Effect of Coniferous Tree–Shrub Mixtures on Traffic Noise Reduction in Public Spaces

Abstract

Despite the well-established ability of urban green belts to reduce traffic noise, a comprehensive analysis of the specific role played by mixed coniferous trees and shrubs in noise mitigation remains lacking. This study aimed to clarify how different planting patterns and the characteristics of plants affect their noise-reduction performance. To achieve this, noise reduction was measured at 18 roadside green spaces comprising mixed coniferous trees and shrubs in Harbin, China, and Moscow, Russia. The results indicate that in lanes 5–15 m wide, the ‘Abreast’ planting pattern consistently offered greater noise reduction than the ‘Taffy’ configuration at all measured distances (5, 10 and 15 m). In addition, in winter the effectiveness of noise reduction improved due to snow cover, which enhanced the sound-absorbing properties of the vegetation. In our analysis, key factors such as diameter at breast height, minimum height under branches and road width emerged as crucial predictors of traffic noise reduction. Among these, carriageway width and sidewalk width exhibited the strongest correlations with noise attenuation. Finally, we developed a quantitative model for roadside green spaces that incorporates plant characteristics, planting schemes and road features. This model allows us to assess the contribution of each factor to overall noise reduction. The results of this study provide a scientific basis for designing and optimising vegetation-based noise-mitigation strategies to enhance the urban acoustic environment while also offering an analytical framework to support evidence-based urban forestry planning and policy.

1. Introduction

As urban areas expand and transportation networks grow, noise pollution has emerged as a significant environmental issue [1], particularly in densely populated cities where hard surfaces and compact designs intensify the problem. This noise negatively impacts human health [2] and overall quality of life [3], making acoustic comfort a crucial goal for sustainable urban design. Research conducted in Europe reveals that 49% of the population frequently experiences discomfort due to escalating noise levels [4]. In response, numerous countries are actively seeking strategies to mitigate noise pollution. While sound barriers are a commonly adopted method for reducing noise [5,6], their high cost and adverse impact on urban aesthetics make green spaces as multifunctional infrastructure presents a more economical and efficient alternative, particularly for alleviating traffic noise [7,8].

The effectiveness of green spaces in reducing noise is influenced by various factors. Early research identified green space width as the primary determinant of noise reduction, with a minimum width of 20 m generally considered necessary for effective mitigation. However, an increasing number of studies indicate that appropriate plant configurations and higher planting densities can significantly enhance the noise-reduction performance of narrower green spaces. For instance, Ow and Ghosh demonstrated that even a 3 m-wide green space could achieve notable noise reduction by employing suitable species combinations and increasing canopy density [9]. Furthermore, Van Renterghem et al. investigated the noise-reduction potential of narrow green spaces, finding that dense hedges between 1.3 and 2.5 m wide could reduce noise by 1.1–3.6 dB, confirming the considerable potential of narrow green spaces in noise protection [10]. This finding is crucial for retrofitting existing urban areas, shifting research focus toward optimizing the noise-reduction capacity of green spaces at widths commonly available in cities.

Beyond width, specific design elements are critical. Research has shown that densely planted trees and shrubs can reduce noise by up to 6 dB(A) more than areas interrupted by pathways or covered with lawns [11], underscoring the importance of planting density in noise mitigation. Notably, the relationship between planting density and noise reduction is not linear [12]. Once a certain density threshold is reached, optimising planting methods, species selection and spatial arrangements becomes more effective in enhancing the noise-reducing performance of green spaces. For instance, the spatial layout and planting patterns within green spaces significantly influence noise reduction. Green spaces with irregular layouts, such as random, staggered or crisscrossed patterns, and those with regular structures, such as cubic, rectangular or triangular arrangements, exhibit notable differences in noise reduction performance [13,14]. Van Renterghem et al. found that under equal planting density, organised patterns with clear geometric arrangements can significantly enhance noise attenuation. In particular, for a 15 m-wide green space, the optimal noise-reduction effect was achieved when the planting gaps were <3 m and the tree diameter at breast height (DBH) exceeded 0.11 m, using a planting method that included a row of densely planted street trees followed by some open spaces [15].

In addition to planting methods, plant species are a critical factor affecting noise reduction. Research has shown that coniferous plants offer superior noise reduction due to their rougher bark [16,17]. Comparative studies reveal that coniferous belts (e.g., Pinus brutia) can achieve approximately 6 dB higher noise reduction than hard-wood plantations of the same width [18]. There are also notable differences in the noise attenuation effectiveness among various coniferous tree species. Kellomäki et al. found that spruce exhibits a stronger noise attenuation effect compared to pine [19]. It has shown that the use of spruce, cedar, yew, thuja, juniper, and mountain pine species in plantations is among the most effective [20,21]. These studies are essential for understanding how different plant species enhance noise absorption, thereby contributing to the development of more effective noise barriers. In addition, the complexity of the vertical structure of green spaces can effectively enhance noise reduction. For example, Koptseva found that planting shrubs beneath trees can effectively reduce noise, achieving a reduction of ~4 dB over a distance of 18 m [16]. Furthermore, combinations of coniferous trees, such as Eastern Red Cedar and Ponderosa Pine, with shrubs can achieve noise reduction of 5–10 dB(A) in a 22.8 m-wide forest belt [22]. While existing research has sufficiently demonstrated the influence of species, planting methods and density on the noise reduction performance of broadleaf and mixed coniferous–broadleaf green spaces, a gap remains in quantifying the impact of these factors on mixed coniferous tree-and-shrub green spaces.

