1. Introduction
It is widely recognized that air pollution is a major environmental issue that poses a significant threat to public health on a global scale. Numerous studies and assessments have indicated that air pollution has adverse effects on health, and it is regarded as one of the leading global challenges [1,2,3]. Over the past few decades, several protocols and conventions have been established to address harmful emissions and to enhance air quality on national and global levels. The most significant improvement has been made in developed countries, mainly in Europe, North America, and Japan [4]. Low- and middle-income countries are most affected by unregulated sources of emissions and industrial-expansion prioritization, usually followed by the effects of rapid urbanization and overpopulation in urban areas. Among various air pollutants, particulate matter (PM), particularly PM2.5, is highly dangerous. Its impact on morbidity and premature mortality has been well-documented [5,6,7]. Furthermore, ground-level ozone and nitrogen dioxide are commonly known for their detrimental impact on human health [8]. Although developed countries have attempted to decrease air pollution, the mentioned pollutant levels still exceed the air-quality guidelines recommended by the World Health Organization (WHO) and other national and international laws that aim to protect public health. The most-known effects on health include respiratory and cardiovascular disease, lung cancer, central nervous system dysfunctions, cutaneous diseases, problems with reproductive health and pregnancy, and cognitive problems from an early age [8,9,10,11,12]. It is challenging to fully comprehend all of the various impacts and consequences of polluted air on ecosystems, climate systems, and human health. A question that arises from these facts is how individuals perceive the risks associated with air pollution and how it impacts their behaviors. Additionally, the examination of any variations in insights and among societies, together with the underlying factors that influence these differences, is important.
This study investigated the awareness of and the behaviors related to exposure to air pollution in Serbia. The population in Serbia is exposed to air pollution levels that exceed the limits set to protect public health, particularly during the winter when solid fuels are burned for heating purposes. In addition, emissions from industry and traffic significantly contribute to air quality [13]. Countries in southeastern Europe, including Serbia, have negative records of pollutant concentrations, particularly regarding PM [14]. Juginović et al. [15] showed that the highest population-weighted PM2.5 concentrations are in the countries in this part of Europe (such as Macedonia, Bosnia and Herzegovina, Serbia, Montenegro, and Bulgaria), leading to higher values of disability-adjusted life years and years of life lost (which are far above the European average). The estimated impact of population exposure to ambient PM2.5 concentrations for 11 cities in Serbia includes nearly 3600 premature deaths yearly [16]. Stanojević et al. [17] showed an increasing trend in urban population exposure to PM2.5 in Serbia.
There are many developing efforts to create policies that reduce emissions and protect public health. Based on a nationwide survey, a set of multidimensional drivers and determinants was used in this study to gain insight into the public’s perceptions and behaviors related to air pollution in Serbia. This study aims to bring attention to the issue and to provide evidence to help future decision-makers.
2. Literature Review
An increasing number of studies focus on how individuals perceive air pollution and ways to minimize exposure and enhance public health. Pignocchino et al. [18] emphasized that human behavior directly relates to how environmental risk is perceived based on the mutual actions of an entire set of factors and determinates. Socio-demographic characteristics of the population are most often identified as factors that influence the perception of air quality [19,20,21,22]. Likewise, local environment features (proximity to sources of pollution such as industry, traffic, etc.) and individual experiences due to exposure to pollution are important for perceived risk [18,23,24,25,26]. Certain studies suggest that people’s behavior is influenced more by their perception of air pollution than by the actual air quality levels measured by monitoring stations [27]. Ramírez et al. [28] noticed that a city’s population size is a predictive factor for perceived risk. In Colombia, they found a relationship between the population size of a residential area and the perception of emission sources, and that local sources of pollution (e.g., forest fires and mining) have more relevance in small cities; industry has more relevance in mid-size cities; and road traffic has more relevance in big cities. Additionally, their findings indicated that even in the absence of measurements, in small and mid-size cities people are aware of air pollution problems due to some visual observations (unpleasant smells and smog) that can be further used as provisional indicators for local air-quality management and monitoring. On the contrary, the conducted research for the city of Mexicali, Mexico, showed inconsistency between the perception of health effects and observed exposure levels; the level of perception of health risks was particularly low in city zones populated with vulnerable groups and having low air quality [22]. Reames and Bravo [29] also found an inverse spatial pattern between exposure to PM2.5 and O3 and residents’ concerns about health due to worsening air quality in the metropolitan area of Kansas City (US). Interestingly, rural areas are often perceived as places where pollution is low or absent, completely ignoring emissions from agriculture or ozone concentrations that can be higher than in urban areas [27,30]. Pollutant emissions from industrial centers and urban areas are caught and transported by air masses, affecting the levels of pollution in background and remote areas and increasing the environmental pressure in them, including the impact on human health. Based on the monitoring data for the southern basin of Lake Baikal, Obolkinet al. [31] found that emissions from large thermal power plants reach heights of several hundred meters in the atmosphere and are carried by air masses over a distance of hundreds of kilometers. Likewise, Molozhnikova et al. [32] analyzed the snow-pollution levels for the same region and found that emissions from the larger cities were transferred to distances of tens of kilometers by prevailing winds. The cross-regional transport of air pollutants by air flows from cities to rural backgrounds and even between cities contributes significantly to local air quality [33,34,35,36]. That raises a complex question about what underlies people’s responses to environmental risks. The perception and the level of perceived risk significantly affect how the public responds to implementing measures to mitigate it [37].
The lack of knowledge about air pollution in general (sources, pollutants, impact, etc.) is frequently cited as a primary problem in environmental-risk perception. According to Maione et al. [38], there is a significant discrepancy between people’s perceptions of air-pollution sources and the actual contribution of various emission sectors in several European countries. In addition to the limited knowledge about major sources of air pollutants, that study showed that people most often mark industry and traffic as the main emission sources, while agriculture is completely underestimated. Canha et al. [39] conducted a national survey about air-quality perception in Portugal and found poor perception of air pollution sources and limited knowledge about air pollutants; half of the population was unable to recognize any pollutant, and half of the population lacked adequate knowledge about the air quality in their places of residence.
