Next Article in Journal
Contrasting Altitudinal Patterns and Composition of Soil Bacterial Communities along Stand Types in Larix principis-rupprechtii Forests in Northern China
Previous Article in Journal
Fine Classification of Urban Tree Species Based on UAV-Based RGB Imagery and LiDAR Data
Previous Article in Special Issue
Restorative Effects of Pocket Parks on Mental Fatigue among Young Adults: A Comparative Experimental Study of Three Park Types
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 2
10.3390/f15020391
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Assessment Framework for Mapping the Air Purification Service of Vegetation at the Regional Scale

by
Yu Liu
1,
Wudong Zhao
1,
Liwei Zhang
1,*,
Xupu Li
1,
Lixian Peng
2,
Zhuangzhuang Wang
3,
Yongyong Song
1,
Lei Jiao
1 and
Hao Wang
1
1
Department of Geography, School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
2
School of Geographic Sciences, East China Normal University, Shanghai 200062, China
3
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(2), 391; https://0-doi-org.brum.beds.ac.uk/10.3390/f15020391
Submission received: 2 January 2024 / Revised: 26 January 2024 / Accepted: 17 February 2024 / Published: 19 February 2024
(This article belongs to the Special Issue Urban Forests and Human Health)
Browse Figures
Versions Notes

Abstract

:
Efficiently mitigating the severe air pollution resulting from rapid progress is crucial for the sustainable development of the socio-ecological system. Recently, concerns about nature-based solutions have emerged in the research on the treatment of air pollution. Studies on the purification of PM2.5 using vegetation currently concentrate on the individual scale of tree species or urban vegetation, ignoring the regional scale, which could better assist ecological governance. Therefore, taking the Fenwei Plain of China as the study area, an assessment framework of the air purification service’s spatial distribution reflecting regional vegetation was constructed. The dry deposition model and GeoDetector were used to quantify the spatial-temporal pattern and explore natural driving factors on the removal of PM2.5. The results showed that (1) the PM2.5 purification services offered by various types of vegetation exhibit notable variations. The average removal rates of PM2.5 by vegetation were 0.186%, 0.243%, and 0.435% in 2000, 2010, and 2021, respectively. (2) Meanwhile, a wide range of spatial mismatch exists between the PM2.5 concentration and PM2.5 removal. Insufficient supply regions of PM2.5 purification services account for 50% of the Fenwei Plain. (3) PM2.5 removal was strongly influenced by the types of vegetation and the Normalized Vegetation Index (NDVI), followed by the Digital Elevation Model (DEM), and less affected by meteorological factors; a strong joint effect was shown among the factors. The findings in this research provide a new perspective on regional air pollution management at the regional scale.

1. Introduction

In recent years, sustained economic and social development has accelerated the urbanization process [1,2]. Air pollution has become a serious environmental problem that needs to be addressed across the world [2]. Goal 3 and Goal 11 of the United Nations Sustainable Development (SDG) emphasize the impact of air pollution on residents. This requires providing good health and well-being to residents and building sustainable cities and communities [3,4,5]. Fine particulate matter (particles with a diameter of less than 2.5 µm, PM2.5) can be breathed deep into the lungs and, in some cases, enter the bloodstream. Epidemiological studies have confirmed that it has a direct negative impact on humans, especially respiratory and cardiovascular diseases. It threatens human health and well-being through complex interactions [6,7,8]. As a developing country, China has formulated and implemented the Air Pollution Prevention and Control Action Plan as well as the Three-year Action Plan to Fight Air Pollution [9]. Continuous improvements have been made to the ecological environment and air quality by updating industries and optimizing the energy structure [10]. These efforts have led to significant reductions in atmospheric PM2.5 pollution levels in many areas [11]. However, the emission reduction potential of traditional treatment methods is gradually narrowing, especially when air pollution enters moderate and mild conditions, and landscape planning and optimization are considered effective ways to mitigate air pollution [12,13]. Nature-based solutions are beginning to receive attention from scholars, which can effectively mitigate PM2.5 pollution by enhancing ecosystem services at the regional level [14]. The assessment of the dust retention capacity of vegetation has gradually become a hot research topic in this field.
Vegetation possesses a unique leaf structure that allows it to remove fine particulate matter from the atmosphere [15]. Current research suggests that ecosystems can help reduce PM2.5 emissions in highly urbanized areas [16,17]. Green infrastructure such as urban trees plays a crucial role in providing ecosystem services such as air purification [18]. The government can effectively enhance regional air quality and promote sustainable urban growth by prioritizing the development of forest cities and strategically planning green spaces [19]. However, the existing research on vegetation dust retention primarily centers on assessing the dust-holding capacity of diverse tree species and investigating the removal effect of different urban vegetation on PM2.5 [20]. In terms of this small scale, the assessment of the effects of tree species selection and vegetation structure on urban air purification focused on street trees and urban parks [21,22]. At the urban scale, they quantified the capacity of vegetation to remove air pollution in different cities. This can provide policy suggestions for optimizing urban green space construction [23,24,25]. Meanwhile, there are also comparative studies on the differences in air purification services of vegetation in multiple cities. Meanwhile, few studies have produced regional quantitative estimates of the capacity of vegetation to reduce PM2.5 levels more effectively. Hence, it is imperative to merge high-resolution data from various sources and utilize mechanistic modeling to establish a framework. This could elucidate the connection between vegetation and the enhancement of air quality currently. The framework in this research will help us better understand how vegetation affects air quality and will be instrumental in developing a novel model for air pollution management.
Due to the complex topographic characteristics and industrial composition, Fen Wei Plain, where the accumulation of PM2.5 has been elucidated, has now become a key area for air pollution control in China [26]. The PM2.5 purification capacity of vegetation in this region is investigated based on high-resolution remote sensing data and spatial analyses. The objectives were to (1) quantify the spatial and temporal variability and spatial mismatch between regional PM2.5 concentrations and PM2.5 removal from air pollution amelioration by vegetation; (2) identify the natural factors that affect the PM2.5 removal; and (3) incorporate nature-based air purification programs into regional-scale ecological construction. This research aims to provide nature-based solutions for air quality improvement and regional ecological environment optimization in key air pollution areas.