Furthermore, the effectiveness of noise attenuation depends not only on the spatial configuration and arrangement of vegetation but also on the climatic conditions that determine the growth of coniferous species. According to studies, temperate continental and moderately humid climates exert a significant influence on the development of species most adapted to such environmental conditions [21,23]. Under these conditions, it was observed that the increase in the width of coniferous forest belts significantly enhanced the noise reduction effect. Moreover, existing research primarily focuses on the noise-reduction effects of green spaces during summer, with limited investigation into their performance after snow cover in winter. Noise attenuation in green spaces is influenced by ground effects, particularly at frequencies of <500 Hz [20,22]. For example, Samara et al. found that grass cover can achieve ground noise attenuation of ~4 dB(A) at a distance of 10 m from the road [11]. However, winter snow cover alters these ground effects. Due to their porous structure, looseness and the presence of air pockets, snow layers act as effective sound barriers, capable of absorbing and scattering sound waves. In particular, in the case of freshly fallen snow with a density of <100 kg/m³, studies have confirmed [24] that snow layers can achieve sound attenuation of 30–60 dB/m in the frequency range of 500–2000 Hz, which is a key parameter determining sound wave velocity and attenuation. Sound attenuation in snow is particularly significant under low-temperature and loose snow conditions, reducing noise by 10–40 dB/m in the frequency range of 500–2000 Hz, effectively acting as a natural sound barrier [25]. Therefore, in winter, snow layers not only regulate climate but also act as natural ecological barriers, enhancing the noise-reduction potential of vegetation. Nevertheless, despite extensive research on summer noise mitigation, the winter performance of coniferous plants in reducing noise remains underexplored. Further research is required to evaluate the noise-reduction performance of mixed coniferous plants under winter conditions, which will contribute to a more comprehensive assessment and provide actionable design guidelines for the implementation of coniferous green spaces in cold region cities and seasonal conditions.

This study involved on-site measurements to assess the noise reduction effects of 18 street green spaces featuring a mix of coniferous trees and shrubs, conducted during both winter and summer. Noise maps of the test sites were generated for two seasons, and a systematic analysis was performed to evaluate the plant parameters influencing noise reduction. The research aimed to quantify the noise reduction performance of these green spaces under different planting patterns, densities, and seasonal conditions, specifically seeking to address the following questions:

• What are the morphological parameters of mixed green spaces that affect noise reduction?
• How does planting density affect noise reduction?
• What are the planting patterns of coniferous plantations and their predictive models of impact noise reduction in summer and winter?

2. Materials and Methods

2.1. Research Site

This study systematically investigated street green spaces in Harbin and Moscow, selecting 18 typical and representative street plots based on criteria such as plant species, vertical structure, and the width of green spaces. To simplify analysis, the 9 locations (marked with red dots on the map) were labeled with letters (A–I), and each measurement point received a unique numerical identifier (Figure 1). The research was conducted during both summer (June to August) and winter (December to February). In the summer (26 °C to 28 °C), testing was performed on sunny days immediately following heavy rainfall to ensure consistent soil moisture across all test sites. In winter (−1 °C and −5 °C), measurements were taken on sunny days after snowfall. To comply with outdoor acoustic environment testing standards, all measurements were conducted on days with wind speeds below 2 m/s, thereby minimizing external influences on the results.

During summer, ground conditions were homogeneous, characterised by sparse grass cover, which minimised the influence of terrestrial factors on noise reduction. To further reduce the impact of soil conditions, summer tests were performed on sunny days after heavy rainfall, ensuring stable soil moisture—an essential factor for sound absorption. Air humidity was maintained between 74% and 85%. In winter, the presence of snow cover added an extra layer of sound insulation. Freshly fallen snow, ranging from 1 to 7 cm in thickness and with a density between 168 and 286 kg/m³, was observed at all locations. Research indicates that the sound absorption effect of snow cover is primarily governed by the properties of the snow layer, particularly its porous structure and density. Consequently, the uniform ground conditions and stable snow layer contributed to favourable acoustic characteristics throughout the winter season.

2.2. Measurement Method

To evaluate how effectively outdoor green spaces of different widths reduce noise in summer and winter, this study employed five BSWA801 (BSWA Technology Co., Ltd. Beijing, China) sound level meters for field measurements following international standards for outdoor acoustic testing [26]. Data were collected at 18 predefined locations, each equipped with five noise meters. As illustrated in Figure 2, the receivers were arranged based on two planting configurations for conifers and shrubs: (a) Abreast and (b) Taffy. The first receiver was installed at the roadside to record traffic noise, and the second was located immediately before the green belt. Three additional receivers were placed at 5 m, 10 m, and 15 m from the road to capture sound levels at increasing depths. Before measurements, all instruments were calibrated with a reference sound source. Data were acquired at a height of 1.5 m over 5 min intervals, sampled at 1 Hz, resulting in 300 readings per site [27]. All five devices operated synchronously throughout the testing period.

Based on the straight line formed by the receivers, plant parameters within a 2 m range on both sides were measured [28] and averaged for subsequent analysis. Detailed information regarding the parameters of conifers and shrubs, as well as the scope of the green spaces, can be found in

Table A1. Basic parameters of coniferous trees and shrubs at research points.

