmospheric air pollution. After 2017, the share of fuels used by households changed. The share of energy obtained from materials that constituted a competitive offer compared to classic coal increased. They were classified as “other” fuels by the Central Statistical Office (CSO). Apart from those mentioned, there are two additional groups on the market: carbon fuels and liquid fuels [26,27]. For the purposes of this article, they were marked as HEU-C (coal), HEU-L (liquid), and HEU-O (other) [27,33,39,49,51,55,57,59,60]. The mentioned changes became the basis for analyzing the emission characteristics of the mentioned classification group. This analysis is important because the consumption of this group is growing and will probably dominate in future years. Currently, the energy obtained from it constitutes over 20% of the total energy used in households. Coal should be replaced from the market with fuels that must guarantee emission reduction (Figure 3).

The answer to the above statement was obtained by comparing data on energy consumption and pollutant concentrations. Using regression models, it was possible to approximately answer the question of whether there was a relationship between energy use in selected fuel groups and individual concentrations of PM₁₀ and PM_2.5 dust components. Additionally, the emission characteristics between the discussed fuel groups were interesting. Finally, using the mentioned models, it was possible to estimate the level of pollutant concentrations with increasing energy consumption in selected HEU-C and HEU-O groups.

The data came from official reports of the Central Statistical Office (GUS) [26,27] and the Institute of Meteorology and Water Management–National Research Institute (IMGW-PIB) [15]. Information on concentrations was obtained from all measurement stations in the Małopolskie Voivodship from 2008 to 2022. They were used in two ways. They were used to determine the general characteristics of air pollution in the Małopolskie Voivodship and to build regression models.

At the initial stage, the following models were used: polynomial, power, exponential, and logarithmic. After observing the fit indices of the regression models, polynomial and power models were selected for the final analyses.

Empirical relationship models were constructed based on data on energy consumption in households from 2010 to 2021 and time-matched data on air pollution concentrations. For this purpose, the concentration data were averaged into uniform time series reflecting their annual changes. Whole procedures were conducted using Python-Pandas 2.2 module. This way, it was possible to compare them with average annual energy consumption data. The concentration data were scaled according to the group of home users, whose market share was 41% for PM₁₀ and 49% for PM_2.5 [39].

2.3. Analytical Tools

Regression is a statistical method that determines the relationship between a pair of variables or more independent variables [77]. Linear regression assumes a linear relationship between variables, while nonlinear regression allows for more complex relationships using nonlinear functions [78,79].

Nonlinear relationships can take the form of various mathematical functions. Their basic forms are polynomial, exponential, logarithmic, or trigonometric functions. Their choice depends on the types of data and the research problem undertaken. The basic task of nonlinear regression is to find the best-fitting parameters of the selected function in an iterative process. This is achieved by minimizing the sum of squared errors between the predicted and actual values [80].

The least squares method (LSM) was used to perform the regression analysis. This algorithm is widely used in regression analyses to optimally adjust the parameters of a mathematical model to a given set of data. This is an effective approach for models with linear parameters. It involves the iterative minimization of the sum of squares of differences between the input data and the corresponding values calculated on the basis of the adopted model. Given data in the form of a set of points (xᵢ,yᵢ), where xᵢ is the initial variable and yᵢ is the output variable, it is possible to estimate the parameter (β) of the model (f(xᵢ, β)) [81]. The mentioned calculations can be performed for a linear or nonlinear model. Nonlinearity does not negate the use of the LSM model due to a trivial transformation called linearization of the dependency function.

Linear, exponential, and polynomial function regression analyses were conducted to examine whether there existed a relationship between the concentration of air pollutants and householder energy usage in the Małopolskie Voivodship. The most appropriate regression model was chosen for each air pollutant and energy data type. All the regression analysis was performed with the use of Python’s SciPy library.

3. Results and Discussion

This section presents the results of regression analyses for individual components of air pollution and household energy consumption (HEU). The calculated correlation coefficient (R) is included in the composite graph (Figure 4,

Table 1. Statistical parameters of estimated regression models for HEU-C.