2. Materials and Methods

2.1. Study Area

Fenwei Plain is a general term for the Fen River Plain, Wei River Plain, and its surrounding terraces. It is located in the middle reaches of the Yellow River, with the Loess Plateau in the northwest and the Qinling Mountains in the south, making it the fourth-largest plain in China [27]. The geographical distribution of the study area in a northeast-southwest band is not conducive to the dispersal of pollutants. The climate of this region is mainly affected by the East Asian monsoon, with annual precipitation of less than 600 mm in most areas [28], and the prevailing winds throughout the year are northeasterly and usually around 2 m/s [26]. The interaction of pollution emissions between cities is significant. In addition, the energy structure of the region is heavy, and the coal industry is relatively developed. The river valley is densely populated and has severe air pollution. The availability of clean air for the inhabitants of the region is not guaranteed. At present, the FenWei Plain is a key area in China’s air pollution control. As defined in the “Three-Year Action Plan to Fight Air Pollution”, it includes 11 cities and 1 district. Among them, five cities and one district are in Shaanxi Province, including Xi’an (XA), Tongchuan (TC), Baoji (TC), Xianyang (XY), Weinan (WN), and the Yangling Demonstration Area; four cities are in Shanxi Province: Jinzhong (JZ), Yuncheng (YC), Linfen (LF), and Lvliang (LL); and it also includes Luoyang (LY) and Sanmenxia (SMX) in Henan Province. This study focuses on a region with 11 prefecture-level cities (Figure 1).

2.2. Research Framework

The research framework includes five main stages (Figure 2), which mainly include (1) index selection and parameter calculation; (2) the quantification of vegetation PM2.5 purification services; (3) analysis of influencing factors for PM2.5 removal; and (4) proposal of measures to improve regional air quality.
In the first stage, natural indicators related to PM2.5 pollution and vegetation dust retention were selected, followed by the collection of PM2.5 spatial distribution data, the Normalized Vegetation Index (NDVI), the China Land Cover Dataset (CLCD), the Digital Elevation Model (DEM), meteorological data, and other basic data. Secondly, based on the retention effect of vegetation leaves on particulate matter, the dry deposition model [24] was used to quantify the PM2.5 purification service, which was used to visualize the effect of vegetation on PM2.5 removal and the temporal and spatial distribution characteristics. Thirdly, spatial matching relationships between PM2.5 concentration and PM2.5 removal were determined. These can be used to identify areas at risk of PM2.5 pollution. In the fourth stage, natural drivers of PM2.5 removal were revealed using GeoDetector [23]. The main influencing factors of PM2.5 removal can be determined. Finally, combined with the above content, it can provide scientific support for the formulation of air quality improvement measures and regional ecological construction decision-making in key areas of national air pollution.

2.3. Data Sources and Processing

The multi-source data used in the study include land-use classification, DEM data, meteorological elements, NDVI, and PM2.5 spatial distribution data. The CLCD dataset published by Yang et al. [29] was adopted as the land-use classification data from Wuhan University. The PM2.5 spatial distribution data were from the Chinese high-resolution and high-quality near-surface air pollutant (CHAP) dataset published by Wei et al. [30,31]. The dataset of a 30 m annual maximum NDVI in China was from the Resource and environmental science data registration and publication system (https://www.resdc.cn/DOI/DOI.aspx?DOIID=68, accessed on 26 January 2024). The datasets of precipitation, wind speed, and temperature came from the NOAA’s National Centers for Environmental Information (https://www.ncei.noaa.gov, accessed on 26 January 2024) through sorting, computation, and spatial interpolation generation. The atmospheric boundary layer height data are from the ECMFW European Center, the fifth-generation atmospheric reanalysis dataset (https://cds.climate.copernicus.eu, accessed on 26 January 2024) for the global climate of the ERA5 meteorological data. DEM data were obtained from the National Earth System Science Data Center (http://www.geodata.cn, accessed on 26 January 2024). The NDVI data were from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences with a resolution of 30 m (Table 1).

2.4. Methods

2.4.1. Simulating PM2.5 Removal

Vegetation possesses a unique leaf structure that allows it to remove fine particulate matter from the atmosphere [15]. PM2.5 removal per unit area can be estimated as [12]:
Q d = C × V × L A I × T × ( 1 − r )
where Q d (μg/m2) is the PM2.5 removal per unit area of vegetation; C (μg/m3) is the concentration of PM2.5, which is resampled to 30 m; V (cm/s) is the rate at which PM2.5 settles onto the surface of vegetation leaves; LAI is the leaf area index of the vegetation (m2/m2); T is the dust retention time; and r is the resuspension rate (%) [32]. Annual mean PM2.5 concentration raster data were used in this research. The PM2.5 deposition rate and resuspension rate were determined from the annual average wind speed (Table 2). The dust retention time was calculated from the continuous non-precipitation time (less than 15 mm daily precipitation) [33]. LAI was calculated based on the NDVI using the formula found in a literature review (Table 3).
P Total vegetation PM2.5 removal can be estimated as [34]:
Q z = Q d × S
where Q z (μg) is the total amount of PM2.5 removed by each type of vegetation and S (m2) is the area of each type of vegetation, which was extracted and counted by CLCD.

2.4.2. Simulating of PM2.5 Removal Rate

The removal rate ( P i ) of PM2.5 from vegetation foliage was calculated as [24]:
P = Q z Q z + E
E (μg) was the total amount of PM2.5 in the atmosphere calculated as [37]:
E = C × B L H × A × 365 × 24
where BLH (m) is the height of the atmospheric boundary layer. The average value was calculated using the spatial distribution. A (m2) is the area of the region.

2.4.3. Identification of Coldspots and Hotspots

The spatial distribution of PM2.5 concentration in this region is uneven. PM2.5 removal in spatial heterogeneity due to different regional vegetation types. To accurately identify the risk areas of air pollution, Gi*statistics was employed to spatially identify the coldspots and hotspots of PM2.5 concentration and PM2.5 removal. Meanwhile, this study explored the spatial match between PM2.5 concentration and PM2.5 removal based on intersect analysis. Gi* is an indicator of local autocorrelation, which incorporated each adjacent raster into the calculation. This approach could visualize the spatial clustering patterns of hotspots and coldspots. Features with high z-scores and low p-values indicated statistically significant hotspots. Conversely, features with low negative z-scores and small p-values indicated statistically significant coldspots [38,39].