Site Number	DBH, m	MHB, m	Height Trees, m	Height Shrubs, m	Crown Trees, m	Crown Shrubs, m	Horizontal Distance, m	Vertical Distance, m	Width of the Roadway, m	Sidewalk Size, m	Density Planting %	Traffic Volume Summer Vehicles/ min	Traffic Volume Winter Vehicles/ min	Number of Lanes
1-A	0.39	0.62	2.65	0.92	2.93	1.4	0.66	2.94	9.60	7.3	52.95	26	22.2	3
2-B	0.33	0.66	3.06	0.7	2.28	1.1	4.83	1.5	8.7	4.7	20.65	24.8	8.2	3
3-B	0.058	0.66	2.85	0.7	2.31	1.1	4.8	1.5	8.7	4.7	20.65	22.8	9.4	3
4-C	0.133	1.02	4.96	1.18	3.78	1.9	1.23	1.3	11.00	4.1	62.5	10	23	3
5-C	0.11	1.07	4.8	1.02	4.1	3.55	2.7	2.05	11.00	4.1	51	26	21	3
6-C	0.15	1.21	3.83	1.13	2.8	3.03	0.81	1.36	11.00	4.1	50	26	22	3
7-D	0.137	1.04	6.21	0.75	3.5	0.68	1.65	2.49	9.60	2.5	37	26	23.8	3
8-E	0.065	0.18	3.5	1	2.6	2.1	3.76	0.26	9.4	0	83	2	2	1
9-F	0.033	0.031	3.76	3.3	1.83	1.76	2.26	3.9	3.6	7	5.28	87	16.6	1
10-F	0.035	0.1	3.2	3.9	1.9	2.03	2.1	2.16	3.6	7	7	33.8	17.6	1
11-F	0.33	0.04	4.03	3.5	1.93	1.73	2.56	1.86	3.6	7	11.16	5.8	12	1
12-G	0.08	0.31	3.46	1.2	0.76	1.8	1.0	1.16	10.5	0	89	7	10.8	2
13-G	0.065	0.15	1.32	0.88	4.3	1.24	1.28	3	10.5	0	45.33	8.8	8.6	2
14-H	0.075	0.05	2.6	0.93	1.16	1.4	0.07	1.26	10.5	0	54.33	8	10.8	2
15-H	0.04	0.05	1.63	1.1	1.2	0.9	0.18	1.46	10.5	0	37.6	26.2	27	2
16-I	0.08	0.15	2.5	0.44	2	1.33	4.66	7.43	9.6	5	99.5	28	30	3
17-I	0.066	0.11	1.68	1	3.36	1.53	2	0.66	9.6	5	99.7	26.4	28	3
18-I	0.056	0.2	3.5	1.5	2.43	1.3	1.5	0.66	9.6	5	99.5	23.6	25.8	3

and

Table A2. Basic criteria for selecting measurement points in urban noise conditions.

Site Number	Planting Patterns	Trees	Shrubs	Weather °C Sunny	Air Humidity, %	Width of Research Point, m	Leq dB(A)	Leq Max dB(A)	Leq Min dB(A)
1-A	Taffy	Juniperus chinensis	Horizontal juniper	28	77	10	74.3	93.2	59.8
2-B	Abreast	Picea pungens	Juniperus	28	77	10	74.6	83.8	60
3-B	Abreast	Picea pungens	Juniperus	28	77	10	74.3	91.3	58.4
4-C	Taffy	Picea abies	Juniperus	28	74	5	69.3	78.9	56.5
5-C	Abreast	Picea abies	Juniperus	28	74	5	70.4	83.6	55.6
6-C	Abreast	Picea abies	Juniperus	18	74	5	71.6	82.2	58.0
7-D	Abreast	Picea pungens	Thuja occidentalis	28	74	15	69.8	80.5	57.7
8-E	Taffy	Rocky Juniper Blue Arrow	Juniperus sabina, Horizontal juniper, scaly blue juniper	24	85	15	65.6	83.7	60.8
9-F	Abreast	Thuja occidentalis Columna	Cotoneaster lucidus, Thuja occidentalis Globosa, Juniperus sabina, Thuja occidentalis	27	85	15	54.8	86	41.1
10-F	Abreast	Picea abies	Thuja occidentalis	27	85	15	55.8	86	43.2
11-F	Abreast	Picea abies	Thuja occidentalis	27	85	15	56.4	86	42.5
12-G	Taffy	Picea abies	Thuja occidentalis	27	84	15	67.4	90.5	57.2
13-G	Taffy	Mountain pine (Pinus mugo), Picea pungens Glauca Globosa, Thuja occidentalis Columna	Juniperus horizontalis Blue Chip, Thuja occidentalis f. Globosa	27	84	15	71.5	88.1	61.3
14-H	Taffy	Mountain pine (Pinus mugo), Picea pungens Glauca Globosa, Juniper Scopulorum	Juniperus sabina, Horizontal juniper, scaly blue juniper.	27	84	15	66.5	93.6	55.1
15-H	Taffy	Picea abies, Picea pungens Glauca Globosa	Juniperus sabina, Horizontal juniper, scaly blue juniper, Thuja occidentalis Globosa	27	84	15	66.3	94.4	54.2
16-I	Taffy	Picea abies	Horizontal juniper	25	84	10	75.1	96.3	55.9
17-I	Taffy	Picea abies	Horizontal juniper	25	84	10	76.1	96.2	55
18-I	Taffy	Picea abies, Picea pungens, Mountain pine (Pinus mugo)	Thuja occidentali, Horizontal juniper	25	84	10	74.1	96.0	54.9

.

