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Article

Effects of Urban Green and Blue Space on the Diffusion Range of PM2.5 and PM10 Based on LCZ

by
Ruiyuan Jiang
,
Changkun Xie
,
Zihao Man
,
Rebecca Zhou
and
Shengquan Che
*
School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(5), 964; https://doi.org/10.3390/land12050964
Submission received: 30 March 2023 / Revised: 13 April 2023 / Accepted: 24 April 2023 / Published: 26 April 2023
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Abstract

:
Urban green and blue space (GBS) significantly impacts the diffusion range of atmospheric particulate matter. By determining the diffusion distance of atmospheric particulate matter (PM2.5 and PM10) in Shanghai, combined with the GBS landscape pattern index, this study completed a stepwise multiple regression equation and correlation analysis to explore the relationship between the morphological structure and spatial configuration of GBS on the diffusion distance of atmospheric particles. The results show that the landscape shape index (LSI) and the number of patches (NP) of GBS have a significant positive correlation with the diffusion distance of atmospheric particles, while the coefficient of the percent of landscape (PLAND), as a key influencing factor, has a negative correlation. The mean Euclidean nearest neighbor distance (ENN_MN) and splitting index (SPLIT) in the spatial configuration metrics positively affect the diffusion distance. Studies have proved that complex and dispersed GBS will lead to the weakening of its purification ability, thereby increasing the pollution range of atmospheric particulate matter. The order of the influence of different GBS types on the diffusion distance of atmospheric particles is as follows: DT > BS > LP > ST > WA. Therefore, high-density GBS with simple shapes and concentrated distribution should be considered in the future construction of new cities.

1. Introduction

Atmospheric particulate matter is solid, and liquid particles with an aerodynamic diameter of less than 100 μm in the atmosphere, especially those less than 2.5 μm (PM2.5) and 10 μm (PM10), have adverse effects on the human body’s trachea, lungs, and even the heart [1,2,3,4]. In the context of global urbanization, frequent human activities such as transportation, industrial production, and fuel power generation have increased atmospheric particulate matter emissions [5,6,7,8]. Meanwhile, the population in developing countries such as China continues to migrate from rural areas to urban areas, resulting in a sharp increase in the frequency of exposure to air pollution, which seriously threatens the health of urban residents.
GBS in cities has been proven as an effective measure to reduce the concentration of atmospheric particulate matter, because the surface of vegetation in the green space has an adsorption effect on PM2.5 and PM10, which directly affect the concentration of atmospheric particulate matter [7,9,10,11,12,13,14,15]. At the same time, vegetation can indirectly affect PM2.5 and PM10 by changing the microclimate of the surrounding environment [14,16]. Studies have found that vegetation in green spaces, such as forests and parks, can increase the humidity in the air and generate local turbulence, which promotes the deposition and diffusion of particulate matter [6,17,18,19]. In addition, green spaces absorb less radiation and store more heat than impermeable surfaces, thus formulating temperature differences, promoting airflow, and controlling the migration of atmospheric particles [20,21,22,23]. Similarly, blue space can enhance local air circulation with its greater specific heat capacity and evaporation [17,24,25,26,27]. As a result, particles will diffuse with the airflow, thus moving toward a lower humidity.
The morphological structure and spatial distribution of GBS are the key factors affecting the distribution and diffusion of atmospheric particulate matter. Regarding vertical structure, forests with complex canopy structures generally have a better blocking and trapping effect on atmospheric particles than grassland [9,16,28,29,30]. In terms of morphology, the ability of GBS to remove atmospheric particulate matter will be enhanced with increases in area and quantity. When the shape of a GBS has more complexity, meaning a larger contact area, this has a significantly larger marginal effect, which may lead to a decrease in the particulate matter removal ability of the GBS, while increasing the influence range of PM2.5 and PM10 [10,24]. Similarly, in terms of spatial distribution when compared with scattered and fragmented GBS patches, the patches with higher continuity, higher density, and a larger area can purify atmospheric particulate matter more efficiently [31,32,33]. In addition, the morphological structure and spatial configuration of GBS also significantly impact the surrounding ambient temperature, which further affects the convection and dilution of PM2.5 and PM10 [34].
Current studies on the impact of urban GBS on PM2.5 and PM10 mostly use the LULC classification system [35,36]. However, this classification method may overlook small GBSs in built-up areas, and cannot explain the diffusion mechanism of PM2.5 and PM10 from the perspective of how the landscape type affects atmospheric particulate matter through the microclimate. Previous studies have demonstrated that the LCZ method is more effective than the traditional LUCC method in explaining the relationship between urban landscapes and atmospheric particles [37]. However, current research on LCZs mainly focuses on urban microclimates, and there are few related studies on the influence of the distribution and diffusion of atmospheric particulate matter. In addition, the interpretation of the relationship of GBS to the extent, magnitude, and intensity of particle dispersion needs to be clarified.
Therefore, this study used the spatial interpolation method to identify the concentration characteristics of PM2.5 and PM10 in Shanghai and at select sample points, as well as determined the diffusion range of the sampling points in the four directions of south, east, north, and west, in order to set the buffer zone. Combined with the LCZ map, we explored the relationship between the landscape pattern of GBS and the diffusion range of PM2.5 and PM10. The main research objectives are: (1) clarify the influence of GBS morphological structure on the diffusion distance of PM2.5 and PM10; (2) analyze the influence of GBS spatial configuration on the diffusion distance of atmospheric particulate matter; (3) explore the differences in the impact of various GBS types on the diffusion range of PM2.5 and PM10. This study proposes a strategy to improve the ecological service of urban GBS and provides a theoretical reference for urban adaptive planning.