Lynch and Mirabelli [40] found that the awareness and perception of air quality were the highest among US adolescents whose parents had high educational levels (a bachelor’s degree or higher). According to their findings, even though the adolescents were aware of the negative impact of air pollution on health, they still needed further education on air quality alerts and how to reduce exposure. Liu et al. [41] indicated that public awareness in Wuhan (China) was closely related to education and the availability of information to all citizens, but mostly to their active participation in various activities raising awareness. A study conducted on China’s megacities [42] analyzed the public’s perception of urban air pollution and its associated health risks. The study found that adequate education and communication are necessary to increase awareness about air pollution’s impact and health effects. Based on the last Eurobarometer survey for 2022, about 60% of residents of the EU think that they are not sufficiently informed about air-quality problems in their country (from 79% in Portugal to 30% in Finland), which was slightly more (on average, + 6%) than the figures from the previous report from 2019 [43]. Additionally, the percentage of those who have not heard about EU air-quality standards was higher (73%). Based on these percentages, education and providing information and knowledge to the public are very important for understanding the air-pollution problem and how it affects people’s lives.
Communication strategies are very important for establishing effective policies, strategies, and adaptation measures in the field of environmental risks and for the protection of public health. Additionally, effective communication of scientific results should provide relevant and comprehensible information for the general public. A limited number of scientific studies have focused on public health information and communication regarding the risks associated with air pollutants, particularly ultra-fine particles [44]. Despite certain policies implying suitable mitigation measures to reduce emissions and increase air quality, changes happen very slowly and should be supported by individual action [45]. Also, particular communication approaches for target population groups can improve the implementation of policies [44]. Information provision and activities on the local level are also of great importance [46,47]. According to Ramírez et al. [48], there are several communication issues related to the efficiency of risk management, such as the quality of sources of air-quality information, information on behaviors to prevent long-term health impacts, and a specific focus on vulnerable groups. Indeed, the research on public attitudes and behavior toward air pollution should be an important preceding step for effective communication in public policy implementation.
3. Materials and Methods
The empirical part of this study was based on an online questionnaire survey conducted within the Serbian population during the period from March to May 2023. The leading air-quality issue in most places in Serbia is emissions from the heating sector during the winter season, while some areas are particularly affected by industrial pollution [13]. The survey was conducted immediately after the winter, when air-quality warnings are common, to obtain the most reliable answers regarding the population’s behavior and perception. The target population was surveyed through an online survey with the help of local nongovernmental organizations in the field of environmental protection, social media networks, and personal contacts. In particular, an effort was made to include respondents from places where air quality is a significant issue. To prevent potential bias in collecting answers, the following steps preceded the questionnaire: a preliminary survey was conducted to eliminate illogicalities due to poorly formulated questions and ambiguity, and the survey included cross-sections to ensure a representative sample (e.g., territorially equal representation and representation according to population structures). Nevertheless, there were some limitations: the sample coverage of people with the lowest educational levels was weaker, and the response rate of older individuals was also weaker. Additionally, there was less participation from less-populated and economically less-developed parts of the country. The main obstacles were filling out an online questionnaire or a lack of knowledge about modern communication media. The total number of respondents was 848 people who came from all parts of the country; residents from 26 administrative regions (NTSJ3) participated (the only missed data were for the four regions from Kosovo and Metohija). Considering the population size of the places of residence of the respondents, the following distribution was represented: settlements of up to 1000 inhabitants participated with 2.78% of the respondents; settlements of 1000–5000 participated with 6.58% of the respondents; settlements of 5000–10,000 participated with 6.22% of the respondents; settlements of 10,000–25,000 participated with 7.06% of the respondents; settlements of 25,000–50,000 participated with 5.62% of the respondents; settlements of 50,000–100,000 participated with 22.37% of the respondents; settlements of 100,000–400,000 participated with 19.62% of the respondents; and settlements with more than a million inhabitants (the capital city of Belgrade) participated with 29.67% of the respondents.
Multidimensions of the respondents’ features were targeted, such as demographic features (age, gender, education, number of household members aged ≤ 12 years, 13–18 years, and ≥ 65 years), socioeconomic features (employment and monthly income), and health status data (presence of chronic diseases), as well as living environment data about the type of housing facility and settlement features, the preferred heating type during the winter season, traffic intensity, and the proximity of industry to the place of residence.
Several questions were defined to analyze the resident’s perception and behavior related to air pollution:
Are you interested in air quality?
Do you think you are sufficiently informed about the impact of polluted air on health and the environment?
To what extent do you think air pollution is dangerous to your health (from 1—no dangerous to 5—very dangerous)?
Has a healthcare professional ever told you or anyone in your household to reduce your level of physical activity outdoors when the air quality was poor?
Respondents were asked whether they had changed their behavior for eight activities in the last six months (during the cold season matching the heating season) due to air pollution or air-pollution warnings:
A1—Spent less time outdoors;
A2—Avoided exercise or strenuous physical activity outdoors;
A3—Avoided opening windows and airing the rooms;
A4—Avoided busy roads to reduce exposure to air pollution when walking, cycling, or exercising;
A5—Thought about changing your place of residence;
A6—Used a face mask;
A7—Felt irritation of respiratory organs;
A8—Have been worried about our own health or the health of family members.
To express the extent to which they changed their behavior related to the above activities, respondents answered with “no”, “yes, sometimes”, or “yes, often”.
Before analysis, validation procedures were applied to remove answers that were due to illogicality or inconsistency. Finally, 838 respondents were included in the study. For this purpose, the chi-square test of independence, the Mann–Whitney U, and the Kruskal–Wallis H [49] test were applied to explore the variation in answers to isolate factors and determinants that influenced perception and behavior. The null hypothesis stated that there were no differences in the behavior and perception of the respondents based on their demographic features, socioeconomic features, health features, and living-environment determinants. Otherwise, there were differences in the answers depending on which group of characteristics the respondents belonged to.
