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Relationship between types of urban forest and PM2.5 capture at three growth stages of leaves
J O U RN A L OF E N V I RO N ME N TA L SC IE N CE S 2 7 (2 0 1 5) 3 3– 4 1
Available online at www.sciencedirect.com
ScienceDirect www.journals.elsevier.com/journal-of-environmental-sciences
Relationship between types of urban forest and PM2.5 capture at three growth stages of leaves Thithanhthao Nguyen1,3 , Xinxiao Yu1,⁎, Zhenming Zhang2 , Mengmeng Liu1 , Xuhui Liu1 1. College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China. E-mail: [email protected] 2. College of Nature Conservation, Beijing Forestry University, Beijing 100083, China 3. Science and Technology Department, Binh Duong Province, Viet Nam
AR TIC LE I N FO
ABS TR ACT
Article history:
Particulate matter diameter ≤ 2.5 μm (PM2.5) causes direct harm to human health. Finding
Received 7 January 2014
forms of urban forest systems that with the ability to reduce the amount of particulate
Revised 10 March 2014
matter in air effectively is the aim of this study. Five commonly cultivated kinds of urban
Accepted 17 April 2014
forest types were studied in Beijing city at three stages of leaf growth. Results show that the urban forest system is capable of storing and capturing dust from the air. The types of shrubs and broadleaf trees that have the ability to capture PM2.5 from the air are most
Keywords:
effective when leaves have fully developed. In the leafless season, the conifer and mixed
Particulate matter
tree types are the most effective in removing dust from the air. For all kinds of forest
PM2.5
types and stages of leaf growth, the PM2.5 concentration is highest in the morning but lower
Type of urban forest
in the afternoon and evening. Grassland cannot control particles suspended in the air,
Growth stages of leaves
but can reduce dust pollution caused by dust from the ground blown by the wind back into the air. © 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
Introduction In recent years large-scale urbanization and industrialization has rapidly increased, causing natural forest decline and even forest loss in many parts of the world, and bringing more people into contact with air pollution than ever before. The problem of particulate matter (PM) is a leading environmental concern in most major cities across the world. Beijing city (China) is among the areas in which aerosol particulate pollution is the highest in the world. According to NASA-Released Images of Beijing Air Pollution in China by politicolonel.com on January 15th, 2013, China was shown to be smothered by a layer of haze. The gray and yellow-tinged clouds visible on the map are areas heavily affected by air pollution. At the time the image was taken, ground sensors at the U.S. Embassy in Beijing recorded PM2.5 measurements of 291. According to the World Health Organization, PM2.5 levels above 25 are considered unsafe (http://politicolonel.com, 2013). According
to figures released by the Beijing Environmental Protection Bureau on January 02, 2014, PM2.5 was 2.5 times the national standard. The intensity of major air pollutants remained much the same in 2013 as in the previous year. The PM2.5 reading in 2013 was on average 89.5 μg/m3. By comparison, the national standard stands at 35 μg/m3. PM2.5 was found to be the major pollutant – accounting for 77.8% – on most smoggy days. Southern Beijing saw much higher PM2.5 readings in 2013 compared with the north (http://www. chinadaily.com.cn/china/2014-01/03/content_17212753.htm). In addition, in recent years, the Chinese Government has undertaken various initiatives to reduce the PM pollution in the air. Trees are efficient scavengers of PM and forested areas are characterized by higher rates of dry deposition than other land types (McDonald et al., 2007). Trees can act as obstacles to dispersal of particle pollution and thus remove a significant amount of PM from the atmosphere (Janne et al., 2012). Trees can retain particles from the air (Beckett et al., 1998). Removal of dust
⁎ Corresponding author. E-mail: [email protected] (Xinxiao Yu).
http://dx.doi.org/10.1016/j.jes.2014.04.019 1001-0742/© 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
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particles by vegetation through interception from the atmosphere enhances air quality in urban areas (Beckett et al., 1998, 2000; Freer-Smith et al., 2004, 2005; Nowak et al., 2006; Litschke and Kuttler, 2008), near roadways and in public places (Smith and Staskawicz, 1977; Liu et al., 2004; Acero et al., 2012). Urban forests can strongly influence the physical/biological environment and mitigate many impacts of urban development by moderating climate, conserving energy, carbon dioxide and water, improving air quality, controlling rainfall runoff and flooding, lowering noise levels, harboring wildlife and enhancing the attractiveness of cities. Urban forests can be viewed as a ‘living technology’ — a key component of the urban infrastructure that helps maintain a healthy environment for urban dwellers (Dwyer et al., 1992; Nowak, 1994; Nowak et al., 2006; Wu et al., 2007). Many studies have analyzed air pollution removal by urban forests, reporting results for an entire city or modeling region (e.g., Nowak, 1994; Nowak et al., 2006, 2008; Yang et al., 2005; Santosh, 2012). Nowak et al. (2008) reported that, through pollution removal and other tree functions (e.g., air temperature reduction), urban trees can help improve air quality for many different air pollutants in cities, and consequently can help improve human health. It is clear that careful planning and design are critical to increasing the net benefits of trees and forests in urban environments. A change in species or location of trees with respect to each other or buildings and other components of the urban infrastructure can have a major impact on benefits and costs. In addition, some studies investigated the effectiveness of trees in accumulating airborne particles on foliage (Beckett et al., 2000; Freer-Smith et al., 2005; Yang et al., 2005; Zhou and Kang, 2008; Dzierźanowski et al., 2011; Sæbø et al., 2012; Popek et al., 2013), and recommended some efficient urban tree species to optimize the design of urban plantings to capture air pollutants (Dzierźanowski et al., 2011; Sæbø et al., 2012; Popek et al., 2013). Beckett et al. (1998, 2000) and Freer-Smith et al. (2004, 2005) provided input on the selection of species for urban areas to decrease the exposure of the urban population to air pollution, and identified species of trees and shrubs with low and high PM accumulation. Broad-leaved species with rough leaf surfaces are more efficient in capturing PM than those with smooth leaf surfaces. In addition, needles of coniferous trees, which produce a thicker epicuticular wax layer, are more effective in PM accumulation than broad-leaved species, and evergreen conifers have the potential to accumulate pollutants throughout the year. Dzierźanowski et al. (2011), Sæbø et al. (2012) and Popek et al. (2013) evaluated and compared the effectiveness of some trees commonly planted in urban areas in accumulating airborne particles on leaf surfaces and leaf wax layers. The results show that on foliage of all tested species, both surface PM and in-wax PM were deposited, but species with densely-haired leaves are more effective in capturing bigger particles while species characterized by a very thick layer of epicuticular waxes are much more effective at capturing smaller particles; leaf hair and wax content were traits with a positive correlation to PM accumulation. But the results of these studies were only limited to the choice of species used for the investigation and did not indicate the form of urban forest system having the ability to reduce the amount of PM in air effectively. No model for urban forest areas with different pollution reduction strategies for various air pollution types has been proposed. The study aims to compare the relationship between different urban forest types with the PM2.5 concentration in the air and diurnal variation of PM2.5 concentration at the three stages of leaf growth; and analyze the impact of the climate conditions on changes to the levels of PM2.5 in the air brought about by different kinds of urban forest types. The achieved results can be used as a basis for designing urban forest systems that are most effective in reducing particulate matter in the air.