2. Methodology

2.1. Study Area

Shanghai is an important economic and financial center on the southeast coast of China, covering an area of 6340.5 km2. It is a city with high urbanization and high density. In the last decade, Shanghai’s permanent resident population has increased by an average of 185,170 people per year, reaching 23.871 million in 2020, of which 89.3% live in urban areas. At the same time, the population density of Shanghai’s central area reached 23,092 people per km2, with a population density ratio comparison of 7.68:1 to that of the suburbs. Shanghai has a subtropical monsoon climate. According to meteorological statistics, Shanghai’s annual average temperature in 2020 was 17.87 °C. The average relative humidity was 72.7%, and the monthly average precipitation was 139.75 mm. From 1 January to 31 December 2020, for 284 days out of 366 days, the primary pollutants in Shanghai were PM2.5 and PM10. Atmospheric particulate matter pollution has always been the primary factor affecting the air quality in this region.

2.2. Distribution Simulation of PM2.5 and PM10 in Shanghai

The PM2.5 and PM10 concentration data were obtained from the Continuous Ambient Particulate TEOM™ model RP 1400a Monitors (ThermoFisher Scientific, Inc., Waltham, MA, USA), set up by the Shanghai Bureau of Ecology and Environment. The measurement accuracy of the instrument is ±1.5 μg/m3 (average mass concentration per hour), ±0.5 μg/m3 (24-h average mass concentration), and the resolution is 0.01 μg/m3. The data consist of the hourly recorded concentration data of PM2.5 and PM10 at 51 sites from 1 January to 31 December 2020.
In order to avoid the influence of the background wind speed, the particle data used in this study excluded wind speeds greater than or equal to level 1 (0.3 m/s). At the same time, this study used typical pollutant concentration scenarios in Shanghai instead of the annual average to limit the influence of extreme weather conditions. The method of determining a typical scenario is first to calculate the nearest integer of the daily average concentration in 2020, and then count the concentration value with the highest frequency and the corresponding date. Finally, the date corresponding to the median particle concentration for those days was chosen to represent a typical scenario. Taking the typical scenario of PM2.5 as an example, the highest frequency of the daily average concentration in 2020 was 23 μg/m3 for 13 days. Among them, 27 June was the median of the daily average concentration in those 13 days, so the daily average PM2.5 concentration on that day represents a typical PM2.5 pollution scenario in Shanghai. Similarly, the typical PM10 pollution scenario in Shanghai was demonstrated by the conditions of 23 August.
After determining the pollution scenarios, Kriging interpolation was used to simulate the distribution characteristics of PM2.5 and PM10 concentrations in Shanghai. Forty-five of the fifty-one stations were set as the experimental group for simulation (Figure 1), and six stations were used for testing, in which t-tests analyzed the difference between the simulated value and actual values. The t-value of the PM2.5 concentration simulation scenario was −2.314, the degree of freedom was 5, and the p-value was 0.069. The t-value of the PM10 concentration simulation scenario was −1.018, the degree of freedom was 5, and the p-value was 0.355. The test results show that the difference is insignificant, proving that the Kriging interpolation method is reasonable.

