Does Urban Green Space Pattern Affect Green Space Noise Reduction?

Abstract

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space’s noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects.

1. Introduction

The development and renewal of cities have brought higher noise levels and more noise worries [1,2,3,4,5,6,7]. Traffic noise is a common noise source in urban environments [8]. It is also the main source of urban space noise pollution [9,10]. Traffic noise is believed to disturb residents’ sleep, increase cardiovascular disease, have adverse effects on mental health, and cause more noise annoyance [11]. It was shown that a green belt between the noise source and the receiver can reduce the noise level perceived by the receiver in a 1946 investigation in the Panama jungle [12]. As the noise reduction function of vegetation has been confirmed by many studies, green space noise reduction has been the focus of an increasing number of studies.

How to reduce noise and the relevant annoyance by configuring urban internal vegetation has been followed by studies in the field of green space noise reduction [13,14,15,16,17]. The noise reduction potential of green space in cities has attracted much attention. At present, the studies on the relationship between urban green space and noise mainly focus on the vegetation composition and structure configuration on a small scale, such as local green belts and small green spaces. At the level of urban planning, little of the literature guides the reduction effect of urban green space on the urban noise environment. At the same time, few studies can analyze the effect of the green space form on urban regional environmental noise separately at the spatial level. The reason is that the impact of urban green space on noise reduction is weaker than urban structures such as buildings, and the impact mechanism of urban green space and buildings on urban environmental noise has not been explored, so it is difficult to separate the green space form and urban environmental noise from the overall urban morphological characteristics. A study discussed the impact of urban green space on traffic noise through the measured data of acoustic instruments and the ordinary least square linear regression model [18]. One study showed that noise barriers significantly affect the dispersion of noise-borne air pollutants near roads on the receptor side [19,20]. However, whether it is the data provided by environmental noise detection stations or the noise data directly measured by sound pressure meters, it contains the impact of various urban form factors on the results, The type of sound source at the measuring point is difficult to be represented by a certain noise.

According to the existing research, urban green space has a significant effect on regulating the urban atmospheric environment, including noise [21,22,23]. From the perspective of green space influencing factors [24], more green space area [25], vegetation density [26], and more compact vegetation configuration can more effectively reduce the transmission efficiency of noise and form acoustic shadow areas [13,14,27]. However, in the urban environment, a large green space is often determined by the original natural environment of the city, and it is difficult to improve the acoustic environment simply by increasing green space area and biomass. Therefore, how to use the limited green space in the city to produce a better noise reduction effect has become a topic worthy of discussion. In the research of morphological spatial patterns, MSPA research methods are widely used in various studies. Morphological spatial pattern analysis (MSPA) is an image processing method that uses corrosion, expansion, open calculation, and closed calculation to segment, recognize, and classify the graphics, mainly describing the geometric arrangement and connectivity of map elements [28,29]. At present, most studies based on MSPA focus on species’ habitat environment, migration corridor construction [30,31], and green space landscape pattern [32]. This method can rapidly identify the morphological pattern of elements in space. Through the morphological spatial pattern analysis of urban green space, the spatial pattern characteristics of urban green space can be quickly extracted; Combined with ecological interpretation and correlation analysis, we can further explore the law of change in noise reduction capacity of urban green space with different pattern characteristics.

Some studies have explored the relationship between urban green space and noise levels at the urban scale [26]. In addition, some scholars have discussed the impact of auditory and thermal perception on people’s acoustic perception in different environments [25,26]. However, the research on the relationship between urban green space and noise level from the perspective of green space noise reduction is limited now, and the influencing factors of green space noise reduction effect are still unclear. To explore the change in noise reduction capacity of urban green space under different pattern characteristics, we aim to ① explore the change in noise reduction capacity of green space under different patterns characteristics in urban environments; ② identify the influencing factors of urban green space noise reduction capacity; ③ propose a method to enhance the noise reduction capacity of urban green space.

Therefore, this study mainly focuses on urban elements such as urban green space and, according to the MSPA morphological spatial pattern analysis method, rapidly identifies the spatial pattern characteristics of urban green space and summarizes the distribution pattern characteristics of green space in the Fuzhou high-tech zone. It also obtains noise reduction maps of urban green spaces by simulating noise with and without green spaces and performing difference calculations and summarizes the spatial distribution characteristics of the net noise reduction of green spaces. It analyzes the correlation between the characteristics of the green space distribution pattern and green space noise reduction to explore the impact of urban green space pattern characteristics on noise reduction under the condition of low interference. Through further geographically weighted regression analysis, it summarizes the role of green space pattern characteristics on the noise reduction effect and discusses its endogenous mechanism and reasons.