	HEU-C
Pollutant	Equation	Coefficient of Determination, R2	Correlation Coefficient, R	p-Value *
AS	ln(Y) = 0.0081656084 × X − 1.9431685	0.678	0.823	0.001
BaA	ln(Y) = −0.0022706025 × X + 2.123752	0.037	0.193	0.548
BaP	ln(Y) = 0.004448441 × X + 0.82712642	0.684	0.827	0.001
BbF	ln(Y) = 0.010787225 × X − 1.514722	0.682	0.826	0.001
BjF	ln(Y) = 0.011675137 × X − 1.9402408	0.338	0.581	0.131
Cd	ln(Y) = 0.010167501 × X − 3.0181146	0.657	0.81	0.001
Ni	ln(Y) = 0.0083546791 × X − 1.7209117	0.561	0.749	0.005
NO2	ln(Y) = 0.0020115257 × X + 2.6877998	0.553	0.744	0.006
NOx	ln(Y) = 0.0021108913 × X + 3.4195439	0.457	0.676	0.016
Pb	ln(Y) = 0.010381718 × X − 6.6490436	0.771	0.878	<0.001
PM10	ln(Y) = 0.0057372885 × X + 1.2429274	0.841	0.917	<0.001
PM2.5	ln(Y) = 0.0072697594 × X + 0.75934121	0.758	0.871	<0.001
SO2	ln(Y) = 0.0067546475 × X + 0.36381761	0.813	0.902	<0.001

* p-values indicate how incompatible the data are with a specific statistical model.

and

Table 2. Statistical parameters of estimated regression models for HEU-O.

HEU-O
Pollutant	Equation	Coefficient of Determination, R2	Correlation Coefficient, R	p-Value *
AS	Y = 3.663833 − 0.02459442 × X + 5.3225431 × 10−5 × pow(X,2)	0.801	0.895	<0.001
BaA	Y = 18.750404 − 0.17767996 × X + 0.0005075984 × pow(X,2)	0.117	0.342	0.277
BaP	Y = 12.131281 − 0.044516153 × X + 8.4178022 × 10−5 × pow(X,2)	0.388	0.62	0.03
BbF	Y = 12.150257 − 0.080330643 × X + 0.00016313151 × pow(X,2)	0.605	0.778	0.003
BjF	Y = 13.663362 − 0.11565859 × X + 0.0002766035 × pow(X,2)	0.563	0.751	0.032
Cd	Y = 1.9187541 − 0.010791667 × X + 1.9520742 × 10−5 × pow(X,2)	0.454	0.674	0.016
Ni	Y = 3.5342748 − 0.017647773 × X + 3.5070037 × 10−5 × pow(X,2)	0.157	0.396	0.202
NO2	Y = 48.850372 − 0.28720691 × X + 0.00076877484 × pow(X,2)	0.539	0.734	0.007
NOx	Y = 121.56022 − 0.8223544 × X + 0.0022021104 × pow(X,2)	0.734	0.856	<0.001
Pb	Y = 0.072237366 − 0.00054926434 × X + 1.270877 × 10−6 × pow(X,2)	0.626	0.791	0.002
PM10	Y = 36.956761 − 0.22437645 × X + 0.00051324584 × pow(X,2)	0.668	0.817	0.001
PM2.5	Y = 34.674047 − 0.19433828 × X + 0.00038722552 × pow(X,2)	0.709	0.842	0.001
SO2	Y = 21.669981 − 0.13737027 × X + 0.0003111479 × pow(X,2)	0.647	0.805	0.002

* p-values indicate how incompatible the data are with a specific statistical model.

). The correlation coefficient describes the strength of the relationship between two variables. It can reach values between −1 and 1. A coefficient of +1 indicates a perfect positive correlation.

The regression analysis was optimized to achieve the highest possible level of model fit to the data, with a satisfactory level of statistical significance. The resulting statistical parameters of the models are presented in tabular form (Table 1 and Table 2). Regression curves were plotted as distinct series corresponding to the analyzed fuel groups. It is evident that the trend between pollutant concentrations and HEU-C was increasing, regardless of the PM₁₀ or PM_2.5 particulate matter. This suggested that under current conditions of household energy consumption, HEU-C usage had a substantial potential for emitting air pollutants. The mentioned forecast was supported by the fact that, excluding BaA, BjF, and NO_x, the correlation coefficient (R) values for the remaining pollutants were greater than 0.7 (Figure 5). Additionally, more than half of all correlated pollutant components exhibited relatively high R² values (>0.5), indicating that the regression models adequately (for PM_2.5, PM₁₀, SO₂, Pb) or moderately (As, BaP, BbF, Cd, Ni, NO₂) explained the dependency of concentration on HEU-C (Figure 6). Furthermore, the p-value, determining the level of statistical significance, was low in the majority of cases (<<0.05).

