Evaluation of the Acoustic Impact of the Public Road Network on a Nature Conservation Area: A Case Study

Abstract

Despite the formal protection of many natural areas, the problem of noise pollution poses a serious challenge to the preservation of their ecological integrity and biodiversity. Traffic noise generated by vehicle traffic on public roads disrupts natural biological processes, negatively affecting animals and the quality of the audiosphere. This research aimed to assess the acoustic impact of the public road network crossing the Roztocze National Park (RPN, Poland) and to characterize noise propagation as a factor polluting the environment and disrupting the functioning of natural forest ecosystems. The equivalent sound pressure level (L_Aeq) was measured at different distances from four public roads crossing the park. A terrain analysis was also taken into account to determine the impact of height differences on sound propagation. To enhance the acoustic analysis, recordings of environmental sounds were made, and their components, including both natural and anthropogenic sounds, were identified. It was found that traffic noise dominated natural sounds at distances 250 m from roads. The results obtained indicate the need for an integrated approach to protected area management, including noise monitoring, the implementation of noise protection regulations, and environmental education.

1. Introduction

Protected areas actively contribute to the conservation of natural resources and wildlife habitats [1]. Environmental pollution in these conservation zones poses a significant challenge to preserving their ecological integrity and biodiversity. Despite formal protection, these areas face various forms of degradation. Increased tourism at protected sites is linked to the development of road infrastructure, which significantly impacts their functioning, affecting ecosystems, biodiversity, and landscape integrity. As tourism grows, the risk of anthropopressure on a protected area increases, hindering the achievement of conservation goals [2]. The conflict between nature conservation objectives and the rising demands of tourists presents a challenge that necessitates a sustainable approach to tourism management, alongside new conservation and remediation solutions [3]. Effective management requires educating tourists about environmental issues and implementing well-considered protection strategies, such as limiting visitor numbers or strictly controlling tourist infrastructure [4,5].

Noise is a prevalent environmental pollutant that significantly affects conservation efforts. In protected areas, anthropogenic noise poses a serious threat to ecosystems. It negatively affects biodiversity, animal behavior, and the quality of ecosystem services [6]. Roads, especially those with heavy traffic, pose a serious threat to many national parks. Traffic noise contributes to habitat degradation by masking natural sounds, thereby diminishing the quality of biodiversity conservation [7]. Studies conducted in the United States indicate that, although protected areas typically have sound levels a few decibels lower than those in adjacent areas, 15% of all protected areas exhibit sound levels 10 dB higher than predicted natural levels [8]. In the natural environment, anthropogenic noise increasingly harms ecosystems. It has a more substantial impact on fauna than on humans, primarily due to differences in hearing sensitivity and responses to sounds, which serve as a means of gathering information about the surrounding environment for animals [9]. While humans can adapt to noise through habituation mechanisms, animals often perceive it as a threat.

Studies of wild animals’ reactions to noise have clearly identified changes in their behavior and spatial distribution [10] and disturbances in community structure [11]. Noise pollution exacerbates problems associated with habitat fragmentation. The noise generated by tourist traffic and transportation infrastructure near protected areas also causes stress in animals, which in turn affects their reproductive behavior and migration patterns. Research results presented in [12] suggest that low-frequency noise, typical of transportation activities, can penetrate over long distances and affect a wide range of species simultaneously, altering their spatial distribution and social behavior. Acoustic disturbance can also hinder communication between individuals, which is crucial for many species [13]. High noise levels interfere with the acoustic signals used by animals for social interaction, territorial defense, and predator detection [14,15]. Research indicates that the presence of roads leads to a decrease in activity and a reduction in the number of individuals observed in areas highly exposed to noise. This type of pollution can impact reproductive processes and foraging ability, thereby altering population dynamics [16]. Many species, especially birds and mammals, utilize sounds to attract mates and establish bonds. Traffic noise reduces the effectiveness of these behaviors. Prolonged noise exposure can lead to increased stress hormones and contribute to health disorders, including reduced reproductive capacity and fewer offspring [17]. In particular, forest species may exhibit reduced activity near roads [18].

A review covering more than 130 species [19] found that the negative impacts of roads on animal numbers are five times greater than the positive impacts. Studies of road ecology in the vicinity of busy motorways provide estimates of the distances at which wildlife is affected by chronic noise exposure. In a study [20], it was shown that car noise is a major factor affecting bird density. The distance of impact ranges from 120 to 1200 m. A meta-analytical study involving 234 species of birds and mammals showed that bird populations decline at distances of 1 km, and mammals up to 5 km from road infrastructure [21]. In contrast, a paper [22] showed that the average 24-h L_eq noise load for a frog population in Ontario was 43.6 dBA, and for a grassland bird population in Massachusetts was 38.3 dBA [20]. These values are similar to similarly derived 24-h L_eq estimates for forest birds (42–52 dBA) and grassland birds (47 dBA) in the Netherlands [23]. These data provide a starting point for a discussion on ecological thresholds for chronic noise exposure.

In summary, noise negatively impacts wildlife, disrupts ecosystems, and has far-reaching adverse effects [8,24,25]. Additionally, the hidden costs of noise exposure in ecosystems, which can be significant [26], must be taken into account, and their consequences remain largely unexplored [27]. Research is needed to further quantify the ecological consequences of chronic noise exposure in terrestrial environments. Noise in protected areas is a significant ecological problem that requires a systematic approach to monitoring, regulation, and management in order to effectively protect biodiversity and ecosystem services.

Monitoring the soundscape and protecting natural sounds should play an integral role in safeguarding the health of ecosystems and nature conservation [28,29]. Shaping the acoustic climate in protected areas is vital for maintaining the integrity of ecosystems and ensuring animal health, making it a key element of environmental protection strategies. Properly shaping the acoustic climate and reducing noise emissions are among the priority tasks, which include, but are not limited to, monitoring noise levels, implementing noise protection programs, and applying suitable urban, technical, and legislative solutions [30]. Noise protection in protected areas necessitates a comprehensive approach. The introduction of appropriate legal regulations, such as setting permissible noise levels and utilizing technical measures, can help improve the acoustic climate in these areas [31,32].

Despite the increasing awareness of the risks associated with noise pollution, as evidenced by both the scientific literature and the legal system, significant gaps persist that necessitate further research and regulation. Noise monitoring and environmental studies are rarely conducted in protected areas in Poland and Europe. Previous environmental studies in protected areas in Poland have focused on other aspects, such as monitoring groundwater quality [33,34] and surface water quality [35]. The problem of soil contamination along transport routes in protected areas with heavy metals [36] and toxic gaseous compounds [37] has been recognized. However, these are not systematic studies conducted on a large scale. On a global scale, the monitoring of organic pollutants that are transported over long distances is becoming increasingly important [38]. Attention has also been drawn to microplastic pollution appearing in protected areas [39]. There is a growing need to support biodiversity and ecosystems against increasing pressure on nature and to monitor the impact of anthropogenic pressure on protected areas.