4. Results
4.1. Demographic, Socioeconomic, and Health Status Features of the Respondents
Several aspects of the demographic and socioeconomic characteristics of the sample were considered to explore their importance for behavior and perception (Table 1). Respondents declared their ages according to four predefined age groups. These groups were slightly modified demographical age groups: young population (≤19 years); middle age with two subgroups—younger (20–39 years) and older middle age (40–64 years); and older population (≥65 years). Although the young population was not the primary interest of this research, under the assumption that they have not yet completed their education and many factors influence their behavior, a particular sample was surveyed (4.89%). Younger and older middle age constituted the most significant part of respondents (42.72% and 47.61%, respectively). The older population (≥65 years) vulnerable to air pollution participated with 4.77% of the respondents, due to difficulties in collecting answers (challenges in completing an online questionnaire, mistrust of research, etc.).
Women were more willing and interested in participating in the survey and the research. They took part as 63.60% of the respondents, compared to 36.40% for the men’s share. If we look at the distribution of the age groups by gender, there was relatively equal representation of the age groups for both men (4.59% for ≤19 years, 46.23% for 20–39 years, 42.95% for 40–64 years, 6.23% for ≤65 years) and women (5.07% for ≤19 years, 40.71% for 20–39 years, 50.28% for 40–64 years, 3.94% for ≤65 years).
When it came to education, the sample showed that the highest occurrence was for individuals with high school diplomas (27.17%), bachelor’s degrees (24.88%), and master’s degrees (39.01%) (Table 1). The most numerous respondents were employees at 73.67%, followed by financially supported people (such as students, housewives, or pregnant women,) with 8.45%, unemployed people with 7.23%, retirees with 5.92%, and people with personal income (5.68%). The monthly income was defined based on the average net salary in Serbia of about RSD 80,000 [50]. Based on this average, several groups were defined (Table 1). Most respondents were in the range of 50% more or less than the average monthly income at the national level. The relationships between education, employment, and monthly income are presented in Figure 1.
The data about the number of household members of the respondents are presented in Table 2; the highest shares were those with three and four members, 23.03% and 24.70%, followed by two-person households (20.88%) and single households (11.10%). Multi-member households with five or more people accounted for 20.29%. The share of respondents with household members aged ≤ 12 years was 42.72%; the share of respondents with household members aged 13–18 years was 24.94%, while the share of respondents with older household members (aged ≥ 65 years) was 26.85%.
People suffering from chronic diseases are potentially vulnerable groups. Almost one-third of the respondents (32.46%) declared that they or someone in their household suffered from a chronic disease (Table 3). The most prevalent were respiratory diseases, followed by cardiovascular or a combination of cardiovascular and respiratory diseases (Figure 2).
Despite efforts to obtain a sample of respondents representing all demographic and socio-economic structures, there was a particular bias, including higher female participation. However, a similar distribution of age groups among men and women was observed, which improved the accuracy of the results. A similar issue was with the involvement of a less-educated population and those in the age group ≥ 65 years. To reduce uncertainty, efforts were made to ensure that the distribution of other observed structures followed the general trends in the entire population.
4.2. Living-Environment Characteristics
In determining the leading sector’s contribution to air-pollutant emissions in Serbia, several components of the environment were considered to assess the potential impact on residents’ perceptions and behaviors (Table 4). There was a higher proportion of those who lived in flats (56.32%) than those who lived in houses (43.68%); when considering the settlement type, there was an almost equal number of those who lived in settlements where houses were the prevailing type of facility (36.75%) and those who lived in settlements where buildings were dominant (30.31%) or where there was a mixture of houses and buildings (32.94%). The type of housing was related to the heating type during the winter season. Figure 3 shows the relationship between settlement type, type of housing facility, and heating type. Most people who lived in flats had remote heating systems (city heating plants) or used electricity and gas that were mainly from emission sources that were far from the place of residence. On the contrary, people who lived in houses used individual systems, with the prevalence of wood as fuel resulting in exposure to area pollution sources due to emissions from heating.
Only a small share of respondents lived in places with very low and low traffic intensity (about 10%), while about 90% rated traffic intensity as moderate (30.07%), high (28.64%), or very high (30.67%). There was a smaller proportion of those who lived close to the industry (37.23%). Regarding the type of industry, the energy and mining, construction, metallurgy, chemical, and food industries, or a combination of these types of industries, were most common near the places of residence.
4.3. Perception, Response, and Behavior Related to Air Pollution
It was found that 96.06% of the respondents expressed an interest in air quality. However, almost half (47.7%) believed they were not sufficiently informed about the impact of air pollution on health and the environment, while 14.3% were unsure and 37.9% answered “yes”. The chi-square test of independence was performed to check which determinants influenced these variations in answers, but only statistically significant statistics were found only for heating types with small-effect sizes according to Cramer’s V statistics (Table 5 and Table 6). For the question “Has a health care professional ever told you or anyone in your household to reduce your level of physical activity outdoors when the air quality was poor?” only 15.51% answered “yes”, while 84.49% answered “no”.
The respondents were asked to rate the extent to which polluted air is dangerous for their health, on a scale of 1 (no dangerous) to 5 (very dangerous). Even though most thought they were not sufficiently informed, about 51% rated the highest grade as very dangerous (rate 5) and 32.46% rated the highest grade as dangerous (rate 4), while a very small proportion (about 3%) rated the highest grade as not dangerous (rate 1) or slightly dangerous (rate 2) (1.31% for rate 1 and 1.91% for rate 2). About 13.25% rated the highest grade as moderate (rate 3). The Mann–Whitney U and Kruskal–Wallis H tests were applied to check whether there was a difference in the rates between groups according to demographics, socioeconomic status, health, and the living environment characteristics of the respondents (Table 7 and Table 8). For demographics and socioeconomic features, statistically significant results were obtained for age, gender, education, employment, monthly income, and chronic diseases. On average, the young population had a lower rate for the impact of air pollution on health and the environment, compared to the rates of other age groups. Similarly, a lower rate was found for men than for women, for respondents with lower education (primary school and high school), for unemployed and financially supported respondents, and for those without income compared to persons with above-average income. People who suffered from chronic diseases had a higher rate. Although people with children and older adults in the household rated as very dangerous, statistically significant test statistics were only for those with children aged from 13 to 18 years. Heating type and traffic intensity in the residence place appeared to be statistically significant. People surrounded by low to medium traffic intensity in their places of residence provided lower rates than those who were surrounded by high traffic intensity. In the case of heating type, despite statistically significant test statistics, it was impossible to determine differences between groups of respondents.