2.3. GBS Data Extraction Based on LCZ

The GBS data were extracted from the Shanghai LCZ map (Figure 2) made by the World Urban Database and Access Portal Tools (WUDAPT) platform generator, which is currently the most advanced way to obtain LCZ data. In this study, 1554 samples were manually selected, and 34 pre-processed earth observation satellites were integrated to extract landscape features in combination with WUDAPT. The data were then input into the random forest classifier and repeatedly analyzed 25 times; 70% of the samples were randomly selected for training and the remaining 30% of the samples were selected for testing. The GBS data used in this study are currently the LCZ map with the highest accuracy (average accuracy rate 0.81, weighted accuracy rate 0.96) in Shanghai (as of 20 December 2022) and meet the passing standards of the WUDAPT platform [38]. The GBS data include dense trees (DTs), scattered trees (STs), bush or scrub (BS), low plants (LPs), and water (WA)—a total of five categories.

2.4. Sector Buffer Generation

Previous studies mostly used the method of creating a circular buffer zone centered on sample points to explore the impact of GBS on the ecological service function of the surrounding environment. However, this method cannot distinguish the difference between the regulatory ability of GBS in every direction, especially in the diffusion process of PM2.5 and PM10. Therefore, this study adopts the method of establishing sector buffer zones in the four directions of south, east, north, and west, and attempts to better explain the influence of GBS on the diffusion distance of atmospheric pollutants. The specific steps for making the fan-shaped buffer zone are as follows (Figure 3): (1) according to the distribution characteristics of PM2.5 and PM10 concentrations in Shanghai, 50 sample points with noticeable color differences compared to surrounding areas were screened (including 25 high concentration points and 25 low concentration points); (2) a total of 200 recording points were set at intervals of 100 m on the diffusion path in the four directions of east, south, west, and north of each sample point; (3) a fitting curve was generated according to the particulate matter concentration at the recording point, and the distance from the first turning point of the fitting curve to the sample point was defined as the diffusion range of the atmospheric particulate matter; (4) afterwards, with the sample point as the center and the diffusion range as the radius, a sector buffer zone was made at a 45° angle on both sides of the corresponding direction. Finally, the landscape features of the LCZ within the range were extracted using the sector buffer zone as a mask and calculated with the landscape pattern index. Excluding part of the diffusion path being close to the edge of Shanghai, which caused some data loss, this study made 138 sector buffers for PM2.5 and 136 for PM10.

2.5. Landscape Pattern Index Selection

Landscape pattern index is a quantitative index describing the composition and spatial configuration of a landscape, and has been widely used to study the relationship between land cover change and air pollution. This study uses the class-level indicators of Fragstats software version 4.2 to explore the impact of the GBS’s morphological structure and spatial configuration on the diffusion range of PM2.5 and PM10. The morphological structure metrics (Table 1) include percent of landscape (PLAND), number of patches (NP), mean area (AREA_MN), and landscape shape index (LSI); the spatial configuration metrics include the mean Euclidean nearest neighbor distance (ENN_MN) and splitting index (SPLIT). The metrics were subjected to Pearson correlation analysis with the diffusion distance of PM2.5 and PM10 to explore the influence of a single factor on the diffusion distance of atmospheric particulate matter. Finally, the six landscape pattern indexes of the five GBSs, equaling a total of thirty independent variables, were introduced into the stepwise multiple regression equation to identify the key influencing factors. The termination condition of the regression analysis was that the last variable added to the model was p > 0.05 or VIF > 5.