This study examined the effect of snow on noise reduction by analysing parameters such as ground snow height, snow density, ambient temperature during measurements and snow accumulation on branches. According to international classifications of seasonal snow cover [29,30], these characteristics significantly impact noise reduction. Winter measurements were conducted in Moscow and Harbin at temperatures ranging from 0 °C to −5 °C. Portable electronic scales of the Support KGF50CJ10 brand (Zhejiang Supor Co., Ltd., Hangzhou, China), with an accuracy of 0.01 kg, were used to measure snow density. A box measuring 17.5 cm × 11.5 cm × 5 cm was filled with freshly fallen snow, and the mass was transferred for measurement. To improve accuracy, this process was repeated four times, and average snow density was calculated using the following formula:

$$p ={\displaystyle \frac{m}{V}}$$

(1)

where m represents the average mass of snow (g) and V denotes the volume within a box (cm³).

Noise level data in dB(A) were collected before and after transmission through the snow cover. The theoretical formula used to calculate the degree of noise reduction at each measurement point is as follows:

$$\mathsf{\Delta }L= Li - Lr$$

(2)

where ΔL denotes the noise reduction due to snow cover (dB), Li represents the source traffic noise level (dB) and Lr indicates the sound level after transmission through the snow cover (dB).

This study demonstrates that the snow heights range from 1 to 41 cm on branches and up to 7 cm on the ground. Snow density at measurement points ranged from 283 to 286 kg/m³ in China and from 168 to 175 kg/m³ in Russia, with freshly fallen loose snow prevailing in both regions. The density values are shown in Figure 3 and

Table A3. Basic parameters of snow cover on branches and snow density at the research points.

Site Number	Snow Density, kg/m	Snow Characteristics	Weather, °C	Height of Snow Cover on the Ground, cm	The Size of the Snow on the Branches, cm	Li dB(A)	Lr dB(A)	ΔL dB
1-A	284	freshly fallen settled snow	−5	1	6.3	70.5	58.17	12.33
2-B	285	freshly fallen settled snow	−5	4	3	80.5	54.29	26.21
3-B	285	freshly fallen settled snow	−5	4	5.5	70.4	49.8	20.6
4-C	286	freshly fallen settled snow	−5	4	3.1	66.5	57.8	8.7
5-C	286	freshly fallen settled snow	−5	4	3.6	68.6	57.1	11.5
6-C	286	freshly fallen settled snow	−5	3.8	3	80.5	63.1	17.4
7-D	283	freshly fallen settled snow	−5	3.8	3.6	75.1	54.49	20.61
8-E	168	wet freshly fallen snow	−2	5	3.6	65.1	61.4	3.7
9-F	173	wet freshly fallen snow	0	3	1	74.6	57.81	16.79
10-F	173	wet freshly fallen snow	0	3	1.26	64.2	59.4	4.8
11-F	173	wet freshly fallen snow	0	3	18	78.6	56.9	21.7
12-G	174	wet freshly fallen snow	0	3	17.4	59.5	53	6.5
13-G	174	wet freshly fallen snow	0	7	24.75	64.8	54	10.8
14-H	173.1	wet freshly fallen snow	−1	7	4.75	72.3	55.7	16.6
15-H	173.1	wet freshly fallen snow	−1	5	41	71.3	54.9	16.4
16-I	175	wet freshly fallen snow	−1	5	24	66.1	57.7	8.4
17-I	175	wet freshly fallen snow	−1	5	21	70.9	58.2	12.7
18-I	175	wet freshly fallen snow	−1	5	24.2	78.6	56.9	21.7

.

3. Results

3.1. Noise-Reduction Effects of Coniferous Trees and Shrubs with Different Planting Patterns in Summer and Winter

3.1.1. Noise Mapping

This study highlights the noise-reduction effects of coniferous tree and shrub mixtures using the Taffy (Figure 4) and Abreast (Figure 5) planting schemes, as analysed through corresponding noise maps for summer and winter. Key plant combinations examined include spruce and juniper, thuja and juniper, mountain pine and juniper and spruce.

In summer, at a distance of 5 m, the Taffy planting pattern achieved noise reductions primarily ranging from 3 to 5 dB. The lowest recorded reduction was 1.6 dB, attributed to smaller coniferous shrubs (60–90 cm) that created gaps between plants, allowing sound to pass through more easily. This indicates limited noise protection compared to taller species. The most effective noise reductions, reaching 8.4 and 7 dB, resulted from high-density plantings that formed a continuous barrier, significantly enhancing acoustic shielding. In winter, this noise reduction significantly improved, ranging from 7 to 10 dB. The least effective reduction recorded was 3 dB, while the highest reached 13 dB. These variations are largely attributed to the enhanced sound-absorbing and scattering properties of vegetation influenced by snow cover, resulting in greater noise reduction than that observed in summer. At a distance of 10 m, the Taffy planting pattern in summer provided noise reductions of 5–8 dB. The least effective reduction was 2.7 dB, while the highest reached 10 dB, highlighting how taller plantings with denser crowns form a more effective acoustic barrier. In winter, noise reduction improved further, ranging from 10 to 15 dB, with the lowest recorded at 9 dB and the highest at 16 dB. The presence of denser snow significantly contributed to these improved noise-reduction levels. At a distance of 15 m, the Taffy planting pattern in summer provided noise reductions ranging from 8 to 10 dB, with the lowest recorded reduction at 3 dB and the highest at 11 dB. These results indicate that increasing plant species diversity enhances noise reduction during the summer season. In addition, a trend was observed indicating that wider green strips contribute to greater noise-reduction efficiency. In winter, noise reduction at this distance ranged from 10 to 15 dB, with the lowest recorded effect at 10 dB and the highest reaching 16 dB. These findings underscore the substantial impact of seasonal changes on the noise-reduction capabilities of coniferous mixtures.