The primary research subject was individual behavior and response due to air pollution and quality warnings during the previous six months (October 2022 to March 2023). Respondents answered “no”, “yes, sometimes”, or “yes, often” to evaluate their activity to eight predefined activities (A1–A8—see Section 3, Materials and Methods). The results are presented in Figure 4. Looking at the whole sample, the largest numbers were for those who sometimes or often felt irritation of the respiratory organs (47.85% and 20.17%) or those who were worried about their own health or the health of family members (46.9% and 30.79%). A very small percentage of respondents used face masks (8.59%), while the vast majority did not (65.04%) or only did sometimes (26.37%). A smaller number of respondents were thinking about changing their places of residence (18.14%) compared to those who did not think about this at all (58.83%) or those who only did so sometimes (20.23%). There was an almost an equal number of those who avoided airing rooms (30.34%) and those who did not avoid airing rooms (30.28%), while 39.38% declared that they practiced this behavior sometimes. About 20% of the respondents practiced spending less time outdoors due to increased air pollution, about 43% sometimes practiced spending less time outdoors due to increased air pollution, and 35.20% did not enforce such restrictions. Behaviors such as avoiding outdoor physical exercise and avoiding busy roads to reduce exposure to air pollution when walking, cycling, or exercising showed similar distributions of answers; about 40% never did this, about 35% did so sometimes, and about 20% followed these restrictions.
The chi-square test of independence was performed to study the relationship between demographic features, socioeconomic features, health status data, and behavior and response due to air pollution and quality warnings for the six months after the heating season. The results are presented in Table 9. For most activities, statistically significant differences were found for age, education, employment, monthly income, and health status (i.e., the presence of chronic diseases). However, considering the effect size, i.e., the Cramer’s V values, these variables had a weak effect on the analyzed activities in most cases. The largest differences in behaviors among age groups (the largest χ2 and Cramer’ V), especially young respondents (≤19 years) and old respondents (≥65 years), were shown for spending time outdoors, outdoor physical exercise, and avoiding busy roads to reduce exposure to air pollution. Most young people did not change their behavior when outdoor air pollution increased; 65.85%, 70.73%, and 78.05% did not spend less time outdoors, did not avoid exercise outside, and did not avoid busy roads, respectively. On the contrary, the largest share—the old population—spent less time outdoors (55%), while 45% did not avoid busy roads, 27.50% did so sometimes, and 27.50% did so often. At the same time, the young and old populations did not think about changing their places of residence (78.05% and 80%, respectively, never thought to do so). The percentage of those wearing face masks was lowest in all age groups. Most of those in the middle contingent (aged 20–39 and 40–64 years) felt irritation of the respiratory organs (for group 20–39 years, 22.70% sometimes and 44.13% often; for the group 20–39 years, 18.30% sometimes and 52.63% often), and they were worried about their health (for group 20–39 years, 32.4% sometimes and 45.50% often; for the group 20–39 years, 30.80% sometimes and 49.60% often). Regarding gender differences, statistically significant test statistics were obtained for A1, A3, A6, A7, and A8, with the largest effect size for A6 and A7. Women felt more irritation of the respiratory organs, were more worried about their health, and practiced more behaviors that reduced exposure. Education, employment, and monthly income seemed to follow a similar pattern. The higher education level was encompassed with activities that reduced exposure to air pollution: about 50% of people who completed only primary school and/or high school did not avoid spending time outdoors, while the opposite was true for those with master’s and doctoral educational levels—more than half often reduced their exposure by spending less time outdoors. The differences were even greater in the case of reducing outdoor physical activity and avoiding busy roads (over 70% of people with primary school education did not practice these behaviors). The more-educated people thought more about changing their places of residence, and they worried more about health effects. Such behavior patterns were also recognized for employees and persons with above-average incomes. The number of household members had no greater significance for behavior, except that exposure reduction was more present in those with children (but this was mostly statistically insignificant), while regularity was not observed in those who lived with older adults. The biggest differences (about 20%) were in the behaviors of people who suffered from chronic diseases or lived with people affected by these diseases for A7 and A8; they sometimes or often felt irritation of respiratory organs (about 80%) and they were worried about health (about 90%).
The results of the chi-square test of independence for living-environment determinants and behavior activities are presented in Table 10. Settlement population size was statistically significant for most activities (A1, A2, A3, A5, A7, and A8). The effect of settlement population size on a person’s behavior was visible for those living in cities with more than 10,000 inhabitants. The biggest differences were for those who lived in cities with 50,000–100,000 inhabitants; for example, 33.69% often and 40.64% sometimes spent less time outdoors due to increased air pollution, while 28.23% never did so. On the contrary, 58.33% of the residents of settlements of up to 1000 inhabitants did not have any restriction regarding spending time outside. The larger share of those who spent less time outdoors often or sometimes was also found for the categories of settlement of 25,000–50,000 inhabitants and more than a million inhabitants, but with the difference that there was a larger proportion of those who answered “sometimes”. A similar distribution of answers was shown for other activities. Over 80% of the respondents from these settlements were worried, often or sometimes, about health due to increased air pollution. Concerning A6 (usage of a face mask), there were no differences among the respondents.
