Skip to Content
MDPIMDPI

Advanced

AtmosphereAtmosphere
PDF
  • Review
  • Open Access

26 October 2025

36 Pages

The Positive and Negative Effects of Green Space on PM2.5 Concentrations: A Review

,
and
1
School of Architecture and Art, Central South University, Changsha 410083, China
2
Department of Engineering, King’s College London, London WC2R 2LS, UK
*
Author to whom correspondence should be addressed.
Atmosphere2025, 16(11), 1235;https://doi.org/10.3390/atmos16111235
This article belongs to the Section Aerosols
Version Notes
Review Reports
Order Reprints

Abstract

Fine particulate matter (PM2.5) can have considerable negative effects on human health. An increasing number of scholars are finding that green space can not only decrease PM2.5 levels but also exacerbate PM2.5 levels. Few scholars have provided comprehensive reviews on this subject. This study reviews research from 1995 to 2024, including 118 studies based on a search of three databases (Web of Science, Engineering Village, and ResearchGate). We found that at the macro (e.g., city-wide) and meso (e.g., high-density built-up areas) scales, most studies report that green space can play a positive role in mitigating PM2.5 concentrations. However, at the micro-scale under specific temporal conditions, green spaces may increase PM2.5 concentrations in some micro-environments. Whether vegetation reduces or elevates local PM2.5 levels, these processes are influenced by various factors, including green space configuration, microclimatic conditions, built-environment characteristics, and emission source distributions. Mechanistically, vegetation can both decrease ambient PM2.5 levels through deposition, adsorption, and absorption and block its dispersion. In the process of exploring and optimizing the effect of greening on PM2.5, we should not only consider these factors in isolation but also account for the environmental factors that can significantly change the effect. Based on our review of a myriad of studies from different disciplinary backgrounds and scales, we propose an optimization strategy consisting of promoting ventilation through weakening sources and strengthening sinks.

1. Introduction

Particulate matter (PM) in the air can have significant negative effects on the health of humans, animals, and plants [1,2]. PM can be further divided based on particle sizes. These categories include total suspended particulate matter (TSP), with a particle size less than 100 μm; PM10, with a particle size less than 10 μm; PM2.5, with a particle size less than 2.5 μm; and PM1, with a particle size less than 1 μm [2]. Kim et al. [3] compared the difference between PM2.5 and PM10 based on a review of numerous relevant articles and pointed out the considerable difference between the two:
(1)
the lifetime of PM10 is in the range of minutes to hours, while for PM2.5, it is in the range of days to weeks [4,5];
(2)
the travel distance of PM10 is only 1–10 km, whereas this distance is always 100–1000 km for PM2.5;
(3)
for PM2.5, the sources can be summarized as ‘combustion of coal, oil, gasoline; transformation products of NOx, SO2, and organics including biogenic organics’ [6]. For PM10, the sources can be summarized as ‘resuspension of soil tracked onto roads and streets; Suspension from disturbed soils’ [6];
(4)
the chemical compositions of PM2.5 and PM10 are also quite different [4,5].
(5)
some researchers have pointed out that the coarse particles in the PM10 range mainly stem from natural sources (such as crustal elements), while PM2.5 particles mainly originate from anthropogenic sources, such as sulfates, nitrates, and black carbon [5,6].
Thus, there are considerable differences between PM2.5 and PM10. More targeted research is needed for PM2.5 control. Simply borrowing previous research results on PM10 or TSP for the control of PM2.5 pollution may lead to erroneous or even disastrous consequences. In addition, the World Health Organization (WHO) has recommended managing PM2.5 instead of PM10 because of the closer relationship between PM2.5 and human health [7].
PM2.5 can have significant negative effects on humans’ respiratory and cardiovascular systems. Exposure to PM2.5 increases the risk of many diseases, such as asthma, ischemic heart disease, type 2 diabetes, lung cancer, chronic kidney disease, lower respiratory tract infections, myocardial infarction, chronic obstructive pulmonary disease, Alzheimer’s disease, and strokes [8,9,10]. PM2.5 can also have more severe and specific effects on vulnerable groups such as children, the elderly, and pregnant women. For example, PM2.5 exposure has been linked to adverse effects on children’s neurodevelopment and immune function [11].
Many researchers have pointed out that urban green space (i.e., areas with vegetation coverage, including parks, street trees, residential green spaces, and vertical greening systems) [12,13] can have a significant effect on PM2.5. These green spaces vary in scale and structure, influencing PM2.5 through deposition, blockage, adsorption, and absorption [14,15]. The deposition effect is mainly caused by atmospheric particles falling on the underlying surface through aerodynamic effects, such as gravity-induced settling and turbulent diffusion, and plants changing the conditions of the underlying surface and thus influencing the aerodynamic effects. The blocking effect is dependent on the complex structures of branches and leaves, which block the movement of particles. The adsorption effect is mainly dependent upon the surface structures of plant leaves and special secretions for dust retention. Absorption mainly refers to the ability of plant stomata to inhale PM2.5 [14,15]. However, not all researchers have found that green space plays a significant role in reducing PM2.5. For example, Qiu et al. [16] pointed out that larger particles can easily be retained by plants, while smaller particles are susceptible to the influence of atmospheric diffusion and diffuse over longer distances. In addition, Zhou et al. [17] used Wuhan, China, as a case study to explore the effects of urban forest spatial patterns on PM2.5 concentrations. Their findings showed that different forest layouts had little effect, with only a modest reduction of 1–2% in PM2.5 levels. Nevertheless, they recommend planting multiple trees on the outskirts of the urban metropolitan development zone to create a forest belt, aiming for a forest coverage rate of over 60%.
Since the 1990s, the number of studies related to the effect of green spaces on PM2.5 concentrations has increased rapidly. With various studies conducted at different times, in different places, at different scales, and using different methods, researchers have gradually realized that green space can not only reduce PM2.5 concentrations but also exacerbate PM2.5 pollution, especially in some micro-environments. To the best of our knowledge, no one has comprehensively reviewed this topic from a global perspective. Thus, in this study, we aim to comprehensively review this subject and guide urban green space managers in managing green space to reduce atmospheric PM2.5 pollution in a more effective way. To achieve this aim, the following objectives are set:
  • Comprehensively review articles related to the effect of green space on PM2.5 concentrations from different scales;
  • Compare the positive and negative effects of green space on PM2.5 based on relevant articles;
  • Consider how to make full use of green space to reduce PM2.5 concentrations based on relevant research.

2. Method

The global databases Web of Science, Engineering Village, and ResearchGate were selected as our search engines. The timeframe we set to search for articles ranged from 1995 to 2024. The types of articles selected were journal articles and reviews. In order to retrieve all the relevant articles related to the effects of green space on PM2.5 concentrations, we did not limit the fields, scales, countries, or cities investigated. As long as an article was related to our search aim (i.e., it explored the positive and/or negative effects of green space on PM2.5), it was selected. To comprehensively retrieve all the relevant articles, we listed all the terms synonymous with PM2.5 and green space and combined them one by one (Figure 1 shows all the keywords used). Every time we used one combination (like fine particles and parks) in a search, we then downloaded all the relevant articles and then changed to another combination. We continued to employ this approach until we had used all the effective combinations. Ultimately, we retrieved and downloaded 118 articles.
Figure 1. The scheme used for selecting articles.

3. Effects of Regional or Urban Green Space on PM2.5

Many researchers have explored the effects of green spaces on PM2.5 over broad areas, including the globe, regions, entire cities, central urban areas, core urban areas, and urban administrative districts. These areas feature many different land-use types and include residential areas, industrial areas, road and transportation facility areas, and public management and public service areas. The sources of PM2.5 pollution in these areas are also quite various, including transportation, domestic environments, industry, dust, and agriculture.

3.1. The Amount of PM2.5 Reduced by Regional or Urban Green Spaces

Feng et al. [18] used satellite image products and an empirical algorithm to study vegetation-related dry deposition and found that under the combined influence of the PM2.5 mass concentration and deposition velocity, the global average dry deposition and the mean vegetation-related dry deposition from PM2.5 sources reached 0.47 g/(m2·yr) and 0.26 g/(m2·yr), respectively. In the Amazon, Central Asia, and East Asia, levels of vegetation-related dry deposition are high because of dense vegetation or severe pollution. Zhai et al. [19] revealed that forests in the Beijing–Tianjin–Hebei (BTH) region, China, reduced 19,400 t, 19,200 t, 16,400 t, and 12,700 t of PM2.5 in 2015, 2016, 2017, and 2018, respectively, according to the results of a mathematical model. Li et al. [20] explored the removal of PM2.5 by green infrastructure in Shenyang, China, based on a dry deposition model. They found that between 2000 and 2019, PM2.5 removal, PM2.5 removal flux, and the PM2.5 removal rate increased by 20.64 mg/year, 0.0258 g/m2, and 0.377%/year, respectively. The removal of PM2.5 by green spaces in the central urban area is relatively low. Areas with high green space coverage remove more PM2.5. Jo et al. [21] used several South Korean cities as an example to explore the reduction in PM2.5 levels induced by street trees in South Korea. They found that the average street tree density and street tree and shrub coverage rate were approximately 1.0 trees/100 m2 and 12.5 ± 1.4%, respectively. The PM2.5 deposition per unit area was 2.0 ± 0.3 kg/(ha·yr). Thus, from a quantitative perspective, all the above research found that green space reduces the total amount of PM2.5 suspended in the air. However, this does not mean that green space only has a positive effect on PM2.5 concentrations. It can precipitate the accumulation of PM2.5 in local environments, resulting in excessively high local PM2.5 concentrations. The later parts of this article provided a more detailed introduction to the negative effects.