2.4.4. GeoDetector

Seven indexes including the NDVI, Vegetation Type (VT), the Digital Elevation Model (DEM), Precipitation (Pre), Wind Speed (WS), Temperature (Tem), and Atmospheric Boundary Layer Height (BLH) were involved in quantifying PM2.5 removal. Based on the vegetation type data, each indicator was classified into 4 categories using the natural breakpoint method, and the natural meteorological drivers of PM2.5 removal were analyzed using the GeoDetector model.
The GeoDetector model is a new statistical method proposed by Wang Jinfeng et al. [40]. It can fully consider the degree of spatial dissimilarity between the dependent and independent variables, and it was widely used in driving factor analysis. The core of the method assumed that there is a certain similarity in the spatial distribution between variables if the change in the independent variable has an important effect on the dependent variable. GeoDetector can detect both numerical and qualitative data, and they can also detect the interaction of two factors on the dependent variable. This paper used two detectors of the software (http://www.geodetector.cn/, accessed on 26 January 2024):
Factor_detector: This was employed to investigate the existence of spatial heterogeneity and identify the factors causing such differences using the q-value, which ranges from 0 to 1. The q-value represents the extent to which X explains 100 × q% of Y. A higher q-value indicates more pronounced spatial heterogeneity.
Interaction_detector: This was used to detect the relationship between the independent variables of the risk factors. First, the q-values of the two factors and the q-values of the combined polygon were calculated. Then the three values are compared and analyzed, and finally, we can determine if the independent variables are truly independent or if there are broader interactions at play.

3. Results

3.1. Quantification of PM2.5 Removal and Identification of Risk Areas

3.1.1. Spatial and Temporal Distribution of PM2.5 Concentration and Removal

The annual PM2.5 concentration showed an overall increasing and then decreasing trend from 2000 to 2021. The area with a relatively high PM2.5 concentration was distributed in the center of the Fenwei Plain (Figure 3a–c), perhaps due to its low topographic conditions, sparse vegetation, and large numbers of artificial surfaces. In comparison, the mountainous region southwest of Baoji and the central mountainous area of Lüliang have relatively low PM2.5 concentrations, meaning they have relatively better air quality. This is likely related to the presence of large vegetation areas in these regions. The average of the three-year PM2.5 concentration far exceeded the PM2.5 concentration limits (5 μg/m3) set by the World Health Organization (WHO). The maximum PM2.5 values in 2000 and 2010 were mildly polluted according to China’s PM2.5 pollution standards (35 μg/m3) [41] (Figure 3g).

3.1.2. Effect of Different Vegetation Types on PM2.5 Removal

The level of PM2.5 removal per unit area of vegetation had a high consistency with the vegetation types (Figure 3d–f). The removal of each vegetation type in order of magnitude is forest > shrub > grassland > cropland (Table 4). Both the maximum and average values of PM2.5 removal per unit area increased in the given time (Figure 3h). The increase in 2010 compared with 2000 was mainly due to the larger concentration of PM2.5, and the significant increase in 2021 was mainly due to the increased wind speed and precipitation. The total amount of PM2.5 removed by different vegetation types was different from the amount removed per unit area. Forests remain the vegetation type that removes the most PM2.5. Due to the large proportion of cultivated land, the total removal of PM2.5 in the FenWei Plain is higher than that of shrubs and grasslands. The highest capacity for the removal of PM2.5 was in forests, followed by cropland (Table 4).

3.1.3. Comparison of PM2.5 Removal Effect in Different Cities

The removal rate of PM2.5 in the Fenwei Plain was 0%~0.634% in 2000, 0%~0.899% in 2010, and 0%~0.919% in 2021, totaling 71,187.01 t, 96,570.89 t, and 108,973.47 t, respectively. The removal rate of PM2.5 in forest land accounted for the largest proportion, up to more than 90%, and the year average removal rate was 0.288% (Figure 4). Since there are large forest area in Baoji, its total annual PM2.5 removal and removal rate was the largest, followed by Luoyang. The lowest total amount of PM2.5 removed in 2000 and 2010 was in Xianyang, and in 2021, it was in Tongchuan.

3.1.4. Identification of Risk Areas

A certain spatial mismatch between PM2.5 concentration and PM2.5 removal was shown in the study area (Figure 5). The high PM2.5 concentration combined with low PM2.5 removal (high pollution with low PM2.5 removal) accounted for the largest proportion (approximately 50%), mainly distributed in the plains area. In the eastern middle part of the study area, there are some areas with PM2.5 hotspots superimposed on PM2.5 removal hotspots (high pollution with high PM2.5 removal). Areas with PM2.5 concentration coldspots were superimposed on PM2.5 removal coldspots (low pollution with low PM2.5 removal), mainly in the western mountainous areas, with a significant decrease in the proportion in 2021 compared with the previous two years. The PM2.5 concentration coldspots combined with PM2.5 removal hotspots are mainly distributed in mountainous areas (low pollution with high PM2.5 removal).

3.2. Influencing Factors of PM2.5 Removal Services in the Fenwei Plain

All factors passed statistical significance tests (p < 0.01). The order of explanatory power of each factor for PM2.5 removal in order from strong to weak in 2000, 2010, and 2021 was Vegetation Types, NDVI, DEM, Precipitation, Temperature, Wind Speed, and Boundary Layer Height. Among them, vegetation type and NDVI had explanatory power for PM2.5 removal greater than 60%. The others showed slightly lower explanatory power, and the explanatory power of the annual mean wind speed decreased to 0.001 in 2021. This indicated that the spatial distribution of annual mean wind speed and PM2.5 removal in that year had low similarity, so the effect was small, whereas the similarity of the distributions in 2000 and 2010 was relatively high (Figure 6). Overall, vegetation has a significant effect on PM2.5 removal services, followed by elevation as PM2.5 and vegetation cover in the Fenwei Plain region were strongly influenced by topographic conditions.
PM2.5 removal was affected by multiple external factors, which have interactions with each other. In this study, the joint effect of any two factors would increase the effect on the removal of PM2.5, mainly manifested as two-factor enhancement and nonlinear enhancement of two types, and the joint effect made the explanatory power significantly larger. As shown in Figure 7, the green circle was the nonlinear enhancement factor. Because vegetation type and NDVI have stronger explanatory ability for PM2.5 removal, the explanatory ability of the combination with other factors is also larger. In conclusion, the removal of PM2.5 is a combination of many factors, rather than a single factor that plays a decisive role.

