- Email: [email protected]
Understanding the washoff processes of PM2.5 from leaf surfaces during rainfall events
Atmospheric Environment 214 (2019) 116844
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Understanding the washoff processes of PM2.5 from leaf surfaces during rainfall events
T
Changkun Xiea,b, Lubing Yanb, Anze Liangb, Shengquan Chea,* a b
School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Particulate matter Air pollution PM2.5 washoff processes Rainfall Leaves
The PM on plant surface being washed into soil during the rain is one of the key processes for plants to reduce atmospheric PM. This study attempted to explore the PM2.5 washoff processes from different tree leaves during rain events (< 16 mm/h) using an artificial rainfall simulation experiment. We aimed to improve our knowledge of processes associated with PM2.5 reduction and to provide a basis for accurately evaluating the ability of plants to reduce PM2.5. This is the first study showing that the PM2.5 washoff processes from leaves follow quartic functions and 4 pattern curves under different conditions were categorized. They respectively explained the PM2.5 washoff processes in broad-leaved trees with large leaves and simple crowns (bimodal curved), in conifer species with small leaves and complex crowns under high rainfall intensity (unimodal curved), in trees with extremely complex crowns under high rainfall intensity (continually-rising curved), and in conditions under which extremely high water interception efficiency but rather low water storage capacity occur (U-shaped curved). These curves indicate that the amount of PM2.5 on leaves was not necessarily reduced in rainfall events. The general ranking of the average values of PM2.5 number on leaves surface during rain events was Cedrus Deodara (40.3 × 103/cm2), Japanese Maple (33.0 × 103/cm2) > Dragon Juniper (14.7 × 103/cm2), Dawn Redwood (12.6 × 103/cm2) > Common Boxwood (6.4 × 103/cm2), Lotus Magnolia (4.1 × 103/cm2). The cycles of PM2.5 accumulation and removal on broad-leaved trees might be shorter than that of conifers, meaning that they may have a better PM2.5 washoff efficiency during rain, which is opposite to the PM2.5 deposition efficiency. Higher rainfall intensity can reduce the cycle length and enhance the PM2.5 washoff efficiency.
*
Corresponding author. 800# Dongchuan Road, Shanghai, 200240, PR China. E-mail address: [email protected] (S. Che).
https://doi.org/10.1016/j.atmosenv.2019.116844 Received 14 March 2019; Received in revised form 13 July 2019; Accepted 16 July 2019 Available online 20 July 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
1. Introduction
washoff processes of chemical elements from plant surfaces. The washoff of sprays (pesticides, herbicides, etc.) from plant surfaces has been studied as part of crop management in rainfall intensities from 11 mm/h to 111 mm/h (Cohen and Steinmetz, 1986; Dang et al., 2016; Mcdowell et al., 1984; Reddy and Locke, 2015; Willis et al., 1994). The washoff processes of sulfate and potassium from four species were studied using experimental acid rainfall intensity of 84 mm/h (Potter and Ragsdale, 1991). The washoff processes of KNO3 were tested for three conifer species using simulated rainfall intensity of 220 mm/h (Pullman, 2008). The common understanding of the above studies was that the washoff processes of trace elements from plant surfaces followed negative exponential curves. One of the limitations in the chemical element washoff processes examined in previous studies is missing considered the accumulation process. The washoff processes of PM during rain events composed of accumulation and removal processes might be more complex than those of the chemical elements mentioned above. The other limitation of the previous studies is that the simulated rainfall intensities might be too strong to represent the high-frequency rainfall intensities in nature (Dang et al., 2016). Most of these rainfall intensities are far heavier than those measured during rainstorms (16 mm/h), which do not occur frequently (Dang et al., 2016; Reddy and Locke, 2015). In this respect, the PM2.5 washoff processes might be more complex than those of chemical elements in rainstorms. Understanding the washoff processes of PM2.5 from leaf surfaces under high-frequency rainfall intensities will be helpful in assessing the ability of plants to remove particles, in guiding plant selection and in promoting air purification. This research aimed to identify the patterns of PM2.5 washoff processes, the differences in PM2.5 washoff processes between different tree species, and the influence of rainfall intensity on PM2.5 washoff processes. We also sought to determine which plant types and rainfall conditions had a positive effect on PM washoff. In this study, three kinds of rainfall intensities (4 mm/h, 8 mm/h, 12 mm/h) were simulated, and the washoff processes of PM2.5 were studied for six trees with different branch structure and leaf surface characteristics. The effects of rainfall intensity, species, and exposure time on the PM washoff processes were analyzed. Ultimately, these results will improve our understanding of how plants reduce PM and may be applied to guide the practice of urban greening.
Particulate matter (PM) pollution has a seriously detrimental effect on human health (Anderson et al., 2012; Kim et al., 2013; Oprea et al., 2017). According to the World Health Organization (WHO), fine particulate matter (PM10 and PM2.5) pose the greatest risks to human health, contributing to approximately 7 million deaths each year (WHO, 2018). PM in air is a heterogeneous mixture of solid and liquid particles, which varies in size and chemical composition. The chemical constituents of PM, including metals, nitrates, sulfates, organic compounds, and biological compounds, might cause disease (Emmanouil et al., 2017). PM of various sizes is inhaled to different extents. PM below 10 μm in diameter can penetrate the respiratory tract and be deposited in the tracheobronchial tree (Pražnikar and Pražnikar, 2012). Eventually, those smaller than 2.5 μm (PM2.5) might escape into respiratory bronchioles and alveoli, translocating further into the cells and tissues of the circulatory system (Kim et al., 2013). These particles and their chemical constituents can be deposited deeply in the body, causing significant health problems (Wu et al., 2014). PM2.5 pollution has been acknowledged as the fourth leading risk factor of environmental problems in China; this type of pollution may cause a 2.00% GDP loss and 25.2 billion USD in health expenditure in 2030 (Xie et al., 2016). Consequently, reducing PM, especially fine particles (PM2.5), in air is important for our health. It has been confirmed that trees/forests have a strong ability to remove PM from the air and subsequently mitigate the damage caused by PM (Grote et al., 2016; Nowak et al., 2013; Salmond et al., 2016). Numerous studies have been performed to measure and quantify PM removal by plants (Beckett et al., 2000; Chen et al., 2017; Janhäll, 2015; Petroff et al., 2008a; Sæbø et al., 2012). Vegetation removes PM from the atmosphere through a combination of processes (Beckett et al., 1998; Popek et al., 2013). The main processes are dry depositionand washoff (Schaubroeck et al., 2014). Dry deposition refers to the combined PM deposition on vegetation surfaces caused by Brownian motion, gravity, impact and interception (Beckett et al., 2000). Washoff is the process whereby PM is transferred from plants to the soil during precipitation events (Pullman, 2008). If the particulate matters deposited on the leaf surface cannot be washed off in time, it may be resuspended in the air due to the disturbance of wind and other factors, resulting in secondary pollution. In addition, it prevents the leaves from adsorbing more airborne particulates (Pullman, 2008). Rainwater washoff is one of the key processes for plants to reduce the atmospheric particulate matter. Understanding the washoff processes of PM2.5 from leaf surfaces under rainfall events will be helpful in assessing the ability of plants to remove particles, in guiding plant selection and in promoting air purification. To our knowledge, most of the previous studies focused on PM deposition and resuspension (Qian and Ferro, 2008; Gromke et al., 2008; Petroff et al., 2008b Peachey et al., 2009; Power et al., 2009; Räsänen et al., 2013; Jin et al., 2014; Song et al., 2015; Wang et al., 2015), few studies have discussed PM washoff processes. An overview indicated that PM washoff process was related to rain water accumulation and removal on leaf surface (Schaubroeck et al., 2014). Firstly, there are additional PM in rainwater and stemflow from top crown inputting on leaf surface. Then the PM will be removed with drops and flows when the water on leaf is over the storage capacity of leaf. The processes of accumulation and removal will exchange or recycle during rain event (Schaubroeck et al., 2014). Differences in rainfall intensity might cause variations in PM washoff processes (Pullman, 2008). The species characteristics are the key factors that affect the rainwater interception efficiency and storage capacity (Holder, 2012). Relative to local features of leaf surface, e.g. microsurface roughness, trichome density, stomatal density, the overall features (leaf area and leaf density (leaf number)) have stronger impact on the rainwater interception efficiency and storage capacity of plants (Holder, 2013). However, some studies have been performed to determine the