3. Results

3.1. Descriptive Statistics of GBS Landscape Metrics in Shanghai

The landscape pattern characteristics of the Shanghai GBS are represented by the average value of 274 sector buffer zone samples from the GBS landscape pattern index (Figure 4). The results show that in terms of PLAND, LP accounts for the most significant proportion of the landscape area (17.44%), followed by WA (7.49%), ST (7.03%), and BS (2.53%). DT is the smallest, accounting for only 1.32% of the landscape area. Overall, Shanghai GBS accounts for about 35.81% of the area, of which green space accounts for 28.32%. Regarding NP, the number of ST reaches the highest at 167.49, 573.60% of WA. However, WA, with less NP, is much higher than green space in terms of AREA_MN, which is four times that of DT, fifteen times that of ST, nineteen times that of DT, and twenty-seven times that of BS. The shape of the green space is more complex than that of WA, especially LP (13.06) and ST (12.67). In terms of spatial configuration, the two levels of differentiation are apparent. The respective plaques of ST and LP were more closely connected, and the distance between the plaque edges was shorter. The DT, BS, and WA plaques were more scattered, and the SPLIT index reached 54.85 × 104, 45.19 × 104, and 38.03 × 104, respectively.

3.2. Descriptive Statistics of the PM2.5 and PM10 Concentration

The particle concentration distribution of PM2.5 and PM10 in Shanghai is significantly different (Figure 5). Overall, the concentration ranges of PM2.5 and PM10 in Shanghai under typical scenarios are 10.95–29.81 μg/m3 and 26.79–43.13 μg/m3, respectively. Among them, most areas in Shanghai are higher than the midrange of PM2.5 concentration. For PM10, it is the opposite, with the overall level being lower than the midrange. The high concentration areas of PM2.5 in Shanghai are mainly located in the center and northwest corner of the urban area. Similarly, in addition to the northwest corner, the high concentration area of PM10 is also distributed in a small amount in the south of the city center. The low concentrations of PM2.5 and PM10 in Shanghai are mainly distributed in the southwest corner of Shanghai.

3.3. Descriptive Statistics of the PM2.5 and PM10 Diffusion Distance

Shanghai PM2.5 and PM10 have noticeable two-level differences in the diffusion range (Figure 6), with standard deviations reaching 3469.78 and 3877.11, respectively. The maximum diffusion distance of PM2.5 reaches 17629.99 m, which is close to 12 times the minimum value. The maximum value of PM10 is 18454.32 m, and the minimum is 2429.65 m. Regarding average diffusion distance, PM10 (8964.51 m) is slightly higher than PM2.5 (8755.79 m).
Regarding overall distribution characteristics, the distribution of low values in the diffusion range is relatively uniform. The PM2.5’s high values were mainly distributed on the central axis from northwest to southeast of Shanghai, while PM10’s were primarily distributed on the west and south sides of Shanghai’s urban area.

3.4. Relationship between GBS and Atmospheric Particulate Matter in Correlation and Stepwise Multiple Regression Equations