3.2. The Effect of Regional and Urban Green Space Structure on PM2.5

Many researchers have explored the effects of green space structure on PM2.5 concentrations. Relevant studies have been conducted at different times, in different areas, on different scales, and using different methods. Even though some studies have been conducted around similar green space structural features, and there have been some clearly disparate viewpoints. For example, a study on 277 Chinese prefecture-level cities found that the higher the degree of ecological land use, the lower the PM2.5 concentration. The order of influence, from high to low, is as follows: forests, shrubs, and grassland. The PM2.5 distribution showed spatial clustering and decreased from the inner to the outer circles [22]. However, Chen et al. [23] indicated that the effects of green space coverage on PM2.5 are various under different conditions. For example, the correlation between green coverage and PM2.5 is high in the spring at the 1–2 km scale but moderate in the summer at the 2 km, 5 km, and 6 km scales. When the PM2.5 concentration is greater than 75 µg/m3, the correlation between PM2.5 concentration and green cover is non-significant. In addition, some researchers have noted that different structural green spaces do not cause significant differences in PM2.5 concentrations, even if this can cause significant differences in PM10 concentrations, based on a study conducted in Baoji, China [16]. In contrast, different sizes of green spaces can lead to obvious differences in PM2.5 pollution in the surrounding environment. In addition, green spaces covering less than 2 ha do not have a significant effect on PM2.5 in the surrounding environment [16]. One study found that the effect of greening on reducing PM2.5 is stronger in built-up areas in which there is frequent human activity, particularly in developed areas with low vegetation coverage. However, this effect is limited in areas with high green coverage, and it is more pronounced in areas with low PM2.5 concentrations than in areas with high concentrations [24]. In addition, Zhang et al. [25] found that on the leaf scale, forest species are quite active in accumulating PM2.5 on the surfaces of their leaves. However, on the canopy scale, horizontal vegetation collection is the major process whereby PM2.5 is removed, and the contribution of vertical sedimentation/emission can be ignored. At the landscape scale, Ai et al. [26] found that dry deposition is the main PM2.5 removal process during rain-free days. One study found that PM2.5 concentration changes are more sensitive to normalized difference vegetative index (NDVI) in the southern part of China than in the north of China. They attributed this phenomenon to the fact that vegetation in the northern part of China predominantly consist of coniferous, mixed coniferous and broad-leaved, and deciduous broad-leaved forests. In contrast, in the south, the vegetation predominantly consists of evergreen broadleaf forests. The NDVI values in the south are higher than those in the north. When similar green space changes occur in north and south of China, there is a greater influence on PM2.5 concentration in the south of China than the north. Furthermore, it is important to note that the most effective urban greening structure for PM2.5 reduction is not static but can vary with conditions. This is exemplified by the winter season, when PM2.5 pollution is typically more severe and the demand for mitigation by green spaces is higher. During this period, however, the capacity of deciduous trees to remove PM2.5 is significantly limited due to the loss of their foliage. This seasonal disparity was quantified in a 2015 study of Beijing, which found that within the city’s Sixth Ring Road, evergreen trees removed PM2.5 at 2.7 times the efficiency of deciduous tree [27]. Niu et al. [28] have conducted research in Shenyang, but they found that canopy density has a significant positive relation with PM2.5 concentrations and that shrub richness is negatively correlated with PM2.5 concentrations. They attributed these findings to the existence of a certain threshold: canopy density shows a negative correlation with PM2.5 concentrations when it is below this threshold, while a positive correlation may occur when this threshold is exceeded.
In contrast to the differences in the views held by the researchers mentioned above, there is a high degree of consistency in the findings regarding the effect of vertical greening features (green walls and green roofs) on PM2.5. Srbinovska et al. [29] highlighted the significant role of green walls in reducing PM2.5 pollution. Similarly, based on their studies conducted in Santiago, Chile, Viecco et al. [26] and Vera et al. [30] found that green roofs and living walls can effectively reduce PM2.5 levels. Furthermore, Vera et al. [31] noted that the greater the biodiversity pertaining to green roofs and walls, the more effective they are at reducing PM2.5 pollution. This phenomenon may be attributed to the fact that relevant vertical greening studies were conducted within a small research scope to exclude the influence of some irrelevant variables and only focused on one side of the vertical greening (i.e., they did not consider the PM2.5 concentration in the narrow space between the vertical-greening area and walls or roofs).

3.3. The Effect of Regional and Urban Green Space Spatial Patterns on PM2.5 from the Perspective of Landscape Ecology and Morphology

As the landscape pattern analysis of landscape ecology and morphological spatial patterns becomes increasingly acknowledged and popular, an increasing number of researchers are using these methods to explore the effects of the spatial pattern features of green space on PM2.5 concentrations. By comparing the relevant studies in Table 1, Table 2 and Table 3, it can be found that regional- or urban-scale studies exhibit distinct patterns between green space landscape pattern characteristics and PM2.5 concentrations due to differences in research scope, time, scale, and other factors. Previous researchers have extensively used different landscape ecology and morphological indicators to consider the effect of green patch amount, area, shape, aggregation or dispersion, and edge characteristics on PM2.5 [32,33,34,35,36,37,38,39,40,41,42,43]. Among the studies [32,33,34,35,36,37,38,39,40,41,42,43], the indicator related to green patch amount characteristics includes number of patch (NP), patch density (PD); the indicators related to green patch area characteristics include percentage of landscape (PLAND), Green space coverage ratio (G_Ratio), total area (TA), largest patch index (LPI), total core area (TCA), perimeter-area ratio (PARA), area-weight perimeter-area fractal dimension index (AWFRAC); the indicators related to green patch shape characteristics include landscape shape index (LSI), shape index (SI), area-weighted mean shape index (Shape_AM), area-weighted mean patch fractal dimension (FRACE_MN); the indicators related to green patch accumulation or dispersion degree include aggregation index (AI), landscape division index (LDI), contiguity index (CONTIG), aggregation index (AI), Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI) cohesion index (COHESION), mean Euclidean nearest-neighbor index (ENN_MN); the indicators related to green patch edge characteristics include total edges (TE), edge density (ED).
Table 1 shows that when studies are conducted at different times and scales, the green space spatial pattern optimization strategies considered optimal can differ. For example, Cai et al. [32] conducted a study in southeastern Fujian, China, and found that the green space LSI was positively correlated with PM2.5 at scales of 1000 m, 2000 m, 3000 m, 4000 m, and 5000 m, but at the 500 m scale, there was no significant correlation between LSI and PM2.5. Zhan et al. [33] pointed out that PLAND is closely related to the reduction in PM2.5 exposure risk on a larger scale, while AI, ED, and SI are closely related to PM2.5 exposure on a local scale. The various conclusions reached based on the above factors in regional- and urban-scale studies further demonstrate the necessity of further categorizing PM2.5 sources (such as transportation, domestic factors, and industry) and greening types (such as road, park, and residential greening) for related research.
The studies in Table 2 show that the optimal spatial optimization strategy also differs based on location or land use. For example, Yang et al. [34] found that patch type (square, subsidiary, or other park green spaces) has a greater effect on PM2.5 concentrations than the type of corridor (protective, ribbon-garden-in-a-park, or road subsidiary green spaces). Table 3 shows studies conducted using different methods, these studies can have some slight differences in findings. For example, in Bi et al.’s [35] study, both geographically weighted regression and the ordinary least squares method indicated that both branches and islands are positively correlated with PM2.5. Geographically weighted regression further indicated that 20.88% and 33.62% of regional branches and islands are negatively correlated with PM2.5. The authors’ [35] geographically weighted regression results indicated that perforations and loops can effectively reduce PM2.5 pollution in local spaces (such as Wuchang District), while the ordinary least squares method showed that there is no correlation between perforations and loops and PM2.5.
Therefore, different cities have some similarities and differences in terms of optimizing green space patterns to reduce PM2.5 concentrations. In general, most studies in Table 1, Table 2 and Table 3 [32,33,34,35,36,37,38,39,40,41,42,43] have found that increasing the green space ratio and ensuring there is a more concentrated distribution of large green spaces are more effective in reducing PM2.5 concentrations. However, there are certain differences in the optimal spatial pattern optimization measures at different times, locations, and scales. Therefore, the optimization strategy for using urban greening to reduce PM2.5 needs to be tailored to local conditions, and there is no optimal method that is suitable for all situations.
Table 1. Studies showing, at different times and scales, that optimal green space spatial pattern optimization strategies differ.
Table 2. Studies showing that the optimal green space spatial pattern optimization strategy differs depending on location land use.
Table 3. Using different study methods can also lead to differences in findings.

4. The Effect of Different Types of Green Space on PM2.5

PM2.5 originates from different sources, such as traffic, industrial activity, domestic factors, and agriculture. PM2.5 from different sources can have different characteristics in relation to chemical composition, physical characteristics, and emission patterns. Thus, the treatment of PM2.5 pollution requires measures tailored to different sources. There are also different types of green spaces, such as road, residential, and industrial. Different types of green space influence different sources of PM2.5 pollution differently for various reasons, like the spatial patterns of different green spaces and the proximity between green spaces and pollution sources. For example, traffic PM2.5 is considered a line-source pollution; road green spaces are distributed in a strip shape on both sides of the roadway. Industrial sources are considered non-point source pollution, and green spaces in industrial areas are usually scarce and scattered around factory yards.

4.1. The Effect of Road Green Space on PM2.5 in Street Canyons

Many researchers have explored the effects of street greening on PM2.5 concentrations in street canyons. Table 4 and Table 5 are studies conducted based on monitoring and computational fluid dynamics (CFD) simulation respectively. Both tables include studies which found (1) road green space can reduce PM2.5; (2) road green space can exacerbate PM2.5 pollution level; (3) depend on specific situation, green space can either increase or reduce PM2.5 concentration. Because studies have been conducted in different street canyon environments and used different criteria to judge the effects, there are many obvious differences in their findings. The most significant difference, as found by some researchers, relates to the positive effects of road greening on PM2.5 concentration reduction [44,45,46,47,48,49]. However, others [50,51,52,53,54,55] have pointed out that road greening can exacerbate PM2.5 pollution in specific micro-environments. All these researchers have provided reasonable causes. For example, Qin et al. [46] found that road greening had positive effects with respect to reducing PM2.5 concentrations. Their evidence was mainly based on a comparison of PM2.5 concentrations between scenarios with trees and a no-tree base-case scenario. However, Jin et al. [50], by comparing the reduction rates of PM2.5 in the vertical direction between scenarios with and without trees, found that road greening can intercept PM2.5 in street canyon environments. In addition, some researchers [56,57,58,59,60,61,62,63] have pointed out that depending on the situation, road greening may have both positive and negative effects on PM2.5. For example, Chen et al. [57] conducted a study based on actual measurements and found that some vegetation barriers have a positive reduction effect, while others have a negative effect, as found by comparing the differences in PM2.5 concentrations before and after plant barriers. However, their research was limited by two factors. Firstly, their experimental research could not completely rule out the interference of PM2.5 sources other than traffic. Secondly, the turbulence of PM2.5 caused by the wind itself is a very complex turbulent flow. The situation is easily affected by turbulence. In some cases, plant barriers exacerbate PM2.5 levels, while in other cases, they reduce PM2.5 levels. This also means that the differences mainly stem from the different criteria used in different studies to determine whether the effect is positive or negative in different situations, and all research findings are reasonable in specific research environments.
Currently, a great deal of research suffers from limitations in regard to analyzing the effect of road greening on PM2.5 by using the difference in PM2.5 concentrations between observation points and control points. Firstly, it is difficult to distinguish the extent to which a reduction in the PM2.5 concentration in a green space is due to a series of complex processes, including deposition, blockage, adsorption, and absorption, and the extent to which the difference in PM2.5 concentration before and after road greening is caused by the large increase in PM2.5 concentrations in front of an area subjected to road greening due to a reduction in wind speed induced by plants. In addition, regardless of whether there is a high concentration of PM2.5 in the roadway area, as long as there is a large concentration difference between the roadside (the control point) and the area subjected to road greening (the experimental point), a good reduction effect can be recorded. However, if the PM2.5 concentration in the roadway area is excessively high, it is undoubtedly detrimental to the health of passengers who open their windows for ventilation and pedestrians crossing the road. Therefore, we call for more attention to the absolute concentration changes in different areas rather than the relative concentration differences between different areas.
In addition, currently, researchers only focus on selecting specific indicators to explore the effect of road greening on PM2.5 in a chosen street canyon environment. Although these efforts can reasonably explain the reasons behind the phenomenon, they often fail to provide insight into some differences between the research in question and previous studies. Therefore, we suggest that in the future, researchers, when conducting research on the effect of road greening on PM2.5, should first fully recognize the dual nature (positive and negative) of the effect of road greening on street canyon PM2.5. New experiments should be designed based on existing research and experimental settings and findings, and several sets of experiments should be set up with reference to the research of researchers who have put forward different views in the past in order to better reveal the special circumstances under which road greening will have a positive effect on reducing PM2.5 concentrations and when it will exacerbate PM2.5 pollution. Contrary to the traditional view that greening is beneficial for improving air quality, more and more researchers are finding that green spaces may also exacerbate local pollution. However, there is currently a lack of insight into the conditions and mechanisms behind the occurrence of related contradictory phenomena.
Table 4. Monitoring studies that explore the effect of street green space on PM2.5 concentrations.
Table 5. CFD simulation studies that explore the effect of street green space on PM2.5 concentrations.