2. Materials and methods 2.1. Species and samples selection To present different patterns of PM washoff processes, samples should have significantly different characteristics and represent a range of leaf areas as well as branch and leaf densities (leaf numbers and subbranch numbers). We chose three broad-leaved and three coniferous trees for this study (Table 1): the broad-leaved trees were Magnolia grandiflora L. (Lotus Magnolia), Acer palmatum Thunb (Japanese Maple), and Buxus sinica (Rehder et E. H. Wilson) M. Cheng (Common Boxwood), and the coniferous trees were Metasequoia glyptostroboides (Dawn Redwood), Sabina chinensis (L.) Ant (Dragon Juniper) and Cedrus deodara (Roxb. et Lamb.) G. Don (Cedrus Deodara). These species have significantly different leaf area, leaf numbers and sub-branch numbers (Table 1). We cut 57 similar tips of branches approximately 25 cm in length from each species in regions with low frequency of human activity and transportation in the center of Shanghai Jiao Tong University campus (31°01′12〃N, 121°25′33〃E) one day after rainfall. The branch samples of each species were collected from one tree or nearby trees in the same direction and height, so as to ensure the consistency of the surrounding environment. These were randomly assigned to the control group, three experimental groups (three different rainfall intensities), and a reserve group. There were 9 branch samples in the control group, 15 in each experimental group and 3 in the reserve group. A total of 342 2
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Table 1 Chosen species and their characteristics. Tree type
Species
Individual leaf area (cm2)
Leaf number
Sub-branch number
Broad-leaved
Lotus Magnolia Japanese Maple Common Boxwood Dawn Redwood Dragon Juniper Cedrus Deodara
98.70 ± 27.62 18.36 ± 3.84 1.01 ± 0.13 3.48 ± 0.23 0.25 ± 0.02 0.27 ± 0.02
9±2 12 ± 4 58 ± 23 16 ± 3 118 ± 8 487 ± 46
1±0 5±3 8±4 1±0 2±1 17 ± 3
Coniferous
Note: Data in the table indicate mean ± standard deviation.
branch samples were collected from the six species. Before the experiment started, the individual leaf area, leaf number and sub-branch number of each species were measured. The number of leaves and sub-branches were counted, and the average values were calculated for each species. The individual leaf areas were estimated from the total leaf surface area divided by the leaf number per branch. The total leaf surface area (S) of each branch was calculated in two steps: digital leaf images were produced using a scanner, and then the total area of the digital leaf images was calculated using Photoshop CS5 (Adobe, CA, USA). The average individual leaf areas of 3 reserved branches per species were calculated and are presented in Table 1, which shows that the ranges of the individual leaf area, leaf number and sub-branch number of six species were 0.25–98.70, 9–487 and 1–17 cm2, respectively.
The total leaf areas of each branch in the control and experimental groups were determined empirically through relationships between fresh weight and surface area developed in preliminary work. We measured the total leaf area (S) of 3 reserved branches (section 2.1). The fresh leaf weight (W) of each reserved branch was measured in g using a Sartorius BS423S balance (Gottingen, Germany) with 0.0001 g precision. The coefficient (C) of leaf area and fresh weight was obtained by averaging S/W of the 3 reserved branches. The fresh leaf weight (W1) of each branch in the control and experimental groups was measured after the experiment, and the total leaf areas were calculated as W1 × C. 2.3. Particle selection and preparation and dosing experiments To obtain accurate results in experiments determining PM2.5 washoff processes from leaf surfaces under artificial rainfall, we applied tracer particles (SiO2) to the leaf surfaces. Silica micropowder with an average diameter of 2.5 μm on a mass basis was chosen as a tracer for the experiments due to its low solubility and high chemical stability. Silica micropowder was certified by Aladdin Company (Beijing, China) and purchased from their official website (www.aladdin-e.com). These dosing experiments were performed in a wind tunnel (in Shanghai Jiao Tong University) with dimensions (working section) of 0.8 × 0.8 × 2 m (Fig. 1). Wind speeds inside the wind tunnel were confirmed before each trial using a digital converter PC14391951977 (Peng Cheng Mechanical and Electrical Co., Ltd., Jiangsu, China). In this study, 6 species and 57 branch samples per species were collected. Except from the
2.2. Measurement of the total leaf area of branches in the control and experimental groups To be comparable, the PM retained on each branch during a rain event should be standardized by the total leaf area. In preliminary work, we found that there were strong linear relationships between leaf fresh weight and surface area, and R2 (Goodness of fit) of six experimental tree species (Lotus Magnolia, Japanese Maple, Common Boxwood, Dawn Redwood, Dragon Juniper and Cedrus Deodara) are 0.98, 0.98, 0.93, 0.96, 0.93 and 0.91, respectively. The PM number per leaf area was determined from the total PM number and the total leaf area.
Fig. 1. Equipment and procedure used in the dosing and rainfall experiments. 3
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
three branch samples in the reserve group, the other 54 branch samples were dosed with tracer particles (SiO2). They were into three groups for dosing experiments with 18 branches per group. 18 branches were supported horizontally in the working section pointing into the upstream airflow, approximating the position of small branches on trees. Then, the SiO2 particles were applied for 10 min from the upper wind position using an air pump JUBA600W-9L (Zhejiang Tengjing Air Compressor Co., Ltd., Zhejiang, China) with a speed of 1000 mg/min in adding particulate matter. The particles were transported to the branches by a uniform wind (1 m/s) produced by the fan system inside the wind tunnel.
2.6. Measurement of PM2.5 The PM2.5 retained on the leaves after the rainfall events was measured as follows (Fig. 1). First, the leaves of each branch sample removed from the artificial rainfall simulator were cut and placed into clean buckets containing 400 ml of distilled water. In order to make the particulate matter on the leaves immerse in the liquid better, the samples were covered and soaked for 2 h before being removed (Xie et al., 2018). To separate the PM2.5 from leaf surface and ensure an even distribution in solution, the buckets were shaken for 1 min before testing (Xie et al., 2018). We labeled 324 solution samples using tags. Then, the PM2.5 number in the solution in each bucket, representing the total PM2.5 number retained on different branches during the rainfall events, was measured using a laser particle counter SMIT-Y (Shanghai Ming Zi Jia Electronics Co. Ltd, Shanghai, China) with a measuring range of 0–120000 and a measurement error of 1%. The working principle of the particle counter is as follows: When the solution with particles passes through the counter by a transparent tube which is irradiated with lasers, then the particles will form different sizes of shadows to a resistance plate, the number of shadows can be estimated and the particle number can be obtained. Finally, the PM2.5 number retained was calculated on a per leaf area basis to ensure comparability between branches and species; this was determined from the total PM2.5 number and the total leaf area per branch. After all the samples were tested at different time points (0, 5, 10, 15, 20, and 25 min), we calculated the variations in the PM2.5 number retained on the leaves over time during each rainfall event for the different species. In addition, the net change in the PM2.5 number at different time points was compared to the PM2.5 value of control group samples.