4. Discussion

4.1. Effect Analysis of Vegetation PM2.5 Purification Services

The vegetation in the Fenwei Plain possesses distinct abilities to purify PM2.5. Forests possess the highest removal capacity for PM2.5 due to their higher deposition rate and larger distribution area, accounting for over 96% of the total removal. Among the 11 cities in the Fenwei Plain, the removal rates ranged from 0 to 0.63%. The PM2.5 removal rates in this region align closely with the findings of similar studies in the world. The forest in Jiangsu Province has an average PM2.5 removal rate of approximately 0.03% [37]. In Beijing, urban green spaces have a PM2.5 removal rate ranging from 0.07% to 0.19% [42]. Additionally, research conducted abroad also demonstrated that San Francisco experienced an average annual air quality improvement of 0.05%, while Atlanta saw a larger improvement of 0.24% [24]. PM2.5 deposition rates vary across vegetation types, with trees generally having a greater effect. Existing research suggests that coniferous forests, in particular, play a significant role in retaining PM2.5 due to their higher deposition rate [43]. In Taipei City in 2016, the contribution of vegetation to PM2.5 removal was found to be highest in mixed forests, followed by broadleaf forests, coniferous forests, greenfield trees, and street trees [34]. Few researchers are focusing on the deposition rate of grassland and cropland, although some studies have indicated that they have a certain level of retention effect on particulate matter [44].
Various factors influence the dispersion and accumulation of air pollutants in the Fenwei Plain, particularly PM2.5. The findings showed that vegetation and NDVI exert the most significant influence on PM2.5 removal. The type and amount of vegetation play a significant role in the rate at which vegetation captures and removes PM2.5 particles. Previous studies have shown a connection between Net Primary Productivity (NPP) and the concentration of PM2.5. This includes selecting tree species with higher sedimentation rates or net productivity, optimizing the layout of urban green spaces, and implementing effective vegetation management practices. These measures contribute greatly to enhancing regional air quality and maintaining ecological balance, and nature-based air purification services hold great potential for future air pollution management [37]. In addition, the spatial distribution of PM2.5 concentrations and PM2.5 removal cold hotspots in the Fenwei Plain show obvious spatial mismatch patterns. Purification of PM2.5 is lower in vegetation at lower elevations, where PM2.5 pollution is more severe and the vegetation distribution is smaller. Undeniably, the key to effectively solving air pollution still remains controlling the emission of PM2.5 or minimizing the PM2.5 pollution by treating it through physical and chemical methods. However, the emission reduction capacity of traditional solutions is gradually reduced, and upgrading the industrial structure and energy transformation are arduous tasks. However, nature-based solutions to air pollution are also a direction that could be explored in depth in the future [13].
Additionally, topography plays a substantial role in air purification. However, meteorological factors have a comparatively small direct impact on PM2.5 removal but are intricately connected to the rate of PM2.5 reduction. In northwest China, wind speed and temperature have a more pronounced effect on PM2.5 levels [45,46]. Typically, elevated wind speeds under normal conditions promote pollutant dispersion, resulting in a gradual decline in PM2.5 concentration [47]. However, within the Fenwei Plain, distinctive topographical features give rise to diminished wind speeds, impeding pollutant dispersion and potentially exacerbating pollutant aggregation. Additionally, wind speed affects the deposition rate of PM2.5 by vegetation, with higher wind speeds leading to a higher relative deposition rate. There are generally two types of PM2.5 removal in the natural environment, one is dry deposition selected in this study and the other is wet deposition. Notably, it is only intense precipitation that exerts a potent scouring effect on PM2.5 concentrations, leading to a substantial reduction in PM2.5 levels. For the meteorological conditions of the Fenwei Plain, dry deposition assumes a more prominent role. Vegetation loses its trapping capacity after a certain duration of the dry deposition and precipitation is needed to purify the vegetation for recovery.

4.2. Policy Recommendations

Currently, certain success has been achieved in air pollution control in China. But when urban air quality reaches moderate or low pollution levels, landscape regulation and optimization have been recognized as an effective way to mitigate air pollution [48]. In areas with high population densities, there is a growing demand for air improvement. Hence, it is necessary to implement more effective air purification measures to protect public health in the future [49].
In the long term, the services provided by ecosystems have a significant positive impact on the removal of PM2.5 particles from the air [17]. Nature-based solutions have the potential to enhance ecosystem services, resulting in significant economic and social advantages when compared to traditional solutions. It helps in conserving natural resources and managing intangible assets in China [37]. Increasing the area of green space is also an effective way to achieve collaborative control of various air pollutants [50] and the significant removal capacity of forests for PM2.5. It also has a good temperature regulation effect. The proper allocation of forests and green spaces can potentially reduce energy consumption, thereby delaying climate change [51,52]. Therefore, in areas with high pollution and high vegetation coverage, priority should be given to energy transformation and industrial structure optimization. In areas with high pollution and low vegetation coverage, while considering energy emission reductions, it is also necessary to consider the reasonable allocation of natural resources [53]. These could give full play to the air purification role of vegetation. In the process of urban expansion, the rational design of green space layout needs to be considered. Planning policies should also be based on natural laws, taking into account local geographical conditions and dominant tree species. In addition, urban construction should focus on strengthening green infrastructure, such as the rational use of shrubs and hedges for vertical greening and the construction of green roofs [54]. In mountainous areas with high vegetation cover, forests and grasslands need to be restored and protected. Those can further consolidate the results of governance [55].
Above all, nature-based solutions to air pollution are of great significance to developing countries. In future air pollution control, policymakers need to break away from traditional thinking and entrenched patterns and implement diversified and innovative measures. In particular, regionally coordinated development should be integrated into urban planning and management, which is crucial to jointly promote regional pollution prevention and control precise pollution management.

4.3. Limitations

Nonetheless, there are still some uncertainties that need to be addressed in further research. Firstly, the deposition rate of forests and shrubs, as well as the resuspension rate, were derived from the existing study. The sedimentation rates of grassland and cropland were determined using the minimum value of the vegetation PM2.5 sedimentation rate. Additionally, LAI was calculated using an empirical function. These factors could have an effect on the variability of the outcomes; given this, the subsequent stage would be focusing on the setting of the parameters. In addition, the spatial resolution of NDVI and vegetation distribution is 30 m, which could not accurately depict the distribution of green areas in spaces that are smaller than 600 m2. Higher-resolution data should be explored in future studies to parse the PM2.5 removal status of vegetation within the city in a more in-depth manner. Secondly, the impact of vegetation and its natural and ecological factors on PM2.5 purification services was taken as the focus. Socio-economic factors such as industrial emissions, transportation, energy, population, and GDP were not taken into account, to explore the socio-ecological effects of vegetation air purification services. Nevertheless, nature-based solutions for mitigating air pollution represent a promising avenue of progress due to their substantial socio-economic benefits. A primary benefit lies in the augmentation of ecosystem services, which can positively influence the environment. Moreover, these solutions can inspire novel research on air purification services and foster a deeper understanding of how ecosystems can retain and absorb air pollutants.