1. Materials and methods 1.1. Study sites and experimental design The Beijing Olympic Forest Park is the organic component of the Beijing Olympic Green, and is located in the north of urban Beijing. The area of the park is about 680 ha, with about 480 ha of green area. It was constructed for the 2008 event, but its long-term target is to form a sustainable environment and serve as a multi-function public park. The design merges traditional Chinese landscape arts with ecological techniques to form an urban green lung and site for public recreation. The trees used for the study were located at the Centre of the North Garden of Beijing Olympic Forest Park. The field studies did not involve endangered or protected species and no specific permits were required for the described field studies. Five urban forest systems were designated: shrub and small tree forest system (deciduous trees), coniferous tree forest system (evergreen trees), broad-leaf tree forest system (deciduous trees), mixed broadleaf and coniferous tree model system, and grassland. These urban forest types are commonly grown in urban forest parks in Beijing and were selected for this study, and an area out of the forest was selected for doing comparative analysis (Control). The distance between the urban forest systems and the Control site was from 5 to 10 m. All trees and shrubs had already been growing in the selected locations for several years and were in good condition and healthy. They varied in size with diameters from 29.0 to 73.4 cm for broadleaf trees; 9.0 to 14.0 cm for coniferous trees; 7.0 to 11.0 cm for shrubs and height from 11.6 to 19.8 m; 4.0 to 6.0 m; 2.0 to 3.5 m; 0.3 to 0.8 m for trees, coniferous trees, shrubs and grass, respectively. Grassland (grass type): Iris ensata Thunb (Iris lactea Pall. Var chinense), Roof Iris (Iris tectorum), Violet orychophragmus (Orychophragmus violaceus), Herb of Denticulate Ixeris (Ixeris denticulata (Houtt.) Stebb.), White flower Lagopsis (Lagopsis supina (Steph.) Ikonn.-Gal. ex Knorr.) and other grass species are some herb species grown commonly in winter and summer. The area was 1500 m2. Shrubs and small forest trees (shrubs type): Flowering plum (Amygdalus triloba) and Flowering peach (Prunus persica Batsch. var. duplex Rehd) are becoming more popular due to their ornamental value and tolerance for the urban environment. The area was 1500 m2. Coniferous forest trees (conifers type): Chinese red pine (Pinus tabuliformis) and Arborvitae (Platycladus orientalis (Linn.) Franco) are commonly used evergreen trees used to maintain green coverage. The area was 1800 m2. Broadleaf forest trees (broadleaf trees type): Tomentosa (Tomentosa Populus tomentosa Carr) and Sophora japonica Linn are some species utilized to reforest along highways for their more comprehensive adaptability than other species. The area was about 3000 m2. Broadleaf and Coniferous mixed forest (mixed trees type): White wax (Fraxinus chinensis Roxb and Fraxinus bungeana), Sophora japonica Linn and Chinese red pine (Pinus tabuliformis) are mainly used in protected forest belts and to cover the
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space around residential areas, campuses, urban gardens and along major roads. The area was about 3000 m2.
Boothwyn, Pennsylvanis, USA) including temperature, wind speed, humidity and barometric pressure.
1.1.1. Control
1.2. Measurements
Control was an area about 1500 m2 in the Olympic park zone, 5–10 m from the forest types, land with no trees located next to the internal road, not affected by emission sources, and not much human involvement. Experimental conditions were similar to those of the forest (meteorological conditions, emission sources, low human activity). Air samples were collected at 5 urban forest types and the Control (Fig. 1). Sampling locations were located in the center of each system at a height of 1.5 m above the ground (human respiration level). The experiment was conducted for 3 consecutive days per type (3 replications) in the developmental period, with results given as the mean of replicate measurements. Sampling times were chosen as follows: each stage of leaf growth (late winter before the leaves grow, in the spring when the leaves are growing and early summer when the leaves mature, 2013) averaging repeated samplings every 3 days without rain; the sampling time of the day was early in the morning from 7:00 to 11:00, in the afternoon from 11:00 to 15:00 and in the evening from 15:00 to 19:00. The tested species were randomized within test fields that were relatively small in area. Thus, variations in the environment within the locations should not significantly influence the results. Hourly meteorological data for the 3 leaf stages were obtained by a Kestrel 4000-pocket weather tracker (Nielsen-Kellerman,
The PM2.5 concentration in air was collected at different urban forest system sites of the Beijing Olympic Forest Park North garden, using the total suspended particle measurement in the flow of an intelligent sampler TH-150C model, flow rate: 100 L/min (Westernization instrument (Beijing) Technology Co., Ltd., Beijing, China). The sampling procedure was given by MDHS-14/3 (2000); and a weighing method was used in this study (HJ 618-2011; TCVN 5067-1995). The Micro-quartz fiber filters, T293 (retention 90 mm) (Munktell, Sweden) used for the analysis were first dried for 240 min at 60°C in a DHG-9123A drying chamber (Biocotek, Shanghai, China) and then left in the weighing room to stabilize the humidity of the hygroscopic paper filters before weighing. After a further 24 hr, the first weighing was a determination of the mass of a clean filter (without dust on the filter, m1), the second step, weighing a filter with dust content (m2). A XS105DU balance (Readability: 0.01 mg/0.1 mg) (Mettler-Toledo International Inc., Greifensee, Switzerland) was used to weigh the filters. Repeat weighing of unexposed substrates over several days is a better guide to true performance. The weighed filter papers were placed in sealed plastic containers for field sampling. Each sample was labeled so that it could be uniquely identified, and sealed with its protective cover to prevent contamination.
Fig. 1 – Sample experiment field map layout. Source of map: www.map.baidu.com.
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One unused loaded sampler from each batch of ten prepared was retained as a blank; a minimum of three blanks were always kept. These were treated as far as possible in the same manner as those actually used for sampling, regarding transport to and from the sampling site, but did not have air drawn through them. The samples were transported to the laboratory in a container designed to prevent damage in transit, and labeled to ensure proper handling, then left in the weighing room with the conditioning procedure described above repeated before re-weighing. At the same time, weighed filter blanks had mass (b = mb2 − mb1) (TCVN 5067-1995). The concentration of PM2.5 in the air was calculated according to the following equation: C 4h ¼
1000 ðm2 −m1 −bÞ V0
where, C4h(mg/m3) is the dust concentration in 4 hr; m1, m2 (g) are the filter mass before and after sampling; b (g) is the blank filter mass; b = mb2-mb1, in, and Vo (m3 at standard state) is the air sample volume calculated as the product of volumetric intensity of uptake air flow and sampling time.
1.3. Statistical analysis Data were subjected to one-way and multi-way analysis of variance using StatGraphics Centurial XVI software (StatPoint Technologies, Inc., Warrenton, Virginia, USA). Significance of differences between mean values was tested using Tukey's honestly significant difference test (HSD) at α = 0.05. Values presented on bar charts are means, while values on dot charts are individual values with trend line and correlation coefficient (r). The latter two were calculated using Microsoft Excel (Microsoft Corp., Redmond, Washington, USA).
2. Results and discussion The mean PM2.5 concentration of the five tested urban forest types varied between diurnal time periods and between stages of leaf growth. Charts a, b and c of Fig. 2 show the different PM2.5 concentrations for all five urban forest types (Forest types) plus control for different diurnal time periods, and illustrate the relationships between PM2.5 levels and forest types at the three leaf growth stages. ANOVA showed that between the 3 leaf growth stages, the levels of PM2.5 concentration in the air are different significantly. PM2.5 was highest at the bud break stage and lower when the leaves were fully grown (Table 1, Fig. 2).
2.1. Daily variation of PM2.5 concentrations in the air for different urban forest types On average for all forest types and leaf stages studied, the highest PM2.5 concentration was found in the morning and was lower in the afternoon and evening. There is no significant difference between the three diurnal time periods at the bud break stage. However, for growing leaves and full leaf stages, the PM2.5 concentration is significantly different between these time periods (Table 1). Similar results were
reported by Wu et al. (2007) for six urban greenlands that showed variations of air PM concentration; the PM2.5 concentrations were higher at dawn and dusk and lower at noon. This may be due to the fact that the period from 7:00 to 11:00 is the time of day with heavy work-related high-density traffic flow, high humidity in the air, and high air pressure, leading to high levels of PM2.5. The period from 19:00 to 21:00 was not studied here; however, the study of Wu et al. (2007) has shown that during this time period PM2.5 concentration was also higher than in the afternoon and evening (Wu et al., 2007). In the morning, the mean value for PM2.5 in the five urban forest types was mostly higher compared with the Control. This can be due to the Control (no trees) having a clearer atmosphere than the modes with different types of trees, leading to a lower level of humidity, which could be the cause of a suspended PM2.5 concentration in the air lower than the other five types. The results from Fig. 2 and Table 1 show that the mean PM2.5 level is highest in the morning and declines during the day, with the lowest levels occurring in the evening, and that leaves with greater surface area had the greatest effect. This may be due to differences in the moisture concentration of the forest system in the morning compared to the afternoon and evening, but could not explain differences between grass and Control. The cause may be due to the presence of active transportation, because the morning is the time with the highest traffic density (Wu et al., 2007; Acero et al., 2012).