4.2. The Effect of Green Spaces on PM2.5 Within and Around Residential Areas

Residential areas are a major aspect of people’s daily lives. The extent of PM2.5 pollution in these areas is also a major focus. Green space in residential areas can be regarded as a unique type (residential green space) and have a considerable influence on people’s daily lives. Because green space can reduce the total amount of PM2.5 suspended in the air, increasing the amount of green space in residential areas is generally believed to be beneficial in terms of reducing the risk of death due to several diseases, such as acute pneumonia [64,65]. Wang et al. [66] used a small town—Tao Liu, in Zibo, China—as a case and focused on green space in residential areas. They found that green space structure can have significant effects on PM2.5 concentrations. However, the spacing between vegetation and buildings had no significant effect on PM2.5 concentration distribution. When trees are evenly distributed, their effect on reducing PM2.5 concentration is minimal. However, a rich distribution of trees can play quite an active role in reducing PM2.5 concentrations. Kim et al. [58] also pointed out that the residential areas located behind planting bands can have very low PM2.5 concentrations. In their study, it was found that the slope with the highest amount of greenery in the spring had a better effect on reducing PM2.5 levels, while the flat type with the highest amount of shrub greenery had a better effect in the winter. Zhang et al. [67] found that large residential green spaces are typical PM2.5 sink landscapes. The PM2.5 concentration inside residential green spaces is significantly lower than that outside green spaces, and the area and perimeter of residential green spaces have a significant effect on PM2.5. However, according to the results of a regression analysis, as the circumference and area increase, the PM2.5 removal rate shows a trend of first increasing and then decreasing.
However, green space in residential areas can also increase PM2.5 concentrations in certain micro-environments. For example, Xiong and Chen [68] explored the effect of residential green space morphology on PM2.5 via CFD simulation analysis. They pointed out that plants in residential areas are not always conducive to reducing PM2.5, and an inappropriate green space layout may lead to an increase in PM2.5 concentrations in specific residential areas. Densely vegetated urban roads and residential areas may have a strong negative blocking effect on PM2.5 dispersion. Although green spaces can positively contribute to PM2.5 removal through deposition, the net effect in certain cases could be an increase in local PM2.5 concentrations if the obstruction effect outweighs the removal mechanisms.

4.3. The Effect of Green Spaces on PM2.5 Within and Around Industrial Areas

Industrial activity is also a significant source of PM2.5. Because studies focusing on green space and industrial sources of PM2.5 have not limited their scope to industrial land use areas, this part mainly focuses on the effect of green space on industrial-sourced PM2.5 within industrial areas or in areas adjacent to them. Han et al. [69] used a land use regression model to study PM2.5 during the hot season in Xi’an, China. The study included a large number of monitoring points, buffer zones, and localized emission source variables (covering information on land use, road traffic source facilities, socio-economic conditions, emission sources, and geographic spatial data). A linear model was constructed through stepwise regression, and it was found that PM2.5 during the hot season in Xi’an, the spatial distribution of PM2.5 is closely related to the layout of industrial land, with industrial emissions contributing more than transportation. Green spaces, especially large-scale green spaces, can effectively reduce PM2.5 concentrations. In addition, Bikis [70] found that the green spaces near roads and in industrial areas of Addis Ababa, Ethiopia, are lacking. The correlation between the occurrence of air-quality-related health problems and the air quality index at 12 transportation stations corresponded to a value of 0.9547 for R2 in terms of average concentration. In addition, the correlation between the air quality index and air-quality-related health problems with respect to manufacturing industry employees was stronger (R2 = 0.9728). These results confirmed that the manufacturing industry emits more pollutants than transportation. These factors can raise PM2.5 concentrations in these areas. Increasing the amount of green space in industrial and road areas is an effective way of solving this problem. Li et al. [71] pointed out that the expansion of industrial land is a cause of PM2.5 pollution. However, the increase in vegetation coverage in their study did not lead to a decrease in PM2.5 concentrations. On the contrary, the authors found that either both increase over time or there is no significant negative correlation between the two.

4.4. The Effect of Green Spaces on PM2.5 Within and Around School Campuses

School campuses are important places of study from kindergarten to university. A healthy school campus environment is quite important for the student population. Some studies have explored how green spaces within and around school campuses affect the PM2.5 levels within the campuses. Campus green spaces can significantly reduce PM2.5 pollution that spreads from car lanes to classrooms, playing an important role in providing a healthier learning environment for young people. Matthaios et al. [72] found that the degree of greenery within a 270-m buffer zone around a school is associated with indoor traffic, with the following PM2.5 correlation: (−0.068, 95% confidence interval: −0.124, −0.013). For greenery further away from the school (within a 1230-m buffer zone), this association is stronger, with the following correlation regarding PM2.5: (−0.101, 95% confidence interval: −0.156, −0.046). Wang et al. [73], in their field-monitoring research conducted around university campuses, found that the PM2.5 concentration in lakes and green leisure areas on campus was significantly lower than the average level. Therefore, increasing the distribution of centralized green spaces on campus can provoke people to relax and provide a healthier environment in these areas. Jiang et al. [74] pointed out that air temperature is negatively correlated with PM2.5 (r = −0.280, p < 0.01), while wind speed and relative humidity are positively correlated with PM2.5 (relative humidity and PM2.5: r = 0.476, p < 0.01; wind speed: r = 0.355, p < 0.01). Adjusting campus green spaces (plants communities, underlying surfaces, and the surrounding environment) can influence meteorological factors (temperature, humidity, wind speed, and illumination) and reduce PM2.5 concentrations.

4.5. The Effect of Parks on PM2.5

The abundant vegetation coverage in urban parks undoubtedly has a significant effect on PM2.5. In addition, the parks themselves generate less pollution due to their distance from areas where PM2.5 pollution is generated, such as roadways, factories, and residential areas. Many researchers have explored the effect of urban parks on PM2.5. These efforts include the experimental research conducted by Junior et al. [75], who obtained PM2.5-concentration-related information from PM2.5 collectors at two locations. One was located in a botanical garden with high traffic flow in the surrounding area, and the others were located near tunnel entrances and exits with heavy traffic. Through box plot comparison, it was found that the PM2.5 concentration at the observation points in the botanical garden was 33% lower, leading to the conclusion that botanical gardens have a significant effect on reducing PM2.5 levels. By setting up sampling points inside and outside a park for comparison and using CFD to simulate the differences in PM2.5 concentrations under multi-scenario conditions, Heshani et al. [76] found that the vegetation inside the park had a good barrier effect, effectively reducing the inflow of PM2.5 from outside the park, thereby making the PM2.5 concentration inside the park lower than outside. LAD has a significant effect on the removal efficiency of PM2.5 at the height of human respiration; that is, the higher the LAD value, the greater the PM2.5 removal efficiency. In addition, hedgerows with high LAD values are more effective in reducing PM2.5 concentrations than trees with deeper crowns. Liu et al. [77] compared the difference between the vertical PM2.5 concentration in a city center and a forest park by monitoring data collected using DustTrak II 8532 aerosol detector (TSI, Saint Paul, MN, USA). Their collection area included a 33-floor-high building (100 m) and a national forest park located in the same city. They collected PM2.5 concentrations at heights of 1.5 m, 10 m, 25 m, 35 m, 50 m, 65 m, 80 m, and 100 m at the building collection point and the forest park. They found that in the forest park, the PM2.5 concentration throughout the year showed an overall decreasing trend with an increase in height. At a height of 100 m, the average PM2.5 concentration decreased to 66.8%, 61.9%, 74.1%, and 80.8% of the maximum value at about 1.5 m, respectively. However, in the city center, the peak concentrations were not found at ground level but at higher altitudes. The PM2.5 concentration in the city center showed an upward trend from the ground to a height of about 25 m and then decreased with an increase in height. At a distance of 100 m, the proportions of the decrease during the four periods were only 7.0%, 12.9%, 18.1%, and 19.7% of the PM2.5 concentration, respectively. The authors believe that the increasing trend regarding the vertical distribution at low altitudes (0 to 25 m) in the city center is related to roadside trees’ interception effect on PM2.5. Roadside trees can reduce the vertical diffusion of PM2.5, resulting in higher concentrations of PM2.5 at low altitudes. Yan et al. [78] conducted an observation study on near-ground PM2.5 in a forest park in Beijing using a grassland sampling point and a bare-ground control sampling point, with a distance of about 30 m between the two and sampling conducted from 7:00 to 18:00 on the first 4 days of each month from September 2014 to September 2015. It was found that the annual concentration of PM2.5 in the forest is highest in the winter, followed by the spring and autumn, and it is lowest in the summer. The average concentration of PM2.5 near the ground reached a peak from 07:00 to 09:30, and its lowest value occurred from 12:00 to 15:00. The concentration of PM2.5 in forests is significantly positively correlated with relative humidity and significantly negatively correlated with temperature. In the forest examined, the highest removal efficiency in the summer occurs between 7:00 and 9:30, while the lowest removal efficiency in the winter occurs between 9:30 and 12:00. Based on observations of PM2.5 concentrations made in a forest park in Guangzhou, China, Xiao et al. [79] found that the PM2.5 concentration in the dry season was higher than that in the wet season. Industrial activities, transportation emissions, and soil dust are the main sources of metals in PM2.5 in the forest park.
Different degrees of vegetation coverage in an urban park can have different effects on PM2.5. In Su et al.’s [80] study, the rate of reduction in PM2.5 by parks ranged from 2.51% to 35.57%. Through an analysis of heavy metals deposited in leaves in urban parks, the authors found that the particles in the parks were less toxic, indicating that the air in the parks was cleaner. In addition, parks with dense vegetation showed stable PM2.5 emission reduction effects during the year monitored and exhibited stronger performance in providing cleaner air in severe PM2.5 pollution situations. In contrast, parks with sparse vegetation had unstable emission reduction effects, leading to rapid dissipation or accumulation of pollutants within the park.
In urban parks, different vegetation configurations can have significant effects on PM2.5. Yin et al. [81] used i-tree software to conduct a simulation and found significant differences in PM2.5 interception among different tree species in parks in Beijing. Tree coverage, tree species richness, average tree height, average crown width, and number of trees have a significant effect on the PM2.5 retention in green spaces, with average tree height having the greatest influence. The estimated degree of annual PM2.5 retention in urban parks and green spaces within the Fifth Ring Road in Beijing is about 6380 tons. The heterogeneity of park plant communities and its effect on PM2.5 have also been a focus for researchers. A study was conducted in forest park in Wuhan, with 44 sample plots selected. The authors found that the composition and location of plant communities are significant factors affecting PM2.5 [82]. Wetlands in urban parks have also been identified as playing quite an active role in reducing PM2.5 concentrations. They are highly efficient in removing PM through dry deposition and vegetation collection. Therefore, wetlands should be protected and constructed in future urban development projects. More regular wetland patches accelerate deposition processes. Long and dense wetland patches at the edges can lead to faster deposition. In contrast, fragmented landscapes are not conducive to dry deposition [82].