5. Conclusions

The framework in this research was constructed to quantify the PM2.5 purification services of vegetation and explore the natural drivers of PM2.5 removal at the regional scale. The expression of the degree of spatial matching between the PM2.5 concentration and PM2.5 removal has also been visualized. The results showed that spatial heterogeneity of PM2.5 purification services provided by vegetation exists in the Fenwei Plain due to the uneven distribution of vegetation. Meanwhile, vegetation type and NDVI significantly affected the PM2.5 removal in the Fenwei Plain. PM2.5 removal is high in mountainous areas with high vegetation coverage. PM2.5 removal is low in plains and urban areas with high emissions. The air-purifying capacity of vegetation in densely populated areas is limited, resulting in a spatial mismatch between PM2.5 concentration and PM2.5 removal. The under-supply of PM2.5 purification services from vegetation in areas with high pollution concentrations is close to 50%, indicating the critical role of vegetation in regional air pollution management. The implementation of regional air pollution control measures needs to be accompanied by the prioritization of improving green infrastructure and optimizing the layout of green spaces in urban development. This is a potential future means of addressing air pollution and a scientific basis for regional ecological construction.

Author Contributions

Conceptualization, Y.L. and L.Z.; methodology, Y.L. and L.Z.; visualization, Y.L. and L.Z.; formal analysis, Y.L.; writing—original draft, Y.L.; writing—review and editing, Y.L., W.Z., L.Z., X.L., L.P., Y.S., Z.W., L.J. and H.W.; supervision, W.Z.; funding acquisition, L.Z., Y.L. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (2024JC-YBMS-196), National Natural Science Foundation of China (41871187), and the Excellent Graduate Training Program of Shaanxi Normal University (LHRCCX23170).