2. Research Methods

2.1. Overview of the Study Area

The study site is located in Fuzhou City, Fujian Province, in the east of Fujian Province, the lower reaches of the Minjiang River and coastal areas, and is the capital of Fujian Province. Its landform is a typical estuary basin, dominated by mountains and hills, and belongs to a typical subtropical monsoon climate. Fuzhou has a permanent population of 8.448 million. According to the Environmental Quality Standard for Noise (GB3096-2008) and the data from the government environmental department [33], the average annual values of daytime environmental noise in the built-up area of Fuzhou in 2019, 2021, and 2022 were 57.2 dB, 56.7 dB, and 56.6 dB, respectively. Urban environmental noise refers to all kinds of noise generated by activities such as building construction, transportation, industrial production, and daily life in the city. These noises interfere with residents’ work, study, rest and sleep, and in severe cases, even endanger human health, causing diseases and noise-induced deafness. Urban noise mainly includes traffic noise, industrial noise, construction noise, and social life noise. Traffic noise mainly refers to the sound generated in traffic activities that interfere with the surrounding living environment, including the sound generated by vehicles, aircraft, ships, and other vehicles during operation. This kind of noise not only affects people’s daily lives but also may impact traffic safety; therefore, the control and management of traffic noise is crucial in urban noise control. The annual average values of road traffic noise in Fuzhou City were 68.7 dB, 68.1 dB, and 68.3 dB, respectively (both day and night). Fuzhou City has a forested area of 7792 square kilometers, accounting for 65% of the city’s total area, with a total forest area of 9,470,000 acres and a total forest stock of 16.8 million cubic meters, with forest cover of 57.8% and a degree of greening reaching 88.6%. The city’s built-up area has a garden green area of 16,434 hectares, with urban parks covering 5930 hectares (116 city parks). The green coverage rate of the built-up area is 43.10%, the green space rate is 40.08%, and the urban per capita park green space area is 14.83 square meters.

As shown in Figure 1, the study area of this research is located in the Fuzhou high-tech zone (Fuzhou high-tech industrial development zone) with a total area of about 193.07 square kilometers (of which about 66 square kilometers are constructed), which is an important advanced manufacturing, office area, and university area of Fuzhou City, which has certain requirements for the serene area. This area extends from the 316 and 324 national highway connection in the north, Dazhang Stream to the east, and Qishan Mountain and Jingyanshan Mountain to the southwest. This area is conveniently connected to Ningde City, Fuqing City, and Putian City around Fuzhou City, as well as a necessary route to other provinces.

2.2. Data Sources and Processing Procedures

2.2.1. Urban Construction Data

The remote sensing image data are from the 10 m resolution Sentinel-2 high-resolution satellite on March 2023, which is used for green space contour extraction and MSPA green space spatial pattern calculation. The data such as building outline and number of floors are downloaded from Open Street Map (OSM). The roads in the study area are visually interpreted, and the road network data in the study area are manually identified and obtained in combination with Baidu map information. After summarizing and processing various urban construction data according to ArcGIS Pro 3.1 software, the green space, buildings, and main road network of the Fuzhou High tech Zone are obtained (Figure 2).

2.2.2. Noise Data

This study uses the online model calculation platform of China: the V4 online calculation platform for environmental noise assessment of Huanan Science and Technology, which was built according to the Technical Guidelines for Environmental Impact Assessment Sound Environment HJ2.4-2021 issued by the Ministry of Ecology and Environment of the People’s Republic of China and implemented in 2022. It was upgraded from the Noise Environmental Assessment Online V3, which can calculate the sound propagation process of industrial sound sources, highway sound sources, railway sound sources, and meteorological sound sources, as well as calculate the sound propagation process of noise in sound barriers, green forest belts, and meteorology, and output the results of the noise assessment.

The trunk traffic flow data used for noise modeling is derived from the on-site observation of vehicles at major road intersections from 10:00 a.m. to 4:00 p.m. on weekdays, and the traffic flow data of each road within 5 min are recorded and counted. At the same time, the noise monitoring of handheld equipment is carried out as on-site observation.

The 5-min traffic flow of each road is taken as one of the indicators, and the number of road lanes, road grade, and other data are input into the calculation platform, and an urban model including terrain, green space, and buildings is built in the platform, and the calculation platform is calculated according to the urban model we built according to the technical guidelines in the “Technical Guidelines for Environmental Impact Assessment Acoustic Environment”.

In the technical guidelines, outdoor acoustic propagation attenuation includes geometric divergence (A_div), atmospheric absorption (A_atm), ground effect (A_gr), barrier shield (A_bar), and other multifaceted effects (A_misc) attenuation. In the environmental impact assessment, the sound level of the prediction point is calculated according to the sound power level of the sound source or the sound pressure level at the reference position, and the outdoor sound propagation attenuation, is calculated separately according to Formula (1) or (2).

L_p(r) = L_w + D_C − (A_div + A_atm + A_gr + A_bar + A_misc)

(1)

In the formula, L_p(r)—sound pressure level at the prediction point, dB;

• L_w—sound power level (A-weighted or octave band) generated by a point sound source, dB;
• D_C—directivity correction, which describes the degree of deviation between the equivalent continuous sound pressure level of a point sound source and the sound level in a specified direction from an omnidirectional point sound source producing sound power level L_w, dB;
• A_div—attenuation due to geometric divergence, dB;
• A_atm—attenuation due to atmospheric absorption, dB;
• A_gr—attenuation due to ground effects, dB;
• A_bar—attenuation due to obstacle shielding, dB;
• A_misc—attenuation due to other multifaceted effects, dB.