However, there is a notable lack of comprehensive studies on the impact of noise on biodiversity within national parks and nature reserves. The absence of empirical analyses, particularly those exploring long-term seasonal and annual variations in noise levels and their effects on the behavior and populations of wildlife, represents a critical gap in the literature [40]. Additionally, the lack of uniform methodological standards hinders a thorough assessment and monitoring of noise in forest ecosystems, considering the unique characteristics of these regions, which may result in an underestimation of the adverse effects of acoustic disturbances. Existing noise measurement techniques are often ill-suited to the specific conditions in protected areas, complicating the comparison of findings across different studies [41]. In Poland and numerous other nations, there are no specific noise standards aimed at national parks or forests, which poses challenges for implementing effective protective measures and conducting consistent noise monitoring. Noise standards are typically developed for urban, residential, industrial, or recreational areas where noise directly affects human populations [42].

The research conducted is part of a broader research project on the environmental threats posed by traffic noise in the protected area of Roztoczański National Park. Given the negative impact of noise on ecosystems, noise often hinders or even prevents these areas from fulfilling their protective functions. Monitoring studies of traffic noise levels in the Roztocze National Park were conducted over 20 years ago. Growing automotive pressure is exacerbating environmental pollution, which is why it is necessary to identify, assess, and take action to reduce noise. Research on traffic noise in protected areas can help highlight the importance of this problem on a national and regional scale.

This research aimed to assess the acoustic impact of a network of public roads crossing protected areas, using Roztocze National Park (RPN) as a case study, and to characterize noise propagation as a factor that pollutes the environment and disrupts natural forest ecosystems. The research was conducted to answer the following questions: (i) what is the traffic noise load along road routes? (ii) what is the spatial range of this impact (250 m, 500 m)? (iii) what is the nature of sounds at different distances from the road (anthropogenic, natural)?

A protected area was selected for the research, given the limited availability of scientific studies and the lack of systematic noise monitoring in such areas in Poland. An aim that supports the main objective of the study was to emphasize the importance of systematic monitoring and protecting these areas from the harmful effects of noise. Field measurements were conducted at various distances from traffic routes to correlate sound intensity values with the proximity to the source. The analysis also accounted for the terrain relief, which, along with the distance from the noise source, affects sound wave propagation. Furthermore, recordings of the soundscape were made and analyzed to determine the relationship between the sources of the identified sound phenomena.

It is essential to emphasize the need for systematic noise monitoring and protective measures in protected areas. Research is needed on instrumentation and research methods, as well as on expanding monitoring capabilities. Experimental research should become an integral part of future management plans to identify the most effective and efficient methods. In protected areas such as national parks, noise constitutes environmental pollution and disrupts natural forest ecosystems.

2. Materials and Methods

2.1. Characteristics of the Research Area

This research focuses on the protected area of Roztocze National Park in Poland, which spans 8456.83 ha, with forests covering about 95% of this area. Strict protection applies to 1000 ha (12%), while the park’s buffer zone extends over 38,000 ha. Roztocze National Park features a unique transportation network, intersected by regional and district roads, hiking and cycling trails, and a railway line [43]. Major thoroughfares, such as Roads 835 and 844, provide access to the park’s eastern and southern sections. However, public transportation within Roztocze National Park is limited. While bus and rail connections operate between larger towns in the park, many residents and tourists prefer private transportation due to the sparse and irregular nature of these connections. The natural beauty of Roztocze National Park—including its rich flora and fauna and unique landscapes—draws numerous visitors each year, increasing the anthropopressure on the area. Tourist traffic in the park has risen notably, especially in recent years. In 2015, the number of tourists was approximately 120,000 [44]. According to a tourist attendance report from 2022 [45], the park welcomed 337,800 visitors in that year. It is estimated that tourist numbers reached 400,000 in 2024. The protection plan for Roztocze National Park does not explicitly define noise standards in technical units but implements measures to minimize its environmental impact [46]. Noise standards are governed by general environmental regulations, which establish a framework for the park’s activities [47,48].

2.2. Measurement of Equivalent Continuous Sound Level

In the first stage of the study, field measurements were taken of the equivalent sound pressure level (L_Aeq), a key indicator of noise pollution. The direct measurement method was employed, providing an accurate representation of the environmental acoustic character. This approach allows for the most precise depiction of the acoustic climate. The study utilized the direct measurement method alongside sampling.

The measurements were carried out in accordance with ISO 1996-1:2016 [49] and ISO 1996-2:2017 [50] standards. The measurements were conducted under suitable atmospheric conditions: the ambient temperature was above 5 °C, the wind speed did not exceed 5 m·s⁻¹, there was no precipitation, and the road surface was dry. The field tests were conducted between 26 May and 30 May 2024, from 8:00 a.m. to 4:00 p.m. The measurement time at each measuring point was five minutes.

A four-channel SVAN 958 meter (Svantek, Poland) was used for measurements—a first-class device that includes a preamplifier, 1/2″ measurement microphone, 10-m microphone cable, windscreen, tripod, and calibrator (1000 Hz, 114 dB). The specifications are presented in Table 1. The device was configured with the following parameters: the elementary signal recording time was 5 min, correction characteristic A, FAST response time, a measuring range of 110 dB, and an integration time set to 1 s. During measurements, the microphone was positioned 1.5 m above the measuring surface and perpendicular to the axis of the vehicles. The sound pressure level, expressed in dB and corrected according to the A frequency response curve (L_pA), is calculated as follows:

$$L_{pA}=10log{\displaystyle \frac{p_A}{p_0}}$$

(1)

where:

p_A—sound pressure level corrected according to the A-frequency characteristic,

p₀—reference pressure level of 2 × 10⁻⁵ Pa, hearing threshold for 1000 Hz.

Table 1. Technical specifications of the SVAN 958A sound and vibration meter (Svantek, Poland).

Parameter	Characteristics
Compliance standards	IEC 61672-1:2013 [51] (sound, class 1), ISO 8041-1:2017 [52] (vibration)
Frequency range	0.5 Hz–20 kHz
Microphone type	MK 255, 50 mV/Pa
Total SPL measurement range	16 dBA RMS ÷ 140 dBA Peak
Linear operating range in accordance with IEC 61672	26 dBA Leq ÷ 140 dBA Peak
Internal noise	<16 dBA
Corrective filters	A, C, Z (for sound), G (for vibrations)
Time constants	Slow, Fast, Impulse
Frequency analysis	Analysis in 1/1 and 1/3 octave bands
Internal memory	32 MB of memory

Table 1. Technical specifications of the SVAN 958A sound and vibration meter (Svantek, Poland).