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors acknowledge the journal editors and anonymous reviewers for their hard work, which undoubtedly led to a substantial improvement in the quality of this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. McDuffie, E.E.; Martin, R.V.; Spadaro, J.V.; Burnett, R.; Smith, S.J.; O’Rourke, P.; Hammer, M.S.; Van Donkelaar, A.; Bindle, L.; Shah, V.; et al. Source Sector and Fuel Contributions to Ambient PM2.5 and Attributable Mortality across Multiple Spatial Scales. Nat. Commun. 2021, 12, 3594. [Google Scholar] [CrossRef]
  2. Chen, H.-S.; Lin, Y.-C.; Chiueh, P.-T. Nexus of Ecosystem Service-Human Health-Natural Resources: The Nature-Based Solutions for Urban PM2.5 Pollution. Sustain. Cities Soc. 2023, 91, 104441. [Google Scholar] [CrossRef]
  3. Sweileh, W.M. Bibliometric Analysis of Scientific Publications on “Sustainable Development Goals” with Emphasis on “Good Health and Well-Being” Goal (2015–2019). Glob. Health 2020, 16, 68. [Google Scholar] [CrossRef] [PubMed]
  4. Bailey, J.; Ramacher, M.O.P.; Speyer, O.; Athanasopoulou, E.; Karl, M.; Gerasopoulos, E. Localizing SDG 11.6.2 via Earth Observation, Modelling Applications, and Harmonised City Definitions: Policy Implications on Addressing Air Pollution. Remote Sens. 2023, 15, 1082. [Google Scholar] [CrossRef]
  5. Junwu, D.; Yanhui, W.; Lili, W.; Wenji, Z.; Chong, H. Assessment of PM2.5 Exposure Risk towards SDG Indicator 11.6.2—A Case Study in Beijing. Sustain. Cities Soc. 2022, 82, 103864. [Google Scholar]
  6. Li, C.; Managi, S. Spatial Variability of the Relationship between Air Pollution and Well-Being. Sustain. Cities Soc. 2022, 76, 103447. [Google Scholar] [CrossRef]
  7. Shi, Y.; Matsunaga, T.; Yamaguchi, Y.; Li, Z.; Gu, X.; Chen, X. Long-Term Trends and Spatial Patterns of Satellite-Retrieved PM2.5 Concentrations in South and Southeast Asia from 1999 to 2014. Sci. Total Environ. 2018, 615, 177–186. [Google Scholar] [CrossRef]
  8. Son, J.-Y.; Lane, K.J.; Miranda, M.L.; Bell, M.L. Health Disparities Attributable to Air Pollutant Exposure in North Carolina: Influence of Residential Environmental and Social Factors. Health Place 2020, 62, 102287. [Google Scholar] [CrossRef] [PubMed]
  9. Yang, X.; Lin, H.; Yang, X.; Cai, Z.; Jiang, P. Analyzing Synergies and Efficiency of Reducing CO2 and Air Pollutants in the Case of China’s Three-Year Action Plan to Fight Air Pollution. Environ. Res. Lett. 2023, 18, 114028. [Google Scholar] [CrossRef]
  10. Cai, S.; Wang, Y.; Zhao, B.; Wang, S.; Chang, X.; Hao, J. The Impact of the “Air Pollution Prevention and Control Action Plan” on PM2.5 Concentrations in Jing-Jin-Ji Region during 2012–2020. Sci. Total Environ. 2017, 580, 197–209. [Google Scholar] [CrossRef]
  11. Zeng, Y.; Cao, Y.; Qiao, X.; Seyler, B.C.; Tang, Y. Air Pollution Reduction in China: Recent Success but Great Challenge for the Future. Sci. Total Environ. 2019, 663, 329–337. [Google Scholar] [CrossRef] [PubMed]
  12. Li, J.; Han, L.; Zhang, Z.; Zhou, W.; Li, W.; Qian, Y. Assessment on the reduction of atmospheric PM2.5 by urban forest and its ratio to the total PM2.5 pollutant in the Chinese major cities. Acta Ecol. Sin. 2023, 43, 2640–2649. [Google Scholar] [CrossRef]
  13. Gopalakrishnan, V.; Ziv, G.; Hirabayashi, S.; Bakshi, B.R. Nature-Based Solutions Can Compete with Technology for Mitigating Air Emissions Across the United States. Environ. Sci. Technol. 2019, 53, 13228–13237. [Google Scholar] [CrossRef] [PubMed]
  14. Villani, M.G.; Russo, F.; Adani, M.; Piersanti, A.; Vitali, L.; Tinarelli, G.; Ciancarella, L.; Zanini, G.; Donateo, A.; Rinaldi, M.; et al. Evaluating the Impact of a Wall-Type Green Infrastructure on PM10 and NOx Concentrations in an Urban Street Environment. Atmosphere 2021, 12, 839. [Google Scholar] [CrossRef]
  15. Riondato, E.; Pilla, F.; Sarkar Basu, A.; 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]
  16. Han, L.; Tan, X.; Zhou, W.; Li, W.; Qian, Y. Better Urban Vegetation Planning for Maximum Utility in Air Pollutant Reduction: A Theoretical Perspective and Preliminary Analysis in Chinese Cities. Sustain. Cities Soc. 2020, 62, 102377. [Google Scholar] [CrossRef]
  17. Luo, X.; Jiang, P.; Yang, J.; Jin, J.; Yang, J. Simulating PM2.5 Removal in an Urban Ecosystem Based on the Social-Ecological Model Framework. Ecosyst. Serv. 2021, 47, 101234. [Google Scholar] [CrossRef]
  18. Manes, F.; Marando, F.; Capotorti, G.; Blasi, C.; Salvatori, E.; Fusaro, L.; Ciancarella, L.; Mircea, M.; Marchetti, M.; Chirici, G.; et al. Regulating Ecosystem Services of Forests in Ten Italian Metropolitan Cities: Air Quality Improvement by PM10 and O3 Removal. Ecol. Indic. 2016, 67, 425–440. [Google Scholar] [CrossRef]
  19. Li, X.; Zhao, C. Can National Forest City Construction Mitigate Air Pollution in China? Evidence from a Quasi-Natural Experiment. Env. Geochem. Health 2023, 45, 3003–3024. [Google Scholar] [CrossRef]
  20. Zhang, R.; Ma, K. New Prospects to Systematically Improve the Particulate Matter Removal Efficiency of Urban Green Spaces at Multi-Scales. Forests 2023, 14, 175. [Google Scholar] [CrossRef]
  21. Ozdemir, H. Mitigation Impact of Roadside Trees on Fine Particle Pollution. Sci. Total Environ. 2019, 659, 1176–1185. [Google Scholar] [CrossRef] [PubMed]
  22. Vieira, J.; Matos, P.; Mexia, T.; Silva, P.; Lopes, N.; Freitas, C.; Correia, O.; Santos-Reis, M.; Branquinho, C.; Pinho, P. Green Spaces Are Not All the Same for the Provision of Air Purification and Climate Regulation Services: The Case of Urban Parks. Environ. Res. 2018, 160, 306–313. [Google Scholar] [CrossRef] [PubMed]
  23. Escobedo, F.J.; Nowak, D.J. Spatial Heterogeneity and Air Pollution Removal by an Urban Forest. Landsc. Urban Plan. 2009, 90, 102–110. [Google Scholar] [CrossRef]
  24. Nowak, D.J.; Hirabayashi, S.; Bodine, A.; Hoehn, R. Modeled PM2.5 Removal by Trees in Ten U.S. Cities and Associated Health Effects. Environ. Pollut. 2013, 178, 395–402. [Google Scholar] [CrossRef]
  25. Selmi, W.; Weber, C.; Rivière, E.; Blond, N.; Mehdi, L.; Nowak, D. Air Pollution Removal by Trees in Public Green Spaces in Strasbourg City, France. Urban For. Urban Green. 2016, 17, 192–201. [Google Scholar] [CrossRef]