L_p(r) = L_p(r₀) + D_C − (A_div + A_atm + A_gr + A_bar + A_misc)

(2)

In the formula, L_p(r)—sound pressure level at the prediction point, dB;

• L_p(r₀)—sound pressure level at reference position r0, dB;
• D_C—directivity correction, which describes the degree of deviation between the equivalent continuous sound pressure level of a point sound source and the sound level in a specified direction from an omnidirectional point sound source producing sound power level L_w, dB;
• A_div—attenuation due to geometric divergence, dB;
• A_atm—attenuation due to atmospheric absorption, dB;
• A_gr—attenuation due to ground effects, dB;
• A_bar—attenuation due to obstacle shielding, dB;
• A_misc—attenuation due to other multifaceted effects, dB.

Considering that the computer simulation model may have certain limitations, it is difficult to completely restore the actual propagation process and the specific situation of noise in the urban environment, and it is necessary to make up for the shortcomings of computer simulation. Therefore, we used Sakieh et al.’s research to calculate the traffic flow in the field and monitor the noise in the field with a handheld sound pressure level meter. We used the actual noise situation in some areas that had been acquired to verify the noise data obtained after the computer simulation, and the accuracy of the computer model simulation exceeded 75% in all 25 noise monitoring points. At the same time, we used actual noise monitoring values to correct the simulation results with large deviations to ensure the accuracy of the computer simulation model in representing real-world noise propagation and attenuation.

After the platform calculation, the noise simulation data are obtained, and we calibrate them according to the noise data samples measured in the field. After simulation calculation and correction, a spatial statistical analysis was carried out by ArcGIS Pro 3.1 software, from which the noise distribution status of the Fuzhou high-tech zone was obtained.

2.3. Research Framework

Figure 3 outlines the methods, data, and research steps used in this study. This study is mainly divided into three parts: (1) Analysis of spatial distribution characteristics of the acoustic environment and reduction distribution characteristics of green space noise; (2) Analysis of the spatial pattern characteristics of urban green space; (3) Analysis of the influence of green space MSPA elements on noise. For details, see Section 2.4 (Research Steps).

2.4. Research Steps

2.4.1. Analysis of Spatial Distribution Characteristics of Acoustic Environment and Green Space Noise Reduction Distribution Characteristics

First, we conducted a noise reduction calculation. To reduce the interference of factors other than green space in the noise simulation and calculation and focus on the correlation law between the spatial pattern of green space and noise reduction capacity, the model constructions with green space and without green space are carried out, respectively, in the simulation of the noise environment. After the noise simulation is carried out separately, the corresponding spatial distribution grating diagram of sound pressure level is generated, and the two sets of raster maps are used for the difference calculation to obtain the net reduction value of the green space to the noise (ΔN). This index is the net reduction value of the green space’s noise reduction capacity to the noise in the study area, which will be used as the index of the noise part in this study. In this process, we also obtained the noise distribution with green space, i.e., the noise distribution status of the Fuzhou high-tech zone (Figure 4). After setting up the control group, the study on regional environmental noise eliminated the building, terrain, and other interference factors, which can purely reflect the change of green space on noise reduction effect. After noise model simulation, difference operation, and correction, ArcGIS Pro 3.1 software was used for the spatial statistical analysis, from which the net noise reduction map of the Fuzhou high-tech zone was summarized (Figure 5). These data can represent the spatial distribution of the green space noise reduction net value. Based on these data, this study can further analyze and discuss the correlation between green space and noise and the influencing factors of green space noise reduction capacity.

After basic data processing and noise simulation calculation and analysis, the spatial distribution characteristics of the acoustic environment and the reduction distribution characteristics of the green space noise in the study area are obtained. According to different spatial distribution characteristics, combined with the main green space and road network data in the study area, the relationship between urban noise and green space was preliminarily explored.

2.4.2. Analysis of Urban Green Space Pattern Characteristics

In the second step, we conducted MSPA to quickly identify the seven types of MSPA elements, which corresponded to the actual elements within the city based on their ecological definitions, to analyze the characteristics of the overall green space pattern in the study area and to make an assessment.

We use the remote sensing data with the resolution of 10 m in March 2023 and preprocess the radiometric calibration, atmospheric correction, ortho correction, image fusion, etc., based on the ENVI 5.3 software platform to output the binary grid data of the green space and background. Based on the Guidos Toolbox 2.9 software platform, the binary grid data with green space as the foreground and non-green space as the background are processed. After MSPA, the binary grid map is divided into 7 types that do not overlap each other (

Table 1. Types of Urban Green Space Morphology Based on MSPA and Their Ecological Implications.