For the Abreast planting pattern at a distance of 5 m in summer, noise reductions ranged from 4 to 6 dB, with the lowest recorded at 2.1 dB and the highest at 8.2 dB. The greater reduction in this case is attributed to the staggered arrangement of taller plants without gaps, forming a continuous acoustic barrier that enhances noise-reduction efficiency. In winter, noise reduction improved significantly, ranging from 5 to 10 dB. The lowest recorded effect was 1.6 dB, while the highest reached 18 dB—an outcome also influenced by snow cover, which enhances the sound-absorbing properties of vegetation. At a distance of 10 m, the Abreast planting pattern yielded summer noise reductions primarily between 6 and 9 dB, with the lowest at 4.2 dB and the highest at 9.7 dB. The increase in the width of green spaces contributes to greater noise protection, as the continuous planting forms a solid green barrier that enhances acoustic shielding. In winter, noise reduction at this distance improved significantly, ranging from 9 to 17 dB. The lowest recorded reduction was 6 dB, while the highest reached 20.4 and 17.4 dB. These differences highlight how snow cover enhances the absorption and scattering of sound by vegetation. At a distance of 15 m during summer, the Abreast planting pattern provided noise reductions between 8 and 9 dB, with the lowest recorded at 6 dB and the highest at 10 dB. This variation is attributed to the absence of gaps between plants, which forms a continuous acoustic screen and enhances noise reduction. In winter, noise reduction significantly improved, primarily ranging from 12 to 20 dB, with the lowest recorded at 9.3 dB and the highest reaching 22 and 21 dB.

Overall, results indicate that planting coniferous trees and shrubs in widths ranging from 5 to 15 m offers greater noise protection potential in winter than in summer. This effectiveness is amplified by snow cover, which enhances the sound-absorbing properties of vegetation. In addition, a clear pattern emerges, indicating improved noise-reduction efficiency with increasing width of green strips.

3.1.2. Noise Reduction Effect

The results of an independent samples t-test indicate a statistically significant difference in noise attenuation effectiveness between the two planting designs: the Taffy pattern and the Abreast pattern (p < 0.05). Further investigation into the data shows that the planting density for the Taffy pattern was consistently higher than that for the Abreast pattern across all measured distances. As shown in Figure 6a, at a distance of 5 m, the average planting density for the Taffy pattern was 72.4%, compared to only 25.3% for the Abreast pattern. A similar trend was observed at 10 m, where the densities were 72.36% for the Taffy pattern and 16.9% for the Abreast pattern (Figure 6b). However, at 15 m (Figure 6c), the average densities decreased to 57% for Taffy and 15.14% for Abreast.

As the distance increased to 5, 10 and 15 m, the Abreast planting pattern consistently achieved significantly greater noise reduction than the Taffy pattern. The summer analysis revealed that at 5 m, noise reduction for the Taffy pattern ranged from 3.3 to 5 dB, while the Abreast pattern achieved reductions ranging from 3.7 to 6.5 dB (Figure 6a). At 10 m (Figure 6b), the Taffy pattern produced a noise reduction of 5.3–8.2 dB, whereas the Abreast pattern exhibited reductions from 5.5 to 9 dB. By 15 m (Figure 6c), the noise reduction for the Taffy pattern ranged from 4.5 to 9.5 dB, while the Abreast pattern achieved a reduction of 7.5–10 dB. This enhanced performance of the Abreast compositions can be attributed to their well-defined structure, which allows for a more uniform distribution of sound waves, preventing their focusing and thereby improving noise suppression efficiency compared to the Taffy pattern. In addition, the analysis confirmed that while the density of green plantings was considerably higher in the Taffy pattern, the noise-reduction effects were more pronounced in the Abreast pattern.

In cold-climate urban settings, snow cover measurably influences sound pressure levels. During winter, both “Abreast” and “Taffy” planting configurations demonstrated consistent, additional road traffic noise reduction compared to summer conditions, an effect attributable to the seasonal snow. Winter measurements indicated that at a distance of 5 m (Figure 6d), the Taffy pattern resulted in noise reductions ranging from 5.5 to 11 dB, while the Abreast pattern achieved reductions from 4.5 to 15 dB. At 10 m (Figure 6e), the Taffy pattern produced a noise-reduction effect of 5–8.3 dB, whereas the Abreast pattern provided reductions ranging from 8.7 to 16.5 dB. At 15 m (Figure 6f), the Taffy pattern yielded noise reductions from 4.5 to 9.5 dB, while the Abreast pattern achieved a more significant reduction ranging from 13 to 21 dB. The most pronounced results occurred with the “Abreast” planting pattern, particularly those with moderate density and a reduced height to the base of the crown (MHB). This configuration achieved significant extra noise reduction at typical distances of 5–15 m from the source. This analysis of snow’s role as a natural sound absorber is a key scientific and practical contribution, advancing the understanding of seasonal variations in the noise-mitigation properties of urban vegetation. These findings enable planners in northern cities to incorporate seasonal dynamics into green infrastructure design, thereby increasing its year-round efficiency.