Parameter	Characteristics
Compliance standards	IEC 61672-1:2013 [51] (sound, class 1), ISO 8041-1:2017 [52] (vibration)
Frequency range	0.5 Hz–20 kHz
Microphone type	MK 255, 50 mV/Pa
Total SPL measurement range	16 dBA RMS ÷ 140 dBA Peak
Linear operating range in accordance with IEC 61672	26 dBA Leq ÷ 140 dBA Peak
Internal noise	<16 dBA
Corrective filters	A, C, Z (for sound), G (for vibrations)
Time constants	Slow, Fast, Impulse
Frequency analysis	Analysis in 1/1 and 1/3 octave bands
Internal memory	32 MB of memory

Before and after the measurements, the sound level meter was checked in the environmental conditions under which the tests were conducted using a calibrated class I acoustic calibrator, SV 33B, 1000 Hz/114 dB. If the meter did not pass the test, it was recalibrated. The background noise level during calibration did not exceed 75 dB. During the field tests, the meter was calibrated at least once before starting measurements in each test area, meaning at least once for every nine measurements. The remaining parameters of the meter were checked with similar frequency. The measurements focused on traffic noise but also included natural environmental noises such as birds singing and the sound of the wind. The background noise was recorded when there was no vehicle traffic and was included in the environmental analysis. The contribution of the acoustic background was subtracted from the total sound energy level based on Equation (2).

$$L_{AeqT}=10{log}_{10}\left({10}^{{\displaystyle \frac{L_{total}}{10}}}-{10}^{{\displaystyle \frac{L_{background}}{10}}}\right)$$

(2)

where:

L_AeqT—sound level,

L_total—total sound level including background noise,

L_background—background noise level.

The measured equivalent sound pressure levels for points at the same distance from the traffic route in a single test area were averaged using the following formula:

$$L_{Aeq \left(mean\right)}=10 \mathrm{l}\mathrm{o}\mathrm{g}\left[{\displaystyle \frac{1}{N}}{\sum }_{i=1}^N{10}^{{0.1L}_{Aeq T}}\right]$$

(3)

The formula used to calculate the standard deviation is as follows [53]:

$$\sigma =\sqrt{{\displaystyle \frac{1}{n-1}}{\sum }_{i=1}^n{\left(L_{AeqT}-L_{AeqT\left(mean\right)}\right)}^2}$$

(4)

where:

L_AeqT(mean)—average equivalent sound level for the dataset,

n—number of samples (data points) used in the calculation,

L_AeqT—equivalent sound level for a given sample.

2.3. Location of Measuring Points

The measuring points are situated along four public roads: Provincial Road No. 858 and district roads 3252L, 2951L, and 3250L. The measuring points were spaced at intervals of 1.5 km in three lines along each of the four traffic routes. Each line corresponds to specific distances from the road, presented in the following order: a point 5 m from the traffic route (7.5 m from the road axis according to the standard [49]), a point 250 m from the traffic route, and a point 500 m from the traffic route. Consequently, four test areas were established: P1, P2, P3, and P4 (Figure 1), where nine measuring points were distributed. All test surfaces were allocated throughout different parts of the test area to ensure maximum variability. For each profile, the Pearson correlation coefficient was calculated between the sound intensity value (in dB) and the distance of the measuring point from the traffic route (in meters).

2.4. Analysis of Audio Recordings

Audio recordings were made at all measurement points using a condenser microphone with omnidirectional characteristics. The microphone sensitivity was set to its highest value, and the sampling frequency was configured at 48 kHz. For environmental recordings, the sampling frequency should be chosen based on the recording’s purpose and the required level of detail. A sampling frequency of 48 kHz ensures sufficient quality to capture the general nature of the environmental sounds [55]. The recordings were made simultaneously with the measurements taken by a noise meter. In a subsequent step, the recordings were analyzed concerning the components of sound phenomena, primarily regarding their origin: natural and anthropogenic sounds, as defined in [56].

2.5. Landform Analysis and Wave Propagation Modeling

The locations of the measuring points were presented on various map backgrounds, facilitating further analysis. A high-resolution orthophotomap was utilized for this purpose, depicting the approximate spatial arrangement of the areas under investigation, such as the distribution of greenery and communication routes [57]. Another base map used was the hypsometric map, which represents the surface relief of the terrain using isohypses and corresponding colors. Its purpose is to visualize height differences in land areas. The key colors used on these maps are green for lowlands, yellow for highlands, and orange and brown for mountains. This color system is designed not only to accurately represent altitude but also to convey a three-dimensional impression [58]. Additionally, the hypsometric map is juxtaposed with a digital terrain model. A digital terrain model (DTM) is a digital representation of the Earth’s surface, mapped using points with known spatial coordinates (X, Y, Z). The study employed a regular GRID model (meter-by-meter grid), where each node is assigned a height value. With the aid of the DTM, it became possible to determine the height of each measuring point with an accuracy of 0.1 m [59]. Utilizing the above map layers at a later stage enabled the comparison of the measurement results obtained with the meter with various spatial data.

In the context of the complexity of acoustic wave propagation in a forest environment, a spatial analysis was conducted to illustrate the impact of noise sources in conditions of varied vegetation structure and terrain in Roztocze National Park. Particular emphasis was placed on analyzing the impact of topography and land cover. The Noise Prediction: Calculate Noise Level From Site plugin in the QGIS (3.34.12 version) environment, based on an algorithm compliant with the BS 5228-1:2009 standard [60], was used for modeling. The choice of this standard was justified by its compliance with the requirements of environmental documentation such as the Environmental Impact Statement (EIS) and its direct applicability in the natural environment.

The model was based on the Digital Terrain Model (DTM) in the PL-EVRF2007-NH system, with a 1 m grid, provided by the Main Office of Geodesy and Cartography (Poland). The sound reception height was set at 1.5 m above ground level and the space was divided into hexagons with sides of 30 m. Soft ground and no reflections (reflection value = 0) were assumed, in accordance with the acoustic properties of a typical forest habitat. The noise source was located by the road, and its emission was determined on the basis of actual field measurements.

2.6. Statistical Analysis

The statistical analysis investigated the relationship between distance from the road (in meters) and noise level, measured in decibels (dB). Data were collected from measurements taken at three distinct distances from the road: 5 m, 250 m, and 500 m, allowing for an assessment of how noise levels change with distance from the source of sound emission. Pearson’s correlation coefficient (r) was applied to evaluate the strength and direction of the relationship between these variables. To determine the statistical significance of the results, the p-value was calculated using a standard significance level of α = 0.05. In all instances, the p-value was below 0.05, indicating that the calculated correlation coefficients are statistically significant. For each study area, the standard deviation (σ) was also computed for the average value of L_Aeq (dB) at points representing specific distances from traffic routes. When comparing the four areas, observable differences in values arise, influenced by various factors (e.g., spatial conditions).

3. Results

Acoustic measurements were carried out in an area with varied terrain, typical of Roztocze National Park, characterized by an undulating landscape with numerous hills and depressions. It was predominantly forested, with grassland–forest habitats, particularly oak–hornbeam and mixed forest communities. The undergrowth and litter layer were well developed, featuring a structure conducive to sound absorption. The substrate mainly consisted of brown and luvisols formed on loess and loamy sands. At the measurement points along forest roads, mineral substrate was present—soil with turf and fragments of organic cover. The periphery of the park also included arable fields and areas of homestead development with extensively transformed soil. The variation in land cover, habitat types, and substrate properties significantly influenced the propagation of acoustic waves, altering their reflection, absorption, and scattering.