Spatial Pattern Types	Ecological Implications
Core	Large natural or semi-natural patches and the main source of the blue-green infrastructure network. In urban areas, cores are usually large urban green spaces such as comprehensive parks, wetland parks, nature reserves, and scenic spots.
Islet	Small natural or semi-natural patches that are relatively isolated and fragmented with low connectivity, equivalent to “stepping stones” in the ecological network. It usually corresponds to the subordinate green space and park in the city.
Perforation	The natural or semi-natural patches on the inner boundary of the core area, and the transition zone with the paved land or urban construction land inside the core area, are equivalent to the ecological interlacing zone or buffer zone and have the edge effect. It is usually the pioneer zone where the core area has been degraded by natural or human activities, such as the green belts on both sides of the roads in the nature reserve.
Edge	The natural or semi-natural patches on the outer boundary of the core area, and the transition zone with the urban construction land on the periphery of the core area, are equivalent to the ecological intertwining zone or buffer zone and have the edge effect. It is usually the pioneer zone where the core area has been degraded by natural or human activities, such as the forest belt in the periphery of parks and scenic spots.
Loop	Natural or semi-natural corridors within the same core area help to enhance ecological processes such as species migration and energy flow within large patches. Corresponding to green belts or bridges within parks or nature reserves.
Bridge	Natural or semi-natural corridors connecting two different core areas in close proximity to each other are conducive to the migration of species and energy flow between core areas. It corresponds to the green belts of roads, windbreaks, rivers, and other green belts in the city.
Branch	Natural or semi-natural corridors connect the core area and peripheral non-natural patches and are channels for species migration and energy exchange between the core area and peripheral non-natural patches. It corresponds to the green belts on roads connecting parks and residential or commercial areas in the city.

), including branch, bridge, core, edge, islet, loop, and perforation, after which the results of the green space layout structure characteristics are obtained (Figure 6 and Figure 7). In urban blocks, cores are large-scale green spaces, including park green space, community parks, etc.; islets are small and isolated green spots, such as pocket parks, scattered street trees, etc.; perforation is the boundary zone between the interior of the core and non-UGI (urban green infrastructure) caused by human interference; the edge is the junction of the core and the outside non-UGI area, such as the peripheral forest belt of the community park; The loop, bridge, and branch are three kinds of linear spaces that, respectively, correspond to the road, green belt, and landscape belt connecting the same core, connecting adjacent cores, and connecting edges, perforations, loops, and bridges at one end.

2.4.3. Analysis of the Impact of Green Space MSPA Elements on Noise

In the third step, through ArcGIS Pro 3.1 software, the study area was divided into 500 m × 500 m grids, and the indicators such as the percentage of area of each greenfield MSPA element in the grid, the area of the element, the perimeter of the element, and the average value of noise reduction in the grid were extracted through the in-scope summarization, and the mean value of noise reduction in the grid was taken as the dependent variable, and the percentage of area of each greenfield MSPA element in the grid, the area of the element, the perimeter of the element as the explanatory variables for correlation analysis. The correlation analysis was conducted to preliminarily investigate the effects of different indicators of greenfield MSPA elements on noise; Through geographically weighted regression (GWR) analysis, the spatial distribution characteristics of green space noise reduction capacity in the study area are analyzed, and the impact of green space spatial pattern on noise reduction is further explored, which helps to search for the methods to improve urban green space noise reduction capacity.

3. Result Analysis

3.1. Spatial Distribution Characteristics of the Acoustic Environment and Noise Reduction Distribution Characteristics of Green Space

According to the analysis of the noise distribution status in the study area (Figure 4), we found that noise impacts are high in the northern part of the study area adjacent to the university town area and the southeastern part of the environment area. Combining the green space, buildings, and main road network in Figure 2, we believe that the area with a large noise impact is roughly consistent with the area with a dense road network. In addition, we also found that there are large mountains and forests in the west and southwest of the study area, and the development level of this area is relatively low, so there are fewer human activities.

As shown in Figure 5, overall, the green land in the Fuzhou high-tech zone contributes relatively high net noise reduction value. The attenuation value of green space noise shows obvious spatial distribution characteristics; the areas with a high net value of noise reduction are all located in the areas with high noise incidence, such as intersections, and the average value of regional noise reduction is as high as 37.962 dB to 44 dB, which indicates that green space has an obvious noise reduction effect on high decibel noise.

Furthermore, although the noise reduction net value of green space in some areas is lower than that in the area near the noise source, it still has a good noise reduction effect, and the average regional noise reduction is 10–26 dB. These noise-reducing areas have a long impact distance over a wide region and play a relatively important role in the optimization of the overall acoustic environment, which we believe is attributed to the dense protective forest belts in the non-intersection areas at the edge of the road. The green space, which is based on grass and shrubs in a large number of human activity impact areas of the built-up areas, and the dense protective forest belt along the roads can play a uniform and more extensive noise reduction effect on the surrounding large areas, and there are different biases in the noise reduction effect of the green space near the noise source.

3.2. The Spatial Pattern Characteristics of Green Space in Fuzhou High-Tech Zone

Based on the MSPA, the area and percentage of green space elements in the seven categories were obtained (Figure 6 and Figure 7). The total area of the seven categories of ecological landscapes is about 310,793 m². The bridge has the largest area, about 161,038 m², accounting for 51.82% of the total area of green space elements. The edge area is the outer boundary of the core area and is a transition zone between the core area and its external non-green space elements. The core area and edge area, respectively, account for 19.33% and 5.26% of the total area of green space elements. Perforation is the transition area between the core area and its internal non-green space elements, accounting for 0.27% of the total area of the green space elements. Islets scatter in the study area, accounting for 12.01% of the total area of green space elements. Branches, bridges, and loops all have connecting roles; more numbers and larger areas mean better connectivity. Among them, the branches, as strip-like areas connecting the core area and other green space elements, accounted for 6.01% of the total area of the green space elements, indicating that the connectivity of the green space elements in the study area is relatively high. The loops, as corridors for the exchange of material information within the core area, accounted for 5.32% of the total area.