2.4. Rainfall intensity settings The imposed rainfall intensity should be consistent with the characteristics of rainfall in the region. The conventional method of rainfall intensity classification did not apply to this experiment. The conventional rainfall intensity is the rainfall volume over a 12- or 24-h period, which does not accurately reflect the real characteristics of individual rainfall events. A study conducted on individual rainfall events in Shanghai showed that the rainfall intensity of almost 90% of rainfall events was below 10 mm/h, the 25%, 50%, and 75% percentiles of individual rainfall events are approximately 1 mm/h, 4 mm/h and 8 mm/h, respectively (Gao et al., 2012). A rainfall intensity of 1 mm/h might be too low to obtain accurate results because of the short duration (25 min) of the experiment for this study. According to the rainfall characteristics in Shanghai and the experimental requirements, we designed a gradient consisting of three rainfall intensities, i.e., 4 mm/h, 8 mm/h, and 12 mm/h. It is difficult to realize the rainfall below 16 mm/h by the traditional rainfall device. We improved the traditional rainfall device, controlled the water pressure by the frequency modulation pump, controlled the water volume by the number of sprinklers, adjusted the sprinklers angle to control the spraying range. This rainfall simulator with dimensions (Length, width and height) of 6 × 4 × 5 m (Fig. 1) located in a sealed glass house in Shanghai Jiao Tong University. The glass house isolated the pollution of the air particles from the outside and prevented the rain from being disturbed by wind. We put 40 rainfall barrels evenly under the rainfall device to test the intensity and uniformity of the rainfall in different pressure, number and setting angle of sprinklers. When we set the pressure of the water pump to 1.5 kg, the sprinkler angle to 15 degrees of elevation, opened 2, 4 and 6 sprinkler tips, we got rainfall events with average intensities of 4.2, 7.8, and 12.3 mm/h, and coefficient of variation of 0.08, 0.10, 0.12 respectively. In the new device, we could get more stable, more uniform, smaller rainfall.
2.7. Data analysis and statistics Data analysis was conducted using the SPSS PASW 18.0 program (SPSS Inc., Chicago, USA) and R for Windows 3.3.3 (Bell Laboratories Lucent Technologies; Mount Laurel, New Jersey, USA). 6 tree species under 3 different rainfall intensities constitute the 18 PM2.5 wash off processes. The patterns of washoff processes were analyzed using descriptive statistics and are presented as line graphs. The 18 processes that exhibited common patterns were classified by hierarchical cluster analysis, with the net changes of PM2.5 number at 5, 10, 15, 20, and 25 min compared to 0 min respectively. The washoff processes of the subsequent groups were simulated using polynomial regression based on the average values. Analysis of variance (ANOVA) was used to determine the significant factors influencing the PM2.5 washoff processes. The data were first tested for normality using the one-sample Kolmogorov–Smirnov test. 3. Results
2.5. Artificial rainfall simulation experiments
3.1. General characteristics of washoff processes
Exposure to the SiO2 particles ceased, and three branch samples from each group were randomly selected as the control group samples, recorded as having experienced 0 min of rain fall washoff; the 15 remaining branch samples were moved to an artificial rainfall simulator. The water used for simulating rainfall was distilled water, which contained 0.1 × 103 number of PM2.5 per cubic centimeter. Since the rainfall simulation test start, three of the samples were randomly selected as a group and taken out in every 5 min, and the whole experiment would last 25 min. That means every group of branches suffered from rain time is different, with the duration of 5, 10, 15, 20, 25 min respectively. After measuring PM2.5 number maintained on the leaf surface of all branch samples, the change processes of PM2.5 number on leaves surface over time in rain events would be shown. Such an experiment will be carried out under three different rainfall intensities (4, 8 and 12 mm/h).
The changes in the PM2.5 numbers retained by the six species under the three different rainfall intensities (4, 8, and 12 mm/h) are shown in Fig. 2. The washoff processes are complex and changed under different rainfall intensities and species. In addition, the cycles and patterns differed between processes. The maximum/minimum (Max/Min) value indicates variation of PM2.5 amounts and the average value represents the ability of leaves to retain PM2.5 during rain events (Table 2). The max/min can eliminate the influence of dimension and magnitude of variables, making different data comparable. A greater the max/min ratio indicates a greater variation of variables. The Max/Min values of PM2.5 retained by the six species ranged from 3.4 to 23.4, indicating that rain had a noticeable impact on PM2.5 retention. The ranking of Max/Min values was Lotus Magnolia (23.4) > Common Boxwood (20.6) > Dawn Redwood (8.5) > Dragon Juniper (4.8) > Cedrus Deodara (4.5) > Japanese 4
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 2. PM2.5 retention on leaves over time under different rainfall intensities. The uncertainty bars represent standard deviations of results from replicate experiments on replicate foliage samples.
trees PM2.5 number at 5, 10, 15, 20, and 25 min compared to that at 0 min respectively. The members of the groups are presented in Table 3. Groups I, II, III and IV had 9, 5, 2, and 2 members, respectively (Table 3). Almost all the washoff processes of the broad-leaved trees belonged to Group I except those of Japanese Maple and Common Boxwood under rainfall intensities of 8 mm/h and 12 mm/h, respectively. Group II contained 2 processes of Dawn Redwood, 2 processes of Cedrus Deodara and 1 process of Japanese Maple. Group III contained 2 processes of Dragon Juniper under rainfall intensities of 8 and 12 mm/ h. Group IV contained 1 processes of Common Boxwood under a rainfall intensity of 12 mm/h and 1 processes of Cedrus Deodara under a rainfall intensity of 4 mm/h. An analysis of these processes alongside branch characteristics (Table 1) indicated that the processes of broadleaved trees with large leaves and simple crowns always belonged to Group I. The processes of Group II may be found in species with small leaves and complex crowns under high rainfall intensity. The processes of Group III consisted of Dragon Juniper (with an extremely complex crown) under high rainfall intensity. The processes of group IV may occur in trees with small leaves but under different rainfall conditions.
Table 2 Characteristics of PM2.5 retention determined by leaf area during rain events. Life forms
Broad-leaved Coniferous
Species
Lotus Magnolia Japanese Maple Common Boxwood Dawn Redwood Dragon Juniper Cedrus Deodara
Number of PM2.5 (103n/cm2) Mean
Max
Min
max/min
4.1 33.0 6.4 12.6 14.7 40.
11.7 67.4 14.4 28.2 26.6 68.2
0.5 19.7 0.7 3.3 5.6 15.1
23.4 3.4 20.6 8.5 4.8 4.5
Maple (3.4). The ranking of the average values was Cedrus Deodara (40.3 × 103/cm2) > Japanese Maple (33.0 × 103/cm2) > Dragon Juniper (14.7 × 103/cm2) > Dawn Redwood (12.6 × 103/ cm2) > Common Boxwood (6.4 × 103/cm2) > Lotus Magnolia (4.1 × 103/cm2). These results indicate that there were considerable variations in the PM2.5 numbers retained by the trees at different time points and under different rainfall intensities. The rankings showed that rainfall had a greater effect on the ability of broad-leaved trees to retain PM2.5 compared with conifers, and trees with more complex structures and smaller leaves could better retain particles and had more stable PM2.5 retention during rainfall events. The washoff processes exhibited different cycles and patterns under different species and rainfall intensities (Fig. 2). The PM2.5 washoff processes of broad-leaved trees generally had two peaks, while there was only one or no peak for coniferous trees. This means that the PM2.5 washoff processes of coniferous trees may be more stable than those of broad-leaved species. In general, the processes under a rainfall intensity of 12 mm/h exhibited larger fluctuation ranges and smaller cycles than those under rainfall intensities of 4 and 8 mm/h. This indicates that the PM2.5 washoff processes are more variable under higher rainfall.