The Pearson correlation (Table 2 and Table 3) showed that the morphological characteristic index NP and LSI of GBS were greater than 0.6 in the diffusion distance of PM2.5 and PM10, showing a strong correlation and a positive coefficient. However, among the spatial distribution indicators, only the ENN_MN of DT had a negative correlation with the diffusion distance of PM10, while the other indicators did not show a significant correlation. In the stepwise multiple regression equation, in addition to the LSI-related indicators, LP-SPLIT and DT-SPLIT in the PM2.5 model, and BS-ENN_MN, LP-ENN_MN and DT-SPLIT in the PM10 model, all show a positively correlated relative diffusion distance. However, the related indicators of PLAND show a negative correlation in both models.
In addition, 8 variables entered the PM2.5 regression equation (Table 4), among which DT had the most variables (3), while ST, LP, and WA each had only 1 variable. As for the landscape pattern index, 3 metrics entered the equation, including LSI (3), PLAAND (3), and SPLIT (2). In addition, BS-LSI, DT-LSI, and BS-PLAND have the highest absolute values of standardized coefficients, reaching 0.720, 0.354, and 0.306, respectively, while those of ST and WA are relatively low, only 0.020 and 0.048. Similarly, 8 variables were screened out in the PM10 regression equation (Table 5), with the DT variable accounting for 37.5%, followed by BS (25%), LP (25%), and ST (12.5%). However, compared with the PM2.5 equation, the BS-ENN_MN and LP-ENN_MN variables were added to the category of landscape pattern index in the PM10 equation, and the standardized coefficients were 0.081 and 0.111, respectively. Regarding absolute values of standardized coefficients, LSI-related indicators are the highest, with BS_LSI, DT-LSI, and LP-LSI reaching 0.435, 0.400, and 0.237, respectively.

4. Discussion

4.1. Complex GBS Morphological Features Will Extend the Diffusion Distance of Atmospheric Particles

The morphological characteristics of GBS have a significant impact on the diffusion range of PM2.5 and PM10, and the NP of GBS has a significant positive correlation with the diffusion distance. The increase in the number of plaques may decrease the average plaque area, thereby slowing down the adsorption of atmospheric particles by GBS. Taking LP and WA as examples, the proportion of LP to the entire landscape area in Figure 4a is about twice that of WA, but in Figure 4c, the average patch area drops to a quarter of WA. Compared with the GBS with a larger area, the GBS with a small area also has the ability to adsorb and capture particulate matter, but the removal efficiency is significantly lower. Some studies have shown that the size of the GBS does not have a linear relationship with the surrounding microclimate [39,40]. When the area is smaller than a certain threshold, the GBS is more affected by the surrounding environment. It leads to a reduction in the temperature difference between the GBS and the surrounding environment, thereby inhibiting the generation of airflow and reducing or even eliminating the ability to repel particulate matter. At the same time, an increase in the number of plaques also means an increase in the boundaries of the plaques, leading to more complex morphological characteristics of the GBS in the buffer zone. Therefore, the landscape pattern indicators NP and LSI show similar trends.
Interestingly, the relevant indicators of the LSI in this study also showed a positive correlation with the diffusion distance of atmospheric particles and the highest standardized coefficient. This means that the more complex shape of the GBS, the weaker the purification ability of the PM2.5 and PM10 concentration is, which is consistent with the research of Bo, Li, and Cai [10,31,41]. However, some studies have proved that the rise in the LSI is also helpful for the GBS to intercept atmospheric particulate matter [26,42,43]. Complex morphological features usually mean a larger contact area with the outside world, as well as more frequent exchanges of matter and energy requiring the GBS’s higher purification capabilities. The GBS with a larger area and stronger purification ability can keep its own repelling efficiency unchanged while increasing the LSI, increasing the influence range, and adsorbing particles at a higher level. However, the smaller GBS is affected by the edge effect, and reduces atmospheric particulate matter’s adsorption and deposition capabilities, thereby increasing the diffusion distance of PM2.5 and PM10 [31]. Furthermore, the PLAND metrics show a negative correlation with the diffusion distance. The reason may be that the increase in the GBS area means more vegetation and water bodies, which enhances the purification ability of the plaques in relation to PM2.5 and PM10, resulting in the shortening of the particle diffusion distance [41,42,44,45].