By analyzing the structural characteristics of green space distribution in the Fuzhou high-tech zone (Figure 6 and Figure 7), it can be obtained that the green space MSPA elements of the Fuzhou high-tech zone are dominated by the bridge, followed by the core and islet, branch, edge, loop, and perforation. The number and size of cores in the study area are relatively large, mostly concentrated in the west and south, while in other areas of the study area, they are scattered and fragmented, with the cores in the form of encircling construction land mainly existing as mountain forests, parks, and green spaces.

The number and size of core areas near the main road network are relatively small; the bridge connecting multiple green cores has become the main part of the green space in the study area. At the same time, a large number of scattered small- and medium-sized green cores have increased the area of the edge. The edge, as the pioneer zone of the core relative to the human activity area, mainly exists in the form of forests outside mountain woods, forests on the periphery of parks, and protected forest belts. The main road network in the study area has cut the green space, resulting in a large number of islets and perforations, which are the closest to the noise source in the green space MSPA elements and occupy a relatively low proportion in the elements.

3.3. Impact of Green Space MSPA Elements on Noise

3.3.1. Correlation Analysis

Based on the element area, element area share, and element perimeter of each element of the green space MSPA within a 500 m × 500 m grid as explanatory variables and the average noise reduction within the grid as the dependent variable, the results of the correlation analysis are as follows (

Table 2. Correlation between green space structural characteristics indicators and noise reduction.

Average noise reduction in each green space structure	Proportion of Branch element area	Branch Element Area	Branch Element Perimeter
	−0.005	0.078	0.065
	Proportion of Bridge element area	Bridge Element Area	Bridge Element Perimeter
	−0.039	0.078	0.079
	Proportion of Core Element Area	Core Element Area	Core Element Perimeter
	−0.019	0.098	0.149 *
	Proportion of Edge Element Area	Edge Element Area	Edge Element Perimeter
	0.041	0.167 *	0.181 **
	Proportion of Islet Element Area	Islet Element Area	Islet Element Perimeter
	0.137 *	0.139 *	0.114
	Proportion of Loop Element Area	Loop Element Area	Loop Element Perimeter
	−0.208 *	−0.053	0.219 *
	Proportion of Perforation Element Area	Perforation Element Area	Perforation Element Perimeter
	−0.322	−0.301	0.017

* indicates that the correlation is significant at 0.05 level (single tail), ** indicates the correlation is significant at 0.01 level (single tail).

).

Noise reduction and proportion of the islet element area along with its element area have a significant positive correlation, while there is a significant negative correlation with the proportion of Loop element area at 0.05 level (single tail), a significant positive correlation with its element perimeter, a significant positive correlation with the Edge element area, and a positive correlation with its element perimeter.

The correlation between noise reduction and the proportion of element area is islet > edge > other (branch, core, bridge, loop, perforation). Increasing the area proportion of islands and edges within the scope can significantly enhance the noise reduction capacity of green space, and improving the proportion of core, bridge and branch area can harm the noise reduction capacity of the green space to a weak degree, while increasing the proportion of loops and perforation area can significantly reduce the noise reduction capacity of green space.

The correlation between noise reduction and element area is edge > islet > other (core, branch, bridge, perforation, loop). Increasing the area of the edge, islet, and core elements can significantly enhance the noise reduction capacity of green space. Increasing the area of the bridge and branch elements can also enhance the noise reduction capacity of the green space. Increasing the area of ring elements will harm the noise reduction capacity to a weak degree. Increasing the perforation element area will significantly reduce the noise reduction capacity of the green space.

The correlation between noise reduction and element perimeter is loop > edge > core > other (islet, bridge, branch, perforation). Increasing the element perimeter of the loop, edge, core, and islet can significantly enhance the noise reduction capacity of the green space, and increasing the element perimeter of the branch and bridge can also enhance the noise reduction capacity of the green space. The perforation element perimeter has a weak impact on the noise reduction capacity of green space.

3.3.2. GWR Geographically Weighted Regression Analysis

According to the GWR model of urban green space pattern indicators and net noise reduction, each green space pattern indicator has different noise reduction capacities in different spatial units (Figure 8). The spatial characteristics of noise reduction capacity for each green space pattern indicator are explored by overlaying the urban green space distribution characteristics map on the GWR model results and using the method of spatial comparison.

Through Figure 8A,B, we found that branches with a small number and area have a strong noise reduction capacity in the green corridor evenly distributed along the river in the east of the study area and in the green patch area around the green core area in the southwest. The areas with strong noise reduction capacity of branches are mainly concentrated in the east of the study area along the river and the southwest of the study area near the mountain and forest areas, which are all areas with high green coverage and sparse distribution of branch green space elements. To some extent, this reflects the phenomenon that a large number of branch element clusters found in the previous study led to the decline of green space noise reduction capacity.