5. Discussion
During 2021, in some places in Serbia, the number of days exceeding the daily limit of 50 μg/m3 for PM10 (defined by national legislation) occurred on almost half the days of the year—e.g., 174 days for the station in the city of Valjevo, with the highest measured daily value in this city of 317 μm/m3 [13]. How extreme it could be is shown by the fact that in 2017, the highest daily value was 806 μm/m3 [51]. Considering that the upper limits from national legislation are significantly higher than WHO recommendations (15 μm/m3 for annual and 45 μm/m3 for the daily concentration of PM10 and 5 μm/m3 for annual and 15 μm/m3 for daily concentrations of PM2.5 [52]), exposure to PM significantly impacts public health in Serbia. During 2021, the dominant sector in PM10 and PM2.5 emissions, with shares of 65% and 80%, respectively, were heating plants with less than 50 MW power and individual heating in households. The other sources included road traffic, industry, and agriculture [13]. The findings from the nationwide survey indicated that air quality was a significant concern among the respondents, and most believed that exposure to polluted air can be highly hazardous. The highest rate of air-pollution impact was observed for older persons, women, those who were more educated, employees with above-average incomes, and respondents who suffered from a chronic disease or who had one of their household members suffering from such a disease. High traffic intensity in the place of residence place was related to the highest rate on the impact scale. At the same time, the majority believed that they were not sufficiently informed about the impact of air pollution on health and the environment—47.7% thought that they were not informed, while 14.3% were unsure. However, the problem of lack of information can be found to a similar extent in other European countries [43]. None of the observed factors stood out as a determinant affecting the level of information. Potentially, a “gap” in the knowledge of possible impacts triggered behaviors in a non-protective manner that was not in line with the observed air-quality levels. In general, the respondents were most concerned about their health and felt irritation of the respiratory organs. At the same time, in most cases, they did not wear face masks (due, in general, to a lack of habit) or even think about changing their residence due to poor air quality (due to the unfavorable economic situation). However, limitations in terms of reducing exposure due to spending time outdoors or outdoor physical activities were not dominant observed behaviors.
Many studies reported a lack of knowledge and a misunderstanding of pollution sources, pollutants, air-quality levels, standards and alerts, and adverse effects, as general trends in many societies [27,38]. Kelly and Fussell [27] emphasized the importance of understanding the biological response of organisms and the health effects of exposure to PM for the effective development of public policies and attitudes toward this issue. This is more significant than relying solely on pollutant-limiting concentration values and air-quality levels. Riley et al. [47] stated that changing the public’s behavior is a complex and challenging issue due to drivers from the physical and social environment.
The conducted statistical procedures revealed certain determinates of perception and behaviors in the observed population sample, but with a small effect size. Statistically significant differences were found for age, gender, education, employment, and monthly income. Individuals who are young, had lower educations, were unemployed, or had below-average monthly incomes did not take measures to reduce their exposure to outdoor pollution. Women tended to have a better perception of risks and to take steps to reduce their exposure; they were more likely to experience irritation in their respiratory organs and to prioritize their health concerns. A similar conclusion was found for people who suffered from a chronic disease or who had one of their household members suffering from such a disease. Having children or older individuals in the household did not reduce exposure. Those with the highest educations and those with the higherst monthly incomes were mainly considering changing their residences. This agreed with the findings of several studies, that higher educations and higher incomes are accompanied by a higher level of risk perception of air pollution [19,20]. Women, older people, and people with chronic illnesses were also predictors of perceived risk [19,26]. Depending on the society being observed, certain specificities can be noticed. A study by Pignocchino et al. [18] found a greater awareness of the issue among male and smoker populations in Italy, as well as among older individuals and those with particular political orientations in Italy and Sweden.
The population size of a place of residence appeared to be a significant predictor for most activities, especially for respondents living in cities with 50,000–100,000 inhabitants. Residents living in settlements with up to 1000 inhabitants were not following restrictions to decrease their exposure to air pollution and were not concerned about their health due to increased air pollution. Generally, rural areas are perceived as places where air pollution is not present [24,53,54], which is actually a concern. Further, as the population sizes of settlements grow, people tend to decrease their exposure to pollution. The only behavior with no difference was wearing a face mask, which is unrelated to the size of the place of residence or any other predictor. It is noticeable that there was a strong correlation between the amount of traffic in an area and the behavior of individuals. Specifically, those who lived in areas with heavy traffic were more inclined to take measures to decrease their exposure to air pollution. Generally, traffic is considered to be the most significant source of pollution [28,38]. Surprisingly, respondents from settlements dominated by houses with individual heating using solid fuels and area emissions were less likely to engage in exposure-modifying behaviors during cold months. It can be concluded that sources of emissions in the local living environment are not being recognized and addressed as potential risks; people tend to perceive air pollution as higher in their neighborhood than in their own place [47]. Avoiding the airing of rooms and wearing face masks are more common among the population living in apartments with remote heating systems. The proximity of industry does not seem to impact efforts to reduce exposure, except in regard to health concerns. Finally, behaviors that reduce exposure are not dominant among residents in Serbia, or are present to an insufficient extent. The prevailing opinion among people that exposure to polluted air is dangerous should be supported by information on how pollution affects health and how information on behaviors reduces negative consequences. The results suggest that familiarization with the potential risks in a living environment is necessary.
It is noteworthy that medical professionals are not individuals’ primary sources of information and guidance, although they have a significant role [39]. Approximately 85% of the surveyed population reported not receiving any advice from medical professionals about reducing outdoor physical activity in response to poor air quality. Mirabelli et al. [55] and Lynch and Mirabelli [40] also reported that healthcare professionals were not participating in communicating the risks of health effects. This is not encouraging, considering that the dominant chronic diseases in the general population—respiratory and cardiovascular diseases—both at the sample level and for the entire population in Serbia [56], are found in vulnerable groups affected by air pollution. Furthermore, it has been found that having children or older adults in a household does not lead to decreased exposure, which is a significant concern, given their vulnerability [57,58].
As air pollution is a growing global burden, it has attracted increasing attention from scientists and the public. Dealing with this issue is complex, but activities to protect public health must be prioritized. Due to the COVID-19 pandemic, researching the public perception of air quality has become even more important [18,59,60,61,62]. The difference in objective exposure to air pollution and subjective perception of air quality can present a significant problem and challenge in creating policies to mitigate adverse effects [63,64,65]. Understanding how such mitigation is affected by differences in the demographic and socioeconomic characteristics of the population [19,20,21,22] is important for effective communication and intervention strategies. Frequently, this is accompanied by spatial inequalities in exposure to polluted air [62], making the issue even more complex and rendering this research subject multidisciplinary [64].