3.3. Fitted models of washoff processes The net change in PM2.5 and the average of each group at different time points were calculated. To interpret the PM2.5 washoff processes more clearly and accurately, the relationships of net change in PM2.5 over time were simulated using polynomial regressions (Fig. 4). The features between the four models were notably different, and all of the R2 values were > 0.94, indicating that the PM2.5 washoff processes were clustered into appropriate groups and that all models fitted well. All the processes were well fitted by quartic polynomial functions. They might follow a bimodal curve (y= 0.51x 4 + 27.05x3 449.99x2 + 2396.4 x+ 24.89), unimodal curve 7.63x 3 + 23.64x2 + 1123.2 x+ 134.92 ), continually-rising (y= 0.19x 4 371.15x2 + 2962.9 x 30.22 ) or a Ucurve (y= 0.33x 4 + 19.39x3 5319.3 x 36.08) shaped curve (y= 0.90x 4 50.54x3 + 926.28x2 under different conditions. The processes of Group I were bimodal distributions, indicating that PM2.5 retention changed easily and rapidly over a cycle of 10–15 min. The process of Group II presented a unimodal curve over a cycle of 25 min; the PM2.5 retained by leaves increased initially and
3.2. Clusters of washoff processes The washoff processes shown in Fig. 2 were clustered into four groups using the hierarchical cluster method (Fig. 3). The variables used to develop cluster discrimination were the net changes of different 5
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 3. Hierarchical cluster plot of PM2.5 washoff processes for different tree species and rainfall intensities. The numbers in parenthesis in the figure are the values of the applied rainfall rate in mm/h.
showed that the net changes were normally distributed (P = 0.068). Then, ANOVAs were performed to determine the significant factors that influenced the net change of PM2.5 on leaves during rainfall events. The results are shown in Table 4. The effects of species (P < 0.001), species × rainfall (P = 0.029), species × exposure time (P < 0.001), and species × rainfall × exposure time (P = 0.002) were significant for PM2.5. The results indicate that species, rainfall and exposure time could significantly influence PM2.5 retention on leaves and the effect of tree species on particle retention was greater than that of rainfall intensity and exposure time. The PM2.5 retained by trees during rainfall events is a comprehensive result of multiple factors, and the key factor is the characteristic of the species.
Table 3 The clustered members of PM2.5 washoff processes with common patterns, explored using hierarchical cluster analysis. Serial numbers
Species
Rainfall (mm/h)
Clusters
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Lotus Magnolia Lotus Magnolia Lotus Magnolia Japanese Maple Japanese Maple Japanese Maple Common Boxwood Common Boxwood Common Boxwood Dawn Redwood Dawn Redwood Dawn Redwood Dragon Juniper Dragon Juniper Dragon Juniper Cedrus Deodara Cedrus Deodara Cedrus Deodara
4 8 12 4 8 12 4 8 12 4 8 12 4 8 12 4 8 12
I I I I II I I I IV II I II I III III IV II II
4. Discussion This study presented the PM2.5 washoff processes from leaves under small rainfalls (smaller than rainstorms) with particulate matter, supplementing the theoretical relationship between particulate matter and leaves. In this study, four groups (I, II, III and IV) of washoff processes (bimodal, unimodal, continually-rising and U-shaped curves, respectively) were categorized,. All processes were well-fitted by quartic polynomial functions, although they followed different patterns. The processes of Group I were bimodal distributions, indicating PM2.5 retention changed easily and rapidly with an accumulation-removal cycle of 10–15 min. The processes of broad-leaved trees with large leaves and simple crowns always belonged to Group I. Broadleaved trees have large leaves and simple structures, resulting in highly efficient interception of rainwater and consequently, a short cycle of water accumulation and dripping (Muzylo et al., 2009). The process of Group II was presented as a unimodal curve over a period of 25 min; the PM2.5 retained by leaves increased initially and then decreased, reaching a maximum value in 10–15 min. Most of the processes of Group II occurred in trees with small leaves and complex crowns under high rainfall intensity. The processes of Group II had longer cycles than those of Group I. This may have occurred because of the lower rainfall interception efficiency of small leaves, and the extra water storage capacity associated with the complex crown and leaves could prolong the cycles. The third type of washoff process showed a continuous increase with time and occurred mainly under high rainfall intensity and in trees
Note: I: bimodal curve; II: unimodal curve; III: continually-rising curve; IV: Ushaped curve.
then decreased, reaching a maximum value between 10 and 15 min. The change in PM2.5 in Groups I and II exhibited a cycle that combined the processes of accumulation and attenuation, similar to the accumulation and dissipation processes of rainfall on leaves. The process of Group III was a continuous increase, indicating that the PM2.5 on leaves accumulated continuously throughout the experiment. The process of Group IV was an initial sharp decrease followed by an increase, i.e., a U-shape. Under this scenario, PM2.5 on the leaf surfaces decreased in the preliminary stage and then increased. The rainfall interception efficiency or storage capacity was very low in the preliminary stage, but then increased after the leaves became wet. 3.4. Significant factor test The normality tests for the net change in the PM2.5 on leaves were conducted using the one-sample Kolmogorov–Smirnov test, which 6
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 4. Fitted models of the net change in PM2.5 over time for the four different groups identified by hierarchical cluster analysis. The uncertainty bars represent standard deviations of the PM2.5 net change from the same group.
the two species might interact, improving the water storage and more PM2.5 might be inputted with stemflow. The PM2.5 washoff curves under small rain events (smaller than rainstorm) in this study provides a potential way to optimize the estimation of the ability of plants to purify particulate matter. The washoff processes of PM2.5 in this study were different from those in previous research which follows negative exponential curves (Mcdowell et al., 1984; Pullman, 2008). One of the reasons might be that additional PM2.5 in rainfall and stemflow can be intercepted (Muzylo et al., 2009), whereas this was not considered in the chemical element washoff processes in previous studies. The other reason might be the different rainfall intensities. A rainfall intensity that is too heavy might result in very short washoff cycles and smooth curves, while the fluctuation of water interception and dripping may be more obvious under lower rainfall intensities. The PM2.5 washoff processes in this study is common in reality due to the frequent occurrence of small rainfall events (smaller than rainstorms). However, only negative exponential washoff curves had been considered in existing models when estimating the particulate matter reduction ability of plants (Schaubroeck et al., 2014), which needs to be optimized. Broad-leaved trees probably have a better PM2.5 washoff efficiency than conifers during rains. In this study, we found smaller average values and larger max/min values of PM2.5 retained on broad-leaved tree species than on coniferous species. This indicates that amount of retained particles on leaves of broadleaf species is lower and its variance is higher, which means particles on leaves of broadleaf species can be washed out in rains more easily than that on conifers. In general, there were more peaks in the PM2.5 washoff processes of broad-leaved trees than those of coniferous trees over the duration of the experiment. These results show that there are more fluctuating processes on the PM2.5 retention of broad-leaved tree species compared with coniferous species in rainfall. The trees with a more complex structure and smaller leaves could better retain particles and had more stable PM2.5 washoff processes during rainfall events (Beckett et al., 2000). The cycles of PM2.5 accumulation and removal on broad-leaved trees could be shorter than those of conifers, because the accumulation and removal processes of particulate matter on leaves is highly correlated with that of rainwater on leaves when raining (Schaubroeck et al., 2014). The larger
Table 4 ANOVA results of between-subject effects on the net change in PM2.5 on leaves during rain events. Source
Corrected Model Intercept Species Rainfall Exposure time Species × rainfall Species × exposure time Rainfall × exposure time Species × rainfall × exposure time Error Total Corrected Total R2 Adjusted R2
DF
89 1 5 2 4 10 20 8 40 180 270 269
Test for PM2.5 net-change F
P
2.54 60.50 10.38 1.09 0.16 2.07 3.14 1.44 1.92
< 0.001 < 0.001 < 0.001 0.340 0.957 0.029 < 0.001 0.182 0.002
0.557 0.338