  26. Cao, J.J.; Cui, L. Current Status, Characteristics and Causes of Particulate Air Pollution in the Fenwei Plain, China: A Review. JGR Atmos. 2021, 126, e2020JD034472. [Google Scholar] [CrossRef]
  27. Wang, D.; Hu, J.; Xu, Y.; Lv, D.; Xie, X.; Kleeman, M.; Xing, J.; Zhang, H.; Ying, Q. Source Contributions to Primary and Secondary Inorganic Particulate Matter during a Severe Wintertime PM2.5 Pollution Episode in Xi’an, China. Atmos. Environ. 2014, 97, 182–194. [Google Scholar] [CrossRef]
  28. Hu, X.; Zhou, T.; Cai, S. The Episodic Geomorphological-Sedimentary Evolution of Different Basins in the Fenwei Graben and Its Tectonic Implication. J. Geogr. Sci. 2017, 27, 1359–1375. [Google Scholar] [CrossRef]
  29. Yang, J.; Huang, X. The 30m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
  30. Wei, J.; Li, Z.; Cribb, M.; Huang, W.; Xue, W.; Sun, L.; Guo, J.; Peng, Y.; Li, J.; Lyapustin, A.; et al. Improved 1 Km Resolution PM2.5 Estimates across China Using Enhanced Space–Time Extremely Randomized Trees. Atmos. Chem. Phys. 2020, 20, 3273–3289. [Google Scholar] [CrossRef]
  31. Wei, J.; Li, Z.; Lyapustin, A.; Sun, L.; Peng, Y.; Xue, W.; Su, T.; Cribb, M. Reconstructing 1-Km-Resolution High-Quality PM2.5 Data Records from 2000 to 2018 in China: Spatiotemporal Variations and Policy Implications. Remote Sens. Environ. 2021, 252, 112136. [Google Scholar] [CrossRef]
  32. Nowak, D.J.; Crane, D.E.; Stevens, J.C. Air Pollution Removal by Urban Trees and Shrubs in the United States. Urban For. Urban Green. 2006, 4, 115–123. [Google Scholar] [CrossRef]
  33. She, X.; Gao, Z.; Zhang, B. PM2.5 removal service of green spaces in Shanghai based on the dust retention simulation on urban vegetation. Acta Ecol. Sin. 2020, 40, 2599–2608. [Google Scholar] [CrossRef]
  34. Chen, H.-S.; Lin, Y.-C.; Chiueh, P.-T. High-Resolution Spatial Analysis for the Air Quality Regulation Service from Urban Vegetation: A Case Study of Taipei City. Sustain. Cities Soc. 2022, 83, 103976. [Google Scholar] [CrossRef]
  35. Wang, K.; Zhang, Y.; Liu, X. Modeled Particulate Matters Removal by Urban Green Lands in Beijing. Environ. Sci. Technol. 2020, 43, 121–129. [Google Scholar] [CrossRef]
  36. Sun, P.; Liu, S.; Liu, J.; Li, Z.; Lin, Y.; Jiang, H. Derivation and validation of leaf area index maps using NDVI data of different resolution satellite imageries. Acta Ecol. Sin. 2006, 11, 3826–3834. [Google Scholar]
  37. Lin, Y.; Yuan, X.; Zhai, T.; Wang, J. Effects of Land-Use Patterns on PM2.5 in China’s Developed Coastal Region: Exploration and Solutions. Sci. Total Environ. 2020, 703, 135602. [Google Scholar] [CrossRef]
  38. Getis, A.; Ord, J.K. The Analysis of Spatial Association by Use of Distance Statistics. In Perspectives on Spatial Data Analysis; Anselin, L., Rey, S.J., Eds.; Advances in Spatial Science; Springer: Berlin/Heidelberg, Germany, 2010; pp. 127–145. ISBN 978-3-642-01976-0. [Google Scholar]
  39. Li, Y.; Zhang, L.; Yan, J.; Wang, P.; Hu, N.; Cheng, W.; Fu, B. Mapping the hotspots and coldspots of ecosystem services in conservation priority setting. J. Geogr. Sci. 2017, 27, 681–696. [Google Scholar] [CrossRef]
  40. Wang, J.; Xu, C. Geodetector: Principle and prospective. Acta Geogr. Sin. 2017, 72, 116–134. [Google Scholar] [CrossRef]
  41. Lei, R.; Nie, D.; Zhang, S.; Yu, W.; Ge, X.; Song, N. Spatial and Temporal Characteristics of Air Pollutants and Their Health Effects in China during 2019–2020. J. Environ. Manag. 2022, 317, 115460. [Google Scholar] [CrossRef]
  42. Xiao, Y.; Wang, S.; Li, N.; Xie, G.; Lu, C.; Zhang, B.; Zhang, C. Atmospheric PM2.5 removal by green spaces in Beijing. Resour. Sci. 2015, 37, 1149–1155. [Google Scholar]
  43. Jones, B.A. Mountain Pine Beetle Impacts on Health through Lost Forest Air Pollutant Sinks. Forests 2021, 12, 1785. [Google Scholar] [CrossRef]
  44. Giess, P.; Goddard, A.J.H.; Shaw, G. Factors Affecting Particle Resuspension from Grass Swards. J. Aerosol Sci. 1997, 28, 1331–1349. [Google Scholar] [CrossRef]
  45. Chen, Z.; Xie, X.; Cai, J.; Chen, D.; Gao, B.; He, B.; Cheng, N.; Xu, B. Understanding Meteorological Influences on PM2.5 Concentrations across China: A Temporal and Spatial Perspective. Atmos. Chem. Phys. 2018, 18, 5343–5358. [Google Scholar] [CrossRef]
  46. Jin, H.; Chen, X.; Zhong, R.; Liu, M. Influence and Prediction of PM2.5 through Multiple Environmental Variables in China. Sci. Total Environ. 2022, 849, 157910. [Google Scholar] [CrossRef] [PubMed]
  47. Zhang, B.; Jiao, L.; Xu, G.; Zhao, S.; Tang, X.; Zhou, Y.; Gong, C. Influences of Wind and Precipitation on Different-Sized Particulate Matter Concentrations (PM2.5, PM10, PM2.5–10). Meteorol. Atmos. Phys. 2018, 130, 383–392. [Google Scholar] [CrossRef]
  48. Li, J.; Han, L.; Zhou, W.; Li, W.; Qian, Y.; Zhang, Z. Uncertainties in Research between Urban Landscape and Air Quality: Summary, Demonstration, and Expectation. Landsc. Ecol. 2023, 38, 2475–2485. [Google Scholar] [CrossRef]
  49. Yang, D.; Wang, X.; Xu, J.; Xu, C.; Lu, D.; Ye, C.; Wang, Z.; Bai, L. Quantifying the Influence of Natural and Socioeconomic Factors and Their Interactive Impact on PM2.5 Pollution in China. Environ. Pollut. 2018, 241, 475–483. [Google Scholar] [CrossRef] [PubMed]
  50. 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]
  51. Akbari, H. Shade Trees Reduce Building Energy Use and CO2 Emissions from Power Plants. Environ. Pollut. 2002, 116, S119–S126. [Google Scholar] [CrossRef]
  52. Baró, F.; Chaparro, L.; Gómez-Baggethun, E.; Langemeyer, J.; Nowak, D.J.; Terradas, J. Contribution of Ecosystem Services to Air Quality and Climate Change Mitigation Policies: The Case of Urban Forests in Barcelona, Spain. AMBIO 2014, 43, 466–479. [Google Scholar] [CrossRef] [PubMed]
  53. 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] [PubMed]
  54. Fletcher, D.H.; Likongwe, P.J.; Chiotha, S.S.; Nduwayezu, G.; Mallick, D.; Uddin, M.d.N.; Rahman, A.; Golovátina-Mora, P.; Lotero, L.; Bricker, S.; et al. Using Demand Mapping to Assess the Benefits of Urban Green and Blue Space in Cities from Four Continents. Sci. Total Environ. 2021, 785, 147238. [Google Scholar] [CrossRef] [PubMed]