This study highlighted the multidimensional drivers and determinants influencing public risk perception and exposure-related behaviors. Due to the lack of previous research on this topic and the recent increase in air pollution levels in Serbia, the results have numerous practical and theoretical implications. However, this topic requires further research to reveal the mutual relationships between public behavior and air pollution.
6. Conclusions
This research provided a comprehensive overview of the perception and behavior of the Serbian population due to exposure to air pollution, an assessment of the level of risk awareness and the practicing of exposure-reduction activities and behaviors, and an assessment of the driving factors in these processes. Statistically significant differences were found between the groups of respondents according to their demographic and socioeconomic characteristics, health status data, and determinants from the living environment. In almost all statistically significant cases, the effect size was small; in other words, the observed differences were not large. Despite the fact that there were certain limitations in the observed sample (e.g., a higher participation of female respondents), it can be concluded that mitigation measures should be adapted to the entire population. Differences between less- and more-educated people or between those with lower and higher incomes were more pronounced regarding concerns about health effects but still did not indicate changes in exposure behavior. Also, the findings indicated that those who are exposed to emissions due to the burning of solid fuel from households or from industries in their places of residence did not practice significantly more exposure-reduction activities. Far from engaging in any activities that reduce exposure to air pollution, the wearing of protective face masks was not a behavior that was at all present in the entire sample. Children and the old population in the household were not recognized as sensitive groups and their presence was not an indication of activities to reduce exposure. However, the presence of chronic diseases suggested concern about the harmful effects of polluted air.
These findings suggest several practical implications: the necessity of interventions on local levels, educating people on how to deal with risks from their immediate living environment, identifying and protecting vulnerable groups, and promoting behaviors that reduce exposure to air pollution and negative health effects. It is important to take systematic actions at various levels to address the issue. Healthcare professionals are a crucial part of risk communication, particularly given the prevalence and vulnerability of chronic diseases.
Generating changes in society awareness does not happen quickly, and only a proper perception of the problem leads to long-term results. Increasing awareness and perception of the negative consequences of exposure to polluted air can reduce adverse effects and improve public health. At the same time, it can encourage pro-environmental behavior. Regarding Serbia, this is a crucial matter for overall welfare. Future studies will be focused on the perception of the emission sources and air-quality information in Serbia. The findings from this study can be utilized to develop better policies and regulations regarding air pollution, not only in Serbia but also in neighboring countries and societies that face similar issues.
Conceptualization, G.S. and S.M.-M.; methodology, G.S.; formal analysis, G.S.; investigation, G.S. and S.M.-M.; resources, N.B.Ć., A.R., T.P. and S.Ć.; writing—original draft preparation, G.S.; writing—review and editing, S.M.-M., N.B.Ć. and M.R.; visualization, T.P.; supervision, M.R. and S.Ć. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Geographical Institute “Jovan Cvijić” SASA (114/1, 26 September 2023).
Informed consent was obtained from all subjects involved in the study. Participation in the study was completely voluntary and anonymous. Participants received no compensation.
The research data can be provided upon request to the corresponding author.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. The distribution of the respondents according to education, employment, and monthly income.
Enlarge this image.Figure 2. The prevalence of chronic diseases among respondents and/or household members.
Enlarge this image.Figure 3. Distribution of respondents according to the type of living facilities, settlement type, and type of heating.
Enlarge this image.Figure 4. Distribution of answers about behavior and response due to air pollution and quality warnings for the previous six months.
Summary statistics of demographic and socioeconomic features of the respondents.
| Category | Respondents Abs. | Respondents % | |
|---|---|---|---|
| Age | ≤19 | 41 | 4.89 |
| 20–39 | 358 | 42.72 | |
| 40–64 | 399 | 47.61 | |
| ≥65 | 40 | 4.77 | |
| Gender | Female | 533 | 63.60 |
| Male | 305 | 36.40 | |
| Education | Without education | 3 | 0.36 |
| Primary school | 14 | 1.69 | |
| High school | 225 | 27.17 | |
| Bachelor’s degree or equivalent | 206 | 24.88 | |
| Master’s degree or equivalent | 323 | 39.01 | |
| Doctoral studies | 67 | 8.09 | |
| Employment | Unemployed | 60 | 7.23 |
| Employed | 610 | 73.67 | |
| Retiree | 49 | 5.92 | |
| Financially dependent | 70 | 8.45 | |
| Personal income | 47 | 5.68 | |
| Other | 2 | 0.24 | |
| Monthly income | Without income | 93 | 11.23 |
| RSD < 40,000 | 81 | 9.78 | |
| RSD 40,000–80,000 | 297 | 35.87 | |
| RSD 80,000–120,000 | 241 | 29.11 | |
| RSD 120,000–160,000 | 70 | 8.45 | |
| RSD > 160,000 | 56 | 6.67 |
Summary statistics of data about household members of the respondents.
| Category | Respondents Abs. | Respondents % | |
|---|---|---|---|
| Number of household members | 1 | 93 | 11.10 |
| 2 | 175 | 20.88 | |
| 3 | 193 | 23.03 | |
| 4 | 207 | 24.70 | |
| ≥5 | 170 | 20.29 | |
| Household members ≤ 12 years | No | 480 | 57.28 |
| Yes | 358 | 42.72 | |
| Household members 13–18 years | No | 629 | 75.06 |
| Yes | 209 | 24.94 | |
| Household members ≥ 65 years | No | 613 | 73.15 |
| Yes | 225 | 26.85 |
Summary statistics of respondents’ answers related to chronic diseases.
| Do You or Someone in Your Household Suffer from a Chronic Disease? | Respondents Abs. | Respondents % |
|---|---|---|
| Yes | 272 | 32.46 |
| No | 513 | 61.22 |
| I don’t want to answer | 53 | 6.32 |
Summary statistics of data about the living environment.