5. The Effect of the Structural Characteristics of Vegetation on PM2.5

The effect of plants on PM2.5 is extremely complex. Firstly, trees differ significantly in terms of their effects on PM2.5 concentrations at different heights. Ji and Zhao [83] pointed out that trees (specifically cypress trees) around buildings can effectively reduce concentrations of PM2.5. In their study, compared with a treeless environment, trees increased the PM2.5 removal rate by about 20%. The effect varies depending on the height of the tree, and the bottom of the tree (including pedestrian height) has a more significant effect on reducing PM2.5 than other heights. At maximum tree height (crown height, below building height), trees maintain their influence. Above the treetops, the influence of trees is almost negligible. Yang et al. [84] used a literature search and simple weighting methods to explore the capacity of 100 ordinary trees in 328 cities across 60 countries to reduce PM2.5 levels. They pointed out that trees undoubtedly have a positive effect on reducing PM2.5 levels, but some common trees are not the most effective in reducing PM2.5. A mixed distribution of evergreen and deciduous trees is very effective in reducing PM2.5. However, Shen et al. [85] pointed out that although the presence of leaves can increase the surface area for dry deposition, an excessively high LAD can hinder natural ventilation and the rate of exchange between air and the surrounding environment, leading to an increase in PM2.5 concentrations in the microenvironment. Tree species with rough leaf microstructures and high PM2.5 deposition rates constitute a better choice for improving air quality. Ciro et al. [86] found that even though an increase in tree coverage is beneficial for PM2.5 removal, trees with lower LAIs are better at reducing PM2.5 pollution.
There are significant differences in the effects of different tree species, and even different parts of the same tree, on PM2.5 [87,88]. Chen et al. [89] focused on the differences in PM2.5 capture efficiency among different plant leaves and pointed out that the needle-shaped leaves of coniferous trees make them more effective in pollutant interception and post-rain recovery than broad-leaved trees. The shapes and leaf veins of broad-leaved trees seem to have no effect on pollutant interception, but the number of grooves and trichomes on broad-leaved leaves is positively correlated with pollutant interception. Liang et al. [90] found a strong relationship between the proportion of grooves, stomatal size, and the ability of plants to capture PM2.5 based on a study of 25 plant species in Beijing and Chongqing. There is no significant correlation between stomatal density, leaf hair, and PM2.5 capture efficiency. The leaf morphology of broad-leaved tree species is more complex, and, compared to coniferous trees, they have a stronger ability to capture PM2.5 per unit leaf area. However, due to the larger total leaf area of coniferous tree species, the PM2.5 capture capacity of each tree may be stronger. However, Kim et al. [91] conducted a study on the PM2.5 reduction ability of 13 major greening tree species and found that the PM2.5 reduction of broad-leaved tree species was about 8.6 times that of coniferous tree species. Tree species with a larger leaf area were found to have greater PM2.5 reduction ability, while trees with larger leaf mass were found to have higher PM2.5 reduction capacity. Different tree species will also affect particles of different sizes, such as Cinnamomum camphora (L.) J. Presl and Osmanthus fragrans Lour., which are better at capturing particles smaller than 1 µm, while Ginkgo biloba L. and Platycodon grandiflorus tend to capture larger particles, up to a maximum of 1.6 µm. From the perspective of leaf wax composition, there are significant species differences with respect to the chemical composition of particles captured by leaves. The viscous hydrophobic wax in leaf wax is conducive to the adhesion of particles, while fatty acids and alkanes are conducive to the adhesion of particles rich in organic carbon and heavy metals. The physiological characteristics of leaves, such as stomatal conductance and water potential, significantly enhance the capture of larger particles [92]. Chaparro et al. [93] found that tree bark can also capture a certain amount of PM2.5, especially iron-rich particles. Therefore, the influence of tree bark on PM2.5 concentrations cannot be ignored.
Under different microclimate conditions, the effect of plants on PM2.5 will also differ significantly. Xie et al. [94] pointed out that the plant morphological and structural characteristics that have the greatest effect on PM2.5 vary under different wind speed conditions. Under three wind speed conditions of 1 m/s, 3.5 m/s, and 8 m/s, the surface area/weight density of branches and leaves as well as the overall characteristics of the branches are the most important factors affecting retention, and the efficiency is negative. At wind speeds of 1 m/second and 3.5 m/s, leaf size and surface roughness are the other two key positive factors, while at wind speeds of 8 m/s, the numerical density of branches and leaves is the key positive factor. As wind speed increases, the influence of leaf size and surface characteristics on retention decreases, while the characteristics of tree crown structure become more significant. Compared with leaf morphological characteristics, the variation in crown morphological structure variables among different tree species is greater, and it also has a greater effect on the retention effect of PM2.5. As rainfall can affect plants’ ability to retain dust, there are significant differences in the dust retention ability of different plants after rain. Luo et al. [95] pointed out that after rainfall, different tree species’ adsorption capacity per unit leaf area decreases to varying degrees. On the second day after rainfall, the adsorption capacity of various tree species rapidly increases. The increase in adsorption capacity of broad-leaved tree species after rainfall is greater than that of coniferous tree species.

6. The Regulatory Effect of Plants on PM2.5

PM2.5 has a slow sedimentation rate due to its small size, and it can remain in the air for quite a long time and spread far and wide. Plants can influence PM2.5 through deposition, blockage, adsorption, and absorption [14,15]. Table 6, Table 7, Table 8 and Table 9 reviewed the deposition, blockage, adsorption and absorption effects of plants on PM2.5 respectively [96,97,98,99,100,101,102,103,104,105,106]. The deposition effect mainly refers to PM2.5 falling on plants’ surfaces through aerodynamic effects such as gravity settling and turbulent diffusion (the process by which vegetation directly reduces PM in the atmosphere is called dry deposition). For instance, coniferous trees can have a higher deposition efficiency than broad-leaved trees due to leaf roughness and their high LAI. The blockage effect is achieved through the complex structures of branches and leaves, which obstruct the movement of PM2.5. An excessively high vegetation density may impede air circulation, leading to the accumulation of PM2.5 within street canyons. The adsorption effect mainly relies on the surface structures of plant leaves and special secretions to capture and hold PM2.5. Absorption mainly refers to the ability of plant stomata to take in PM2.5. [14,15].
Table 6, Table 7, Table 8 and Table 9 reveals that vegetation can have obvious deposition, blockage, adsorption, absorption effects on PM2.5 concentrations. These play an important role in the mitigation of PM2.5 pollution. Different leaf surfaces can adsorb certain amounts of PM2.5 and help decrease PM2.5 concentrations. Some researchers [105,106] who have explored the absorption effect of vegetation even believe it is as weak when compared with plants’ deposition and adsorption capacities. These properties positively contribute to decreasing PM2.5 concentrations. However, vegetation can block the dispersion of PM2.5. This may increase PM2.5 concentrations in the micro-environment. Due to the complex mechanism whereby vegetation effects PM2.5 concentrations, the net effect is highly dependent on the specific situation. More studies can be conducted to explore ways of increasing the positive effect and decreasing the negative effect and determine how to more effectively reduce PM2.5 concentrations through vegetation.
Table 6. Studies focusing on the deposition effects of trees.
Table 7. Studies mainly focusing on trees’ blockage effect.
Table 8. Study mainly focusing on trees’ adsorption effect.
Table 9. Studies mainly focusing on trees’ absorption effect.

7. Greening Interacts with Other Environmental Factors to Mitigate PM2.5

Alongside the abovementioned deposition, blockage, adsorption, and absorption effects, green spaces can also change microclimates through cooling and humidification, and also affect the wind environment, thereby affecting PM2.5 concentrations [107]. Feng et al. [107] found significant differences in the correlation between temperature, relative humidity, and PM2.5 concentrations in different seasons. The correlation between temperature and PM2.5 concentrations varies throughout the four seasons, with a positive correlation in the spring (r = 0.163, p < 0.01) and a negative correlation in the summer (r = −0.367, p < 0.01), autumn, and winter, with the strongest correlation being in the summer, with the rest having overall correlation coefficients < 0.4, indicating a weak correlation. In the summer and winter, PM2.5 is significantly positively correlated with relative humidity, with an r = 0.475 (p < 0.01) in the summer and an r = 0.368 (p < 0.01) in the winter. The correlation is weak in the spring and autumn (r < 0.2). In addition, plants can not only significantly affect the dry deposition process but also influence the wet deposition process on rainy days. Xie et al. [108] found that the retention of PM2.5 by trees during rainfall is the result of a combination of multiple factors, with the key factor being the characteristics of the tree species. During rainfall, broad-leaved trees may have a higher efficiency regarding rainwater wash-off than coniferous trees. For broad-leaved trees, the wash-off process usually has two peaks, while coniferous trees have only one peak or no peaks. This means that the particle wash-off process for coniferous trees may be more stable than that for broad-leaved trees. The effect of greenery in different seasons on PM2.5 also shows significant differences. In the study by Hong et al. [109], the average reduction efficiency regarding PM2.5 within the forest was approximately 18.2%; the seasonal average reduction efficiency regarding PM2.5 was highest in the winter (about 28.1%) and lowest in the summer (about 9.6%). Aside from the influence of the seasons, Shao et al. [110] found that there were significant differences in the effect of green spaces on PM2.5 at different times of the day. By comparing the PM2.5 concentration differences between observation points 45 m away from the road, it was found that although the afternoon value was positive, the reduction rate on a spring morning was negative, especially in March, before 9:30 (local time), when the reduction rate was the lowest. In the winter, the decrease rate is always negative, but it is significantly higher in the afternoon than in the morning. In addition, the authors of [110] pointed out that when the leaves are fully mature, the PM2.5 concentration is strongly positively correlated with tree crown density and negatively correlated with porosity. There is no significant correlation between PM2.5 concentration and tree crown density and porosity during the leaf-shedding and leaf-spreading periods.
Determining how to optimize an urban green space pattern to maximize its value is an urban planning issue. Lee et al. (2015) [111] pointed out that urban planners need to match the health benefits pursued with the needs of the community and the functions that urban green spaces will provide. In the process of planning green spaces, the community’s needs extend far beyond just reducing PM2.5 pollution; they include factors like promoting physical exercise and mental health. The functions of green spaces are also influenced by microclimate conditions and other built-environment factors. This also indicates that the effect of green spaces on PM2.5 cannot be viewed in isolation, and the interaction between greenery and other environmental factors should also be considered. In Chi et al.’s [112] study, livability was interpreted from six aspects: environment, health, accessibility, housing, economy, and education. These aspects complement each other. Chen et al. [113] pointed out that the health promotion value of urban green spaces can be extended to brand elements, with a focus on the quality and landscape aesthetic functions of urban green spaces. Guo et al. [114] pointed out that urban spatial structure, land use, spatial form, transportation, and green spaces are all related to exposure to PM2.5 pollution. Reasonably arranging green spaces in the downwind direction of urban pollution sources may play an important role in reducing pollution exposure. It would be prudent to reduce plot ratios and building density in city centers. Improving the spatial continuity of green spaces in urban centers may have a significant effect on reducing PM2.5. Eisenman et al. [115] reviewed relevant research and pointed out that there is currently no scientific consensus that urban trees can reduce asthma by improving air quality. In some cases, urban trees may reduce air quality and increase the incidence of asthma. Therefore, an interdisciplinary and comprehensive understanding of the effect of green spaces on PM2.5 and human health is also crucial.