The bridge shows relatively strong noise reduction capacity in the north of the study area near the university town and in the southwest scenic forest belt connecting different green core areas. There is a significant difference between the area proportion and the area and perimeter of the elements in the noise reduction capacity of the bridge. Increasing the proportion of the element area can produce a strong noise reduction capacity in the southwest, but it will weaken the noise reduction capacity in the north, while increasing the area and perimeter of the elements will strengthen the noise reduction capacity in the north and weaken the impact in the south. According to the characteristics of the bridge elements, we infer that this phenomenon is because the bridge is connected to an area with high green coverage. Increasing the proportion of the area within the area will reduce the noise source and keep the elements away from the noise source, leading to a decrease in the noise reduction of the whole area. Increasing the degree of fragmentation of the green space can make the green space elements close to the noise source and produce a better noise reduction effect.

In the east of the study area, there are a large number of residential areas, several main roads, and evenly distributed noise sources. The core shows relatively outstanding noise reduction capacity and is widely distributed. A number of cores with small areas are evenly distributed. At the same time, the green land close to the noise source has a strong noise reduction effect. In the area along the river in the east of the study area, improving the perimeter of the core elements can provide a stronger noise reduction function. This, to some extent, reflects that the core with a complex shape can make a larger range of green space produce strong noise reduction capacity; The spatial distribution characteristics of the edge’s noise reduction capacity at a high level are consistent with the spatial distribution characteristics of the main noise sources, and the edge’s noise reduction capacity is relatively strong. Increasing the area proportion of the edge in the southeast of the study area can significantly enhance the noise reduction capacity, while improving the area and perimeter of edge elements can enhance the noise reduction capacity in the eastern area along the river.

The islet has a significant positive effect on noise reduction. In the area with a high degree of fragmentation of green space and dense road network in the north of the study area, the islet’s noise reduction capacity is more prominent, and its noise reduction capacity is also strong in the relatively isolated and fragmented small green patches in the south of the study area. The area with high noise reduction potential is more consistent with the area with dense road network and also with the area of high noise level in the study area in the spatial dimension. As a highly fragmented green space around the construction land, the islet is closer to the noise source than other green space elements and can better exert the noise reduction capacity of green space.

The overall noise reduction capacity of loop elements in the eastern part of the study area is relatively weak, but increasing its area proportion can produce a stronger noise reduction effect in the southern part, while increasing the area and perimeter of elements can strengthen the noise reduction capacity in the northern part. Based on the characteristics of the elements, it is inferred that the loop area in the southern part accounts for a lower proportion, with more large areas and continuous green spaces. Increasing the proportion of the loop element area can improve the degree of fragmentation of local green space, while the northern part is short of large and continuous green space. Increasing the area of the loop element and the perimeter of the element will also improve the degree of fragmentation of the local green space. The area with a high degree of green space fragmentation is more consistent with the area with enhanced green space noise reduction capacity in the dimension of spatial distribution.

The overall noise reduction effect of perforation is relatively weak, and its impact on the overall noise reduction capacity is also weak. In terms of spatial agglomeration characteristics, the noise reduction capacity is relatively strong in the north and relatively weak in the south. In the sense of ecology, the perforation is a transitional green space between the interior of the core of the green space and the non-green space area, located away from the noise source, and the noise reduction performance is rather modest in practice, resulting in a lower noise reduction capacity.

4. Discussion: Variations in Noise Reduction Capacity of Urban Green Spaces under Different Pattern Characteristics

4.1. Urban Green Spaces with Different Landscape Pattern Characteristics Show Different Noise Reduction Capacities

The previous research on the impact of urban morphology on noise level pointed out that in a space with a large area and strong continuity of green space, the noise level observed is low. At the same time, many studies have pointed out that green space with large areas and strong continuity can play a stronger ecological function [34]. In our result analysis, we found that the islet and edge, which represent green space elements that destroy the continuity of green space, often have stronger noise reduction capacity. This study focuses on the net value of noise reduction generated by green space and discusses the noise reduction of actual green space. It is found that the inconspicuous noise reduction performance of large-area and continuous green spaces may be because it is far away from the noise source so the noise cannot be fully reduced. The reason for the low noise level is not the noise reduction, but the increased resistance to noise sources in a particular space. In previous studies, the relationship between the artificial neural network index and green patches shows that scattered green spaces are more likely to produce noise sources. Although it is efficient to simply and directly eliminate noise sources, there are large amounts of unavoidable noise generation in cities due to economic development.