2.2. PM2.5 concentration differences between the different urban forest types The PM2.5 concentration results for different urban forest types show that the investigated forest types differ significantly in the total of captured PM2.5, which on average for the three leaf stages ranges between 107.58 and 184.93 μg/m3 (Fig. 3). For these stages the highest mean PM2.5 concentration was captured by coniferous trees (184.93 μg/m3), while the lowest was by grassland and the Control (145.28 and 107.58 μg/m3, respectively). Compared with the forest type containing only broadleaf trees, the ability to absorb PM2.5 of the mixed tree type (broadleaf and coniferous mixed forest) is more effective. However, the broadleaf type is more effective when its leaves have fully developed (Fig. 3). This may be because of their more complex canopy structure; coniferous tree species are considered to be more efficient in capturing particles than broadleaved species (Beckett et al., 2000; Freer-Smith et al., 2004; Reinap et al., 2009; Tallis et al., 2011; Popek et al., 2013). So in winter [the leafless season], different from the other forest systems, the evergreen system (conifers) and mixed tree system are the most effective in removing dust from the air. The mean PM2.5 concentration collected from the five urban forest types is presented in Figs. 2, 3 and Table 1. Significant differences between forest types were recorded. Fig. 3 shows that all five forest types were higher than and significantly different from Control, while the conifer forest type had the highest PM2.5 levels and was significantly different from grassland and the Control but not significantly different from the other forest types. The effects of plant species on particle deposition depend on the size scale considered.
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WS (m/s) RH (%)
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Broadleaf
Conifers
Shrubs
Grass
101.0
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Broadleaf
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35
2.5 LAI-S1
Chart a
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TP (°C) BP (kPa)
17:00
7:00-11:00 15:00-19:00
99.0
Fig. 2 – Average of PM2.5 concentration (μg/m3) in 4 hr and leaf area index (LAI) of five urban forest types and Control at three stages of leaf growth. (a) bud break stage; (b) growing leaf stage; (c) full leaf stage in the morning, afternoon and evening — three diurnal time periods, with climate condition chart (d), (e) and (f) at that time period.
Regarding the plants as a whole, deposition is mainly affected by the shape of the plant and the structure of the leaves or needles. Considering individual leaves, deposition may be increased or reduced by different surface structures (Litschke and Kuttler, 2008). The effects of plant species on particle deposition depend on the size scale considered. The spatial structure of branches and twigs and the shape of leaves and needles play a key role in the filtration properties of a plant. Beckett et al. (2000) researched wind tunnel tests, showing that the deposition of NaCl particles is significantly higher on selected conifers
(pine and cypress) as a result of their more complex spatial structure than on deciduous trees (maple, poplar). Another disadvantage of deciduous trees is the lack of foliage outside the vegetative period which, considered in absolute terms, reduces their filtration performance. Plants play an important role in filtering ambient air by adsorbing PM onto leaf surfaces. Trees, with their large total leaf area, are considered the most effective type of vegetation for this purpose (McDonald et al., 2007). The structure of tree crowns leads to turbulent air movements, which increases PM deposition on leaves (Fowler et al., 1989). Some species-specific features of
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Table 1 – PM2.5 (particulate matter) mean concentration of five urban forest types (F-types) and control at three leaf growth stages (Stages) and three diurnal time periods. Mean of PM2.5 content (μg/m3) in 4 hr (hour) Stages Bud break ⁎
Growing leaf ⁎⁎
Full leaf ⁎⁎⁎
Time
Grass
Shrubs
Conifers
Broadleaf
Mixed trees
Control
7:00–11:00 11:00–15:00 15:00–19:00 7:00–11:00 11:00–15:00 15:00–19:00 7:00–11:00 11:00–15:00 15:00–19:00
201.61 126.28 136.43 195.56 139.89 97.93 216.94 99.15 93.74
213.39 198.82 196.19 236.90 153.06 101.63 209.96 97.25 71.53
257.16 221.60 222.96 247.33 163.71 151.62 219.57 96.47 83.94
240.43 234.75 138.82 207.51 140.61 93.16 259.21 93.25 71.47
207.63 189.00 240.46 235.43 148.80 111.77 195.11 83.09 64.20
122.38 131.85 109.93 164.10 112.89 84.10 155.79 47.38 39.82
Multi-way analysis of variance (α = 0.05). ⁎ PF-types = 0.0089; PTime = 0.7341. ⁎⁎ PF-types = 0.0008; PTime = 0.0135. ⁎⁎⁎ PF-types = 0.0035; PTime = 0.0029.
leaves may enhance this air filtration process, e.g., trichomes and the chemical composition and structures of epicuticular waxes (Jouraeva et al., 2002; Dzierźanowski et al., 2011; Sæbø et al., 2012). For example, leaves of broad-leaved species that have rough surfaces are more effective in capturing PM than those with smooth surfaces (Beckett et al., 2000). This may be due to the greater surface area of rough surfaces than smooth surfaces. In addition, needles of coniferous trees (Arborvitae and Chinese red pine), which produce a thicker epicuticular wax layer, are more effective in PM accumulation than broad-leaved species (Beckett et al., 1998). The amount of deposited particles is higher on structured wax areas than on flat-shaped surfaces (Burkhardt et al., 1995; Reynolds-Henne et al., 2010; Dzierźanowski et al., 2011; Sæbø et al., 2012). Moreover, evergreen conifers have the potential for accumulating toxic pollutants throughout the year. On the other hand, since most of these plants keep their needles for several years, there is no possibility of recycling PM accumulated on needles every year, as is the case for deciduous
PM2.5 concentration (μg/m3)
250
P(α=0.05) = 0.0070 HSD (α=0.05) = 7.63
200
a 150
ab
ab
ab
b 100
c
50
0
Grass
Shrubs
Conifers
Broadleaf Mixed trees Control
Fig. 3 – PM2.5 (particulate matter) concentration of five urban forest types with the Control. Mean values from three stages of leaf growth study with Tukey's HSD (honest significant differences) values at α = 0.05.
species. In addition, conifers are in general less tolerant to high traffic-related pollution, especially if salt is used for road deicing during winter, and they are often not recommended for roadside plantings. Therefore, evergreen conifers may not be as useful as deciduous leafy species, in spite of their high efficiency in PM scavenging (Beckett et al., 2000).
2.3. PM2.5 concentration of five urban forest types at three stages of leaf growth The results show that there is a significant difference in the total PM2.5 concentration during the day between different leaf growth stages of different urban forest systems. Regarding stages before full growth, the PM2.5 concentration is highest for conifers (233.91 and 187.55 μg/m3) and lowest for Control (121.39 and 120.36 μg/m3). Meanwhile, in the full leaf stage, the PM2.5 concentration of the broadleaf forest system is the highest (141.31 μg/m3) and is significantly different compared with the Control (81.0 μg/m3), but not significantly different compared with other systems. Comparison of differences between the leaf growth stages shows that the mean PM2.5 capture in forest types is in general higher in the bud break stage (188.32 μg/m3) than in the leaf growing stage (154.78 μg/m3) and full leaf stage (122.10 μg/m3), while on average for all forest types the mean values of PM2.5 are similar (Table 1). In the bud break stage and growing leaf stage, the highest mean of PM2.5 concentration is recorded for the conifer forest types (between 233.91 and 187.55 μg/m3), but in the full leaf stage, the highest is recorded for broadleaf trees (141.31 μg/m3). For the remaining five types, the mean level of PM2.5 prevented does not differ significantly between the two last stages (p = 0.0035), but there is a significant difference between these two stages of leaf growth at the bud break stage (p = 0.0089 and 0.0008). Statistical analyses performed for all tested urban forest types together show that there exists a positive correlation with the mean PM2.5 concentration. However, the model is only a partial fit (r = 0.3972) (Fig. 4). In this case, negative correlations between mean PM2.5 and status of leaf growth were also recorded.
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y = -2.4743x + 223.11 R² = 0.3972
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Leaf area index (LAI)
Fig. 4 – Correlations between mean of PM2.5 (particulate matter) concentration with stages of leaf growth of the five of urban forest types and the Control.
Fig. 5 – Correlations between mean of PM2.5 (particulate matter) concentration with LAI (leaf area index) of the five of urban forest types and the Control.