8. Discussion

The phenomenon of green spaces potentially reducing PM2.5 pollution and exacerbating PM2.5 in local environments is increasingly being studied by scholars, but there is a lack of comprehensive reviews on this phenomenon. This article represents an attempt to integrate the research conducted by scholars from various disciplines, such as urban and rural planning, landscape architecture, geography, forestry, environmental science, and atmospheric science, in order to gain a more comprehensive understanding of the dual effects of green spaces on PM2.5 and promote better research in the future.
Through relevant reviews conducted on large scales, such as across regional or urban areas, it has been found that green spaces can reduce a significant amount of PM2.5 pollution every year, and this is believed to be beneficial for human health [116,117]. From a macro perspective, afforestation within regions or cities is beneficial for reducing PM2.5 concentrations and creating a healthier environment. In addition, the selection of different research units during a study may also lead to certain differences in research results. Most studies conducted at the macro scale has found that green spaces are beneficial for reducing PM2.5 levels. However, in some studies, specific areas such as waterfront green coverage and areas with high green coverage have also been found to have relatively high PM2.5 concentrations. There are some differences in how different studies optimize the spatial patterns of green spaces, such as patch size, shape characteristics, and the distance between patches, to maximize their effectiveness in reducing PM2.5 concentrations [117,118,119]. This may be due to differences in the sizes of the research units selected and differences in where (in which cities) and when the studies were conducted.
From the perspective of different types of green spaces, there are significant differences in the findings related to road greening in road areas. Some researchers have found that road greening generally has a positive effect on PM2.5 levels, while others have focused on negative effects. There are also some studies revealing that there may be both positive and negative effects under different conditions. The daily traffic-sourced PM2.5 generated in road areas accounts for a large share, and many roads are urban air ducts that are greatly affected by other sources. Some researchers have used CFD or wind tunnels to simulate the ideal conditions of traffic-only sources, while others have not completely ruled out other sources of PM2.5 in road areas as a reason for the differences. Studies are conducted in different regions, and there are significant differences in the characteristics of roads and street buildings. For example, in some cities, street buildings are mainly low-rise and multi-story, with narrow roads, while in some cities, street buildings are mainly high-rise or even super-high-rise, with wide roads. In addition, there are significant differences in the greening conditions of different roads, including with respect to tree species, green coverage, and tree height characteristics. Research on residential areas and surrounding green spaces has also yielded similar findings. Some studies have found that greening mainly has a positive effect on reducing PM2.5 concentrations, while others have found a negative effect. The research conducted around urban industrial zones mostly focuses on the effect of green conditions in and around the entire industrial zone on PM2.5. Currently, there is a lack of research on the spatial distribution of PM2.5 at the meso and micro scales in industrial areas, so the relevant findings are relatively simple. The main advantage is that greening is conducive to reducing complex urban PM2.5 pollution, including industrial-source PM2.5. Research on campus greening and parks has found that greening is beneficial for reducing PM2.5 pollution. These areas themselves produce very little or even no PM2.5 pollution. Large-scale greening can not only block some foreign PM2.5 outside an area but also fully exploit large green spaces’ ability to settle, block, adsorb, and absorb PM2.5, thus reducing PM2.5 levels [120,121,122]. Therefore, the optimization of greening at the meso and micro scales requires refined management tailored to local conditions. Overall, measures such as employing sparse sources and strong sinks, as well as promoting ventilation, can be considered. In areas producing high levels of PM2.5, excessive planting should be avoided. Ventilation and other methods can be used to promote the flow of PM2.5 into areas with large green coverage and low PM2.5 production.
From the perspective of the mechanisms of action, many researchers have focused on the process whereby plants reduce PM2.5 concentrations through dry deposition. They have found that this process has a significant reducing effect. The notion that green spaces can lead to an increase in local PM2.5 pollution is more closely related to research focusing on the blockage effect of plants on PM2.5. Plants’ adsorption of PM2.5 and their effectiveness in reducing its levels in other ways are significantly influenced by the structural characteristics of the plants themselves, such as leaf surface roughness. There is relatively little research on absorption effects, with a focus on the components of PM2.5 that are small and can be absorbed by stomas. Different tree species have significant differences in their ability to reduce PM2.5 due to differences in leaf surface roughness, leaf size, leaf area index, and other characteristics. Therefore, selecting tree species of green spaces to reduce PM2.5 is also an important consideration in optimizing the structure.
There are significant differences in the effect of green spaces on PM2.5 under different environmental conditions, such as seasons, meteorological conditions (temperature, humidity, rainfall, etc.), and land-use characteristics. The numerous economic, social, and environmental benefits of green spaces are closely related. This also indicates that deep exploration of the connection between green spaces and their surrounding environment is necessary to maximize their value.

9. Conclusions

From the macro-regional and city scales, green spaces are mostly negatively correlated with PM2.5 concentrations, but they may not have a significant correlation with PM2.5 in some periods and locations and at certain research scales. At the aforementioned scales, a high green space ratio, large green space patches, and increased concentrated green space distributions are more likely to have a better effect on reducing PM2.5 levels. However, this does not hold true for all times, places, and scales. For example, in one study [32], the AI of green space was found to be negatively correlated with PM2.5 across most buffer zone scales, but there was no significant correlation at the 1000 m buffer zone scale. From the perspective of other indicators, such as urban green space shape complexity, different cities may exhibit significantly different regularity due to differences in factors like urban spatial morphology, pollution source distribution characteristics, and climate conditions, as shown by one study using LSI which found predominantly positive correlations with PM2.5 concentration [32]. Moreover, this was shown by another study [39], which found predominantly negative correlations between SHAPE_AM and PM2.5 concentration. In addition, there is a scale effect, with an optimal scale optimizing the macro-scale green space pattern for reducing PM2.5 levels. For example, Bi et al. [38] pointed out that 2 km × 2 km is the optimal scale for analyzing the spatial pattern of green space morphology and the spatial heterogeneity of PM2.5. Therefore, from the perspective of regional and urban development, increasing the green space ratio is conducive to reducing urban PM2.5 pollution, especially when increasing large patches of green space in cities such as parks and squares. Promoting a more concentrated distribution of green spaces can lead to a better reduction in PM2.5 pollution in most situations relative to a dispersed distribution.
Due to their proximity to pollution sources such as transportation and daily life, urban road areas and residential areas have a particularly pronounced dual effect (through greening) on PM2.5, which can lead to an increase or decrease in local PM2.5 concentrations. These green spaces undoubtedly play a certain role in reducing PM2.5 pollution, but in narrow environments, excessively wide tree crowns or dense leaves may lead to an accumulation of PM2.5 [61,62]. Areas with dense greenery and low PM2.5 production, such as parks and campuses, are widely regarded as PM2.5 sinks, and green spaces are widely believed to be beneficial for reducing PM2.5 pollution. Therefore, we suggest that practitioners in urban planning and design, as well as landscape architecture, avoid planting too many dense plants in narrow areas close to pollution sources. However, promoting ventilation may facilitate the flow of PM2.5 into high-concentration areas such as campuses and parks, thereby efficiently reducing PM2.5 pollution.
The selection of plants is an important factor to consider in the rational planning and greening process. Overall, the rougher the plant leave surface, the more favorable it is for PM2.5 adsorption [91,104]. A dense canopy with a large number of leaves provides a surface allowing for more dry deposition to occur. However, it may also lead to the accumulation of a large amount of PM2.5 in the local environment due to strong blocking effects and reduced wind speed [97,98,99]. Therefore, the selection of plants needs to be tailored to local conditions, and there may not be an absolutely good solution.
There are significant differences in the effect of greening on PM2.5 under different urban spatial characteristics, climate, pollution source locations, and intensities. For example, there are significant differences in the adsorption capacity of plants for PM2.5 before and after rainfall [95,108]. The optimization of greening to reduce PM2.5 pollution cannot be viewed in isolation with respect to greening and PM2.5 pollution. It also needs to be combined with the environment in order to better understand the complexity of the mechanism of greening on PM2.5 and effectively use greening to reduce PM2.5 pollution.

Author Contributions

Conceptualization, J.L. (Junyou Liu) and B.Z.; methodology, J.L. (Junyou Liu); validation, J.L. (Junyou Liu) and B.Z.; formal analysis, J.L. (Junyou Liu); investigation, J.L. (Junyou Liu) and B.Z.; resources, J.L. (Junyou Liu); data curation, J.L. (Junyou Liu); writing—original draft preparation, J.L. (Junyou Liu); writing—review and editing, J.L., B.Z., J.L. (Jiawei Li); supervision, B.Z.; project administration, J.L. (Junyou Liu) and B.Z.; funding acquisition, J.L. (Junyou Liu) and B.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Hunan Provincial Innovation Foundation for Postgraduates (grant number: CX20230091) and the Innovation Project for Postgraduates’ Independent Exploration of Central South University (grant number: 2023ZZTS0001).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Acknowledgments

We thank Xiaoqun Sun and Xiaozhen Lei for their help during the revision process.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AIaggregation index
AW-FRACarea-weight perimeter-area fractal dimension index
BTH regionBeijing–Tianjin–Hebei region
CFDcomputational fluid dynamics
CONTIGcontiguity index
COHESIONcohesion index
EDedge density
ENN_MNmean Euclidean nearest-neighbor index
FRACE_MNarea-weighted mean patch fractal dimension
G_Ratiogreen space coverage ratio
GVIgreen view index
LADleaf area density
LAIleaf area index
LDIlandscape division index
LPIlargest patch index
LSIlandscape shape index
NDVInormalized difference vegetative index
NPnumber of patch
PARAperimeter-area ratio
PLANDpercentage of landscape
PDpatch density
PM2.5fine particulate matter
PMparticulate matter
SIshape index
Shape_AMarea-weighted mean shape index
SHDIShannon’s diversity index
TAtotal area
TEtotal edge
TCAtotal core area
TSPtotal suspended particulate matter
WHOWorld Health Organization