with extremely complex crowns (e.g., Dragon Juniper). The underlying reason might be similar to that for Group II, i.e., the extremely complex crown resulted in cycles even longer than the experimental duration of 25 min. The process of Group III indicated a positive accumulation with a continuous increase, indicating that PM2.5 accumulation exceeded the amount that dripped from the leaves throughout the experiment. A large number of particles on the surface of complex branches might be inputted to leaf surface with stemflow, which helps the PM2.5 accumulation. The process of Group IV was an initial sharp decrease followed by an increase, i.e., a U-shape. This kind of process occurred under the scenario where there was a short storage duration in the preliminary stage that lengthened after the leaves became wet. In the preliminary stage, Common Boxwood intercepted water, which subsequently dripped off rapidly under a rainfall intensity of 12 mm/h, and the PM2.5 retained by Cedrus Deodara decreased rapidly due to the extremely low water storage capacity of the needle leaves under a rainfall intensity of 4 mm/h. After wetting, however, the complex structure and leaves of 7
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
leaves of the broad-leaved trees had higher water interception efficiency, while the more complex structure of conifers improved the water storage capacity (Li et al., 2016). Consequently, broad-leaved trees might have more rapid dripping than conifers and a more changeable PM2.5 balance. The PM2.5 washoff efficiency of broadleaved trees is higher than that of conifers, which is opposite to the PM2.5 deposition efficiency (Janhäll, 2015). This mean that both the adsorption capacity and washoff ability of particulate matter need to be considered when screening trees of particulate matter purification. Higher rainfall intensity can shorten the cycle of PM2.5 accumulation and removal on leaves, resulting in the particulate matter on the leaf surface being effectively washed away. We found that the number of particulate matter on leaf surface change over time under a rainfall intensity of 12 mm/h exhibited larger fluctuation ranges and smaller cycles of accumulation and removal on leaves than those under rainfall intensities of 4 and 8 mm/h. In previous studies, the washoff processes followed negative exponential curves under rainstorm intensities (≥16 mm/h) (Mcdowell et al., 1984; Pullman, 2008). This indicates that the cycle of PM2.5 accumulation changed to dripping shortened with increasing rainfall intensity, which is closely related to the water balance on leaves (Schaubroeck et al., 2014). In rainless areas, the enrichment of particulate matter on leaf surface has a great impact on plant photosynthesis, respiration, disease and other physiological functions (Grote et al., 2016). If it is necessary, spraying plants with rainfall intensity over 16 mm/h can effectively wash out the particulate matter on leaf surfaces and reduce the impact of particulate matter on plant physiology.
Because of the complex and continuous PM2.5 exchange process between stem and leaf surface, we can not separate the net PM2.5 removal from leaf in rain event. In the next stage of work, we will consider the PM2.5 removal from stem and leaf surface of a branch synthetically, so as to calculate the net PM2.5 removal of plants in rain event. In addition, a comprehensive evaluation of the ability of vegetation to reduce PM based on washoff efficiency and deposition effects needs to be conducted. However, this study provides a basis for calculating the PM2.5 removal from plant surface and assessing the ability of plant to purify particulate matter.
5. Conclusion
Appendix A. Supplementary data
In general, the PM2.5 washoff processes were associated with the constant exchange processes between accumulation and removal. Replacement processes also occurred, i.e., where additional PM2.5 in rain would be intercepted, and initial particles would drip off. Therefore, relative to the initial value, the amount of particles on the leaves was not necessarily reduced during rainfall events. The cycles of the exchanges could be short for leaves with a high water interception efficiency and small water storage capacity, or long otherwise. However, if the water interception efficiency is extremely high and the water storage capacity is quite small, the process might begin to decline. The shorter the exchange cycle, the higher the efficiency with which PM2.5 is washed off from leaves. Broad-leaved trees with short cycles might have a higher efficiency to transport PM2.5 from leaves to the soil compared with conifers, which is opposite to the PM2.5 deposition efficiency. In this respect, broad-leaved trees have advantages in reducing particulate matter. Rainfall intensity could change the water interception efficiency, and the concentration of particulate matter in rainwater and stemflow could change the particles that accumulate on the leaves. A comprehensive evaluation of the ability of vegetation to reduce PM2.5 should be carried out based on washoff efficiency and the deposition effect, according to the local precipitation conditions and species characteristics. Due to the significant impact of tree species on the process of PM deposition and washoff, appropriate tree species selection will have a positive effect on particulate matter reduction. There were some limitations of this study, and interesting topics should be expanded in future work. The duration of the experiment in this study may have been too short, and some processes were not completely observed. Future experiments can be considered to extend the experimental time. In addition, we only conducted the experiments using rainfall containing 0.1 × 103 PM2.5 per cubic centimeter and only one type (SiO2) and size (2.5 μm) of PM. The effect of different concentrations of PM2.5 in rainfall on washoff processes should be studied further. However, we found that species was the most important factor that affected PM2.5 washoff processes, and in-depth studies on the influence of species characteristics could be developed in further work.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116844.
Declaration of interest statement Here within enclosed is our manuscript for consideration to be published in Atmospheric Environment. The authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere. The submission is original and that all authors are aware of the submission and agree to its publication. Acknowledgments This work was funded by the Projects in the National Key Research and Development Program of China (2016YFC0502703) All authors would like to give thanks to Bingxin Ma for his help on designing wind tunnel. We would like to thank our team workers Dan Chen, Zhedong Wang, Size Cai, Yunqiang Xia, Wen Pang for their hard work on the experiment and data collection.
References Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8 (2), 166–175. https://doi.org/10.1007/s13181-011-0203-1. Beckett, K.P., Freer-Smith, P.H., Taylor, G., 2000. Particulate pollution capture by urban trees: effect of species and wind speed. Glob. Chang. Biol. 6, 995–1003. https://doi. org/10.1046/j.1365-2486.2000.00376.x. Beckett, K.P., Freer-Smith, P.H., Taylor, G., 1998. Urban woodlands: their role in reducing the effects of particulate pollution. Environ. Pollut. 99 (3), 347–360. https://doi.org/ 10.1016/S0269-7491(98)00016-5. Chen, J., Yu, X., Bi, H., Fu, Y., 2017. Indoor simulations reveal differences among plant species in capturing particulate matter. PLoS One 12 (5), e0177539. https://doi.org/ 10.1371/journal.pone.0177539. Cohen, M.L., Steinmetz, W.D., 1986. Foliar washoff of pesticides by rainfall. Environ. Sci. Technol. 20 (5), 521–523. https://doi.org/10.1021/es00147a014. Dang, A., Silburn, M., Craig, I., Shaw, M., Foley, J., 2016. Washoff of residual Photosystem II herbicides from sugarcane trash under a rainfall simulator. J. Agric. Food Chem. 64 (20), 3967–3974. https://doi.org/10.1021/acs.jafc.5b04717. Emmanouil, C., Drositi, E., Vasilatou, V., Diapouli, E., Krikonis, K., Eleftheriadis, K., Kungolos, A., 2017. Study on particulate matter air pollution, source origin, and human health risk based of PM10 metal content in Volos City, Greece. Toxicol. Environ. Chem. 99 (4), 691–709. https://doi.org/10.1080/02772248.2016.1242005. Gao, Y., Fang, G.L., Hu, L., Xu, L.J., 2012. An analysis of characteristics of rainfalls with short and long durations in Shanghai. In: 2012 International Symposium on Urban Stormwater Management, pp. 153–158. Gromke, C., Buccolieri, R., Sabatino, S.D., Ruck, B., 2008. Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations – evaluation of CFD data with experimental data. Atmos. Environ. 42 (37), 8640–8650. https://doi.org/10.1016/j.atmosenv.2008.08.019. Grote, R., Samson, R., Alonso, R., Amorim, J.H., Cariñanos, P., Churkina, G., Fares, S., Thiec, D.L., Niinemets, Ü., Mikkelsen, T.N., 2016. Functional traits of urban trees: air pollution mitigation potential. Front. Ecol. Environ. 14 (10), 543–550. https://doi. org/10.1002/fee.1426. Holder, C.D., 2012. The relationship between leaf hydrophobicity, water droplet retention, and leaf angle of common species in a semi-arid region of the western United States. Agric. For. Meteorol. 152, 11–16. https://doi.org/10.1016/j.agrformet.2011. 08.005. Holder, C.D., 2013. Effects of leaf hydrophobicity and water droplet retention on canopy storage capacity. Ecohydrology 6 (3), 483–490. https://doi.org/10.1002/eco.1278. Janhäll, S., 2015. Review on urban vegetation and particle air pollution – deposition and dispersion. Atmos. Environ. 105, 130–137. https://doi.org/10.1016/j.atmosenv.