The results of PM2.5 concentration at the three stages of leaf growth show that the highest mean PM2.5 concentration is intercepted by conifer trees in the bud break stage and growing leaf stage but not with full leaves (Fig. 2). The forest type with the smallest concentration of PM2.5 captured from the air is grassland, which is lower and significantly different from the conifer trees but not very different from the other tree types (shrubs, broadleaf and mixed tree types). For two forest types (conifer trees and shrubs), the mean of total PM2.5 concentration differs significantly at all three stages. In contrast, for conifers and mixed tree types there are no significant differences between the three diurnal time periods (morning, afternoon and evening). Between the leaf growth stages, there are differences in concentrations of PM2.5 at the bud break stage compared to the growing leaf and full leaf stages, but no difference between growing and full leaf stages. Results show that the concentration of PM2.5 is reduced by urban forest leaves. But comparison between the forest types cannot explain why broadleaf trees and shrubs with high leaf area index have PM2.5 concentrations higher than the other models (Fig. 2a, b and c). Weak to moderate positive correlations between concentration of PM2.5 were observed with LAI (R2 = 0.0233) (Fig. 5). The interception of the different forest types showed that the PM 2.5 concentration on conifer needles is highest and significantly different from the Control and grass at the bud break and growing leaf stages. However, at the full leaf stage, the broadleaf tree type has the highest capture and is significantly different from the Mixed tree type and Control, but not different from the other forest types.
and filtration performance. In addition, the plant species and planting configuration also affect deposition, as the special structure of vegetation and the shape of leaf surfaces are key factors in the deposition and resuspension of particles (Litschke and Kuttler, 2008). The wind pushes PM into the forest but with reversed position, thereby changing the metamorphic pollution situation. Wind speed increases this process. Temperature has an effect on atmospheric humidity and turbulence. Increased temperature will lead to decreased humidity and increased turbulence, which therefore also impacts the decrease in both PM2.5 concentration and PM2.5 capture by urban forest trees. Particulate matter is shown to deposit more efficiently on leaf surfaces with increasing wind velocity (Beckett et al., 2000). Leaf wax structures are deteriorated by aging and environmental factors such as air pollution, wind, rain and snowfall (Grodzińska-Jurczak and Szarek-Łukaszewska, 1998; Janne et al., 2012). In this study, the data collection times occurred during periods without rain, with light wind and stable air pressure. Therefore, factors affecting PM2.5 concentrations in the atmosphere were mainly temperature and humidity. According to the meteorological charts in Fig. 2c, d, and e, PM2.5 concentration changes during the day can be explained as follows: high PM2.5 concentration in the morning is a result of high moisture and low temperature. The fundamental effect of air humidity on deposition is due to the fact that particles are mainly hygroscopic, and their size varies as a result of the absorption or discharge of water (Smith and Staskawicz, 1977; Winkler, 1988). In turn, this leads to a change in their deposition properties as a function of diameter. In addition, the high level of PM2.5 in the air in the early morning is also affected by heavy traffic density (Wu et al., 2007; Whitlow et al., 2011; Smith and Staskawicz, 1977). Wu et al. (2007) and Xiao et al. (2009) also researched the variation of PM2.5, the concentration differences in six types of urban green lands and concentration changes under different weather conditions; the results showed that the PM2.5 concentration is less in highly vegetated areas than in any other green lands in summer. In spring, autumn and winter, the concentration of PM2.5 in green lands with arbor trees is lower than in those without arbor trees.
2.4. Effect of climatic conditions on PM2.5 concentration between different urban forest types at different times The deposition of particles on plant surfaces is influenced by a variety of factors. Not only the diameter and shape of the particles, but also meteorological parameters such as humidity, wind speed and turbulence are of decisive importance and have considerable impact on plant deposition velocity
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3. Conclusions The results of research and surveys show that shrubs and broadleaf trees have the ability to control particulate matter in the air most effectively at the stage when leaves have fully developed. In winter (the leafless season), the evergreen system (for example coniferous type) and mixed tree systems are likely to be the most effective for preventing high PM2.5 concentrations and exhibit significant differences from other forest systems. The PM2.5 capture efficiency of six urban forest types at three growth stages of leaves in descending order is conifers, broadleaf trees, shrubs, mixed trees, grass and Control. On average for all urban forest types and the three stages of leaf growth studied, PM2.5 capture from the air in morning is highest and lower in afternoon and evening. The forest system is capable of storing and capturing dust in the air, so the area around residential areas, campuses, public parks and alongside roads should be designed with a green belt in order to protect the center. The most effective solution is to develop one or two rows of shrubs, followed by mixed evergreen (coniferous tree) and timber trees, and lastly with shrubs, which can create a landscape and also prevent particulate matter from entering the center. Grassland cannot control particles suspended in the air, but it is likely to reduce dust pollution caused by dust blown by wind from the ground back into the air. Grasslands should be developed for groundgreen-cover in other forest systems in order to increase the ability to effectively prevent the raising of dust and to reduce air pollution.
Acknowledgments This work was supported by the Forestry Public Welfare Project of China (No. 201304301). The authors gratefully acknowledge the contributions of Fengbin Sun, Zhihua Tu and Yang Zhao for their assistance with the field measurements and instrumentation maintenance.
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Fowler, D., Cape, J.N., Unsworth, M.H., Mayer, H., Crowther, J.M., Jarvis, P.G., et al., 1989. Deposition of atmospheric pollutants on forests. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 324 (1223), 247–265. Freer-Smith, P.H., El-Khatib, A.A., Taylor, G., 2004. Capture of Particulate Pollution by trees: a comparison of species typical of semi-arid areas (Ficus nitida and Eucalyptus globulus) with European and North American species. Water Air Soil Pollut. 155 (1–4), 173–187. Freer-Smith, P.H., Beckett, P.K., Taylor, G., 2005. Deposition velocities to Sorbus aria, Acer campestre, Populus deltoides × trichocarpa ‘Beaupré’, Pinus nigra and × Cupressocyparis leylandii for coarse, fine and ultra-fine particles in the urban environment. Environ. Pollut. 133 (1), 157–167. Grodzińska-Jurczak, M., Szarek-Łukaszewska, G., 1998. Conifer epicuticular wax as a biomarker of air pollution: an overview. Acta Soc. Bot. Pol. 67 (3–4), 291–300. HJ 618-2011 (Replace GB 6921-86). Determination of atmospheric articles PM10 and PM2.5 in ambient air by gravimetric method, 2011 (China). Janne, R.V., Pasi, Y.P., Holopainen, T., Jorma, J., Pertti, P., Minna, K., 2012. Soil drought increases atmospheric fine particle capture efficiency of Norway spruce. Boreal Environ. Res. 17, 21–30. Jouraeva, V.A., Johnson, D.L., Hassett, J.P., Nowak, D.J., 2002. Differences in accumulation of PAHs and metals on the leaves of Tilia × euchlora and Pyrus calleryana. Environ. Pollut. 120 (2), 331–338. Litschke, T., Kuttler, W., 2008. On the reduction of urban particle concentration by vegetation — a review. Meteorol. Z. 17 (3), 229–240. Liu, Y.S., Shen, X.X., Mao, X.L., Chen, R., 2004. Indoor air levels of TSP, PM10, PM2.5 and PM1 at public places in Beijing during wintertime. Acta Sci. Circumst. 24 (2), 190–196. McDonald, A.G., Bealey, W.J., Fowler, D., Dragosits, U., Skiba, U., Smith, R.I., et al., 2007. Quantifying the effect of urban tree planting on concentrations and depositions of PM10 in two UK conurbations. Atmos. Environ. 41 (38), 8455–8467. MDHS-14/3, 2000. Methods for the determination of hazardous substances Health and Safety Laboratory — general methods for sampling and gravimetric analysis of respirable and inhalable dust. Health and Safety Executive (February). Nowak, D.J., 1994. Air pollution removal by Chicago's forest. In: McPherson, E.G., Nowak, D.J., Rowntree, R.E. (Eds.), Chicago's urban forest ecosystem: results of the Chicago Urban Forest Climate Project. General Technical Report NE-186. US Department of Agriculture, Forest Service, Radnor PA. Nowak, D.J., Crane, D.E., Stevens, J.C., 2006. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4 (3), 115–123. Nowak, D.J., Crane, D.E., Stevens, J.C., Hoehn, R.E., Walton, J.T., Bond, J., 2008. A ground-based method of assessing urban forest structure and ecosystem services. Arboricult. Urban For. 34 (6), 347–358. Popek, R., Gawrońska, H., Sæbø, A., Wrochna, M., Gawroński, S.W., 2013. Particulate matter on foliage of 13 woody species: deposition on surfaces and phytostabilisation in waxes — a 3-year study. Int. J. Phytorem. 15 (3), 245–256. Reinap, A., Wiman, B., Svenningsson, B., Gunnarsson, S., 2009. Oak leaves as aerosol collectors: relationship with wind velocity and particle size distribution. Experimental results and their implications. Trees 23 (6), 1263–1274. Reynolds-Henne, C.E., Langenegger, A., Mani, J., Schenk, N., Zumsteg, A., Feller, U., 2010. Interactions between temperature, drought and stomatal opening in legumes. J. Environ. Exp. Bot. 68 (1), 37–43. Sæbø, A., Popek, R., Nawrot, B., Hanslin, H.M., Gawronska, H., Gawronski, S.W., 2012. Plant species differences in particulate matter accumulation on leaf surfaces. Sci. Total Environ. 427–428, 347–354.