References

  1. Almeida, S.M.; Pio, C.A.; Freitas, M.C.; Reis, M.A.; Trancoso, M.A. Approaching PM2.5 and PM2.5–10 source apportionment by mass balance analysis, principal component analysis and particle size distribution. Sci. Total Environ. 2006, 368, 663–674. [Google Scholar] [CrossRef] [Scilit]
  2. Hua, S.; Cai, X.; Sun, F.; Yu, X. Effect of Roadside Forest Belts on Particles Including TSP, PM10, PM2.5, and PM1 under Different Seasons in Beijing, China. Nat. Environ. Pollut. Technol. 2016, 15, 1389–1394. [Google Scholar]
  3. Kim, K.H.; Kabir, E.; Kabir, S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015, 74, 136–143. [Google Scholar] [CrossRef] [Scilit]
  4. Cheung, K.; Daher, N.; Kam, W.; Shafer, M.M.; Ning, Z.; Schauer, J.J.; Sioutas, C. Spatial and temporal variation of chemical composition and mass closure of ambient coarse particulate matter (PM10–2.5) in the Los Angeles area. Atmospheric Environ. 2011, 45, 2651–2662. [Google Scholar] [CrossRef] [Scilit]
  5. Chuang, K.-J.; Yan, Y.-H.; Chiu, S.-Y.; Cheng, T.-J. Long-termair pollution exposure and risk factors for cardiovascular diseases among the elderly in Taiwan. Occup. Environ. Med. 2011, 68, 64–68. [Google Scholar] [CrossRef] [Scilit]
  6. Srimuruganandam, B.; Nagendra, S. Source characterization of PM10 and PM2.5mass using a chemical mass balance model at urban roadside. Sci. Total. Environ. 2012, 433, 8–19. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Beckett, K.P.; Freer-Smith, P.H.; Taylor, G. Urban woodlands: Their role in reducing the effects of particulate pollution. Environ. Pollut. 1998, 99, 347–360. [Google Scholar] [CrossRef] [Scilit]
  8. Yu, X.; Meng, Z.; Li, M.; Xie, S.; Hong, X.; Liu, C. The impact of atmospheric PM2.5 exposure on DNA methylation. J. Environ. Occup. Med. 2020, 37, 1124–1131. [Google Scholar]
  9. Chen, Y.; Wang, H.; Dong, Z. The preliminary application of an online calculation platform for deaths attributable to PM2.5 (Mortality@PM). J. Environ. Hyg. 2023, 13, 668–673. [Google Scholar]
  10. Mak, H.W.L.; Ng, D.C.Y. Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches. Int. J. Environ. Res. Public Health 2021, 18, 6532. [Google Scholar] [CrossRef] [Scilit]
  11. Sangkham, S.; Phairuang, W.; Sherchan, S.P.; Pansakun, N.; Munkong, N.; Sarndhong, K.; Islam, A.; Sakunkoo, P. An update on adverse health effects from exposure to PM2.5. Environ. Adv. 2024, 18, 100603. [Google Scholar] [CrossRef] [Scilit]
  12. Fisher, J.E.; Andersen, Z.J.; Loft, S.; Pedersen, M. Opportunities and challenges within urban health and sustainable development. Curr. Opin. Environ. Sustain. 2017, 25, 77–83. [Google Scholar] [CrossRef] [Scilit]
  13. Diener, A.; Mudu, P. How can vegetation protect us from air pollution? A critical review on green spaces’ mitigation abilities for air-borne particles from a public health perspective—With implications for urban planning. Sci. Total Environ. 2021, 796, 148605. [Google Scholar] [CrossRef] [Scilit]
  14. Chinese Academy of Forestry. Promote the Transformation of Achievements and Serve the Construction of a Beautiful China. 2018. Available online: http://www.caf.ac.cn/info/1041/19859.htm (accessed on 30 May 2024).
  15. Yan, S.; Du, Z.; Zhang, Y.; Duan, F.; Shi, D.; Chen, M. Research Advances on Regulating PM2.5 Pollution by Urban Greenland. North. Hortic. 2019, 2019, 168–173. [Google Scholar]
  16. Qiu, L.; Liu, F.; Zhang, X.; Gao, T. The Reducing Effect of Green Spaces with Different Vegetation Structure on Atmospheric Particulate Matter Concentration in BaoJi City, China. Atmosphere 2018, 9, 332. [Google Scholar] [CrossRef] [Scilit]
  17. Zhou, Y.; Liu, H.; Zhou, J.; Xia, M. GIS-Based Urban Afforestation Spatial Patterns and a Strategy for PM2.5 Removal. Forests 2019, 10, 875. [Google Scholar] [CrossRef] [Scilit]
  18. Feng, H.; Ding, Y.; Zou, B.; Cohen, J.B.; Ye, S.; Yang, Z.; Qin, K.; Liu, L.; Gu, X. Vegetation-related dry deposition of global PM2.5 from satellite observations. J. Geogr. Sci. 2022, 32, 589–604. [Google Scholar] [CrossRef] [Scilit]
  19. Zhai, H.; Yao, J.; Wang, G.; Tang, X. Study of the Effect of Vegetation on Reducing Atmospheric Pollution Particles. Remote Sens. 2022, 14, 1255. [Google Scholar] [CrossRef] [Scilit]
  20. Li, K.; Li, C.; Hu, Y.; Xiong, Z.; Wang, Y. Quantitative estimation of the PM2.5 removal capacity and influencing factors of urban green infrastructure. Sci. Total Environ. 2023, 867, 161476. [Google Scholar] [CrossRef] [Scilit]
  21. Jo, H.-K.; Kim, J.-Y.; Park, H.-M. Carbon and PM2.5 Reduction and Design Guidelines for Street Trees in Korea. Sustainability 2020, 12, 10414. [Google Scholar] [CrossRef] [Scilit]
  22. Wang, Y.; Wang, M.; Wu, Y.; Sun, G. Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China. Environ. Int. 2023, 174, 107889. [Google Scholar] [CrossRef] [Scilit]
  23. Chen, J.; Zhu, L.; Fan, P.; Tian, L.; Lafortezza, R. Do green spaces affect the spatiotemporal changes of PM2.5 in Nanjing? Ecol. Process. 2016, 5, 7. [Google Scholar] [CrossRef] [Scilit]
  24. Guo, G.; Liu, L.; Duan, Y. Evaluating the Association of Regional and City-Level Environmental Greenness and Land Over Patterns with PM2.5 Pollution: Evidence from the Shanxi Province, China. Front. Environ. Sci. 2022, 10, 875619. [Google Scholar] [CrossRef] [Scilit]
  25. Zhang, Z.; Liu, J.; Wu, Y.; Yan, G.; Zhu, L.; Yu, X. Multi-scale comparison of the fine particle removal capacity of urban forests and wetlands. Sci. Rep. 2017, 7, 46214. [Google Scholar] [CrossRef] [Scilit]
  26. Ai, H.; Zhang, X.; Zhou, Z. The impact of greenspace on air pollution: Empirical evidence from China. Ecol. Indic. 2023, 146, 109881. [Google Scholar] [CrossRef] [Scilit]
  27. Zhang, R.; Chen, G.; Yin, Z.; Zhang, Y.; Ma, K. Urban greening based on the supply and demand of atmospheric PM2.5 removal. Ecol. Indic. 2021, 126, 107696. [Google Scholar] [CrossRef] [Scilit]
  28. Niu, X.; Li, Y.; Li, M.; Zhang, T.; Meng, H.; Zhang, Z.; Wang, B.; Zhang, W. Understanding vegetation structures in green spaces to regulate atmospheric particulate matter and negative air ions. Atmos. Pollut. Res. 2022, 13, 101534. [Google Scholar] [CrossRef] [Scilit]
  29. Srbinovska, M.; Andova, V.; Mateska, A.K.; Krstevska, M.C. The effect of small green walls on reduction of particulate matter concentration in open areas. J. Clean. Prod. 2021, 279, 123306. [Google Scholar] [CrossRef] [Scilit]
  30. Viecco, M.; Vera, S.; Jorquera, H.; Bustamante, W.; Girona’s, J.; Dobbs, C.; Leiva, E. Potential of Particle Matter Dry Deposition on Green Roofs and Living Walls Vegetation for Mitigating Urban Atmospheric Pollution in Semiarid Climates. Sustainability 2018, 10, 2431. [Google Scholar] [CrossRef] [Scilit]
  31. Vera, S.; Viecco, M.; Jorquera, H. Effects of biodiversity in green roofs and walls on the capture of fine particulate matter. Urban For. Urban Green. 2021, 63, 8. [Google Scholar] [CrossRef] [Scilit]
  32. Cai, L.; Zhuang, M.; Ren, Y. A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter. Urban For. Urban Green. 2020, 49, 126607. [Google Scholar] [CrossRef] [Scilit]
  33. Zhan, Q.; Yang, C.; Liu, H. How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives. Geo-Spat. Inf. Sci. 2022, 27, 95–110. [Google Scholar] [CrossRef] [Scilit]
  34. Yang, L.; Wang, S.; Hu, X.; Shi, T. County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern. Processes 2023, 11, 704. [Google Scholar] [CrossRef] [Scilit]
  35. Bi, S.; Dai, F.; Chen, M.; Xu, S. A new framework for analysis of the morphological spatial patterns of urban green space to reduce PM2.5 pollution: A case study in Wuhan, China. Sustain. Cities Soc. 2022, 82, 103900. [Google Scholar] [CrossRef] [Scilit]
  36. Chen, W.; Zhang, F.; Zhu, Y.; Yang, L.; Bi, P. Analysis of the impact of multiscale green landscape on urban PM2.5. Air Qual. Atmos. Health 2022, 15, 1319–1332. [Google Scholar] [CrossRef] [Scilit]
  37. Cai, L.; Zhuang, M.; Ren, Y. Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces. Int. J. Environ. Health Res. 2022, 32, 1–17. [Google Scholar] [CrossRef] [Scilit]
  38. Bi, S.; Chen, M.; Dai, F. The impact of urban green space morphology on PM2.5 pollution in Wuhan, China: A novel multiscale spatiotemporal analytical framework. Build. Environ. 2022, 221, 109340. [Google Scholar] [CrossRef] [Scilit]
  39. Li, Y.; Huang, L.; Li, S.; Cao, M.; Tan, P.; Wang, Q.; Meng, H.; Yin, S.; Zhang, W.; Tan, P.; et al. The role of urban green space landscape patterns in the synergistic prevention of PM2.5 and ozone pollution: A case study in Shenyang city, China. Atmos. Pollut. Res. 2024, 15, 102278. [Google Scholar] [CrossRef] [Scilit]
  40. Chen, M.; Dai, F.; Yang, B.; Zhu, S.W. Effects of neighborhood green space on PM2.5 mitigation: Evidence from five megacities in China. Build. Environ. 2019, 156, 33–45. [Google Scholar] [CrossRef] [Scilit]
  41. Fan, Z.; Zhan, Q.; Liu, H.; Wu, Y.; Xia, Y. Investigating the interactive and heterogeneous effects of green and blue space on urban PM2.5 concentration, a case study of Wuhan. J. Clean. Prod. 2022, 378, 134389. [Google Scholar] [CrossRef] [Scilit]
  42. Luo, S.; Chen, W.; Sheng, Z.; Wang, P. The impact of urban green space landscape on PM2.5 in the central urban area of Nanchang city, China. Atmos. Pollut. Res. 2023, 14, 101903. [Google Scholar] [CrossRef] [Scilit]
  43. Chen, Y.; Ke, X.; Min, M.; Zhang, Y.; Dai, Y.; Tang, L. Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China. Land 2022, 11, 776. [Google Scholar] [CrossRef] [Scilit]
  44. Hong, B.; Lin, B.R.; Qin, H.Q. Numerical Investigation on the Effect of Avenue Trees on PM2.5 Dispersion in Urban Street Canyons. Atmosphere 2017, 8, 129. [Google Scholar] [CrossRef] [Scilit]