The results show that the distribution of green space near the noise source can effectively prevent noise propagation. This is consistent with Yousef Sakieh’s research results: green space near the pollution center can significantly mitigate the impact of noise transmission mechanisms [18]. Increasing the area and area proportion of islet and edge elements in urban green space can significantly enhance the noise reduction capacity of green space, indicating that the relatively isolated and fragmented small natural or semi-natural patches in urban green space, which correspond to the affiliated green space, parks, and other green spaces in the city, play a key role in the noise reduction capacity of urban green space. Low levels of traffic noise were also observed in areas with small surrounding townhouses and backyards or foreyards [35]. Therefore, we consider that these green space pattern characteristics have the higher noise reduction efficiency in residential areas. At the same time, as a transition zone between the core area and the peripheral urban built-up area, the outer boundary of the core green area, which is equivalent to an ecological ecotone or buffer zone, is the pioneer zone where the core area is degraded due to the interference of natural or human activities, such as the forest belt outside parks and scenic spots, which can also play a strong role in noise reduction. Scattered green spaces may enhance noise reduction, which is not only because of their physical characteristics, but also because they limit high population density and vehicle use [36].

Increasing the element perimeter and area of the core is beneficial to green space noise reduction, which not only represents that a large number of continuous green spaces can effectively reduce noise but also further proves that increasing the element area of the edge can improve the noise reduction capacity because increasing the element perimeter and area of the core is to increase the area of the edge itself. This also confirms that the increase in area and area proportion of perforation in the analysis results is negative for the noise reduction capacity of green space, because the ecological significance of perforation itself makes its area and area proportion increase, which means more construction land in the core area, and destroys the continuity of the core. At the same time, more construction land also means the reduction of green space coverage in a certain space. A study in the UK found that the distribution of urban green space may be an important reason for affecting the noise level. Cities with high porosity and green space coverage are more likely to maintain low noise levels [24]. This seems to conflict with the results of this study, but in fact, the increase in the area and area proportion of islet and edge improves the porosity and green space coverage in a certain space and is closer to the noise sources, especially traffic noise, in an ecological sense.

Although the increase in the area of perforation has improved the porosity, because its ecological significance represents the presence of construction land or buildings rather than roads in the core green space, it does not help to enhance the noise reduction capacity and also reduces the noise reduction capacity of green space because it reduces the green space coverage within a certain range. This involves the difference and change in the noise reduction mechanism of green space vegetation from the macro urban morphology to the meso and micro green space vegetation. According to the analysis results in this study, it is further inferred that the reason why the core, as a large area of green space, has a weak correlation with the amount of noise reduction is that its own nature causes the core area to be far away from the noise source, and there are other elements between each other, so that the noise level in the core area is low and the green space noise reduction capacity to be utilized is limited. Increasing the area and area proportion can effectively improve the noise reduction capacity of the edge and islet. However, due to the structural nature of the elements, the edge itself is attached to the core, which leads to when the area and proportion of the edge itself increase to a certain extent due to the high proportion of green space in the space, the noise source decreases, reducing the noise reduction capacity of the edge. The loop and edge are elements attached to the core in nature and composition. The noise reduction amount is negatively correlated with the loop area proportion and the element area, while it is significantly positively correlated with its element perimeter, which indicates that the loop with a complex shape and small area can enhance the noise reduction capacity. The islet, as a fragmented and relatively isolated green space, is more operable and has a higher noise reduction potential when deployed near the noise source, which is also consistent with the previous research conclusions [18]. The strong correlation between the noise reduction amount and the element perimeter of the core may be because the element perimeter of the core corresponds to the area and shape complexity of the edge.

The loop corresponds to green belts or ecological bridges in parks or nature reserves in the physical urban environment. Increasing its proportion means that more perforation occurs within the core, resulting in an impact on the area and the continuity of the core’s green space. At the same time, the loop often occurs within the core, which is relatively far away from the noise source and has weak noise reduction capacity. Increasing the proportion of its area will significantly reduce the noise reduction capacity of the green space, while increasing the perimeter of the loop element means increasing the shape complexity of the internal corridor of the core green space, which will make the shape of the core more complex and also make the area of the edge larger and the shape more complex, which is very helpful to improve the noise reduction capacity of the green space [19].