3.2. Influence of Planting Density and Gaps on Noise-Reduction Effect

3.2.1. Planting Density

This study (Figure 7) illustrates the impact of planting density on noise reduction at three distances—5, 10 and 15 m—comparing the Abreast and Taffy planting patterns, both maintained at a density of 37%. At a distance of 5 m (Figure 7a), the noise-reduction effect for the Abreast planting pattern was measured at 2.1 dB, while the Taffy planting pattern exhibited a lower effect of 1.59 dB. This finding indicates that the Abreast pattern, characterised by its regular and dense structure, provides significantly greater noise reduction than the less organised Taffy pattern. At a distance of 10 m (Figure 7b), a similar trend emerged. As the distance from the green spaces increased, the noise reduction for the Abreast planting pattern increased to 4.2 dB, while the Taffy pattern achieved a reduction of only 2.75 dB. At 15 m (Figure 7c), the noise-reduction effect for the Abreast pattern further increased to 6.6 dB, whereas the Taffy pattern reached 3.04 dB. These results confirm that noise reduction improves with greater distance from the source. Notably, at all three measured distances—5, 10 and 15 m—the Abreast planting pattern consistently demonstrated greater noise reduction than the Taffy pattern. This enhanced performance can be attributed to a more uniform distribution of vegetation and a higher planting density, both of which play a crucial role in enhancing noise-reduction efficiency.

3.2.2. Planting Gaps

Figure 8 shows the influence of gaps between plants on noise reduction, comparing coniferous green spaces with and without gaps under similar planting densities. The planting density ranged from 50% to 51% in configurations with gaps and from 45% to 51% in those without gaps. In green spaces where gaps existed between plants (Figure 8a), the noise-reduction effect ranged from 3.8 to 4.3 dB. This suggests that when shrubs are low in height, gaps may form between trees and shrubs, resulting in an inadequate barrier against sound waves—particularly at lower frequencies. Conversely, plantings without gaps (Figure 8b) demonstrated a significantly higher noise-reduction effect, ranging from 4.6 to 8.4 dB. This improvement can be attributed to the dense structure of vegetation that eliminates gaps, effectively limiting the direct acoustic visibility of the sound source. This arrangement enhances the reduction and scattering of sound waves within the biomass and reduces the diffraction of sound in the surface layer. These findings underscore that the most effective noise reduction is achieved when trees and shrubs are arranged without gaps. Therefore, plantings comprising closely spaced vegetation offer optimal acoustic protection.

3.3. Influence of Morphologic Parameters on the Noise-Reduction Effect of Mixture Green Spaces

This study employed correlation analysis to evaluate the impact of plant morphological parameters and planting conditions on noise-reduction efficiency in mixed coniferous plantings, utilising the ‘Taffy’ and ‘Abreast’ planting patterns. Figure 9 shows the correlation analysis between noise-reduction levels from these mixed coniferous plantings, which have widths ranging from 5 to 15 m, and various morphological parameters and planting conditions. The analysis revealed that noise reduction in these plantings positively correlates with sidewalk width (p < 0.05) while showing negative correlations with pavement type, DBH, MHB and road width (p < 0.05). Consequently, the most significant factors influencing the noise protection capabilities of green plantings are DBH, MHB and road width. Among these parameters, road width and sidewalk width emerged as the most influential, exhibiting strong correlation values. These findings indicate that optimising morphological parameters can enhance the noise-reduction effectiveness of mixed coniferous green spaces, thereby strengthening their role as acoustic barriers in urban environments.

3.4. Prediction Model for the Noise-Reduction Effect of a Mixture of Coniferous Trees and Shrubs

A stepwise regression model was employed to analyse the morphological parameters influencing noise reduction in mixed coniferous green spaces with widths of 5, 10 and 15 m. The model considered several variables, including tree crown size, shrub crown size, tree height, shrub height, vertical and horizontal distances, MHB, DBH, planting density, sidewalk width and road width. The analysis identified DBH, MHB, road width and sidewalk width as key morphological parameters significantly affecting noise reduction. The model yielded an R-squared value of 0.618, indicating that increasing sidewalk width, reducing MHB, decreasing DBH and minimising road width contribute to more effective traffic noise mitigation. In particular, a reduction in DBH positively impacts noise-reduction efficiency. Trees with smaller diameters typically exhibit a more compact structure with denser foliage in the lower canopy, which helps create a more effective vegetative barrier. This configuration enhances the ability to reduce and scatter sound waves emanating from traffic noise sources.