  55. Babí Almenar, J.; Petucco, C.; Sonnemann, G.; Geneletti, D.; Elliot, T.; Rugani, B. Modelling the Net Environmental and Economic Impacts of Urban Nature-Based Solutions by Combining Ecosystem Services, System Dynamics and Life Cycle Thinking: An Application to Urban Forests. Ecosyst. Serv. 2023, 60, 101506. [Google Scholar] [CrossRef]
Forests 15 00391 g001
Figure 1. Overview of the Fenwei Plain. The spatial location (a); land cover in 2021 (b); land cover changes in 2000, 2010, and 2021 (c); administrative divisions (d); and topographical features (e).
Figure 1. Overview of the Fenwei Plain. The spatial location (a); land cover in 2021 (b); land cover changes in 2000, 2010, and 2021 (c); administrative divisions (d); and topographical features (e).
Forests 15 00391 g001
Forests 15 00391 g002
Figure 2. A framework for the study of PM2.5 purification services for regional vegetation and their natural influence factors.
Figure 2. A framework for the study of PM2.5 purification services for regional vegetation and their natural influence factors.
Forests 15 00391 g002
Forests 15 00391 g003
Figure 3. Spatial distribution of PM2.5 concentration (a–c) and PM2.5 removal per unit area (d–f) in 2000, 2010, and 2021 (LY: Luoyang; SMX: Sanmenxia; JZ: Jinzhong; LF: Linfen; LL: Lüliang; YC: Yuncheng; BJ: Baoji; TC: Tongchuan; WN: Weinan; XA: Xi’an; XY: Xianyang). Comparison of statistical values of PM2.5 concentration (g) and comparison of statistical values of PM2.5 removal per unit area (h) in 2000, 2010, and 2021.
Figure 3. Spatial distribution of PM2.5 concentration (a–c) and PM2.5 removal per unit area (d–f) in 2000, 2010, and 2021 (LY: Luoyang; SMX: Sanmenxia; JZ: Jinzhong; LF: Linfen; LL: Lüliang; YC: Yuncheng; BJ: Baoji; TC: Tongchuan; WN: Weinan; XA: Xi’an; XY: Xianyang). Comparison of statistical values of PM2.5 concentration (g) and comparison of statistical values of PM2.5 removal per unit area (h) in 2000, 2010, and 2021.
Forests 15 00391 g003
Forests 15 00391 g004
Figure 4. Annual total PM2.5 removal (a) and PM2.5 removal rate (b) in 11 cities.
Figure 4. Annual total PM2.5 removal (a) and PM2.5 removal rate (b) in 11 cities.
Forests 15 00391 g004
Forests 15 00391 g005
Figure 5. Spatial matching relationship between PM2.5 concentration and PM2.5 removal in 2000, 2010, and 2021.
Figure 5. Spatial matching relationship between PM2.5 concentration and PM2.5 removal in 2000, 2010, and 2021.
Forests 15 00391 g005
Forests 15 00391 g006
Figure 6. Analysis of the impact of natural factors on PM2.5 removal.
Figure 6. Analysis of the impact of natural factors on PM2.5 removal.
Forests 15 00391 g006
Forests 15 00391 g007
Figure 7. Effect of natural factor interactions on PM2.5 removal (green circles indicate that the two factors acting together exhibit nonlinear enhancement, and the absence of circles indicates two-factor enhancement).
Figure 7. Effect of natural factor interactions on PM2.5 removal (green circles indicate that the two factors acting together exhibit nonlinear enhancement, and the absence of circles indicates two-factor enhancement).
Forests 15 00391 g007
Table 1. Datasets, sources, and calculated parameter data sources used to analyze PM2.5 purification services of vegetation in the Fenwei Plain.
Table 1. Datasets, sources, and calculated parameter data sources used to analyze PM2.5 purification services of vegetation in the Fenwei Plain.
Data ClassificationData NameSpatial Resolution
CLCD2000, 2010, 2021
Land use data		30 m
Vegetation data2000, 2010, 2021 NDVI30 m
PM2.5 data2000, 2010, 2021
Annual average
concentration of PM2.5		1000 m
Meteorological dataAverage annual wind speed-
Average daily precipitation-
Average annual temperature-
Atmospheric boundary
layer height		0.1°
Terrain dataDEM data (ASTER DEM v3)30 m
Table 2. PM2.5 deposition rate (cm/s) of different vegetation types [24,34].
Table 2. PM2.5 deposition rate (cm/s) of different vegetation types [24,34].
Wind Speed (m/s)12345678
Vegetation Types
Mixed Forest0.020.2850.5450.640.7350.830.9251.02
Shrub0.030.240.450.550.660.760.860.96
Grassland0.0060.0120.0180.0220.0250.0290.0560.082
Cropland0.0060.0120.0180.0220.0250.0290.0560.082
Resuspension rate0.0250.0290.0320.0360.0390.0590.0790.099
Table 3. Calculation formula of LAI for different vegetation types [35,36].
Table 3. Calculation formula of LAI for different vegetation types [35,36].
Vegetation TypesRegression Equation
ForestLAI = 4.689NDVI/(1.818 − NDVI)
ShrubLAI = 6.211NDVI − 1.088
GrasslandLAI = 3.227NDVI/NDVIavg
CroplandLAI = 8.547NDVI − 0.932
Table 4. Annual PM2.5 removal of different vegetation types in the Fenwei Plain.
Table 4. Annual PM2.5 removal of different vegetation types in the Fenwei Plain.
Year 200020102021
Vegetation Type Removal (t)Removal Rate (%)Removal (t)Removal Rate (%)Removal (t)Removal Rate (%)
Cropland1375.800.0041731.020.0041763.430.007
Forest68,287.130.17993,504.700.236106,175.110.424
Shrub846.920.002617.020.002421.540.002
Grassland677.170.002718.150.002613.400.002
Sum71,187.010.18696,570.890.243108,973.470.435
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.

Share and Cite

MDPI and ACS Style

Liu, Y.; Zhao, W.; Zhang, L.; Li, X.; Peng, L.; Wang, Z.; Song, Y.; Jiao, L.; Wang, H. An Assessment Framework for Mapping the Air Purification Service of Vegetation at the Regional Scale. Forests 2024, 15, 391. https://0-doi-org.brum.beds.ac.uk/10.3390/f15020391

AMA Style

Liu Y, Zhao W, Zhang L, Li X, Peng L, Wang Z, Song Y, Jiao L, Wang H. An Assessment Framework for Mapping the Air Purification Service of Vegetation at the Regional Scale. Forests. 2024; 15(2):391. https://0-doi-org.brum.beds.ac.uk/10.3390/f15020391

Chicago/Turabian Style

Liu, Yu, Wudong Zhao, Liwei Zhang, Xupu Li, Lixian Peng, Zhuangzhuang Wang, Yongyong Song, Lei Jiao, and Hao Wang. 2024. "An Assessment Framework for Mapping the Air Purification Service of Vegetation at the Regional Scale" Forests 15, no. 2: 391. https://0-doi-org.brum.beds.ac.uk/10.3390/f15020391

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop