## 4 Discussion
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 commission 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.