4.2. Scattered GBS Spatial Layout Will Increase the Scope of Atmospheric Particulate Matter Pollution

Most spatial configuration metrics of GBS do not show a significant relationship in Pearson correlation but frequently appear in stepwise multiple regression, and the two indicators ENN_MN and SPILT are the key influencing factors. This may be due to the impact of GBS on atmospheric particulate matter in terms of spatial configuration, emphasizing the combined effect of multiple variables rather than the direct impact of single variables. Previous studies have emphasized the influence of a single landscape pattern index on the purification ability of GBS itself [46,47,48]. However, in the actual diffusion process, the synergy between GBSs may form ventilation corridors, and the average distance and degree of dispersion between patches jointly determine the diffusion direction and distance of atmospheric particles. In terms of standardized coefficients, both ENN_MN and SPILT showed a positive correlation with the diffusion distance of atmospheric PM2.5 and PM10, which was consistent with previous studies [24,48]. The possible reason for this phenomenon is that when the average proximity distance and dispersion between GBSs increase, they are more affected by other types of plaques and the synergy between the plaques decreases. At the same time, the clustering effect of the GBS itself was reduced and weakened the ability to mitigate pollution [31].
Overall, the morphological structure metrics of GBSs have a higher impact on the atmospheric particulate matter than the spatial configuration index, especially the correlation between single variables. This may be due to most morphological structure metrics, such as the quantity, area, and shape being directly related to the purification ability of the GBS itself. Such metrics indicate the number of trees and their contact area with other types of land, directly affecting the particulate matter concentration. The indicators related to spatial layout mainly explain the synergy between the same GBS and reflect more on the impact on the path and direction of the particle diffusion process. This study’s diffusion distance determination is based on the concentration gradient of PM2.5 and PM10, which cannot fully reflect the synergy between GBSs. In the future, further in-depth research may be carried out on the influence of GBS on the migration direction and path of particulate matter.

4.3. Scattered GBS Spatial Layout Will Increase the Scope of Atmospheric Particulate Matter Pollution

The influence of different GBS types on the diffusion range of atmospheric particulate matter is quite different. Among them, DT-related variables appear the most in both PM2.5 and PM10 equations, which means that DT has the most significant impact on the diffusion range of atmospheric particulate matter. This is because of the adsorption of particulate matter by green space, which is mainly because: (1) the rough surface of the branches and leaves of plants can directly capture PM2.5 and PM10 in the air [9,13]; (2) the canopy of plants can slow down the wind speed and promote the settlement of particulate matter [16,18]; and (3) the complex plant canopy will form turbulence locally when the wind blows over the branches and leaves, which further promotes the settlement of particulate matter [16,18]. The ability of the plant canopy to capture particulate matter mainly depends on the number of branches and leaves and the variability of the canopy structure [49,50]. Therefore, trees have larger and more complex canopies than shrubs and low vegetation, and are thus able to capture more atmospheric particles. At the same time, trees have a larger shade area than shrubs, which can better form temperature differences, promote air circulation, and further enhance air purification capabilities.
The phenomenon mentioned above is also reflected in the relationship between BS and LP. In conclusion, under the same community density conditions, community height is a crucial factor affecting the diffusion range of PM2.5 and PM10. However, the independent variables of BS and LP entering the model and the absolute values of the corresponding standardized coefficients were higher than those of ST, which indicated, that compared to the community’s height, the community density’s impact on the particle diffusion range was more significant. Although the trees in the ST community can better absorb particulate matter, the distribution of trees in the community could be comparatively sparser, resulting in lower average heights of branches, leaves and canopy structure complexity in the overall community than in BS and LP. Therefore, the influence of the ST community on the diffusion range of PM2.5 and PM10 is smaller than that of BS and LP. Moreover, WA increases the humidity in the air through evaporation, providing carriers for gaseous and solid pollutants in the atmosphere, leading to an increase in the hygroscopicity of particulate matter, and thus promoting the deposition of atmospheric particulate matter [51,52]. At the same time, there is a large temperature difference between WA and land, which usually forms an unimpeded fast airflow on the surface of a water body. Although this airflow can dilute the local particle concentration, it will also expand the overall pollution range. Overall, the order of the influence of GBS on the diffusion distance of atmospheric particulate matter is as follows: DT > BS > LP > ST > WA.