8
Atmospheric Environment 214 (2019) 116844
C. Xie, et al. 2015.01.052. Jin, S., Guo, J., Wheeler, S., Kan, L., Che, S., 2014. Evaluation of impacts of trees on PM2.5 dispersion in urban streets. Atmos. Environ. 99, 277–287. https://doi.org/10. 1016/j.atmosenv.2014.10.002. Kim, K.H., Jahan, S.A., Kabir, E., 2013. A review on human health perspective of air pollution with respect to allergies and asthma. Environ. Int. 59, 41–52. https://doi. org/10.1016/j.envint.2013.05.007. Li, X., Xiao, Q., Niu, J., Dymond, S., Doorn, N.S.V., Yu, X., Xie, B., Lv, X., Zhang, K., Li, J., 2016. Process-based rainfall interception by small trees in Northern China: the effect of rainfall traits and crown structure characteristics. Agric. For. Meteorol. 218–219, 65–73. https://doi.org/10.1016/j.agrformet.2015.11.017. Mcdowell, L.L., Willis, G.H., Southwick, L.M., Smith, S., 1984. Methyl parathion and EPN washoff from cotton plants by simulated rainfall. Environ. Sci. Technol. 18, 423–427. https://doi.org/10.1021/es00124a006. Muzylo, A., Llorens, P., Valente, F., Keizer, J.J., Domingo, F., Gash, J.H.C., 2009. A review of rainfall interception modelling. J. Hydrol 370, 191–206. https://doi.org/10.1016/ j.jhydrol.2009.02.058. Nowak, D.J., Hirabayashi, S., Bodine, A., Hoehn, R., 2013. Modeled PM 2.5 removal by trees in ten U.S. cities and associated health effects. Environ. Pollut. 178, 395–402. https://doi.org/10.1016/j.envpol.2013.03.050. Oprea, M., Dunea, D., Liu, H.Y., 2017. Development of a knowledge based system for analyzing particulate matter air pollution effects on human health. Environ. Eng. Manag. J. 16 (3), 669–676. https://doi.org/10.30638/eemj.2017.068. Peachey, C.J., Sinnett, D., Wilkinson, M., Morgan, G.W., Freer-Smith, P.H., Hutchings, T.R., 2009. Deposition and solubility of airborne metals to four plant species grown at varying distances from two heavily trafficked roads in London. Environ. Pollut. 157, 2291–2299. https://doi.org/10.1016/j.envpol.2009.03.032. Petroff, A., Mailliat, A., Amielh, M., Anselmet, F., 2008a. Aerosol dry deposition on vegetative canopies. Part II: a new modelling approach and applications. Atmos. Environ. 42 (16), 3654–3683. https://doi.org/10.1016/j.atmosenv.2007.12.060. Petroff, A., Mailliat, A., Amielh, M., Anselmet, F., 2008b. Aerosol dry deposition on vegetative canopies. Part I: review of present knowledge. Atmos. Environ. 42 (16), 3625–3653. https://doi.org/10.1016/j.atmosenv.2007.09.043. Popek, R., Gawrońska, H., Wrochna, M., Gawroński, S.W., Sæbø, A., 2013. Particulate matter on foliage of 13 woody species: deposition on surfaces and phytostabilisation in waxes – a 3-year study. Int. J. Phytoremediation 15, 245–256. https://doi.org/10. 1080/15226514.2012.694498. Potter, C.S., Ragsdale, H.L., 1991. Dry deposition washoff from forest tree leaves by experimental acid rainfall. Atmos. Environ. 25, 341–349. https://doi.org/10.1016/ 0960-1686(91)90305-Q. Power, A.L., Worsley, A.T., Booth, C., 2009. Magneto-biomonitoring of intra-urban spatial variations of particulate matter using tree leaves. Environ. Geochem. Hlth. 31, 315–325. https://doi.org/10.1007/s10653-008-9217-2. Pražnikar, Z., Pražnikar, J., 2012. The effects of particulate matter air pollution on respiratory health and on the cardiovascular system. Slovenian J. Public Health 51, 190–199. https://doi.org/10.2478/v10152-012-0022-z. Pullman, M., 2008. Conifer PM2.5 Deposition and Resuspension in Wind and Rain Events.
http://hdl.handle.net/1813/11239. Qian, J., Ferro, A.R., 2008. Resuspension of dust particles in a chamber and associated environmental factors. Aerosol Sci. Technol. 42 (7), 566–578. https://doi.org/10. 1080/02786820802220274. Räsänen, J.V., Holopainen, T., Joutsensaari, J., Ndam, C., Pasanen, P., Rinnan, Å., Kivimäenpää, M., 2013. Effects of species-specific leaf characteristics and reduced water availability on fine particle capture efficiency of trees. Environ. Pollut. 183, 64–70. https://doi.org/10.1016/j.envpol.2013.05.015. Reddy, K.N., Locke, M.A., 2015. Imazaquin spray retention, foliar washoff and runoff losses under simulated rainfall. Pest Manag. Sci. 48, 179–187. https://doi.org/10. 1002/(SICI)1096-9063(199610)48:2%3c179::AID-PS457%3e3.0.CO;2-M. 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, 347–354. https://doi.org/10.1016/j.scitotenv.2012.03.084. Salmond, J.A., Marc, T., Sotiris, V., Katherine, A., Andrew, C., Matthias, D., Dirks, K.N., Clare, H., Shanon, L., Helen, M., 2016. Health and climate related ecosystem services provided by street trees in the urban environment. Environ. Health-Glob. 15 (Suppl. l), 36. https://doi.org/10.1186/s12940-016-0103-6. Schaubroeck, T., Deckmyn, G., Neirynck, J., Staelens, J., Adriaenssens, S., Dewulf, J., Muys, B., Verheyen, K., 2014. Multilayered modeling of particulate matter removal by a growing forest over time, from plant surface deposition to washoff via rainfall. Environ. Sci. Technol. 48 (18), 10785–10794. https://doi.org/10.1021/es5019724. Song, Y., Maher, B.A., Li, F., Wang, X., Sun, X., Zhang, H., 2015. Particulate matter deposited on leaf of five evergreen species in Beijing, China: source identification and size distribution. Atmos. Environ. 105, 53–60. https://doi.org/10.1016/j.atmosenv. 2015.01.032. Wang, H., Shi, H., Wang, Y., 2015. Effects of weather, time, and pollution level on the amount of particulate matter deposited on leaves of ligustrum lucidum. Sci. World J 935942. 2015. https://doi.org/10.1155/2015/935942. WHO(World Health Organization), 2018. World Health Organization Releases New Global Air Pollution Data. Geneva, Switzerland. http://www.ccacoalition.org/en/ news/world-health-organization-releases-new-global-air-pollution-data. Willis, G.H., Mcdowell, L.L., Southwick, L.M., Smith, S., 1994. Azinphosmethyl and fenvalerate washoff from cotton plants as a function of time between application and initial rainfall. Arch. Environ. Contam. Toxicol. 27, 115–120. https://doi.org/10. 1007/bf00203896. Wu, S., Deng, F., Wei, H., Huang, J., Wang, X., Hao, Y., Zheng, C., Qin, Y., Lv, H., Shima, M., 2014. Association of cardiopulmonary health effects with source-appointed ambient fine particulate in Beijing, China: a combined analysis from the Healthy Volunteer Natural Relocation (HVNR) study. Environ. Sci. Technol. 48, 3438–3448. https://doi.org/10.1021/es404778w. Xie, C., Kan, L., Guo, J., Jin, S., Li, Z., Chen, D., Li, X., Che, S., 2018. A dynamic processes study of PM retention by trees under different wind conditions. Environ. Pollut. 233, 315–322. https://doi.org/10.1016/j.envpol.2017.10.073. Xie, Y., Dai, H., Dong, H., Hanaoka, T., Masui, T., 2016. Economic impacts from PM2.5 pollution-related health effects in China: a provincial-level analysis. Environ. Sci. Technol. 50, 4836–4843. https://doi.org/10.1016/j.envint.2018.03.030.