Y = −0.143 (DBH) − 1.906 (MHB) + 0.071 (Sidewalk) − 0.484 (Road width) + 10.937
(R² = 0.618)

(3)

Figure 10 presents a predictive model illustrating how green vegetation belts along major roadways influence the distribution of urban noise. The model simulates sound levels for green belts of varying widths (5, 10, and 15 m) and compositions, specifically coniferous trees and shrubs in China and Russia. A color scale indicates sound intensity, ranging from high-intensity noise (>70 dB, red) to comfortable levels (<35 dB, yellow). The calculations reveal that noise reduction is nonlinear: the sound-dampening effect increases significantly as the vegetation belt gets wider. Green zones 10–15 m wide create substantial quiet areas, serving as effective sound buffers between traffic and residential developments. In contrast, a 5 m belt provides only a limited effect, allowing high-noise zones (over 65 dB) to remain close to buildings. With 10–15 m belts, however, these same high-noise areas are pushed 20–30 m further away from the roadway. This predictive model is a practical tool for urban planning and sustainable design. It enables planners to simulate urban sound environments, assess the effectiveness of proposed green corridors, optimize spatial layouts, and develop effective noise mitigation strategies.

4. Discussion

4.1. Scope of Research Application and Its Expansion Possibility

Harbin and Moscow are located in climatic zones featuring cold winters and warm summers. Both cities experience significant temperature fluctuations, with abrupt transitions from winter cold to summer warmth. However, the average temperature in Harbin is 6–8 °C lower than that in Moscow, and the winter duration in Harbin is slightly longer—approximately 4–5 months. During this period, temperatures frequently drop to −20 °C, and snow cover persists throughout the winter. These climatic features contribute to the differences in the morphological characteristics of coniferous plantations in both cities. This comparative approach enables the extension of the research findings to other regions with cold climates. In these conditions, the effect of snow cover on noise reduction serves as a natural acoustic barrier, absorbing and scattering sound waves. This effect is most pronounced at low temperatures and high snow porosity, significantly enhancing its noise-cancelling properties. Numerous studies [24,25,31] have provided data on the structure and properties of snow, supporting the conclusion that snow cover plays a critical role in noise reduction. Therefore, it is essential to incorporate snow cover into noise-reduction models, particularly in cold climates, such as those found in Harbin and Moscow.

In Harbin, mixtures of spruce and juniper (Picea and Juniperus), as well as pine and juniper (Pinus sylvestris and Juniperus), are widely used in lane plantings. In Moscow, pine is often planted in combination with horizontal juniper (P. mugo and Juniperus horizontalis), spruce and juniper (Picea and Juniperus) and thuja and western juniper (T. occidentalis and Juniperus), as well as spruce, juniper and mountain pine (Picea, Juniperus and P. mugo). These coniferous species and shrubs are evergreen throughout the year, a characteristic typical of cities in cold climates. This feature enhances the significance of the findings of this study and increases its relevance. Evergreen coniferous trees and shrubs undergo minimal morphological changes between winter and summer, enabling them to maintain their noise-reducing properties year-round. Some studies [23] have demonstrated that broad-leaved trees are highly effective in reducing noise levels. However, many researchers [11,32,33] have pointed out that compared to coniferous plantations, broad-leaved plantations are significantly less effective in noise reduction. In addition, ordered plantings with a clear geometric layout are more efficient in noise abatement than irregular configurations. The study demonstrated that regular planting patterns, such as the ‘Abreast’ planting pattern, provide more stable and effective noise reduction, as plants interact evenly with sound waves, creating an acoustic barrier that results in higher levels of noise reduction than sparser and irregular plantings, such as ‘Taffy’, particularly over short distances (5–15 m).

Thus, in cold climate regions such as Harbin and Moscow, snow cover plays a crucial role in urban noise reduction, emphasising its importance in noise-mitigation strategies. Future study should explore the differences in noise reduction between combinations of evergreen and deciduous coniferous trees and shrubs, as well as the influence of seasonal changes.

4.2. Noise-Reduction Strategies in Urban Greenery Planning

The planting pattern is essential for effective noise reduction. Our study demonstrates that the highest noise-reduction efficiency occurs in Abreast planting patterns, which are characterised by a well-ordered structure. This finding aligns with previous studies [13,14], indicating that rectangular and Abreast planting patterns with clear geometric layouts provide superior noise suppression compared to irregular configurations. Reducing the distance between neighbouring trees and increasing the average DBH expands the basal area of the plantings, thereby enhancing their noise protection effectiveness. Since the lower portions of most trees contain gaps, many researchers [13,16,34] recommended planting shrubs beneath them to enhance the continuity of the vegetative barrier and maximise noise reduction. In addition, parallel planting patterns, where rows of trees are aligned along the longitudinal axis of the road, have been shown to offer the greatest noise-reduction effectiveness [35]. This is due to the formation of a continuous longitudinal barrier that more effectively reflects, scatters and absorbs sound waves from the source compared to areas with gaps or lawns. In addition, our study indicates that planting density has a more pronounced impact in mixed plantings using the Taffy pattern, whereas the Abreast pattern tends to employ a sparser configuration. These findings are consistent with a previous study [9], revealing that transitioning from minimal to sparse or moderate planting densities significantly enhances overall noise attenuation, while further densification diminishes this effect. Therefore, plantings with moderate density are optimal for noise reduction, offering more effective noise mitigation.