If p-value ≤ 0.05, it indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null hypothesis (no relationship) is correct. In the case of the 13 types of pollutants analyzed, in 50% of cases, the p-value was less than or equal to 0.001 (Table 1).

For each element/chemical compound comprising PM₁₀ and PM_2.5 particulate matter, exponential relationships are provided in Table 1.

Comparing the correlation coefficients within groups, it can be observed that the most significant differences between the “coal” fuel group (HEU-C) and the “other” group (HEU-O) occurred for BaP, BjF, NO_x, and Ni. Unfortunately, in the case of PM₁₀ and PM_2.5, the differences in R were not as pronounced. On this basis, it can be estimated that the elimination of carbon fuel may contribute to a decrease in BaP and Ni; however, we can expect that a growth in HEU-O consumption would increase NO_x and BjF pollutants.

The statistical models presented in the article should be treated as estimates due to the dynamic changes in energy consumption levels observed in recent years (Figure 3), especially in 2018–2021. The availability of different fuels in the household sector depends on regional policies, economic conditions, and technical considerations related to their use. Overall, lower emission fuels face greater challenges in terms of competitiveness compared to carbon-based fuels. It is possible that as a result of market uncertainty, the share of carbon fuels may increase, which may have a negative impact on air quality in the Lesser Poland Voivodeship. The pre-pandemic level of market share in household energy generated from HEU-O suggested that this group of fuels had the potential to dominate over HEU-C.

This was particularly visible in 2018 and 2019. In 2018, both groups had a comparable share in the household energy consumption market. A year later, the HEU-O group had a 25.84% share, a 2% increase compared to HEU-C. The demotion of the discussed growth in favor of HEU-C fell in the middle of the COVID-19 pandemic. This revealed a general decrease in the share of the HEU-O and HEU-C groups in the energy consumption mix of households and a 6% decrease in HEU-O compared to HEU-C. The market share of both groups is on a downward trend, but the fact that HEU-C is growing is disturbing.

The years 2010 to 2017 were very similar in terms of energy consumption in the HEU-O and HEU-C groups. They oscillated around the value of approximately 50% of the entire household energy consumption market, in the proportion of 13% HEU-O compared to 33% HEU-C.

After 2018, there was a significant increase in HEU-O to an average of approximately 23% and a decrease in HEU-C to 24%. This was the period of the largest amplitudes of changes, reaching 10% in the case of HEU-O and 7% for HEU-C. The discussed decreases amounted to approximately 40–50 PJ on average. Assuming that the highest levels of market share of the HEU-C and HEU-O groups in 2018 were approximately 250 PJ and the amplitude of changes was approximately 50 PJ, the pollution levels for selected PM₁₀ and PM_2.5 components were calculated at the level of 300 PJ for the HEU-O and HEU-C groups. A growth scenario was assumed due to the fact that energy consumption on farms is constantly growing, and the percentage share in the market of the HEU-C and HEU-O groups will probably remain at the level of approximately 30–40%.

Thanks to the performed estimations, it was easy to assess the concentration values of individual pollutants with the increase in energy consumption. Due to the persistent issue of pollution exceedances in the Małopolskie Voivodship, this article allowed us to determine how the values of PM₁₀, PM_2.5, and BaP (Figure 7) would change with the expected increases in energy consumption. Nickel (Ni) and benzo(a)pyrene (BaA) were excluded from the calculations due to their low R² coefficient.

The chart in Figure 7 illustrates that an increase in energy consumption up to 300 PJ for HEU-O would result in overall reductions in air pollution compared to a similar level of energy consumption for HEU-C. The precise percentage values for individual pollutants are presented in

Table 3. The table shows the calculated values of pollutant concentrations in the HEU-C and HEU-O groups in the case of an increase in energy consumption to 300 PJ, along with the percentage differences.