5. Conclusions

In this study, by analyzing the concentration distribution characteristics of PM2.5 and PM10 in Shanghai, a total of 274 sector buffer zones were selected according to the diffusion distances of high and low concentration values in the four directions of east, south, west, and north. The GBS landscape pattern index in the buffer zone was calculated and a correlation analysis and stepwise multiple regression equation was established with the diffusion distance of atmospheric particles. The study found that the morphological structure metrics LSI and NP are affected by the edge effect when increasing, which reduces the ability of the GBS to reduce PM2.5 and PM10, increasing the diffusion distance of atmospheric particulate matter. The rise in the PLAND means more vegetation and water bodies, shortening the transmission distance of PM2.5 and PM10. When the ENN_MN and SPLIT spatial configuration metrics increase, the GBS patches are more easily affected by other categories of patches, reducing the clustering effect of the GBS and weakening the ability to purify pollution. We also found that, compared with the spatial layout index of GBSs, the morphological structure index has a more significant impact on the diffusion distance of atmospheric particles. Due to the difference in the number of branches and leaves and the structure of the canopy among the various types of GBSs, the influence on the diffusion distance of PM2.5 and PM10 was significantly different. Overall, the order of the influence of different GBS types on the diffusion distance of atmospheric particles is DT > BS > LP > ST > WA.

Author Contributions

Conceptualization, C.X. and S.C.; methodology, C.X. and S.C.; software, R.J. and Z.M.; validation, R.J., C.X. and S.C.; formal analysis, R.J., C.X. and Z.M.; investigation, R.J. and Z.M.; writing–original draft preparation, R.J., C.X. and R.Z.; writing–review and editing, R.J., C.X., S.C. and R.Z.; visualization, R.J., C.X. and Z.M.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the project of the National Natural Science Foundation of China (No. Attract 32001362)/Science and Technology Commission of Shanghai Municipality (No. Attract 22Z510702352).

Data Availability Statement

Not applicable.