| Category | Respondents Abs. | Respondents % | |
|---|---|---|---|
| Type of housing facility | House | 366 | 43.68 |
| Flat | 472 | 56.32 | |
| Settlement type | Dominant individual housing (houses) | 308 | 36.75 |
| Dominant collective housing (buildings) | 254 | 30.31 | |
| Mixed (houses and buildings) | 276 | 32.94 | |
| Traffic intensity 1 (low intensity) to 5 (high intensity) | 1 | 17 | 2.03 |
| 2 | 72 | 8.59 | |
| 3 | 252 | 30.07 | |
| 4 | 240 | 28.64 | |
| 5 | 257 | 30.67 | |
| Type of heating | Individual—wood | 153 | 18.26 |
| Individual—gas | 94 | 11.22 | |
| Individual—electricity | 171 | 20.41 | |
| Individual—coal | 5 | 0.60 | |
| Individual—pellet | 80 | 9.67 | |
| Individual—mixed | 50 | 5.97 | |
| Remote heating systems | 275 | 32.82 | |
| Other (heating pumps, geothermal energy, fuel oil, etc.) | 10 | 1.19 | |
| Proximity to industry | Yes | 312 | 37.23 |
| No | 526 | 62.77 |
The results of the chi-square test of independence for demographic, socioeconomic, and health data on respondents and information about the harmful impact of air pollution on health and the environment: test statistics χ2, p-values, and Cramer’s V statistics values.
| Age | Gender | Education | Employment | Income | Household |
Haus. Memb. ≤ 12 Years | Haus. Memb. 13–18 Years | Haus. Memb. ≥ 65 Years | Chronic |
|
|---|---|---|---|---|---|---|---|---|---|---|
| χ2 | 11.446 | 2.949 | 10.146 | 12.474 | 16.622 | 6.749 | 0.120 | 1.826 | 4.203 | 2.467 |
| p | 0.076 | 0.566 | 0.255 | 0.131 | 0.083 | 0.564 | 0.942 | 0.401 | 0.122 | 0.300 |
| V | 0.083 | 0.030 | 0.078 | 0.086 | 0.010 | 0.064 | 0.012 | 0.047 | 0.071 | 0.044 |
The results of the chi-square test of independence for living-environment determinants and information about the harmful impact of air pollution on health and the environment: test statistics χ2, p-values, and Cramer’s V statistics values. Statistically significant test statistics χ2 for α = 0.05 are in bold.
| Settlement |
Type of Housing Facility | Settlement Type | Heating Type | Traffic |
Industry | |
|---|---|---|---|---|---|---|
| χ2 | 20.100 | 0.776 | 2.5045 | 19.281 | 12.461 | 1.623 |
| p | 0.127 | 0.685 | 0.644 | 0.037 | 0.052 | 0.443 |
| V | 0.110 | 0.030 | 0.039 | 0.108 | 0.086 | 0.044 |
The results of the Mann–Whitney U test for living-environment determinants and information about the harmful impact of air pollution on health and the environment: test statistics U, p-values, test statistic z, and standardized effect size r. Statistically significant test statistics U for α = 0.05 are in bold.
| Gender | Type of Housing |
Industry | Chronic Diseases | Household |
Household |
Household |
|
|---|---|---|---|---|---|---|---|
| U | 70,335 | 96,625.5 | 77,116 | 81,507 | 88,867 | 58,768.5 | 65,130.5 |
| p | <0.001 | 0.001 | 0.110 | <0.001 | 0.351 | 0.012 | 0.176 |
| z | −3.564 | 3.237 | −1.600 | 4.255 | 0.933 | 2.520 | −1.354 |
| r | 0.12 | 0.11 | 0.006 | 0.15 | 0.032 | 0.009 | 0.047 |
The results of the Kruskal–Wallis H test for living-environment determinants and information about the harmful impact of air pollution on health and the environment: test statistics χ2, p-values), the observed effect size η2, and post hoc Dunn’s test using a Bonferroni corrected α. Statistically significant test statistics χ2 for α = 0.05 are in bold.
| Age | Population Size | Education | Employment | Income | Heating Type | Traffic Intensity | Settlement Type | |
|---|---|---|---|---|---|---|---|---|
| χ2 | 28.52 | 9.83 | 44.43 | 20.90 | 24.7 | 16.15 | 27.12 | 3.49 |
| p | <0.001 | 0.198 | <0.001 | <0.001 | <0.001 | 0.008 | <0.001 | 0.174 |
| η2 | 0.031 | / | 0.004 | 0.020 | 0.024 | 0.014 | 0.029 | / |
| α | 0.008 | / | 0.005 | 0.005 | 0.003 | / | 0.008 | / |
The results of the chi-square test of independence for demographic features, socioeconomic features, and health data on respondents and their behavior (A1–A8 activities); test statistics χ2, p-values, and Cramer’s V statistics. Statistically significant test statistics χ2 for α = 0.05 are in bold.