9
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Understanding the washoff processes of PM2.5 from leaf surfaces during rainfall events
T
Changkun Xiea,b, Lubing Yanb, Anze Liangb, Shengquan Chea,* a b
School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Particulate matter Air pollution PM2.5 washoff processes Rainfall Leaves
The PM on plant surface being washed into soil during the rain is one of the key processes for plants to reduce atmospheric PM. This study attempted to explore the PM2.5 washoff processes from different tree leaves during rain events (< 16 mm/h) using an artificial rainfall simulation experiment. We aimed to improve our knowledge of processes associated with PM2.5 reduction and to provide a basis for accurately evaluating the ability of plants to reduce PM2.5. This is the first study showing that the PM2.5 washoff processes from leaves follow quartic functions and 4 pattern curves under different conditions were categorized. They respectively explained the PM2.5 washoff processes in broad-leaved trees with large leaves and simple crowns (bimodal curved), in conifer species with small leaves and complex crowns under high rainfall intensity (unimodal curved), in trees with extremely complex crowns under high rainfall intensity (continually-rising curved), and in conditions under which extremely high water interception efficiency but rather low water storage capacity occur (U-shaped curved). These curves indicate that the amount of PM2.5 on leaves was not necessarily reduced in rainfall events. The general ranking of the average values of PM2.5 number on leaves surface during rain events was Cedrus Deodara (40.3 × 103/cm2), Japanese Maple (33.0 × 103/cm2) > Dragon Juniper (14.7 × 103/cm2), Dawn Redwood (12.6 × 103/cm2) > Common Boxwood (6.4 × 103/cm2), Lotus Magnolia (4.1 × 103/cm2). The cycles of PM2.5 accumulation and removal on broad-leaved trees might be shorter than that of conifers, meaning that they may have a better PM2.5 washoff efficiency during rain, which is opposite to the PM2.5 deposition efficiency. Higher rainfall intensity can reduce the cycle length and enhance the PM2.5 washoff efficiency.
*
Corresponding author. 800# Dongchuan Road, Shanghai, 200240, PR China. E-mail address: [email protected] (S. Che).
https://doi.org/10.1016/j.atmosenv.2019.116844 Received 14 March 2019; Received in revised form 13 July 2019; Accepted 16 July 2019 Available online 20 July 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
1. Introduction
washoff processes of chemical elements from plant surfaces. The washoff of sprays (pesticides, herbicides, etc.) from plant surfaces has been studied as part of crop management in rainfall intensities from 11 mm/h to 111 mm/h (Cohen and Steinmetz, 1986; Dang et al., 2016; Mcdowell et al., 1984; Reddy and Locke, 2015; Willis et al., 1994). The washoff processes of sulfate and potassium from four species were studied using experimental acid rainfall intensity of 84 mm/h (Potter and Ragsdale, 1991). The washoff processes of KNO3 were tested for three conifer species using simulated rainfall intensity of 220 mm/h (Pullman, 2008). The common understanding of the above studies was that the washoff processes of trace elements from plant surfaces followed negative exponential curves. One of the limitations in the chemical element washoff processes examined in previous studies is missing considered the accumulation process. The washoff processes of PM during rain events composed of accumulation and removal processes might be more complex than those of the chemical elements mentioned above. The other limitation of the previous studies is that the simulated rainfall intensities might be too strong to represent the high-frequency rainfall intensities in nature (Dang et al., 2016). Most of these rainfall intensities are far heavier than those measured during rainstorms (16 mm/h), which do not occur frequently (Dang et al., 2016; Reddy and Locke, 2015). In this respect, the PM2.5 washoff processes might be more complex than those of chemical elements in rainstorms. Understanding the washoff processes of PM2.5 from leaf surfaces under high-frequency rainfall intensities will be helpful in assessing the ability of plants to remove particles, in guiding plant selection and in promoting air purification. This research aimed to identify the patterns of PM2.5 washoff processes, the differences in PM2.5 washoff processes between different tree species, and the influence of rainfall intensity on PM2.5 washoff processes. We also sought to determine which plant types and rainfall conditions had a positive effect on PM washoff. In this study, three kinds of rainfall intensities (4 mm/h, 8 mm/h, 12 mm/h) were simulated, and the washoff processes of PM2.5 were studied for six trees with different branch structure and leaf surface characteristics. The effects of rainfall intensity, species, and exposure time on the PM washoff processes were analyzed. Ultimately, these results will improve our understanding of how plants reduce PM and may be applied to guide the practice of urban greening.