Acknowledgments

We acknowledge all WUDAPT contributors for providing the training areas for our city of interest.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Land 12 00964 g001 550
Figure 1. Geographical location of Shanghai and air quality monitoring stations (45 experimental stations in blue and 6 control stations in red).
Figure 1. Geographical location of Shanghai and air quality monitoring stations (45 experimental stations in blue and 6 control stations in red).
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Figure 2. Shanghai LCZ map and GBS data features.
Figure 2. Shanghai LCZ map and GBS data features.
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Figure 3. Process of sector buffer generation: (a) determine the position of the sample point (green dots); (b) set the value interval; (c) analyze the diffusion distance; (d) make a sector buffer zone.
Figure 3. Process of sector buffer generation: (a) determine the position of the sample point (green dots); (b) set the value interval; (c) analyze the diffusion distance; (d) make a sector buffer zone.
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Figure 4. Comparison of landscape pattern index of five GBSs. Morphological Structure: (a) PLAND; (b) NP; (c) AREA_MN; (d) LSI; Spatial Configuration: (e) ENN_MN; (f) SPLIT.
Figure 4. Comparison of landscape pattern index of five GBSs. Morphological Structure: (a) PLAND; (b) NP; (c) AREA_MN; (d) LSI; Spatial Configuration: (e) ENN_MN; (f) SPLIT.
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Figure 5. Distribution characteristics of atmospheric particulate matter concentration: (a) PM2.5; (b) PM10.
Figure 5. Distribution characteristics of atmospheric particulate matter concentration: (a) PM2.5; (b) PM10.
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Figure 6. Distribution characteristics of atmospheric particulate matter dispersion distance: (a) PM2.5; (b) PM10.
Figure 6. Distribution characteristics of atmospheric particulate matter dispersion distance: (a) PM2.5; (b) PM10.
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Table
Table 1. Selection and description of landscape pattern index.
Table 1. Selection and description of landscape pattern index.
MetricsDescription
Morphological StructurePLAND (%)The proportion of the corresponding GBS type area to the total landscape area.
NPTotal plaque number for every GBS type.
AREA_MN (hm2)Average area of the same type plaques.
LSIThe complexity of the plaque shape with the same type.
Spatial
Configuration		ENN_MN (m)Indicates the distance between plaque edges and briefly describes the aggregation of plaques.
SPLITThe degree of dispersion among plaques of the same type.
Table
Table 2. Correlation analysis between PM2.5 and various GBS landscape pattern indexes (* p < 0.05; ** p < 0.01).
Table 2. Correlation analysis between PM2.5 and various GBS landscape pattern indexes (* p < 0.05; ** p < 0.01).
GBSPLANDNPLSIAREA_MNENN_MNSPLIT
LP−0.0710.840 **0.857 **−0.079−0.0830.039
BS−0.1010.829 **0.879 **0.029−0.1310.012
DT−0.1700.799 **0.825 **0.131−0.1340.069
ST−0.0800.831 **0.861 **0.052−0.1440.004
WA−0.0650.843 **0.849 **0.020−0.058−0.036
Table
Table 3. Correlation analysis between PM10 and various GBS landscape pattern indexes (* p < 0.05; ** p < 0.01).
Table 3. Correlation analysis between PM10 and various GBS landscape pattern indexes (* p < 0.05; ** p < 0.01).
GBSPLANDNPLSIAREA_MNENN_MNSPLIT
LP−0.0850.896 **0.901 **−0.065−0.137−0.027
BS−0.1430.898 **0.927 **−0.004−0.1720.052
DT−0.1180.835 **0.879 **−0.033−0.200 *0.043
ST−0.1340.898 **0.925 **−0.111−0.1600.026
WA−0.0360.867 **0.830 **−0.039−0.114−0.061
Table
Table 4. The stepwise multiple regression equation constructed with 30 independent variables (6 landscape pattern indices of 5 GBSs) and PM2.5.
Table 4. The stepwise multiple regression equation constructed with 30 independent variables (6 landscape pattern indices of 5 GBSs) and PM2.5.
Adjusted R2: 0.95MSE: 961.05C: 4263.69
Landscape MetricsStandardized Coefficientp
BS-LSI0.7200.000
BS-PLAND−0.3060.000
LP-SPLIT0.1020.000
DT-PLAND−0.1690.000
DT-LSI0.3540.000
DT-SPLIT0.1080.000
ST-PLAND−0.0780.020
WA-LSI0.0820.048
Table
Table 5. The stepwise multiple regression equation constructed with 30 independent variables (6 landscape pattern indices of 5 GBSs) and PM10.
Table 5. The stepwise multiple regression equation constructed with 30 independent variables (6 landscape pattern indices of 5 GBSs) and PM10.
Adjusted R2: 0.95MSE: 961.05C: 4263.69
Landscape MetricsStandardized Coefficientp
BS-LSI0.7200.000
BS-PLAND−0.3060.000
LP-SPLIT0.1020.000
DT-PLAND−0.1690.000
DT-LSI0.3540.000
DT-SPLIT0.1080.000
ST-PLAND−0.0780.020
WA-LSI0.0820.048
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Jiang, R.; Xie, C.; Man, Z.; Zhou, R.; Che, S. Effects of Urban Green and Blue Space on the Diffusion Range of PM2.5 and PM10 Based on LCZ. Land 2023, 12, 964. https://doi.org/10.3390/land12050964

AMA Style

Jiang R, Xie C, Man Z, Zhou R, Che S. Effects of Urban Green and Blue Space on the Diffusion Range of PM2.5 and PM10 Based on LCZ. Land. 2023; 12(5):964. https://doi.org/10.3390/land12050964

Chicago/Turabian Style

Jiang, Ruiyuan, Changkun Xie, Zihao Man, Rebecca Zhou, and Shengquan Che. 2023. "Effects of Urban Green and Blue Space on the Diffusion Range of PM2.5 and PM10 Based on LCZ" Land 12, no. 5: 964. https://doi.org/10.3390/land12050964

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