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Santosh, K.P., 2012. Ecological effect of airborne particulate matter on plants. Environ. Skeptics Critics 1 (1), 12–22. Smith, W.H., Staskawicz, B.J., 1977. Removal of atmospheric particles by leaves and twigs of urban trees: some preliminary observations and assessment of research needs. J. Environ. Manag. 1 (4), 317–330. Tallis, M., Taylor, G., Sinnett, D., Freer-Smith, P., 2011. Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London, under current and future environments. Landsc. Urban Plan. 103 (2), 129–138. TCVN 5067-1995. Air quality - Weighing method for determination of dust content, 1995 (Vietnam). Whitlow, T.H., Hall, A., Zhang, K.M., Anguita, J., 2011. Impact of local traffic exclusion on near-road air quality: findings from the New York City “Summer Streets” campaign. Environ. Pollut. 159 (8–9), 2016–2027.
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Winkler, P., 1988. The growth of atmospheric aerosol particles with relative humidity. Phys. Scr. 37 (2), 223–230. Wu, Z.P., Wang, C., Xu, J.N., Hu, L.X., 2007. Air-borne anions and particulate matter in six urban green spaces during the summer. J. Tsinghua Univ. Sci. Technol. 47 (12), 2152–2157. Xiao, J.M., Wang, C., Wu, Z.P., Yang, W.W., Hou, X.J., 2009. Variations of air PM10 concentration in different greenlands in Tsinghua University. Sci. Silvae Sin. 45 (5), 153–156. Yang, J., McBride, J.R., Zhou, J.X., Sun, Z.Y., 2005. The urban forest in Beijing and its role in air pollution reduction. Urban For. Urban Green. 3 (2), 65–78. Zhou, X.W., Kang, X.P., 2008. Study on dust-retention ability of different green plants on campus. J. Anhui Agric. Sci. 36 (24), 10431–10432.
Available online at www.sciencedirect.com
ScienceDirect www.journals.elsevier.com/journal-of-environmental-sciences
Relationship between types of urban forest and PM2.5 capture at three growth stages of leaves Thithanhthao Nguyen1,3 , Xinxiao Yu1,⁎, Zhenming Zhang2 , Mengmeng Liu1 , Xuhui Liu1 1. College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China. E-mail: [email protected] 2. College of Nature Conservation, Beijing Forestry University, Beijing 100083, China 3. Science and Technology Department, Binh Duong Province, Viet Nam
AR TIC LE I N FO
ABS TR ACT
Article history:
Particulate matter diameter ≤ 2.5 μm (PM2.5) causes direct harm to human health. Finding
Received 7 January 2014
forms of urban forest systems that with the ability to reduce the amount of particulate
Revised 10 March 2014
matter in air effectively is the aim of this study. Five commonly cultivated kinds of urban
Accepted 17 April 2014
forest types were studied in Beijing city at three stages of leaf growth. Results show that the urban forest system is capable of storing and capturing dust from the air. The types of shrubs and broadleaf trees that have the ability to capture PM2.5 from the air are most
Keywords:
effective when leaves have fully developed. In the leafless season, the conifer and mixed
Particulate matter
tree types are the most effective in removing dust from the air. For all kinds of forest
PM2.5
types and stages of leaf growth, the PM2.5 concentration is highest in the morning but lower
Type of urban forest
in the afternoon and evening. Grassland cannot control particles suspended in the air,
Growth stages of leaves
but can reduce dust pollution caused by dust from the ground blown by the wind back into the air. © 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
Introduction In recent years large-scale urbanization and industrialization has rapidly increased, causing natural forest decline and even forest loss in many parts of the world, and bringing more people into contact with air pollution than ever before. The problem of particulate matter (PM) is a leading environmental concern in most major cities across the world. Beijing city (China) is among the areas in which aerosol particulate pollution is the highest in the world. According to NASA-Released Images of Beijing Air Pollution in China by politicolonel.com on January 15th, 2013, China was shown to be smothered by a layer of haze. The gray and yellow-tinged clouds visible on the map are areas heavily affected by air pollution. At the time the image was taken, ground sensors at the U.S. Embassy in Beijing recorded PM2.5 measurements of 291. According to the World Health Organization, PM2.5 levels above 25 are considered unsafe (http://politicolonel.com, 2013). According
to figures released by the Beijing Environmental Protection Bureau on January 02, 2014, PM2.5 was 2.5 times the national standard. The intensity of major air pollutants remained much the same in 2013 as in the previous year. The PM2.5 reading in 2013 was on average 89.5 μg/m3. By comparison, the national standard stands at 35 μg/m3. PM2.5 was found to be the major pollutant – accounting for 77.8% – on most smoggy days. Southern Beijing saw much higher PM2.5 readings in 2013 compared with the north (http://www. chinadaily.com.cn/china/2014-01/03/content_17212753.htm). In addition, in recent years, the Chinese Government has undertaken various initiatives to reduce the PM pollution in the air. Trees are efficient scavengers of PM and forested areas are characterized by higher rates of dry deposition than other land types (McDonald et al., 2007). Trees can act as obstacles to dispersal of particle pollution and thus remove a significant amount of PM from the atmosphere (Janne et al., 2012). Trees can retain particles from the air (Beckett et al., 1998). Removal of dust
⁎ Corresponding author. E-mail: [email protected] (Xinxiao Yu).