The use of the Abreast planting pattern offers the most effective noise reduction. To further enhance this effect, modifications to the planting layout are essential. Optimising the arrangement of plants in a parallel orientation along the road strip can create a denser barrier against sound waves. Incorporating shrubs in the lower layer helps fill the gaps between the upper canopies of trees, thereby strengthening the noise-absorbing effect. Trees with smaller DBH are typically planted more densely, which improves noise absorption due to the increased area of vegetation acting as an acoustic screen. For new green plantings, selecting the appropriate plant species is crucial, as certain trees and shrubs exhibit superior noise-absorbing characteristics. Transplanting larger plants can expedite the achievement of desired noise protection levels, as mature plants possess developed root systems and dense foliage capable of blocking sound at greater heights. Thoughtful species selection and proper placement can significantly enhance noise absorption efficiency and improve overall environmental quality.

These strategies are best suited for densely built-up urban corridors with high human presence, such as transit interchanges, bus lanes, and pedestrian zones adjacent to roadways. Soundscape perception is often tied to the function of a space [12,36,37], justifying the prioritization of these areas. In these locations, mixed coniferous stands in an ‘Abreast’ pattern—with parallel rows, moderate density, and a shrub understory—create a continuous vegetative screen. This configuration achieves significant noise reduction (LAeq) at receiver positions 5–15 m from the source. Parameters such as sidewalk setback distance, stand density, DBH, and MHB can all be adjusted to optimize acoustic attenuation.

4.3. Application in Urban Planning and Design

In urban planning, it is vital to consider noise impacts to create comfortable and safe living conditions. This study examined public spaces along major transport corridors, where vegetation-based noise barriers can effectively separate pedestrian and vehicular flows, fostering zones of acoustic comfort. The deployment of green belts with varied densities and configurations can create durable barriers that mitigate noise propagation between traffic arteries and human-occupied areas. This study demonstrates that various landscaping schemes incorporating coniferous trees and shrubs can address multiple critical objectives within urban environments. Linear plantings of coniferous trees, characterised by well-defined structures such as the Abreast pattern, can serve as effective living barriers against noise and environmental pollution along streets. More complex planting configurations, such as the Taffy pattern, can integrate mixed groups of trees and shrubs to create natural partitions or visual accents in urban spaces. Overall, the establishment of mixed green areas in urban settings enhances the microclimate and increases overall comfort. Results indicate that for green spaces located 5–15 m from the noise source, noise reduction is influenced by factors such as DBH, MHB, road width and sidewalk width. The combination of coniferous trees and shrubs is essential for fostering a pleasant urban environment. Species such as pine, spruce, juniper and thuja are particularly well-suited to urban environments owing to their tolerance of air pollution and the arid conditions typical of large metropolitan areas. These species demonstrate resilience to pollution and adaptability to various soil types. Therefore, incorporating a diverse mix of plants into urban landscape design, developing effective planting configurations and planning green spaces all contribute to sustainable and enduring green areas that enhance the quality of life for urban residents year-round.

In summary, the development of scientifically informed landscaping schemes and the rational planning of green spaces, together with the integration of acoustic and environmental research into urban design, will foster balanced and sustainable urban environments. In these settings, natural components not only enhance aesthetic organization but also fulfill functional roles, contributing to the sustainable development of cities and improving the quality of life for their residents year-round.

5. Conclusions

This study investigated the effectiveness of mixed coniferous trees and shrubs, as evergreen vegetation, in attenuating traffic noise. Experimental data from 18 coniferous green spaces in Harbin and Moscow revealed that, notwithstanding interspecies morphological variations, all configurations demonstrated a notable capacity for noise reduction. The principal findings are summarized as follows.

First, the study examined the noise reduction effects of coniferous tree and shrub mixtures. Plantings with a clearly defined structure, such as the Abreast pattern, were more effective at reducing noise levels compared to less organised configurations such as the Taffy pattern. During summer, the noise reduction at distances of 5, 10 and 15 m was significantly greater with the Abreast pattern. This effectiveness is attributed to the structured compositions that promote a more uniform distribution of sound waves, preventing their concentration and enhancing noise suppression compared to the Taffy pattern. Furthermore, as the width of the green plantings increases, the noise-reduction effect becomes more pronounced. In winter, both planting patterns showed improved noise reduction, largely due to the snow cover, which significantly enhances noise attenuation.

Second, it assessed the impact of planting density on noise reduction. Although the Taffy planting pattern exhibited a higher planting density, the noise-reduction effect was substantially greater in the Abreast pattern. In addition, planting shrubs beneath trees further enhanced noise reduction. The absence of gaps between trees and shrubs creates a continuous vegetative barrier that effectively blocks the transmission of sound waves. This combination of plants improves acoustic shielding, leading to a notable reduction in noise levels.

Third, key morphological parameters influencing noise-reduction efficiency were identified. For green plantings comprising mixed coniferous species, significant correlations were found between morphological characteristics—such as DBH, MHB, sidewalk width and road width—and the level of noise reduction. These parameters are crucial for maximising noise attenuation and interact in ways that enhance their overall effectiveness.

Finally, based on these morphological characteristics, a predictive model for noise-reduction effects in mixed green plantings was developed. Key parameters, including DBH, MHB, sidewalk width and road width, were integral to this model. This model provides a reliable scientific basis for developing urban greening strategies, contributing to optimal noise-reduction outcomes.

In summary, the study highlights the significant potential of combining coniferous trees and shrubs for traffic noise reduction, offering valuable insights for urban planners and landscape designers in formulating effective noise-mitigation strategies.