Pollutant	HEU-O (300 PJ)	HEU-C (300 PJ)	[%]
AS	1.659	−24.796	1.659
BaP	8.686	−25.278	8.686
BbF	5.593	−51.186	5.593
BjF	4.770	−16.139	4.770
Cd	1.033	−55.164	1.033
NO2	26.877	18.579	26.877
NOx	57.559	26.757	57.559
Pb	0.029	−47.553	0.029
PM10	19.378	−17.812	19.378
PM2.5	18.921	−43.607	18.921
SO2	10.916	−21.673	10.916

. The analyses revealed increases in NO₂ and NO_x of 18% and 26%, respectively. The most substantial decreases were observed for BbF, Cd, Pb, and PM_2.5. The conducted analyses indicated that increasing the share of HEU-O in the energy market was necessary to reduce air pollution in the Małopolskie Voivodship. It also revealed that the HEU-O fuel group had an elevated potential for emitting NO_x and NO₂ compared to HEU-C.

The mentioned percentage differences were calculated based on regression models for each of the pollutants. The procedure consisted in calculating the percentage difference in relation to the level of pollution generated by HEU-C fuel, according to the equation:

$$Pd={\displaystyle \frac{HEU-C_{P300\left(i\right)}-HEU-O_{P300\left(i\right)}}{HEU-C_{P300\left(i\right)}}}\times 100\%$$

(1)

Figure 7 illustrates that utilizing HEU-O at a level of around 300 PJ resulted in a reduction of 9 out of 11 air components by specific percentages. These values fell into three groups. The first group, related to nitrogen oxides (NO_x, NO₂), showed a 20% increase in emissions compared to HEU-C. A second group, with an average reduction of about 20%, included BaP, As, SO₂, PM₁₀, and BjF, while the third group, comprising Cd, BbF, Pb, and PM_2.5, predicted a 50% decrease in pollution levels.

4. Conclusions

The analysis of the relationship between energy consumption in households and air pollution levels plays a crucial role in the context of sustainable development. The regression models presented in this article should be seen as estimations, given the dynamic changes in energy consumption levels observed in recent years. A long-standing issue with air pollution, particularly in the Małopolskie Voivodship, underscores the importance of understanding how energy consumption, driven by the choice of fuels, influences pollution levels. The distribution of available fuels in the household sector is shaped by regional policies, economic conditions, and technical considerations related to their use, with lower-emission fuels often facing greater competitive challenges compared to carbon-based fuels. As the article highlights, the possibility of an increased share of carbon-based fuels due to market uncertainties could have negative consequences for air quality in the region. The primary finding suggested that the regression models derived for PM₁₀, PM_2.5, and BaP offered a solid predictive framework, validated by appropriate statistical parameters. Additionally, the analysis indicated that the HEU-O fuel category had a heightened potential for NO_x and NO₂ emissions compared to HEU-C. The study also demonstrated that the use of HEU-O at approximately 300 PJ resulted in a reduction of 9 out of 11 air pollutants, categorized into three groups. The first group, comprising nitrogen oxides (NO_x, NO₂), showed a 20% increase in emissions compared to HEU-C. The second group, including BaP, As, SO₂, PM₁₀, and BjF, suggested a 20% reduction, while the third group, consisting of Cd, BbF, Pb, and PM_2.5, predicted a significant 50% decrease in pollution levels.

The analysis draws attention to the importance of education and the demonstration of the impact of energy sources used, as exemplified in the Małopolskie Voivodship, where air pollution has been a persistent issue. Understanding the relationship between energy consumption and air pollution levels can help shape appropriate consumer behaviors, which is critical for improving air quality. Furthermore, assessing household energy consumption provides valuable data for shaping energy and environmental policy, not only in the Małopolskie Voivodship but across Poland. The findings could serve as a foundation for introducing regulations on the trade of energy fuels and for conducting awareness programs aimed at moving away from fossil fuels. In addition to compliance with European and local policies, the cost of energy fuel remains a decisive factor for households. However, the recent increase in the percentage of HEU-O in the total energy consumption market suggests that this group has the potential to exceed HEU-C. The article’s focus on a wide spectrum of suspended dust components, correlated with household energy consumption, sets it apart from other studies related to air pollution. This work, which stands out in the literature, is one of the few studies that analyze data from households, considered a form of diffuse emission.

In conclusion, the article contributes to the discourse on sustainable development by demonstrating that reducing air pollution can be achieved through a deliberate transition from carbon-based fuels to alternative fuel groups. These alternatives must be strategically selected to align with the specific heating patterns and regional characteristics of the population under study. By considering both environmental and socio-economic factors, this transition can ensure a sustainable energy policy that supports public health, environmental protection, and economic growth, contributing to local sustainable development goals and improving the quality of life in Europe.