http://dx.doi.org/10.1016/j.jes.2014.04.019 1001-0742/© 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
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particles by vegetation through interception from the atmosphere enhances air quality in urban areas (Beckett et al., 1998, 2000; Freer-Smith et al., 2004, 2005; Nowak et al., 2006; Litschke and Kuttler, 2008), near roadways and in public places (Smith and Staskawicz, 1977; Liu et al., 2004; Acero et al., 2012). Urban forests can strongly influence the physical/biological environment and mitigate many impacts of urban development by moderating climate, conserving energy, carbon dioxide and water, improving air quality, controlling rainfall runoff and flooding, lowering noise levels, harboring wildlife and enhancing the attractiveness of cities. Urban forests can be viewed as a ‘living technology’ — a key component of the urban infrastructure that helps maintain a healthy environment for urban dwellers (Dwyer et al., 1992; Nowak, 1994; Nowak et al., 2006; Wu et al., 2007). Many studies have analyzed air pollution removal by urban forests, reporting results for an entire city or modeling region (e.g., Nowak, 1994; Nowak et al., 2006, 2008; Yang et al., 2005; Santosh, 2012). Nowak et al. (2008) reported that, through pollution removal and other tree functions (e.g., air temperature reduction), urban trees can help improve air quality for many different air pollutants in cities, and consequently can help improve human health. It is clear that careful planning and design are critical to increasing the net benefits of trees and forests in urban environments. A change in species or location of trees with respect to each other or buildings and other components of the urban infrastructure can have a major impact on benefits and costs. In addition, some studies investigated the effectiveness of trees in accumulating airborne particles on foliage (Beckett et al., 2000; Freer-Smith et al., 2005; Yang et al., 2005; Zhou and Kang, 2008; Dzierźanowski et al., 2011; Sæbø et al., 2012; Popek et al., 2013), and recommended some efficient urban tree species to optimize the design of urban plantings to capture air pollutants (Dzierźanowski et al., 2011; Sæbø et al., 2012; Popek et al., 2013). Beckett et al. (1998, 2000) and Freer-Smith et al. (2004, 2005) provided input on the selection of species for urban areas to decrease the exposure of the urban population to air pollution, and identified species of trees and shrubs with low and high PM accumulation. Broad-leaved species with rough leaf surfaces are more efficient in capturing PM than those with smooth leaf surfaces. In addition, needles of coniferous trees, which produce a thicker epicuticular wax layer, are more effective in PM accumulation than broad-leaved species, and evergreen conifers have the potential to accumulate pollutants throughout the year. Dzierźanowski et al. (2011), Sæbø et al. (2012) and Popek et al. (2013) evaluated and compared the effectiveness of some trees commonly planted in urban areas in accumulating airborne particles on leaf surfaces and leaf wax layers. The results show that on foliage of all tested species, both surface PM and in-wax PM were deposited, but species with densely-haired leaves are more effective in capturing bigger particles while species characterized by a very thick layer of epicuticular waxes are much more effective at capturing smaller particles; leaf hair and wax content were traits with a positive correlation to PM accumulation. But the results of these studies were only limited to the choice of species used for the investigation and did not indicate the form of urban forest system having the ability to reduce the amount of PM in air effectively. No model for urban forest areas with different pollution reduction strategies for various air pollution types has been proposed. The study aims to compare the relationship between different urban forest types with the PM2.5 concentration in the air and diurnal variation of PM2.5 concentration at the three stages of leaf growth; and analyze the impact of the climate conditions on changes to the levels of PM2.5 in the air brought about by different kinds of urban forest types. The achieved results can be used as a basis for designing urban forest systems that are most effective in reducing particulate matter in the air.
1. Materials and methods 1.1. Study sites and experimental design The Beijing Olympic Forest Park is the organic component of the Beijing Olympic Green, and is located in the north of urban Beijing. The area of the park is about 680 ha, with about 480 ha of green area. It was constructed for the 2008 event, but its long-term target is to form a sustainable environment and serve as a multi-function public park. The design merges traditional Chinese landscape arts with ecological techniques to form an urban green lung and site for public recreation. The trees used for the study were located at the Centre of the North Garden of Beijing Olympic Forest Park. The field studies did not involve endangered or protected species and no specific permits were required for the described field studies. Five urban forest systems were designated: shrub and small tree forest system (deciduous trees), coniferous tree forest system (evergreen trees), broad-leaf tree forest system (deciduous trees), mixed broadleaf and coniferous tree model system, and grassland. These urban forest types are commonly grown in urban forest parks in Beijing and were selected for this study, and an area out of the forest was selected for doing comparative analysis (Control). The distance between the urban forest systems and the Control site was from 5 to 10 m. All trees and shrubs had already been growing in the selected locations for several years and were in good condition and healthy. They varied in size with diameters from 29.0 to 73.4 cm for broadleaf trees; 9.0 to 14.0 cm for coniferous trees; 7.0 to 11.0 cm for shrubs and height from 11.6 to 19.8 m; 4.0 to 6.0 m; 2.0 to 3.5 m; 0.3 to 0.8 m for trees, coniferous trees, shrubs and grass, respectively. Grassland (grass type): Iris ensata Thunb (Iris lactea Pall. Var chinense), Roof Iris (Iris tectorum), Violet orychophragmus (Orychophragmus violaceus), Herb of Denticulate Ixeris (Ixeris denticulata (Houtt.) Stebb.), White flower Lagopsis (Lagopsis supina (Steph.) Ikonn.-Gal. ex Knorr.) and other grass species are some herb species grown commonly in winter and summer. The area was 1500 m2. Shrubs and small forest trees (shrubs type): Flowering plum (Amygdalus triloba) and Flowering peach (Prunus persica Batsch. var. duplex Rehd) are becoming more popular due to their ornamental value and tolerance for the urban environment. The area was 1500 m2. Coniferous forest trees (conifers type): Chinese red pine (Pinus tabuliformis) and Arborvitae (Platycladus orientalis (Linn.) Franco) are commonly used evergreen trees used to maintain green coverage. The area was 1800 m2. Broadleaf forest trees (broadleaf trees type): Tomentosa (Tomentosa Populus tomentosa Carr) and Sophora japonica Linn are some species utilized to reforest along highways for their more comprehensive adaptability than other species. The area was about 3000 m2. Broadleaf and Coniferous mixed forest (mixed trees type): White wax (Fraxinus chinensis Roxb and Fraxinus bungeana), Sophora japonica Linn and Chinese red pine (Pinus tabuliformis) are mainly used in protected forest belts and to cover the
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space around residential areas, campuses, urban gardens and along major roads. The area was about 3000 m2.
Boothwyn, Pennsylvanis, USA) including temperature, wind speed, humidity and barometric pressure.
1.1.1. Control
1.2. Measurements
Control was an area about 1500 m2 in the Olympic park zone, 5–10 m from the forest types, land with no trees located next to the internal road, not affected by emission sources, and not much human involvement. Experimental conditions were similar to those of the forest (meteorological conditions, emission sources, low human activity). Air samples were collected at 5 urban forest types and the Control (Fig. 1). Sampling locations were located in the center of each system at a height of 1.5 m above the ground (human respiration level). The experiment was conducted for 3 consecutive days per type (3 replications) in the developmental period, with results given as the mean of replicate measurements. Sampling times were chosen as follows: each stage of leaf growth (late winter before the leaves grow, in the spring when the leaves are growing and early summer when the leaves mature, 2013) averaging repeated samplings every 3 days without rain; the sampling time of the day was early in the morning from 7:00 to 11:00, in the afternoon from 11:00 to 15:00 and in the evening from 15:00 to 19:00. The tested species were randomized within test fields that were relatively small in area. Thus, variations in the environment within the locations should not significantly influence the results. Hourly meteorological data for the 3 leaf stages were obtained by a Kestrel 4000-pocket weather tracker (Nielsen-Kellerman,
The PM2.5 concentration in air was collected at different urban forest system sites of the Beijing Olympic Forest Park North garden, using the total suspended particle measurement in the flow of an intelligent sampler TH-150C model, flow rate: 100 L/min (Westernization instrument (Beijing) Technology Co., Ltd., Beijing, China). The sampling procedure was given by MDHS-14/3 (2000); and a weighing method was used in this study (HJ 618-2011; TCVN 5067-1995). The Micro-quartz fiber filters, T293 (retention 90 mm) (Munktell, Sweden) used for the analysis were first dried for 240 min at 60°C in a DHG-9123A drying chamber (Biocotek, Shanghai, China) and then left in the weighing room to stabilize the humidity of the hygroscopic paper filters before weighing. After a further 24 hr, the first weighing was a determination of the mass of a clean filter (without dust on the filter, m1), the second step, weighing a filter with dust content (m2). A XS105DU balance (Readability: 0.01 mg/0.1 mg) (Mettler-Toledo International Inc., Greifensee, Switzerland) was used to weigh the filters. Repeat weighing of unexposed substrates over several days is a better guide to true performance. The weighed filter papers were placed in sealed plastic containers for field sampling. Each sample was labeled so that it could be uniquely identified, and sealed with its protective cover to prevent contamination.
Fig. 1 – Sample experiment field map layout. Source of map: www.map.baidu.com.
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One unused loaded sampler from each batch of ten prepared was retained as a blank; a minimum of three blanks were always kept. These were treated as far as possible in the same manner as those actually used for sampling, regarding transport to and from the sampling site, but did not have air drawn through them. The samples were transported to the laboratory in a container designed to prevent damage in transit, and labeled to ensure proper handling, then left in the weighing room with the conditioning procedure described above repeated before re-weighing. At the same time, weighed filter blanks had mass (b = mb2 − mb1) (TCVN 5067-1995). The concentration of PM2.5 in the air was calculated according to the following equation: C 4h ¼
1000 ðm2 −m1 −bÞ V0
where, C4h(mg/m3) is the dust concentration in 4 hr; m1, m2 (g) are the filter mass before and after sampling; b (g) is the blank filter mass; b = mb2-mb1, in, and Vo (m3 at standard state) is the air sample volume calculated as the product of volumetric intensity of uptake air flow and sampling time.