3.1. Research Area P1

Research area P1, located along Provincial Road No. 858, was characterized by a significant variation in equivalent sound level depending on the distance from the thoroughfare and the terrain (

Table 2. Results of equivalent sound level measurements in research area P1.

Profile	Distance from Road (m)	LAeqT (dB)	LAeq(mean) (dB)	Height a.s.l. (m)	Dominant Sounds
A	5	70.4	67.3 ± 3.6	270.6	Anthropogenic
B	5	64.2		251.5
C	5	64.0		234.6
A	250	47.0	43.4 ± 4.8	292.4	Natural and anthropogenic
B	250	39.7		256.9
C	250	38.1		246.3
A	500	35.3	41.5 ± 4.4	290.6	Natural
B	500	43.5		274.0
C	500	42.0		276.8

).

At the measuring points located directly by the road, the highest sound intensity was recorded, averaging 67.3 dB, due to the heavy traffic, including trucks. The analyzed traffic route connects the region’s key cities: Biłgoraj, Zwierzyniec, and Zamość. In addition to tourist traffic, there is also transit traffic, which further increases noise emissions in the area. As the measuring points moved further from the road, the noise level decreased significantly. The most considerable decrease, averaging 23.9 dB, was noted between the points closest to the road and the middle points (located 250 m away). This relationship aligns with the theory of acoustic physics, which states that sound intensity diminishes with increasing distance from its source, following the principles of sound wave dispersion in space [61]. The reduction in sound intensity may be slightly non-linear, as indicated by the values, but the trend is clearly downward. In profiles B and C, higher sound intensity values were observed at points furthest from the road, which can be explained by differences in terrain height that modify the propagation of acoustic waves.

The audio recordings reveal the differences between the components of the audiosphere in various parts of the research area. The anthropogenic sounds, mainly traffic noise, were the most dominant nearest to the road. At a distance of 250 m from the road, anthropogenic sounds blended with natural sounds; the road noise became much less noticeable here, while the singing of birds and the rustling of trees became much more audible. At a distance of 500 m from the traffic route, no anthropogenic sounds were recorded; only natural sounds, particularly the singing of birds, were audible.

3.2. Research Area P2

Research area P2, located on District Road No. 3250L, had a lower equivalent noise level in the immediate vicinity of the road (

Table 3. Results of equivalent sound level measurements in research area P2.

Profile	Distance from Road (m)	LAeqT (dB)	LAeq(mean) (dB)	Height a.s.l. (m)	Dominant Sounds
A	5	57.9	63.3 ± 3.9	236.5	Anthropogenic
B	5	64.7		235.5
C	5	64.5		234.1
A	250	44.1	45.3 ± 6.7	243.0	Natural and anthropogenic
B	250	48.6		255.5
C	250	35.4		274.1
A	500	32.8	40.6 ± 7.5	245.9	Natural
B	500	45.0		262.4
C	500	31.4		316.0

) compared to area P1. This difference is attributed to the lower traffic volume typical of local roads. The category of the road influences vehicle traffic intensity; higher road categories, due to their capacity and functionality, generate more traffic. In contrast, local and district roads primarily serve a supplementary role in the transportation network.

The highest sound pressure levels (63.3 dB) were recorded at points closest to the road. As the distance from the traffic route increased, the noise level decreased significantly—on average by 18 dB between the 5 m and 250 m points and by 4.7 dB between the 250 m and 500 m points. The noise levels recorded at measuring points away from the road were similar to those obtained at surface P1. The topography of test area P2 reveals notable differences, particularly in profiles B and C, where significant height differences were recorded—26.9 m and 81.9 m, respectively—between the points closest to and farthest from the road. The sound measurement in profile C, located at an altitude of 316 m above sea level, is particularly noteworthy, as it recorded the lowest sound values (31.4 dB).

The characteristics of the audiosphere at various distances from the road resembled those described at the P1 test site.

3.3. Research Area P3

Research area P3, located on District Road No. 3252L, has the highest equivalent sound level at the points closest to the thoroughfare among all the areas tested, despite its lower road category. The average noise level was 68.4 dB, with a maximum level of 71.2 dB recorded in profile B (

Table 4. Results of equivalent sound level measurements in research area P3.

Profile	Distance from Road (m)	LAeqT (dB)	LAeq(mean) (dB)	Height a.s.l. (m)	Dominant Sounds
A	5	68.1	68.4 ± 5.2	238.5	Anthropogenic
B	5	71.2		245.0
C	5	61.0		246.0
A	250	41.8	42.3 ± 1.7	259.9	Natural and anthropogenic
B	250	40.0		265.8
C	250	43.9		262.1
A	500	38.4	39.6 ± 1.4	265.2	Natural
B	500	38.8		301.6
C	500	41.0		263.0

). As with the other test areas, the sound level decreased with distance from the traffic route. The sound intensity dropped by 26.1 dB and 2.7 dB, respectively, with distance from the road. The high noise level (68.4 dB) recorded in the Park can pose a serious threat to local wildlife and negatively affect the acoustic comfort and health of residents and tourists.

The analysis of the terrain revealed differences in height, with the most significant variation in profile B, reaching a maximum of 56.6 m, where the farthest point is situated on a distinct rise. Profile A is characterized by a lack of acoustic insulation in the form of trees, which further amplifies noise near the road. The distribution of dominant sounds confirms that anthropogenic sources, primarily car noise, prevail near the road, while natural sounds, such as birdsong, are more prevalent at points 500 m away. These results emphasize that both relief and land use influence sound propagation, and that the absence of acoustic barriers, such as trees, can increase the impact of traffic noise in open areas.

3.4. Research Area P4

Research area P4, situated along District Road No. 2951L, was characterized by a high average noise level of 66.8 dB at measurement points directly adjacent to the road (

Table 5. Results of equivalent sound level measurements in research area P4.

Profile	Distance from Road (m)	LAeqT (dB)	LAeq(mean) (dB)	Height a.s.l. (m)	Dominant Sounds
A	5	64.1	66.8 ± 2.3	249.3	Anthropogenic
B	5	66.5		252.1
C	5	68.6		252.0
A	250	37.1	38.3 ± 1.2	269.5	Natural and anthropogenic
B	250	38.3		293.1
C	250	39.4		255.2
A	500	30.3	42.7 ± 9.1	273.7	Natural
B	500	47.2		299.3
C	500	33.0		259.3

). At a distance of 250 m from the road, an average decrease of 28.5 dB was noted, which aligns with the general trend of sound intensity diminishing with distance from the noise source. In measurement profiles A and C, the noise level decreased as the distance from the road increased. However, in profile B, similar to profiles B and C on surface P1, an increase in the sound level of approximately 9 dB was observed between points 250 m and 500 m away. The higher values in profile B at the most distant measuring point may result from other factors. In this profile, the midpoint is located on a slope, and the point farthest from the road is situated on a hill with an elevation of 299.3 m above sea level. At this location, the highest value was recorded at the greatest distance from the road across all test surfaces. This raises further questions about the validity of this point, which sits on the eastern side of the slope, suggesting that it may be somewhat isolated from the source of road noise. The elevated sound level is likely not attributable to spatial conditions.

The analysis of the terrain revealed various spatial conditions. Profile B is characterized by the most significant differences in height (47.2 m), which could affect sound propagation. Profile A includes points situated in a slight depression between hills; however, no anomalies were found in the measurement results at this location. Profile C is almost flat, minimizing the influence of spatial factors on the test results. The apparent variability in sound intensity at different distances from the road, across different measurement profiles, may result from external factors, such as environmental interference, sound reflections, or irregularities in the sound source itself. The composition of the audiosphere at the P4 surface was similar to that recorded at other surfaces: anthropogenic sounds dominated near the road, while natural sounds, such as birdsong, prevailed at points 500 m away, indicating a limited impact of road noise at a greater distance from the source.

3.5. Results of Sound Propagation Modeling

BS 5228-1:2009 [60] and ISO 9613-2:2024 [62] are among the most commonly used standards for predicting noise levels in open areas. However, they differ in both their methodological assumptions and scope of application. ISO 9613-2 is based on an empirical model that takes into account the propagation of sound from large industrial and linear sources, including losses resulting from atmospheric absorption, screening, terrain type, and meteorological conditions. It is a highly specialized model designed for long-distance noise propagation, most often used in analyses related to industrial or transport infrastructure. At the same time, it requires a large amount of precise input data and is most often implemented in specialized, paid software.

Unlike this approach, BS 5228 stands out for its operational flexibility and adaptation to practical field applications, making it extremely useful in environmental analyses conducted in natural conditions—especially where spatial assessment of the impact of individual noise sources is important. Its methodology takes into account key factors affecting sound propagation, such as distance, topography, ground type, and natural terrain obstacles, while limiting the amount of data required. Importantly, BS 5228 can be successfully used in open GIS software such as QGIS (3.34.12 version), which increases the accessibility and usability of this standard in acoustic analyses in forest areas, national parks, and other protected areas. The results of sound propagation modeling from individual research points are presented in Figure 2, Figure 3, Figure 4 and Figure 5. On the P1 research site (Figure 2), modeling showed a significant impact of terrain on sound wave propagation. The highest traffic noise intensity was recorded in measurement profile A, where the L_Aeq sound level reached as high as 70.4 dB. It can be seen that the acoustic impact in this profile covered a much larger area than in profiles B and C. However, its range was clearly limited by the elevation that separates the noise source from the deeper part of the forest—the sound propagated mainly along the road. In profiles B and C, the direct L_Aeq measurements were very similar. Noise propagation modeling showed a regular and comparable impact range in these profiles, with slight deviations resulting from local differences in the relatively flat terrain.

Figure 2. Results of sound propagation modeling on research area P1, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

Figure 2. Results of sound propagation modeling on research area P1, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

In study area P2 (Figure 3), measurement profile A is located on flat terrain. The noise propagation modeling revealed a very regular pattern of sound dispersion, with a relatively limited range of influence. The measured L_Aeq sound level at the roadside measurement point was 57.9 dB. The regularity of propagation and the limited range are due to the absence of terrain-induced interference and the strong attenuation of acoustic waves by the ground surface, which is related to the specific characteristics of the local soil conditions. In profiles B and C, higher L_Aeq values were recorded, resulting in a greater extent of noise impact. The terrain in these profiles is more varied—the model shows small depressions caused by local undulations. A key observation, however, is that in both cases, the range of noise propagation terminates approximately at an elevation, which serves as a natural barrier limiting sound propagation.

Figure 3. Results of sound propagation modeling on research area P2, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

Figure 3. Results of sound propagation modeling on research area P2, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

Study area P3 is characterized by varied terrain morphology (Figure 4). Measurement profile A, where an L_Aeq value of 68.1 dB was recorded, exhibits a relatively regular pattern of noise propagation. Sound mainly disperses toward flat areas—agricultural fields and scattered rural buildings. The acoustic impact is more limited in the southern direction, where the terrain rises, as well as toward the forested area. Particular attention should be paid to measurement profile B, which stands out among those analyzed. This profile recorded the highest L_Aeq value of all study areas. A possible reason for such a high noise level is the presence of a straight road segment, which may encourage drivers to increase their speed. Additionally, the terrain morphology on the opposite side of the road—elevations running parallel to the roadway—may contribute to sound wave reflection and amplification. The modeling indicated a large range of noise propagation in this profile—both along the road and extending into the forest. In measurement profile C, a significantly lower L_Aeq value was recorded, and the range of sound wave propagation was more limited. Nevertheless, the model reveals local variations in terrain morphology that may disrupt the regularity of noise propagation.

Figure 4. Results of sound propagation modeling on research area P3, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

Figure 4. Results of sound propagation modeling on research area P3, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

The final analyzed study area, P4, includes three measurement profiles, which differ significantly in terms of terrain morphology (Figure 5). Noise propagation modeling in measurement profile A highlighted the important role of a small valley—a portion of the sound wave range is seen to ‘penetrate’ into this terrain depression. It can be assumed that under higher noise levels, the range of acoustic impact would extend further along the valley. Modeling results for measurement profile B revealed a relatively regular pattern of propagation. It is possible that the hill in this area plays a dual role—on the one hand, it may amplify sound by focusing it in a particular direction, while on the other, it may act as a barrier limiting acoustic wave propagation deeper into the forest. Measurement profile C is, in terms of terrain characteristics, similar to profile A in study area P2. The terrain is flat, and the modeling showed a regular propagation pattern with minor disturbances caused by small undulations in the northern part of the profile, near the road. Nevertheless, the recorded L_Aeq value was relatively high, and the range of noise impact extended considerably into the forest.

Figure 5. Results of sound propagation modeling on research area P4, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

Figure 5. Results of sound propagation modeling on research area P4, with direct measurement values of L_Aeq (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]).

The results of the analysis demonstrated a clear influence of landscape structure on sound propagation. Areas located behind natural terrain barriers, in valleys, or within densely wooded forest sections exhibited a marked reduction in noise levels, which is consistent with theoretical assumptions regarding the scattering and attenuation of sound waves by vegetation structures. At the same time, it was observed that in many measurement points, the dominant acoustic components were natural sound sources, such as bird songs and the activity of other forest animals. Their presence had a significant effect on the overall soundscape, particularly in locations distant from the road.

The use of modeling based on the BS 5228 standard allowed not only for the spatial representation of noise levels but also for the assessment of the relationship between anthropogenic traffic noise and the natural acoustic background of the forest. By integrating field data with digital terrain models, it was possible to obtain analytical results without the need for expensive expert software. In comparison to ISO 9613-2, the BS 5228 standard proved to be a more flexible, accessible, and suitable solution for the environmental conditions of the study area. Its application in forest environments enables the execution of realistic and spatially differentiated analyses of the acoustic climate, providing a valuable foundation for further research employing more advanced modeling methods and tools.

However, significant discrepancies were identified between the modeled results and the actual values recorded deeper within the park. In many locations, particularly those farther from the road, the measured noise levels were higher than those predicted by the spatial model. This phenomenon was primarily due to the presence of other acoustic events characteristic of the natural environment. Notably, loud bird songs, especially in areas with high avian activity, played a significant role. Natural sound sources were not incorporated into the propagation model, which partially explains the elevated values observed in direct measurements. These observations highlight the need to account for the full, complex soundscape of natural environments in acoustic analyses, particularly in the context of protected areas.

4. Discussion

The highest sound levels were observed at points directly along the road routes, ranging from 57.9 to 71.2 dB, attributed to traffic noise. The recorded sound levels, including those from points 250 m away from the traffic routes, often exceeded 40 dB (35.4–48.6 dB), encompassing both natural and anthropogenic sounds, demonstrating the acoustic impact of the road routes at this distance. At points 500 m from the roads, sound levels primarily ranged from 33.0 to 42.0 dB, with exceptionally high levels measuring between 43.5 and 47.2 dB. Natural sounds dominated at this distance. Although there are no specific regulations on noise threshold values for environmentally protected areas, it can be concluded that the recorded traffic noise levels were high across all study areas. The most significant noise impact was noted in the immediate vicinity and at a distance of 250 m from the traffic routes. The measurement results indicate a clear relationship between the distance from the noise source and sound level; the sound levels at the points closest to the road were the highest, decreasing as the distance increased. However, in test area P1 for measurement profiles B and C and in test area P4 for measurement profile B, higher sound levels were recorded at a distance of 500 m. In all analyzed research areas, the differences in noise levels between points located from 0 m to 250 m from the road ranged from 13.8 dB to 35.6 dB, with further distances from the traffic route showing no similarly significant changes. Similar findings were reported in [30], which stated that traffic noise was significantly reduced at approximately 500 m from the road and was perceived as a relatively monotonous hum. In flat areas, sound propagation was superficial, whereas in hilly regions, sound waves traveled along depressions, reaching greater distances with higher volume. The Pearson correlation coefficients (

Table 6. Pearson correlation coefficients (r) for all profiles (A–C) in all research areas (P1–P4).

P1	r	P2	r	P3	r	P4	r
A	−0.982	A	−0.998	A	−0.914	A	−0.945
B	−0.783	B	−0.939	B	−0.887	B	−0.668
C	−0.787	C	−0.916	C	−0.924	C	−0.938
A	−0.982	A	−0.998	A	−0.914	A	−0.945

) were negative for all measurement profiles on all test surfaces, indicating a strong, negative, and statistically significant relationship between distance and sound intensity. The correlation coefficient values ranged from r = −0.668 to r = −0.998, suggesting a strong to very strong negative relationship between the analyzed variables. The physical principles governing sound waves explain this: as distance increases, sound energy dissipates, resulting in decreased intensity recorded by the meter. This phenomenon is rooted in the laws of physics; in open space, sound intensity diminishes with distance from the source according to the inverse-square law, assuming even dissipation of sound energy. The strong negative correlation emphasizes that distance is a key factor in recorded sound intensity, aiding in monitoring environmental noise and assessing how far from the sound source the noise becomes harmless.

An essential element of the research was considering the impact of the terrain on sound propagation. Sound propagation in natural environments is a multifaceted phenomenon involving interactions between ground surface, atmospheric conditions, and topographical features. The propagation of acoustic waves in roadside areas is determined by both the energy of the sound source and the properties of the medium. Under open terrain conditions, meteorological factors play a significant role in the propagation of sound energy. Wind speed and direction, temperature gradients, humidity, and atmospheric stratification influence sound propagation, causing refraction, scattering, and variations in sound level and attenuation. The effects vary depending on distance, ground surface cover, and sound frequency. Sound pressure level increases by approximately 0.4, 0.8, and 1.9 dB per 1 m/s increase in wind speed [63]. Studies of road traffic noise propagation in open terrain have shown a clear reduction in excessive attenuation with increasing wind speed at distances of 40 and 140 m from the source [64]. Atmospheric effects (stratification and wind shear) can raise sound levels by 10–20 dB at significant distances from the road, potentially causing violations of environmental noise limits [65]. Meteorological phenomena, including temperature gradients, can lead to the refraction of sound waves—bending them either downward or upward depending on the type of temperature inversion [66]. Wind direction and speed also significantly influence sound propagation—observed changes in excessive attenuation strongly correlate with wind parameters [67].

Another important factor affecting sound propagation is topography and surface morphology, which causes reflection, absorption, and diffraction of sound waves as they interact with the ground, vegetation, buildings, and other objects. It has been demonstrated that sound intensity varies with terrain form. Sounds are perceived differently depending on slope and elevation due to complex interactions between sound waves and terrain features [68]. Perceived sounds vary with height differences: in flat terrain, sound propagates evenly in all directions, while variations in terrain act as obstacles that scatter sound, sometimes reducing propagation speed [69]. Complex or irregular geomorphological terrain is associated with increased sound attenuation, amplification, or shielding, depending on its shape and land cover.

Vegetation is also a critical factor influencing noise propagation. Field measurements have shown that sound attenuation in forests averages approximately 7 dB per 30 m (about 100 feet), depending on vegetation type and terrain conditions [70]. However, attenuation effectiveness can vary with tree species, canopy density, and meteorological conditions. Coniferous stands reduce noise propagation in both summer and winter (by up to 18%), while deciduous trees exhibit such properties only during the growing season [71]. Ground surfaces, such as grass or snow, create complex reflection coefficients, altering the sound spectrum. Terrain morphology and surface cover type significantly affect sound propagation. The ground covered with grass and vegetation has a high sound absorption capacity, particularly at higher frequencies, due to its porous structure and moisture retention capacity [72]. In contrast, mineral soils and hard surfaces, such as asphalt, reflect acoustic waves across a wider range of frequencies.

Hilly terrain can create shadow zones or multiple reflections. In sloped terrain, compared to flat ground, sound pressure levels can increase by up to 30 dB at elevated points. Additionally, complex temperature profiles and atmospheric conditions further modify noise levels, resulting in fluctuations ranging from −3 dB to +10 dB compared to a homogeneous atmosphere.

Theoretical analyses of sound propagation in natural environments, particularly in forest ecosystems, indicate complex interactions between acoustic waves and structural elements of vegetation. Trees, forest understory, and terrain morphology play significant roles in attenuating, scattering, and refracting sound waves. Understanding these processes is crucial for effective noise management in spatial planning and the protection of natural environments.

The literature describes several primary acoustic mechanisms occurring in forest environments. Destructive interference results from the interaction between direct waves and those reflected from the ground surface, which is often acoustically soft due to the presence of forest litter. Scattering of acoustic waves by trunks, branches, and twigs leads to a loss of wave coherence, thereby increasing sound attenuation in the mid- and high-frequency ranges. Additionally, the density and morphology of leaves significantly influence sound absorption efficiency—empirical modeling demonstrates a relationship between leaf surface area and attenuation level [73].

Advanced sound propagation models, such as the Finite-Difference Time-Domain (FDTD) method, enable the simulation of reflections and diffraction in forest environments while accounting for variable structural parameters of trees [74]. Energy-based radiative transfer theory allows for the estimation of acoustic fields at forest edges by treating vegetation as an effective medium [75]. Modern predictive models additionally incorporate ground impedance effects and vertical sound speed profiles, which significantly enhance the accuracy of acoustic forecasts [75].

Thus, accurate sound-level predictions require detailed numerical computations that consider both terrain and atmospheric heterogeneity [76]. Accurate noise propagation predictions must simultaneously account for all these phenomena. In this context, ISO 9613 provides an empirical foundation for assessing sound propagation under diverse terrain and meteorological conditions.

The varying elevations, valleys, and slopes within the RPN areas under study altered the intensity of the noise. For example, in elevated regions, despite the greater distance from the road, the noise was sometimes more intense due to the lack of natural sound-dampening barriers. This phenomenon was particularly evident at measurement points located on slopes, where sound could reflect and spread more intensely than on flat areas. However, the measuring point on surface P2, in profile C, 500 m from the road, stands out as the one with the lowest sound intensity of all measuring points in the park. Another example is surface P4 in profile B, where the noise level was relatively high compared to the values of other points on this surface, despite the presence of natural acoustic barriers. Therefore, it can be concluded that the terrain is not the only factor affecting the noise level at the analyzed points.

The average noise levels at different distances from the traffic routes exhibit a general downward trend; however, an analysis of the Pearson correlation coefficients and standard deviations for each sample reveals that an individual approach to sound propagation is necessary for each measurement profile. The observed differences are attributed to topography, vegetation cover, and the overlap of natural and anthropogenic sounds.

As noted in the study [77], the harmfulness of noise in the natural environment depends on several factors, including its amplitude and spectrum, propagation conditions from the source, acoustic background, and the auditory perception of animals. Land use also influences the propagation and immission level of noise. Key factors include the type of development, the presence of greenery, and the type of land use. Buildings serve as sound barriers or sources of noise reflections, while green areas effectively lower noise levels through sound absorption [78,79]. The diversity of land use is especially evident in the P3 research area.

The analysis also included recordings of environmental sounds, enabling a more comprehensive capture of the acoustic characteristics of the area under investigation by distinguishing between anthropogenic (caused by human activity) and natural (originating from the surrounding environment) sounds. The main component was anthropogenic sounds, such as noise generated by road traffic. Conversely, natural sounds, such as birdsong, the sound of the wind, and animal noises, created a background disturbed by human activity. The anthropogenic sounds in the studied area included traffic noise, specifically the sounds of vehicle engines, and tires rolling on the surface. The highest noise levels were recorded near major provincial and district roads. The research revealed that these sounds are continuous, with periodic increases in volume when heavier vehicles pass by. While the traffic noise was predominantly monotonous, varying traffic intensity and differences in road surfaces also caused variable sounds, including vibrations and thundering linked to heavy vehicle passage. The natural sounds typical of the Roztocze National Park ecosystem contrasted sharply with the traffic noise. Nature’s sounds, such as birds singing, the calls of other animals (like deer), rustling leaves, and wind in the treetops, predominated. These sounds positively affect people, enhancing the functioning of the nervous system [80]. However, it should be noted that human activity, including traffic noise, significantly disturbed the park’s natural audiosphere, introducing undesirable changes to the ecosystem. In areas with higher noise levels, natural sounds were often suppressed or completely vanished, negatively impacting animals and their behavior. This phenomenon also affected the comfort of tourists visiting the park, disrupting their wildlife observation experience. Undisturbed natural sounds were only present at points 500 m away from traffic routes, including those with relatively high measured sound intensity. Anthropogenic sounds, primarily traffic noise, were detected at points 250 m away from thoroughfares, even though the sound levels at these points were relatively low in most cases.

The recordings illustrate a clear correlation between the distance from the traffic route and the audiosphere, which refers to the overall sound environment in which individuals perceive sound by interpreting various acoustic stimuli. In the immediate vicinity of the road, the predominance of anthropogenic sounds was expected. In contrast, at points farther away from the road, the recorded audio files enhance the noise immission analysis by incorporating spatial factors. These audio files contain sound phenomena captured during the measurements with a sound level meter. Analyzing the audio recordings from each test area revealed significant differences in the sounds occurring at varying distances from the traffic route. The recordings from the points nearest to the traffic routes primarily exhibit road noise. Other sound phenomena that should accompany these locations, such as the rustling of trees or birdsong, are not discernible. In recordings from points 250 m away from the roads, traffic noise remained present, but natural sounds, including birdsong, were also audible. In recordings from the furthest points away (500 m) from the roads, no anthropogenic sounds were detected; only natural sounds, such as the rustling of trees and birdsong, were heard. Therefore, it can be concluded that despite the higher sound level values detected at some of the measuring points furthest from the traffic routes, these points are the least affected by traffic noise.

A pilot study conducted in 2017 on the impact of traffic noise on birds in Roztocze National Park [81] indicates a clearly negative effect. Birds are the species most affected by road noise, as they rely on sound for their fundamental behaviors [82]. Noise levels exceeding 60–65 dB can disrupt birds’ singing, hindering their ability to communicate effectively during the breeding season and navigate their environments. For birds of prey, noise can diminish their hunting capabilities by interfering with their hearing and their response to sounds that signal the presence of prey [83,84]. This study guided the selection of test areas for the current research, enabling a comparison of noise level measurements at these locations. The measurements from the pilot study are presented in Table 5.

The data presented in

Table 7. Results of equivalent sound level measurements in Roztocze National Park.

Research Area Number	Sound Level at Different Distances from the Traffic Route [dB]
	I Distance Category		II Distance Category		III Distance Category
P1	64.2 *	67.3 **	54.5 *	43.4 **	51.8 *	41.5 **
P2	47.5 *	63.3 **	37.8 *	45.3 **	35.1 *	40.6 **
P3	56.9 *	68.4 **	47.2 *	42.3 **	44.5 *	39.6 **
P4	53.6 *	66.8 **	43.9 *	38.3 **	41.2 *	42.7 **

* Results are presented in [81]. ** Own results.

indicate that the highest noise levels were observed at the measurement points closest to transportation routes. The values were lower by up to approximately 15 dB than those found in the current study. This illustrates the increase in traffic noise pollution in the park and the significant burden this pollution poses on the environment. At the II distance category measurement points, the differences between the noise levels observed in these surveys and those presented in the report were smaller (5–11 dB), with the exception of the P2 area. In the III distance category, higher sound levels compared to the present study were found in areas P1 and P3 (5–10 dB), while lower levels were noted in areas P2 and P4 (1.5–5.5 dB).