  45. Zhao, Y.; Hu, Q.; Li, H.; Wang, S.; Ai, M. Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data. Remote Sens. 2018, 10, 1759. [Google Scholar] [CrossRef] [Scilit]
  46. Qin, H.; Hong, B.; Huang, B.; Cui, X.; Zhang, T. How dynamic growth of avenue trees affects particulate matter dispersion: CFD simulations in street canyons. Sustain. Cities Soc. 2020, 61, 102331. [Google Scholar] [CrossRef] [Scilit]
  47. Riondato, E.; Pilla, F.; Basu, A.S.; Basu, B. Investigating the effect of trees on urban quality in Dublin by combining air monitoring with i-Tree Eco model. Sustain. Cities Soc. 2020, 61, 102356. [Google Scholar] [CrossRef] [Scilit]
  48. Wang, W.; Cheng, X.; Dai, M. Strategies for sustainable urban development and morphological optimization of street canyons: Measurement and simulation of PM2.5 at different points and heights. Sustain. Cities Soc. 2022, 87, 104191. [Google Scholar] [CrossRef] [Scilit]
  49. Jeong, N.-R.; Han, S.-W.; Ko, B. Effects of Green Network Management of Urban Street Trees on Airborne Particulate Matter (PM2.5) Concentration. Int. J. Environ. Res. Public Health 2023, 20, 2507. [Google Scholar] [CrossRef] [Scilit]
  50. Jin, S.J.; Guo, J.K.; Wheeler, S.; Kan, L.Y.; Che, S.Q. Evaluation of impacts of trees on PM2.5 dispersion in urban streets. Atmos. Environ. 2014, 99, 277–287. [Google Scholar] [CrossRef] [Scilit]
  51. Jin, X.M.; Yang, L.J.; Du, X.Z.; Yang, Y.P. Transport characteristics of PM2.5 inside urban street canyons: The effects of trees and vehicles. Build. Simul. 2017, 10, 337–350. [Google Scholar] [CrossRef] [Scilit]
  52. Karttunen, S.; Kurppa, M.; Auvinen, M.; Hellsten, A.; Järvi, L. Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations—A case study from a city-boulevard. Atmos. Environ. X 2020, 6, 100073. [Google Scholar] [CrossRef] [Scilit]
  53. Liu, J.; Zheng, B.; Xiang, Y.; Fan, J. The Impact of Street Tree Height on PM2.5 Concentration in Street Canyons: A Simulation Study. Sustainability 2022, 14, 12378. [Google Scholar] [CrossRef] [Scilit]
  54. Xu, J.; Liu, M.; Chen, H.; Luo, M.; Chen, H.; Luo, M. Spatially heterogeneous influence of street greenery on street-level PM2.5 pollution using mobile monitoring from a three-dimensional perspective. Urban Clim. 2023, 48, 101414. [Google Scholar] [CrossRef] [Scilit]
  55. Liu, J.; Zheng, B. A Simulation Study on the Influence of Street Tree Configuration on Fine Particulate Matter (PM2.5) Concentration in Street Canyons. Forests 2023, 14, 1550. [Google Scholar] [CrossRef] [Scilit]
  56. Wang, X.; Teng, M.; Huang, C.; Zhou, Z.; Chen, X.; Xiang, Y. Canopy density effects on particulate matter attenuation coefficients in street canyons during summer in the Wuhan metropolitan area. Atmos. Environ. 2020, 240, 117739. [Google Scholar] [CrossRef] [Scilit]
  57. Chen, X.; Wang, X.; Wu, X.; Guo, J.; Zhou, Z. Influence of roadside vegetation barriers on air quality inside urban street canyons. Urban For. Urban Green. 2021, 63, 127219. [Google Scholar] [CrossRef] [Scilit]
  58. Kim, S.; Lee, S.; Hwang, K.; An, K. Exploring sustainable street tree planting patterns to be resistant against fine particles (PM2.5). Sustainability 2017, 9, 1709. [Google Scholar] [CrossRef] [Scilit]
  59. Buccolieri, R.; Jeanjean, A.P.R.; Gatto, E.; Leigh, R.J. The impact of trees on street ventilation, NOx and PM2.5 concentrations across heights in Marylebone Rd street canyon, central London. Sustain. Cities Soc. 2018, 41, 227–241. [Google Scholar] [CrossRef] [Scilit]
  60. Liang, F.H.; Liu, Y.; Shi, Y.Y.; Liu, H.Q.; Zhang, C. Influence of spatial structure of road greening on particle diffusion. IOP Conf. Ser. Earth Environ. Sci. 2019, 346, 012023. [Google Scholar] [CrossRef] [Scilit]
  61. Liu, C.; Dai, A.; Ji, Y.; Sheng, Q.; Zhu, Z. Effect of Different Plant Communities on Fine Particle Removal in an Urban Road Greenbelt and Its Key Factors in Nanjing, China. Sustainability 2023, 15, 156. [Google Scholar] [CrossRef] [Scilit]
  62. He, H.; Zhu, Y.; Liu, L.; Du, J.; Liu, L.; Liu, J. Effects of roadside trees three-dimensional morphology characteristics on traffic-related PM2.5 distribution in hot-humid urban blocks. Urban Clim. 2023, 49, 101448. [Google Scholar] [CrossRef] [Scilit]
  63. Tian, X.; Gao, J.; Liu, L.; Zhao, Z.; Hang, J.; Zheng, Y.; Wang, X. Mathematical models for traffic-source PM2.5 dispersion in an urban street canyon considering the capture capability of roadside trees. Sci. Total Environ. 2024, 951, 175513. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  64. Sun, S.; Sarkar, C.; Kumari, S.; James, P.; Cao, W.; Lee, R.-S.; Tian, L.; Webster, C. Air pollution associated respiratory mortality risk alleviated by residential greenness in the Chinese Elderly Health Service Cohort. Environ. Res. 2020, 183, 109139. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  65. Liu, L.; Yan, L.L.; Lv, Y.; Zhang, Y.; Li, T.; Huang, C.; Kan, H.; Zhang, J.; Zeng, Y.; Shi, X.; et al. Air pollution, residential greenness, and metabolic dysfunction biomarkers: Analyses in the Chinese Longitudinal Healthy Longevity Survey. BMC Public Health 2022, 22, 885. [Google Scholar] [CrossRef] [Scilit]
  66. Wang, F.; Sun, B.; Zheng, X.; Ji, X. Impact of Block Spatial Optimization and Vegetation Configuration on the Reduction of PM2.5 Concentrations: A Roadmap towards Green Transformation and Sustainable Development. Sustainability 2022, 14, 11622. [Google Scholar] [CrossRef] [Scilit]
  67. Zhang, Z.; Li, Y.; Li, M.; Meng, H.; Zhang, T.; Peng, Z.; Zhang, W. Improving atmospheric particulate matter removal of residential green space based on Landscape patterns and plant functional types. Air Qual. Atmos. Health 2023, 16, 401–413. [Google Scholar] [CrossRef] [Scilit]
  68. Xiong, C.; Chen, Q. Green belt’s effect on fine particle matter (PM2.5) in residential cluster: Based on CFD simulation. E3S Web Conf. 2021, 237, 01011. [Google Scholar] [CrossRef] [Scilit]
  69. Han, L.; Zhao, J.; Gao, Y.; Gu, Z.; Xin, K.; Zhang, J. Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models. Sustain. Cities Soc. 2020, 61, 102329. [Google Scholar] [CrossRef] [Scilit]
  70. Bikis, A. Urban Air Pollution and Greenness in Relation to Public Health. J. Environ. Public Health 2023, 2023, 8516622. [Google Scholar] [CrossRef] [Scilit]
  71. Li, Q.; Chen, W.; Li, M.; Yu, Q.; Wang, Y. Identifying the effects of industrial land expansion on PM2.5 concentrations: A spatiotemporal analysis in China. Ecol. Indic. 2022, 141, 109069. [Google Scholar] [CrossRef] [Scilit]
  72. Matthaios, V.N.; Holland, I.; Kang, C.M.; Hart, J.E.; Hauptman, M.; Wolfson, J.M.; Gaffin, J.M.; Phipatanakul, W.; Gold, D.R.; Koutrakis, P. The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools. J. Expo. Sci. Environ. Epidemiology 2024, 34, 745–752. [Google Scholar] [CrossRef] [Scilit]
  73. Wang, Q.; Ao, R.; Chen, H.; Li, J.; Wei, L.; Wang, Z. Characteristics of PM2.5 and CO2 Concentrations in Typical Functional Areas of a University Campus in Beijing Based on Low-Cost Sensor Monitoring. Atmosphere 2024, 15, 1044. [Google Scholar] [CrossRef] [Scilit]
  74. Jiang, Y.; Liu, C.; Wen, C.; Long, Y. Study of summer microclimate and PM2.5 concentration in campus plant communities. Sci. Rep. 2024, 14, 3311. [Google Scholar] [CrossRef] [Scilit]
  75. Junior, D.P.M.; Bueno, C.; da Silva, C.M. The Effect of Urban Green Spaces on Reduction of Particulate Matter Concentration. Bull. Env. Contam. Toxicol. 2022, 108, 1104–1110. [Google Scholar] [CrossRef] [Scilit]
  76. Heshani, S.; Winijkul, E. Numerical simulations of the effects of green infrastructure on PM2.5 dispersion in an urban park in Bangkok, Thailand. Heliyon 2022, 8, e10475. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  77. Liu, F.; Zheng, X.H.; Qian, H. Comparison of particle concentration vertical profiles between downtown and urban forest park in Nanjing (China). Atmos. Pollut. Res. 2018, 9, 829–839. [Google Scholar] [CrossRef] [Scilit]
  78. Yan, G.; Yu, Z.; Wu, Y.; Liu, J.; Wang, Y.; Zhai, J.; Cong, L.; Zhang, Z. Understanding PM 2.5 Concentration and Removal Efficiency Variation in Urban Forest park-Observation at Human Breathing Height. PeerJ. 2020, 8, e8988. [Google Scholar] [CrossRef] [Scilit]
  79. Xiao, Y.H.; Liu, S.R.; Tong, F.C.; Kuang, Y.W.; Chen, B.F.; Guo, Y.D. Characteristics and Sources of Metals in TSP and PM2.5 in an Urban Forest Park at Guangzhou. Atmosphere 2014, 5, 775–787. [Google Scholar] [CrossRef] [Scilit]
  80. Su, T.; Lin, C.; Lu, S.; Lin, J.; Wang, H.; Liu, C. Effect of air quality improvement by urban parks on mitigating PM2.5 and its associated heavy metals: A mobile-monitoring field study. J. Environ. Manag. 2022, 323, 116283. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  81. Yin, Z.; Zhang, Y.; Ma, K. Evaluation of PM2.5 Retention Capacity and Structural Optimization of Urban Park Green Spaces in Beijing. Forests 2022, 13, 415. [Google Scholar] [CrossRef] [Scilit]
  82. Zhu, C.; Przybysz, A.; Chen, Y.; Guo, H.; Chen, Y.; Zeng, Y. Effect of spatial heterogeneity of plant communities on air PM10 and PM2.5 in an urban forest park in Wuhan, China. Urban For. Urban Green. 2019, 46, 126487. [Google Scholar] [CrossRef] [Scilit]
  83. Ji, W.J.; Zhao, B. A wind tunnel study on the effect of trees on PM2.5 distribution around buildings. J. Hazard. Mater. 2018, 346, 36–41. [Google Scholar] [CrossRef] [Scilit]
  84. Yang, J.; Chang, Y.M.; Yan, P.B. Ranking the suitability of common urban tree species for controlling PM2.5 pollution. Atmos. Pollut. Res. 2015, 6, 267–277. [Google Scholar] [CrossRef] [Scilit]