1.3. Statistical analysis Data were subjected to one-way and multi-way analysis of variance using StatGraphics Centurial XVI software (StatPoint Technologies, Inc., Warrenton, Virginia, USA). Significance of differences between mean values was tested using Tukey's honestly significant difference test (HSD) at α = 0.05. Values presented on bar charts are means, while values on dot charts are individual values with trend line and correlation coefficient (r). The latter two were calculated using Microsoft Excel (Microsoft Corp., Redmond, Washington, USA).
2. Results and discussion The mean PM2.5 concentration of the five tested urban forest types varied between diurnal time periods and between stages of leaf growth. Charts a, b and c of Fig. 2 show the different PM2.5 concentrations for all five urban forest types (Forest types) plus control for different diurnal time periods, and illustrate the relationships between PM2.5 levels and forest types at the three leaf growth stages. ANOVA showed that between the 3 leaf growth stages, the levels of PM2.5 concentration in the air are different significantly. PM2.5 was highest at the bud break stage and lower when the leaves were fully grown (Table 1, Fig. 2).
2.1. Daily variation of PM2.5 concentrations in the air for different urban forest types On average for all forest types and leaf stages studied, the highest PM2.5 concentration was found in the morning and was lower in the afternoon and evening. There is no significant difference between the three diurnal time periods at the bud break stage. However, for growing leaves and full leaf stages, the PM2.5 concentration is significantly different between these time periods (Table 1). Similar results were
reported by Wu et al. (2007) for six urban greenlands that showed variations of air PM concentration; the PM2.5 concentrations were higher at dawn and dusk and lower at noon. This may be due to the fact that the period from 7:00 to 11:00 is the time of day with heavy work-related high-density traffic flow, high humidity in the air, and high air pressure, leading to high levels of PM2.5. The period from 19:00 to 21:00 was not studied here; however, the study of Wu et al. (2007) has shown that during this time period PM2.5 concentration was also higher than in the afternoon and evening (Wu et al., 2007). In the morning, the mean value for PM2.5 in the five urban forest types was mostly higher compared with the Control. This can be due to the Control (no trees) having a clearer atmosphere than the modes with different types of trees, leading to a lower level of humidity, which could be the cause of a suspended PM2.5 concentration in the air lower than the other five types. The results from Fig. 2 and Table 1 show that the mean PM2.5 level is highest in the morning and declines during the day, with the lowest levels occurring in the evening, and that leaves with greater surface area had the greatest effect. This may be due to differences in the moisture concentration of the forest system in the morning compared to the afternoon and evening, but could not explain differences between grass and Control. The cause may be due to the presence of active transportation, because the morning is the time with the highest traffic density (Wu et al., 2007; Acero et al., 2012).
2.2. PM2.5 concentration differences between the different urban forest types The PM2.5 concentration results for different urban forest types show that the investigated forest types differ significantly in the total of captured PM2.5, which on average for the three leaf stages ranges between 107.58 and 184.93 μg/m3 (Fig. 3). For these stages the highest mean PM2.5 concentration was captured by coniferous trees (184.93 μg/m3), while the lowest was by grassland and the Control (145.28 and 107.58 μg/m3, respectively). Compared with the forest type containing only broadleaf trees, the ability to absorb PM2.5 of the mixed tree type (broadleaf and coniferous mixed forest) is more effective. However, the broadleaf type is more effective when its leaves have fully developed (Fig. 3). This may be because of their more complex canopy structure; coniferous tree species are considered to be more efficient in capturing particles than broadleaved species (Beckett et al., 2000; Freer-Smith et al., 2004; Reinap et al., 2009; Tallis et al., 2011; Popek et al., 2013). So in winter [the leafless season], different from the other forest systems, the evergreen system (conifers) and mixed tree system are the most effective in removing dust from the air. The mean PM2.5 concentration collected from the five urban forest types is presented in Figs. 2, 3 and Table 1. Significant differences between forest types were recorded. Fig. 3 shows that all five forest types were higher than and significantly different from Control, while the conifer forest type had the highest PM2.5 levels and was significantly different from grassland and the Control but not significantly different from the other forest types. The effects of plant species on particle deposition depend on the size scale considered.
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Chart f
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35
2.5 LAI-S1
Chart a
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TP (°C) BP (kPa)
17:00
7:00-11:00 15:00-19:00
99.0
Fig. 2 – Average of PM2.5 concentration (μg/m3) in 4 hr and leaf area index (LAI) of five urban forest types and Control at three stages of leaf growth. (a) bud break stage; (b) growing leaf stage; (c) full leaf stage in the morning, afternoon and evening — three diurnal time periods, with climate condition chart (d), (e) and (f) at that time period.
Regarding the plants as a whole, deposition is mainly affected by the shape of the plant and the structure of the leaves or needles. Considering individual leaves, deposition may be increased or reduced by different surface structures (Litschke and Kuttler, 2008). The effects of plant species on particle deposition depend on the size scale considered. The spatial structure of branches and twigs and the shape of leaves and needles play a key role in the filtration properties of a plant. Beckett et al. (2000) researched wind tunnel tests, showing that the deposition of NaCl particles is significantly higher on selected conifers
(pine and cypress) as a result of their more complex spatial structure than on deciduous trees (maple, poplar). Another disadvantage of deciduous trees is the lack of foliage outside the vegetative period which, considered in absolute terms, reduces their filtration performance. Plants play an important role in filtering ambient air by adsorbing PM onto leaf surfaces. Trees, with their large total leaf area, are considered the most effective type of vegetation for this purpose (McDonald et al., 2007). The structure of tree crowns leads to turbulent air movements, which increases PM deposition on leaves (Fowler et al., 1989). Some species-specific features of
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Table 1 – PM2.5 (particulate matter) mean concentration of five urban forest types (F-types) and control at three leaf growth stages (Stages) and three diurnal time periods. Mean of PM2.5 content (μg/m3) in 4 hr (hour) Stages Bud break ⁎
Growing leaf ⁎⁎
Full leaf ⁎⁎⁎
Time
Grass
Shrubs
Conifers
Broadleaf
Mixed trees
Control
7:00–11:00 11:00–15:00 15:00–19:00 7:00–11:00 11:00–15:00 15:00–19:00 7:00–11:00 11:00–15:00 15:00–19:00
201.61 126.28 136.43 195.56 139.89 97.93 216.94 99.15 93.74
213.39 198.82 196.19 236.90 153.06 101.63 209.96 97.25 71.53
257.16 221.60 222.96 247.33 163.71 151.62 219.57 96.47 83.94
240.43 234.75 138.82 207.51 140.61 93.16 259.21 93.25 71.47
207.63 189.00 240.46 235.43 148.80 111.77 195.11 83.09 64.20
122.38 131.85 109.93 164.10 112.89 84.10 155.79 47.38 39.82
Multi-way analysis of variance (α = 0.05). ⁎ PF-types = 0.0089; PTime = 0.7341. ⁎⁎ PF-types = 0.0008; PTime = 0.0135. ⁎⁎⁎ PF-types = 0.0035; PTime = 0.0029.
leaves may enhance this air filtration process, e.g., trichomes and the chemical composition and structures of epicuticular waxes (Jouraeva et al., 2002; Dzierźanowski et al., 2011; Sæbø et al., 2012). For example, leaves of broad-leaved species that have rough surfaces are more effective in capturing PM than those with smooth surfaces (Beckett et al., 2000). This may be due to the greater surface area of rough surfaces than smooth surfaces. In addition, needles of coniferous trees (Arborvitae and Chinese red pine), which produce a thicker epicuticular wax layer, are more effective in PM accumulation than broad-leaved species (Beckett et al., 1998). The amount of deposited particles is higher on structured wax areas than on flat-shaped surfaces (Burkhardt et al., 1995; Reynolds-Henne et al., 2010; Dzierźanowski et al., 2011; Sæbø et al., 2012). Moreover, evergreen conifers have the potential for accumulating toxic pollutants throughout the year. On the other hand, since most of these plants keep their needles for several years, there is no possibility of recycling PM accumulated on needles every year, as is the case for deciduous
PM2.5 concentration (μg/m3)
250
P(α=0.05) = 0.0070 HSD (α=0.05) = 7.63
200
a 150
ab
ab
ab
b 100
c
50
0
Grass
Shrubs
Conifers
Broadleaf Mixed trees Control
Fig. 3 – PM2.5 (particulate matter) concentration of five urban forest types with the Control. Mean values from three stages of leaf growth study with Tukey's HSD (honest significant differences) values at α = 0.05.
species. In addition, conifers are in general less tolerant to high traffic-related pollution, especially if salt is used for road deicing during winter, and they are often not recommended for roadside plantings. Therefore, evergreen conifers may not be as useful as deciduous leafy species, in spite of their high efficiency in PM scavenging (Beckett et al., 2000).