Studies on species richness and the number of individuals at varying distances from the road revealed the impact of traffic noise on both the number of individuals and the number of species. The lowest number of birds was recorded at the measuring points along the traffic routes. Generally, the number of individuals and species increased in the II and III distance categories. The most substantial negative impact of traffic routes on birds was found in a distance of up to 250 m [81]. This spatial range of the impact of road noise on forest birds is supported by research presented in the paper [85], which states that the acoustic impact distance of the transport route was approximately 300 m. The most noise-sensitive species were those that communicate using low-frequency sounds and nest near the ground. According to [86], traffic noise pollution is the most critical factor influencing bird density in a forest environment at distances greater than 50 m from the road. Similar findings are presented in the paper [40], which concluded that noise pollution in protected areas significantly affects both bird species richness and individual abundance near transport infrastructure, with highways exerting a more pronounced negative impact than railways. In other studies [87], the detrimental effect of traffic volume on species diversity and bird abundance along roads was observed. Available research indicates a negative impact of traffic noise on birds at levels above 47 dB [23], while a slightly higher harmful threshold of 53 dB is noted in the paper [88]. Furthermore, the adverse effects of noise on birds persist for more than 3 h after the noise event. This implies that the acoustic impact on birds is not merely temporary but may have lasting effects on their behavior [89]. Thus, the level of traffic noise in Roztoczański National Park, directly along the traffic routes, as determined by conducted studies and the cited pilot studies, negatively impacts birds.

Noise monitoring in remote or extensive protected areas is costly and often technologically limited. The absence of standardized noise limits in protected areas complicates the implementation of clear and feasible guidelines for industries operating near these zones [90]. Special attention should be given to various sources of noise, such as road traffic and tourism activities [11]. Quantifying noise mitigation as an ecosystem service is crucial for understanding how the distribution, development, or fragmentation of protected areas affects their ability to provide silence and reduce noise [91]. Noise impact assessments on biodiversity should include acoustic monitoring and soundscape analysis, which will provide insights into the level of anthropogenic noise in natural areas and its effect on the soundscape’s composition [92]. Additionally, it is important to consider that methods based on decibel indicators (such as L_Aeq or L_den) do not fully capture the response to environmental noise, hindering the development of an effective management strategy. A more comprehensive approach is needed, taking into account not only the sound level but also the source of the sound and its propagation under specific terrain conditions [32].

5. Conclusions

Knowledge about the acoustic climate and noise pollution in protected areas can contribute to direct noise reduction. To achieve this goal, it is essential to implement effective management strategies, such as monitoring noise sources, providing environmental education, and collaborating with local communities and economic sectors. Incorporating sound measurement and noise monitoring into environmental planning will reduce noise exposure and benefit ecological systems. Road infrastructure produces sounds that can overshadow the acoustic environment, which also includes natural and animal sounds. An increase in anthropogenic noise levels significantly disrupts the perception of natural sounds, which are perceived as positive. Therefore, it is essential to carry out long-term acoustic monitoring and field observations, alongside research on soundscapes in protected areas. Despite the absence of legal regulations on permissible noise levels in environmentally protected areas, the park area can be regarded as significantly affected by noise pollution, particularly near traffic routes, which contribute to anthropogenic noise. The results of the research conducted indicate that high noise levels can severely impact the natural environment. The noise level directly next to the roads was 57.9–71.2 dB and exceeded the values considered safe for many animal species. Additionally, in some cases, the changing terrain led to a local increase in noise levels. The range of harmful effects was noticeable at a distance of 250 m from the traffic routes. At a distance of 500 m from the roads, the acoustic climate was already close to natural conditions.

The approach presented in this study extends the classical methodology of acoustic measurements by integrating measurement data and spatial modeling results with an analysis of sound source characteristics, distinguishing between natural and anthropogenic components. Incorporating this additional interpretive layer provides a deeper understanding of the acoustic context of the study sites, particularly in natural environments where the soundscape is diverse and dynamic. The analysis revealed that in some locations, despite low sound levels expressed in decibels, road traffic noise was still perceptible, whereas in areas with higher measured values, natural sounds—primarily bird songs—predominated. Such findings indicate that interpreting acoustic data based solely on dB levels may lead to incomplete conclusions, especially when assessing the quality of soundscapes in protected areas.

It is therefore essential to conduct long-term acoustic monitoring and field observations, as well as research on soundscapes in protected areas. Although there are currently no legal regulations specifying permissible noise levels in nature-protected areas, it can be concluded that the park is significantly exposed to noise pollution, particularly in the vicinity of transportation corridors, which are sources of anthropogenic noise.

Integrating environmental monitoring with the park’s management activities is crucial for achieving biodiversity conservation goals. An important area of research that should be further developed in protected areas concerns the potential for reducing traffic noise levels and evaluating the effectiveness of proposed mitigation measures, taking into account the specific characteristics of these environments. In locations particularly threatened by traffic noise—where roads are situated below the surrounding terrain or near human settlements with high traffic volumes—acoustic impacts can be mitigated through measures such as lowering speed limits, upgrading road surfaces, preserving as much vegetation (trees and shrubs) as possible, especially close to the noise source, and promoting quieter modes of transport (electric vehicles, bicycles), as well as implementing visitor limits.

In the face of broad environmental changes, ensuring the acoustic tranquility of protected areas will reaffirm the value of wild nature—the very essence that inspired their creation.

The applied research methodology is associated with certain limitations. Supplementing the tests with simultaneous sound level measurements and audio recordings at individual points along the measurement profile would facilitate the precise identification of noise level distribution, assessment of the acoustic climate and soundscape under specific site conditions, and development of detailed noise protection guidelines. The park’s terrain is diverse in terms of relief and land use. Therefore, it is necessary to consider the possibilities of noise reduction using spatial conditions. The study’s results highlight the necessity for enhanced measures to safeguard Roztocze National Park from traffic noise. It utilizes environmental and acoustic analysis, taking into account the area’s topography, the distance from noise sources, and the specifics of sound propagation under varying spatial conditions. They also provide a valuable foundation for ongoing monitoring and safeguarding the environment in this area against the adverse effects of traffic noise. The conclusions drawn can be utilized to implement specific protective measures that will help minimize the impact of noise on wildlife and the quality of life for residents and tourists in this uniquely protected area.

It is worth noting that in recent years, artificial intelligence-based tools for automatic sound source recognition have been increasingly developed. Examples include systems such as Svantek’s SvanNET AI, which enables real-time sound classification based on built-in databases, and Haikubox, used for long-term noise monitoring. These technologies represent the direction of advancement in modern soundscape analysis methods and may, in the future, complement traditional measurements and spatial analyses, particularly in studies conducted in ecologically valuable areas.