  85. Shen, J.-W.; Cui, P.-Y.; Huang, Y.-D.; Luo, Y.; Guan, J. New insights into quantifying deposition and aerodynamic characteristics of PM2.5 removal by different tree leaves. Air Qual. Atmos. Health 2022, 15, 1341–1356. [Google Scholar] [CrossRef] [Scilit]
  86. Ciro, D.V.; Barriga, J.E.C.; Rincón, I.C.H. The removal of PM2.5 by trees in tropical Andean metropolitan areas: An assessment of environmental change scenarios. Environ. Monit. Assess. 2021, 193, 396. [Google Scholar] [CrossRef] [Scilit]
  87. Luo, J.; Zhou, X.; Tian, Y.; Zhang, M. Research report of particulate matter deposited on leaf surface of major ecological tree species. IOP Conf. Ser. Earth Environ. Sci. 2018, 170, 052002. [Google Scholar] [CrossRef] [Scilit]
  88. Bi, Y.F.; Guo, F.Y.; Yang, L.; Zhong, H.; Wang, A.K.; Wang, Y.K.; Wu, Z.Z.; Du, X.H. Phyllostachys edulis forest reduces atmospheric PM2.5 and PAHs on hazy days at suburban area. Sci. Rep. 2018, 8, 12591. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  89. Chen, L.; Liu, C.; Zhang, L.; Zou, R.; Zhang, Z. Variation in Tree Species Ability to Capture and Retain Airborne Fine Particulate Matter (PM2.5). Sci. Rep. 2017, 7, 3206. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  90. Liang, D.; Ma, C.; Wang, Y.Q.; Wang, Y.J.; Zhao, C.X. Quantifying PM2.5 capture capability of greening trees based on leaf factors analyzing. Environ. Sci. Pollut. Res. 2016, 23, 21176–21186. [Google Scholar] [CrossRef] [Scilit]
  91. Kim, K.; Jeon, J.; Jung, H.; Kim, T.K.; Hong, J.; Jeon, G.; Kim, H. PM2.5 reduction capacities and their relation to morphological and physiological traits in 13 landscaping tree species. Urban For. Urban Green. 2022, 70, 127526. [Google Scholar] [CrossRef] [Scilit]
  92. Chen, D.; Yan, J.; Sun, N.; Sun, W.; Zhang, W.; Long, Y.; Yin, S. Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement. J. Hazard. Mater. 2024, 478, 135428. [Google Scholar] [CrossRef] [Scilit]
  93. Chaparro, M.A.E.; Chaparro, M.A.E.; Castañeda-Miranda, A.G.; Marié, D.C.; Gargiulo, J.D.; Juan, M.L.; Marcela, N.; Böhnel, H.N. Fine air pollution particles trapped by street tree barks: In situ magnetic biomonitoring. Environ. Pollut. 2020, 266, 115229. [Google Scholar] [CrossRef] [Scilit]
  94. Xie, C.; Guo, J.; Yan, L.; Jiang, R.; Liang, A.; Che, S. The influence of plant morphological structure characteristics on PM2.5 retention of leaves under different wind speeds. Urban For. Urban Green. 2022, 71, 127556. [Google Scholar] [CrossRef] [Scilit]
  95. Luo, J.; Niu, Y.D.; Zhang, Y.; Zhang, M.; Tian, Y.X.; Zhou, X.L. Dynamic analysis of retention PM2.5 by plant leaves in rainfall weather conditions of six tree species. Energy Sources Part A Recovery Util. Environ. Eff. 2020, 42, 1014–1025. [Google Scholar] [CrossRef] [Scilit]
  96. Jeanjean, A.P.R.; Monks, P.S.; Leigh, R.J. Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale. Atmos. Environ. 2016, 147, 1–10. [Google Scholar] [CrossRef] [Scilit]
  97. Zhang, Y.; Yan, G.; Dai, L.; Cong, L.; Wu, Y.; Zhai, J.; Zhang, Z. Removal ability of different underlying surfaces to near-surface particulate matter. Environ. Technol. 2021, 42, 1899–1910. [Google Scholar]
  98. Gaglio, M.; Pace, R.; Muresan, A.N.; Grote, R.; Castaldelli, G.; Calfapietra, C.; Fano, E.A. Species-specific efficiency in PM2.5 removal by urban trees: From leaf measurements to improved modeling estimates. Sci. Total Environ. 2022, 844, 157131. [Google Scholar] [CrossRef] [Scilit]
  99. Yao, J.; Wu, S.; Cao, Y.; Wei, J.; Tang, X.; Hu, L.; Wu, J.; Yang, H.; Yang, J.; Ji, X. Dry deposition effect of urban green spaces on ambient particulate matter pollution in China. Sci. Total Environ. 2023, 900, 165830. [Google Scholar] [CrossRef] [Scilit]
  100. Wu, J.; Luo, K.; Wang, Y.; Wang, Z. Urban road greenbelt configuration: The perspective of PM 2.5 removal and air quality regulation. Environ. Int. 2021, 157, 106786. [Google Scholar] [CrossRef] [Scilit]
  101. Zhang, X.; Lyu, J.; Han, Y.; Sun, N.; Sun, W.; Li, J.; Liu, C.; Yin, S. Effects of the leaf functional traits of coniferous and broadleaved trees in subtropical monsoon regions on PM2.5 dry deposition velocities. Environ. Pollut. 2020, 265, 114845. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  102. Tong, Z.; Baldauf, R.W.; Isakov, V.; Deshmukh, P.; Max Zhang, K. Roadside vegetation barrier designs to mitigate near-road air pollution impacts. Sci. Total Environ. 2016, 541, 920–927. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  103. Li, F.; Rubinato, M.; Zhou, T.; Li, J.; Chen, C. Numerical simulation of the influence of building-tree arrangements on wind velocity and PM2.5 dispersion in urban communities. Sci. Rep. 2022, 12, 16378. [Google Scholar] [CrossRef] [Scilit]
  104. Zhao, X.; Yan, H.; Liu, M.; Kang, L.; Yu, J.; Yang, R. Relationship between PM2.5 adsorption and leaf surface morphology in ten urban tree species in Shenyang, China. Energy Sources Part A Recovery Util. Environ. Eff. 2019, 41, 1029–1039. [Google Scholar] [CrossRef] [Scilit]
  105. Yang, Q.; Wang, H.; Wang, J.; Lu, M.; Liu, C.; Xia, X.; Yin, W.; Guo, H. PM2.5-bound SO42− absorption and assimilation of poplar and its physiological responses to PM2.5 pollution. Environ. Exp. Bot. 2018, 153, 311–319. [Google Scholar] [CrossRef] [Scilit]
  106. Li, Y.; Wang, Y.; Wang, B.; Wang, Y. The Response of Plant Photosynthesis and Stomatal Conductance to Fine Particulate Matter (PM2.5) based on Leaf Factors Analyzing. J. Plant Biol. 2019, 62, 120–128. [Google Scholar] [CrossRef] [Scilit]
  107. Feng, H.; Feng, H. Correlation Analysis Between PM2.5 Concentration and Meteorological, Vegetation and Topographical Factors in the Urbanized Ecosystem in Beijing, China. Nat. Environ. Pollut. Technol. 2020, 19, 1399–1410. [Google Scholar] [CrossRef] [Scilit]
  108. Xie, C.; Yan, L.; Liang, A.; Che, S. Understanding the washoff processes of PM2.5 from leaf surfaces during rainfall events. Atmos. Environ. 2019, 214, 116844. [Google Scholar] [CrossRef] [Scilit]
  109. Hong, J.; Lee, M.; Huh, W.; Kim, T.K.; Jeon, J.; Lee, H.; Kim, K.; Byeon, S.; Park, C.; Kim, H.S. Comparisons of PM2.5 mitigation with stand characteristics between evergreen Korean pine plantations and deciduous broad-leaved forests in the Republic of Korea. Environ. Pollut. 2023, 334, 122240. [Google Scholar] [CrossRef] [Scilit]
  110. Shao, F.; Wu, H.T.; Li, G.; Sun, F.B.; Yu, L.; Zhang, Y.K.; Dong, L.; Bao, Z.Y. PM2.5 concentrations in the greenbelt near the Lin’an 1109 toll station of the Hang Rui expressway and related influencing factors. Atmosfera 2019, 32, 323–336. [Google Scholar] [CrossRef] [Scilit]
  111. Lee, A.C.; Jordan, H.C.; Horsley, J. Value of urban green spaces in promoting healthy living and wellbeing: Prospects for planning. Risk Manag. Healthc. Policy 2015, 8, 131–137. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  112. Chi, Y.L.; Mak, H.W.L. From Comparative and Statistical Assessments of Liveability and Health Conditions of Districts in Hong Kong Towards Future City Development. Sustainability 2021, 13, 8781. [Google Scholar] [CrossRef] [Scilit]
  113. Chan, C.-S. Health-related elements in green space branding in Hong Kong. Urban For. Urban Green. 2017, 21, 192–202. [Google Scholar] [CrossRef] [Scilit]
  114. Guo, L.; Luo, J.; Yuan, M.; Huang, Y.P.; Shen, H.F.; Li, T.W. The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data. Sci. Total Environ. 2019, 659, 1585–1596. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  115. Eisenman, T.S.; Churkina, G.; Jariwala, S.P.; Kumar, P.; Lovasi, G.S.; Pataki, D.E.; Weinberger, K.R.; Thomas, H.W. Urban trees, air quality, and asthma: An interdisciplinary review. Landsc. Urban Plan. 2019, 187, 47–59. [Google Scholar] [CrossRef] [Scilit]
  116. Meo, S.A.; Almutairi, F.J.; Abukhalaf, A.A.; Usmani, A.M. Effect of Green Space Environment on Air Pollutants PM2.5, PM10, CO, O3, and Incidence and Mortality of SARS-CoV-2 in Highly Green and Less-Green Countries. Int. J. Environ. Res. Public Health 2021, 18, 13151. [Google Scholar] [CrossRef] [Scilit]
  117. Chen, L.; Xie, J.; Ma, T.; Chen, M.; Gao, D.; Li, Y.; Ma, Y.; Wen, B.; Jiang, J.; Wang, X.; et al. Greenness alleviates the effects of ambient particulate matter on the risks of high blood pressure in children and adolescents. Sci. Total Environ. 2022, 812, 152431. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  118. Cao, W.; Zhou, W.; Yu, W.; Wu, T. Combined effects of urban forests on land surface temperature and PM2.5 pollution in the winter and summer. Sustain. Cities Soc. 2024, 104, 105309. [Google Scholar] [CrossRef] [Scilit]
  119. Cao, W.; Wang, L.; Li, R.; Zhou, W.; Zhang, D. Unveiling the nonlinear relationships and co-mitigation effects of green and blue space landscapes on PM2.5 exposure through explainable machine learning. Sustain. Cities Soc. 2025, 122, 106234. [Google Scholar] [CrossRef] [Scilit]
  120. Lorot, G. Impact of Urban Green Spaces on Air Quality in Metropolitan Areas. Am. J. Environ. Stud. 2024, 7, 1–11. [Google Scholar] [CrossRef]
  121. Wei, Q.; Chen, Y.; Zhang, H.; Jia, Z.; Yang, J.; Niu, B. Simulation and prediction of PM2.5 concentrations and analysis of driving factors using interpretable tree-based models in Shanghai, China. Environ. Res. 2025, 270, 121003. [Google Scholar] [CrossRef] [Scilit]
  122. Fry, J.L.; Ooms, P.; Krol, M.; Kerckhoffs, J.; Vermeulen, R.; Wesseling, J.; Elshout, S.V.D. Effect of street trees on local air pollutant concentrations (NO2, BC, UFP, PM2.5) in Rotterdam, the Netherlands. Environ. Sci. Atmos. 2025, 5, 394–404. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco
Previous
Heavy Metal Concentrations in Particulate Matter: A Case Study from Santo Domingo, Dominican Republic, 2022
Next