2.3. PM2.5 concentration of five urban forest types at three stages of leaf growth The results show that there is a significant difference in the total PM2.5 concentration during the day between different leaf growth stages of different urban forest systems. Regarding stages before full growth, the PM2.5 concentration is highest for conifers (233.91 and 187.55 μg/m3) and lowest for Control (121.39 and 120.36 μg/m3). Meanwhile, in the full leaf stage, the PM2.5 concentration of the broadleaf forest system is the highest (141.31 μg/m3) and is significantly different compared with the Control (81.0 μg/m3), but not significantly different compared with other systems. Comparison of differences between the leaf growth stages shows that the mean PM2.5 capture in forest types is in general higher in the bud break stage (188.32 μg/m3) than in the leaf growing stage (154.78 μg/m3) and full leaf stage (122.10 μg/m3), while on average for all forest types the mean values of PM2.5 are similar (Table 1). In the bud break stage and growing leaf stage, the highest mean of PM2.5 concentration is recorded for the conifer forest types (between 233.91 and 187.55 μg/m3), but in the full leaf stage, the highest is recorded for broadleaf trees (141.31 μg/m3). For the remaining five types, the mean level of PM2.5 prevented does not differ significantly between the two last stages (p = 0.0035), but there is a significant difference between these two stages of leaf growth at the bud break stage (p = 0.0089 and 0.0008). Statistical analyses performed for all tested urban forest types together show that there exists a positive correlation with the mean PM2.5 concentration. However, the model is only a partial fit (r = 0.3972) (Fig. 4). In this case, negative correlations between mean PM2.5 and status of leaf growth were also recorded.
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250
250 200 150
y = -2.4743x + 223.11 R² = 0.3972
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PM (μg/m ) 0
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Stages of leaf growth
PM2.5 concentration (μg/m3)
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300
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y = 2.1763x + 151.17 R² = 0.0077
100 50 0
0
1
2
3
4
5
Leaf area index (LAI)
Fig. 4 – Correlations between mean of PM2.5 (particulate matter) concentration with stages of leaf growth of the five of urban forest types and the Control.
Fig. 5 – Correlations between mean of PM2.5 (particulate matter) concentration with LAI (leaf area index) of the five of urban forest types and the Control.
The results of PM2.5 concentration at the three stages of leaf growth show that the highest mean PM2.5 concentration is intercepted by conifer trees in the bud break stage and growing leaf stage but not with full leaves (Fig. 2). The forest type with the smallest concentration of PM2.5 captured from the air is grassland, which is lower and significantly different from the conifer trees but not very different from the other tree types (shrubs, broadleaf and mixed tree types). For two forest types (conifer trees and shrubs), the mean of total PM2.5 concentration differs significantly at all three stages. In contrast, for conifers and mixed tree types there are no significant differences between the three diurnal time periods (morning, afternoon and evening). Between the leaf growth stages, there are differences in concentrations of PM2.5 at the bud break stage compared to the growing leaf and full leaf stages, but no difference between growing and full leaf stages. Results show that the concentration of PM2.5 is reduced by urban forest leaves. But comparison between the forest types cannot explain why broadleaf trees and shrubs with high leaf area index have PM2.5 concentrations higher than the other models (Fig. 2a, b and c). Weak to moderate positive correlations between concentration of PM2.5 were observed with LAI (R2 = 0.0233) (Fig. 5). The interception of the different forest types showed that the PM 2.5 concentration on conifer needles is highest and significantly different from the Control and grass at the bud break and growing leaf stages. However, at the full leaf stage, the broadleaf tree type has the highest capture and is significantly different from the Mixed tree type and Control, but not different from the other forest types.
and filtration performance. In addition, the plant species and planting configuration also affect deposition, as the special structure of vegetation and the shape of leaf surfaces are key factors in the deposition and resuspension of particles (Litschke and Kuttler, 2008). The wind pushes PM into the forest but with reversed position, thereby changing the metamorphic pollution situation. Wind speed increases this process. Temperature has an effect on atmospheric humidity and turbulence. Increased temperature will lead to decreased humidity and increased turbulence, which therefore also impacts the decrease in both PM2.5 concentration and PM2.5 capture by urban forest trees. Particulate matter is shown to deposit more efficiently on leaf surfaces with increasing wind velocity (Beckett et al., 2000). Leaf wax structures are deteriorated by aging and environmental factors such as air pollution, wind, rain and snowfall (Grodzińska-Jurczak and Szarek-Łukaszewska, 1998; Janne et al., 2012). In this study, the data collection times occurred during periods without rain, with light wind and stable air pressure. Therefore, factors affecting PM2.5 concentrations in the atmosphere were mainly temperature and humidity. According to the meteorological charts in Fig. 2c, d, and e, PM2.5 concentration changes during the day can be explained as follows: high PM2.5 concentration in the morning is a result of high moisture and low temperature. The fundamental effect of air humidity on deposition is due to the fact that particles are mainly hygroscopic, and their size varies as a result of the absorption or discharge of water (Smith and Staskawicz, 1977; Winkler, 1988). In turn, this leads to a change in their deposition properties as a function of diameter. In addition, the high level of PM2.5 in the air in the early morning is also affected by heavy traffic density (Wu et al., 2007; Whitlow et al., 2011; Smith and Staskawicz, 1977). Wu et al. (2007) and Xiao et al. (2009) also researched the variation of PM2.5, the concentration differences in six types of urban green lands and concentration changes under different weather conditions; the results showed that the PM2.5 concentration is less in highly vegetated areas than in any other green lands in summer. In spring, autumn and winter, the concentration of PM2.5 in green lands with arbor trees is lower than in those without arbor trees.
2.4. Effect of climatic conditions on PM2.5 concentration between different urban forest types at different times The deposition of particles on plant surfaces is influenced by a variety of factors. Not only the diameter and shape of the particles, but also meteorological parameters such as humidity, wind speed and turbulence are of decisive importance and have considerable impact on plant deposition velocity
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3. Conclusions The results of research and surveys show that shrubs and broadleaf trees have the ability to control particulate matter in the air most effectively at the stage when leaves have fully developed. In winter (the leafless season), the evergreen system (for example coniferous type) and mixed tree systems are likely to be the most effective for preventing high PM2.5 concentrations and exhibit significant differences from other forest systems. The PM2.5 capture efficiency of six urban forest types at three growth stages of leaves in descending order is conifers, broadleaf trees, shrubs, mixed trees, grass and Control. On average for all urban forest types and the three stages of leaf growth studied, PM2.5 capture from the air in morning is highest and lower in afternoon and evening. The forest system is capable of storing and capturing dust in the air, so the area around residential areas, campuses, public parks and alongside roads should be designed with a green belt in order to protect the center. The most effective solution is to develop one or two rows of shrubs, followed by mixed evergreen (coniferous tree) and timber trees, and lastly with shrubs, which can create a landscape and also prevent particulate matter from entering the center. Grassland cannot control particles suspended in the air, but it is likely to reduce dust pollution caused by dust blown by wind from the ground back into the air. Grasslands should be developed for groundgreen-cover in other forest systems in order to increase the ability to effectively prevent the raising of dust and to reduce air pollution.
Acknowledgments This work was supported by the Forestry Public Welfare Project of China (No. 201304301). The authors gratefully acknowledge the contributions of Fengbin Sun, Zhihua Tu and Yang Zhao for their assistance with the field measurements and instrumentation maintenance.
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