| A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | ||
|---|---|---|---|---|---|---|---|---|---|
| Age | χ2 | 23.239 | 25.020 | 16.839 | 32.4966 | 18.909 | 5.897 | 8.487 | 13.834 |
| p | 0.001 | 0.000 | 0.010 | <0.001 | 0.004 | 0.435 | 0.205 | 0.032 | |
| V | 0.118 | 0.122 | 0.100 | 0.139 | 0.106 | 0.059 | 0.071 | 0.091 | |
| Gender | χ2 | 6.577 | 5.757 | 6.103 | 4.721 | 0.837 | 20.261 | 18.781 | 7.911 |
| p | 0.037 | 0.056 | 0.047 | 0.094 | 0.658 | <0.001 | <0.001 | 0.019 | |
| V | 0.089 | 0.083 | 0.085 | 0.075 | 0.032 | 0.155 | 0.150 | 0.097 | |
| Education | χ2 | 50.884 | 46.323 | 33.634 | 40.767 | 32.736 | 10.101 | 19.065 | 27.770 |
| p | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.258 | 0.015 | 0.001 | |
| V | 0.175 | 0.167 | 0.142 | 0.156 | 0.140 | 0.0780 | 0.107 | 0.129 | |
| Employment | χ2 | 27.645 | 18.192 | 32.855 | 25.905 | 30.255 | 8.157 | 7.276 | 24.144 |
| p | 0.001 | 0.020 | <0.001 | 0.001 | <0.001 | 0.418 | 0.507 | 0.002 | |
| V | 0.129 | 0.104 | 0.1402 | 0.124 | 0.135 | 0.070 | 0.067 | 0.120 | |
| Monthly |
χ2 | 35.327 | 25.937 | 26.403 | 18.264 | 21.775 | 8.712 | 10.075 | 30.075 |
| p | <0.001 | 0.004 | 0.003 | 0.051 | 0.016 | 0.560 | 0.434 | 0.001 | |
| V | 0.145 | 0.124 | 0.126 | 0.104 | 0.114 | 0.072 | 0.078 | 0.1340 | |
| Household |
χ2 | 21.579 | 10.708 | 16.626 | 9.633 | 13.342 | 9.491 | 6.497 | 8.283 |
| p | 0.006 | 0.219 | 0.034 | 0.292 | 0.073 | 0.303 | 0.594 | 0.406 | |
| V | 0.1135 | 0.080 | 0.100 | 0.076 | 0.093 | 0.075 | 0.062 | 0.070 | |
| Household |
χ2 | 8.878 | 3.630 | 3.351 | 2.710 | 2.361 | 1.173 | 0.707 | 6.860 |
| p | 0.012 | 0.163 | 0.187 | 0.258 | 0.307 | 0.556 | 0.702 | 0.032 | |
| V | 0.103 | 0.066 | 0.063 | 0.057 | 0.053 | 0.037 | 0.029 | 0.090 | |
| Household |
χ2 | 20.448 | 15.714 | 4.001 | 3.896 | 10.323 | 2.602 | 8.526 | 13.179 |
| p | <0.001 | <0.001 | 0.135 | 0.143 | 0.001 | 0.272 | 0.014 | 0.001 | |
| V | 0.156 | 0.137 | 0.069 | 0.068 | 0.111 | 0.056 | 0.101 | 0.125 | |
| Household |
χ2 | 1.591 | 0.629 | 13.180 | 1.354 | 7.798 | 2.467 | 1.130 | 1.952 |
| p | 0.451 | 0.730 | 0.001 | 0.508 | 0.020 | 0.291 | 0.568 | 0.377 | |
| V | 0.044 | 0.027 | 0.125 | 0.040 | 0.097 | 0.054 | 0.037 | 0.048 | |
| Chronic |
χ2 | 15.497 | 18.339 | 16.194 | 10.637 | 13.695 | 6.950 | 44.385 | 41.448 |
| p | <0.001 | <0.001 | <0.001 | 0.005 | 0.001 | 0.031 | <0.001 | <0.001 | |
| V | 0.141 | 0.153 | 0.144 | 0.116 | 0.132 | 0.094 | 0.238 | 0.224 | |
The results of the chi-square test of independence for living-environment determinants and behavior activities (A1–A8 activities); test statistics χ2, p-values, and Cramer’s V statistics. Statistically significant test statistics χ2 for α= 0.05 are in bold.
| A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | ||
|---|---|---|---|---|---|---|---|---|---|
| Settlement population size | χ2 | 62.245 | 38.416 | 62.453 | 19.324 | 44.465 | 21.867 | 33.058 | 50.611 |
| p | <0.001 | <0.001 | <0.001 | 0.153 | <0.001 | 0.0814 | 0.003 | <0.001 | |
| V | 0.196 | 0.152 | 0.193 | 0.108 | 0.163 | 0.144 | 0.142 | 0.170 | |
| Type of housing facility | χ2 | 9.916 | 5.481 | 15.067 | 4.393 | 29.531 | 9.560 | 8.640 | 14.225 |
| p | 0.007 | 0.064 | 0.001 | 0.111 | <0.001 | 0.008 | 0.013 | <0.001 | |
| V | 0.109 | 0.081 | 0.1341 | 0.072 | 0.188 | 0.107 | 0.102 | 0.130 | |
| Settlement type | χ2 | 4.249 | 5.168 | 11.917 | 5.563 | 20.230 | 14.889 | 9.411 | 14.670 |
| p | 0.373 | 0.271 | 0.018 | 0.234 | <0.001 | 0.005 | 0.052 | 0.005 | |
| V | 0.050 | 0.055 | 0.084 | 0.058 | 0.110 | 0.094 | 0.075 | 0.094 | |
| Heating type | χ2 | 21.313 | 18.207 | 25.366 | 9.085 | 39.372 | 14.332 | 17.083 | 16.927 |
| p | 0.019 | 0.052 | 0.005 | 0.524 | <0.001 | 0.158 | 0.073 | 0.076 | |
| V | 0.114 | 0.105 | 0.124 | 0.074 | 0.155 | 0.093 | 0.102 | 0.101 | |
| Traffic intensity | χ2 | 17.544 | 10.869 | 24.048 | 26.176 | 35.787 | 7.783 | 23.185 | 30.427 |
| p | 0.008 | 0.092 | 0.001 | <0.001 | <0.001 | 0.254 | 0.001 | <0.001 | |
| V | 0.102 | 0.081 | 0.120 | 0.125 | 0.146 | 0.068 | 0.118 | 0.135 | |
| Industry | χ2 | 0.640 | 2.105 | 0.362 | 0.962 | 4.479 | 2.501 | 2.615 | 6.180 |
| p | 0.887 | 0.349 | 0.835 | 0.618 | 0.107 | 0.286 | 0.271 | 0.047 | |
| V | 0.019 | 0.050 | 0.021 | 0.034 | 0.073 | 0.055 | 0.056 | 0.085 | |
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; Malinović-Milićević, Slavica 1
; Ćurčić, Nina B 1
; Radovanović, Milan 1
; Radivojević, Aleksandar 2
; Popović, Teodora 1
; Ćurčić, Srećko 3
1 Geographical Institute “Jovan Cvijić”, Serbian Academy of Sciences and Arts, Djure Jakšića 9, 11000 Belgrade, Serbia;
2 Department of Geography, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia;
3 Faculty of Biology, University of Belgrade, Studentski Trg 16, 11000 Belgrade, Serbia;