4.2. Influence of Green Space Elements on Noise Reduction Capacity through Different Paths

Through the GWR geographically weighted regression model, the spatial differences in the noise reduction capacity of various green space elements are analyzed, and the paths of various green space elements affecting noise reduction capacity are discussed. The increase in the proportion of branch and bridge areas will lead to an increase in the overall connectivity of green space, which will lead to the inability of noise sources to occur in the space adjacent to green space. This means that the noise is transferred from the space rather than reduced. The continuous improvement of green space connectivity will lead to the gradual accumulation of noise sources in high-density construction land lacking green space resources, along with the formation of the noise island effect [37]. A study in northern Ghana, West Africa, shows that protected forests and man-made green spaces often account for a large proportion of the available green spaces in the urban core [38], which is similar to the situation in our study area. Although some spaces have more green space resources, their noise reduction capacity is weak instead. From the perspective of green space noise reduction, this phenomenon means that the city’s relatively limited green space resources have not played their due role. The spatial distribution of the noise reduction capacity of the core and edge is relatively similar, which means that the core part of a large urban green space and its pioneer green belt can play a strong noise reduction capacity when relatively close to the noise source [39,40]. The reasonable distribution of its green space structure can significantly reduce the regional noise level. The islet usually corresponds to the affiliated green space and garden in the city. Its positive effect on noise reduction is more significant in this study. As a green space with a high degree of fragmentation, it is often concentrated in the road network, traffic arteries, and intersections in a realistic scenario. It is the green space closest to the source of noise among all elements, and it is often located inside the high-density noise source space composed of complex noise sources, so it shows a strong noise reduction capacity. The existence of the islet itself can not only reduce the number of noise sources but also provide high net value noise reduction in the high-density noise source space, which has a significant inhibitory effect on relatively serious noise pollution. In this regard, the previous studies have also found a strong correlation between high-density residential green space and changes in noise annoyance [39,41]. Increasing the proportion of the area of loop elements in the city corresponds to increasing the width of green belts such as road green belts and windbreak forest belts. This method will increase the area and continuity of green belts near noise sources, strengthen their noise reduction capacity and produce higher noise reduction net values. At the same time, because vegetation such as road greenbelts and windbreaks generally has higher biomass and vegetation density, it is also one of the reasons why they can play a strong role as noise barriers [19]. Perforation often occurs in a space with high green space coverage. It is relatively far away from the noise source, and its noise reduction capacity is weak. At the same time, the increase in its area means that the green space coverage in a certain space will be reduced, leading to the decline of the overall green space noise reduction capacity. In this regard, there is a relatively consistent conclusion with many previous studies [18,42].

4.3. Regulate the Characteristics of Urban Green Space Distribution to Improve the Noise Reduction Capacity of Green Space

According to Table 2 and Figure 8E, increasing the area and proportion of islets is an efficient method to reduce urban environmental noise, improve the green space coverage of high-density noise source areas, increase the small green space among them, and correspond to the actual urban environment. There are various ancillary green spaces, parks, etc. More allocation of such highly fragmented green space has strong operability and can produce better noise reduction effects, especially, optimizing the location of a green space can enhance its mitigation effect on the level of noise pollution [18].

First of all, we need to pay attention to the strong noise reduction capacity of the core and edge. Therefore, deploying these two green space elements near the noise source can play a better role in noise reduction [18]. However, the large urban green spaces corresponding to the core, such as comprehensive parks, wetland parks, scenic spots, and other large urban green spaces, depend on the natural background, and it is difficult to deploy them from the planning perspective. Therefore, if it is necessary to give better play to the noise reduction capacity of the core and edge, it is necessary to configure the areas with high noise, such as construction land, commercial land, etc., near the large green spaces and use the edge effect of green space core to reduce noise. At the same time, in the interior of the large green space, reducing the interference factors of internal core green space, maintaining the continuity of green space, and configuring complex core green space shapes as far as possible also helps to improve the noise reduction capacity of green space [19].

Secondly, when the area of branch and bridge elements remains unchanged, the more complex the shape is, the more it can improve its noise reduction capacity, corresponding to various road green belts in the urban environment. Therefore, the feasible operation to improve the noise reduction capacity through branches and bridges is to configure the ribbon green space with the smallest area and the most complex shape in the middle and high-density noise source areas of the city [19].

5. Conclusions

Urban green space patterns can significantly affect the net noise reduction of green space. Larger green patches, higher green coverage, more complex green space shapes, and more fragmented urban green space can produce higher noise reduction. The urban green space elements corresponding to the MSPA analysis method are islet area proportion > edge area proportion > other elements area proportion (branch, core, bridge, loop, perforation); edge element area > islet element area > other elements areas (core, branch, bridge, perforation, loop); loop element perimeter > edge element perimeter > core element perimeter > perimeter of other elements (islet, bridge, branch, perforation).

(1)

Areas with high green coverage can produce a stronger green space noise reduction effect. According to the regression results of the GWR geographically weighted regression model, various green space elements have different noise reduction capacities in different spatial locations, but they have better noise reduction performance in areas with high green coverage.

(2)

The green space close to the source of noise can play a stronger noise reduction effect. In an ecological sense, the islet is the closest to the source of the noise, with the highest degree of fragmentation, which can produce a large acoustic shadow area; The different actual noise reduction effects of green spaces with different patterns may be due to the uneven opportunities of their noise exposure. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with high levels of noise pollution should be given priority, which may have better noise reduction effects.

Based on the MSPA, this study classified the characteristics of urban green space patterns and discussed the impact of green space patterns on its noise reduction capacity. In the physical urban environment, the vegetation configuration of the green space itself is also worth discussing. In future research, the research object should be refined, and the vegetation configuration characteristics within the green space should be taken into account from the overall green space pattern characteristics, which can expand more means to improve the regional acoustic environment.

There are also some limitations in our study. On a smaller scale, the noise reduction effect of urban green space will be affected by the properties of the green space itself and the specific situation, such as the composition of vegetation configuration, density, biomass, etc., and different seasons will also have an impact on the green space, which may lead to the configuration of the green space near the source of the noise being unable to produce the expected effect of noise reduction in the actual situation. In the research field of urban green space noise reduction, we need to combine the noise reduction laws and influencing factors of urban green space among different scales, and the planning of urban green space should also include different scales to improve the noise reduction capacity of urban green space and optimize the urban acoustic environment.