Particulate matter (PM) pollution has a seriously detrimental effect on human health (Anderson et al., 2012; Kim et al., 2013; Oprea et al., 2017). According to the World Health Organization (WHO), fine particulate matter (PM10 and PM2.5) pose the greatest risks to human health, contributing to approximately 7 million deaths each year (WHO, 2018). PM in air is a heterogeneous mixture of solid and liquid particles, which varies in size and chemical composition. The chemical constituents of PM, including metals, nitrates, sulfates, organic compounds, and biological compounds, might cause disease (Emmanouil et al., 2017). PM of various sizes is inhaled to different extents. PM below 10 μm in diameter can penetrate the respiratory tract and be deposited in the tracheobronchial tree (Pražnikar and Pražnikar, 2012). Eventually, those smaller than 2.5 μm (PM2.5) might escape into respiratory bronchioles and alveoli, translocating further into the cells and tissues of the circulatory system (Kim et al., 2013). These particles and their chemical constituents can be deposited deeply in the body, causing significant health problems (Wu et al., 2014). PM2.5 pollution has been acknowledged as the fourth leading risk factor of environmental problems in China; this type of pollution may cause a 2.00% GDP loss and 25.2 billion USD in health expenditure in 2030 (Xie et al., 2016). Consequently, reducing PM, especially fine particles (PM2.5), in air is important for our health. It has been confirmed that trees/forests have a strong ability to remove PM from the air and subsequently mitigate the damage caused by PM (Grote et al., 2016; Nowak et al., 2013; Salmond et al., 2016). Numerous studies have been performed to measure and quantify PM removal by plants (Beckett et al., 2000; Chen et al., 2017; Janhäll, 2015; Petroff et al., 2008a; Sæbø et al., 2012). Vegetation removes PM from the atmosphere through a combination of processes (Beckett et al., 1998; Popek et al., 2013). The main processes are dry depositionand washoff (Schaubroeck et al., 2014). Dry deposition refers to the combined PM deposition on vegetation surfaces caused by Brownian motion, gravity, impact and interception (Beckett et al., 2000). Washoff is the process whereby PM is transferred from plants to the soil during precipitation events (Pullman, 2008). If the particulate matters deposited on the leaf surface cannot be washed off in time, it may be resuspended in the air due to the disturbance of wind and other factors, resulting in secondary pollution. In addition, it prevents the leaves from adsorbing more airborne particulates (Pullman, 2008). Rainwater washoff is one of the key processes for plants to reduce the atmospheric particulate matter. Understanding the washoff processes of PM2.5 from leaf surfaces under rainfall events will be helpful in assessing the ability of plants to remove particles, in guiding plant selection and in promoting air purification. To our knowledge, most of the previous studies focused on PM deposition and resuspension (Qian and Ferro, 2008; Gromke et al., 2008; Petroff et al., 2008b Peachey et al., 2009; Power et al., 2009; Räsänen et al., 2013; Jin et al., 2014; Song et al., 2015; Wang et al., 2015), few studies have discussed PM washoff processes. An overview indicated that PM washoff process was related to rain water accumulation and removal on leaf surface (Schaubroeck et al., 2014). Firstly, there are additional PM in rainwater and stemflow from top crown inputting on leaf surface. Then the PM will be removed with drops and flows when the water on leaf is over the storage capacity of leaf. The processes of accumulation and removal will exchange or recycle during rain event (Schaubroeck et al., 2014). Differences in rainfall intensity might cause variations in PM washoff processes (Pullman, 2008). The species characteristics are the key factors that affect the rainwater interception efficiency and storage capacity (Holder, 2012). Relative to local features of leaf surface, e.g. microsurface roughness, trichome density, stomatal density, the overall features (leaf area and leaf density (leaf number)) have stronger impact on the rainwater interception efficiency and storage capacity of plants (Holder, 2013). However, some studies have been performed to determine the
2. Materials and methods 2.1. Species and samples selection To present different patterns of PM washoff processes, samples should have significantly different characteristics and represent a range of leaf areas as well as branch and leaf densities (leaf numbers and subbranch numbers). We chose three broad-leaved and three coniferous trees for this study (Table 1): the broad-leaved trees were Magnolia grandiflora L. (Lotus Magnolia), Acer palmatum Thunb (Japanese Maple), and Buxus sinica (Rehder et E. H. Wilson) M. Cheng (Common Boxwood), and the coniferous trees were Metasequoia glyptostroboides (Dawn Redwood), Sabina chinensis (L.) Ant (Dragon Juniper) and Cedrus deodara (Roxb. et Lamb.) G. Don (Cedrus Deodara). These species have significantly different leaf area, leaf numbers and sub-branch numbers (Table 1). We cut 57 similar tips of branches approximately 25 cm in length from each species in regions with low frequency of human activity and transportation in the center of Shanghai Jiao Tong University campus (31°01′12〃N, 121°25′33〃E) one day after rainfall. The branch samples of each species were collected from one tree or nearby trees in the same direction and height, so as to ensure the consistency of the surrounding environment. These were randomly assigned to the control group, three experimental groups (three different rainfall intensities), and a reserve group. There were 9 branch samples in the control group, 15 in each experimental group and 3 in the reserve group. A total of 342 2
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Table 1 Chosen species and their characteristics. Tree type
Species
Individual leaf area (cm2)
Leaf number
Sub-branch number
Broad-leaved
Lotus Magnolia Japanese Maple Common Boxwood Dawn Redwood Dragon Juniper Cedrus Deodara
98.70 ± 27.62 18.36 ± 3.84 1.01 ± 0.13 3.48 ± 0.23 0.25 ± 0.02 0.27 ± 0.02
9±2 12 ± 4 58 ± 23 16 ± 3 118 ± 8 487 ± 46
1±0 5±3 8±4 1±0 2±1 17 ± 3
Coniferous
Note: Data in the table indicate mean ± standard deviation.
branch samples were collected from the six species. Before the experiment started, the individual leaf area, leaf number and sub-branch number of each species were measured. The number of leaves and sub-branches were counted, and the average values were calculated for each species. The individual leaf areas were estimated from the total leaf surface area divided by the leaf number per branch. The total leaf surface area (S) of each branch was calculated in two steps: digital leaf images were produced using a scanner, and then the total area of the digital leaf images was calculated using Photoshop CS5 (Adobe, CA, USA). The average individual leaf areas of 3 reserved branches per species were calculated and are presented in Table 1, which shows that the ranges of the individual leaf area, leaf number and sub-branch number of six species were 0.25–98.70, 9–487 and 1–17 cm2, respectively.
The total leaf areas of each branch in the control and experimental groups were determined empirically through relationships between fresh weight and surface area developed in preliminary work. We measured the total leaf area (S) of 3 reserved branches (section 2.1). The fresh leaf weight (W) of each reserved branch was measured in g using a Sartorius BS423S balance (Gottingen, Germany) with 0.0001 g precision. The coefficient (C) of leaf area and fresh weight was obtained by averaging S/W of the 3 reserved branches. The fresh leaf weight (W1) of each branch in the control and experimental groups was measured after the experiment, and the total leaf areas were calculated as W1 × C. 2.3. Particle selection and preparation and dosing experiments To obtain accurate results in experiments determining PM2.5 washoff processes from leaf surfaces under artificial rainfall, we applied tracer particles (SiO2) to the leaf surfaces. Silica micropowder with an average diameter of 2.5 μm on a mass basis was chosen as a tracer for the experiments due to its low solubility and high chemical stability. Silica micropowder was certified by Aladdin Company (Beijing, China) and purchased from their official website (www.aladdin-e.com). These dosing experiments were performed in a wind tunnel (in Shanghai Jiao Tong University) with dimensions (working section) of 0.8 × 0.8 × 2 m (Fig. 1). Wind speeds inside the wind tunnel were confirmed before each trial using a digital converter PC14391951977 (Peng Cheng Mechanical and Electrical Co., Ltd., Jiangsu, China). In this study, 6 species and 57 branch samples per species were collected. Except from the
2.2. Measurement of the total leaf area of branches in the control and experimental groups To be comparable, the PM retained on each branch during a rain event should be standardized by the total leaf area. In preliminary work, we found that there were strong linear relationships between leaf fresh weight and surface area, and R2 (Goodness of fit) of six experimental tree species (Lotus Magnolia, Japanese Maple, Common Boxwood, Dawn Redwood, Dragon Juniper and Cedrus Deodara) are 0.98, 0.98, 0.93, 0.96, 0.93 and 0.91, respectively. The PM number per leaf area was determined from the total PM number and the total leaf area.
Fig. 1. Equipment and procedure used in the dosing and rainfall experiments. 3
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
three branch samples in the reserve group, the other 54 branch samples were dosed with tracer particles (SiO2). They were into three groups for dosing experiments with 18 branches per group. 18 branches were supported horizontally in the working section pointing into the upstream airflow, approximating the position of small branches on trees. Then, the SiO2 particles were applied for 10 min from the upper wind position using an air pump JUBA600W-9L (Zhejiang Tengjing Air Compressor Co., Ltd., Zhejiang, China) with a speed of 1000 mg/min in adding particulate matter. The particles were transported to the branches by a uniform wind (1 m/s) produced by the fan system inside the wind tunnel.
2.6. Measurement of PM2.5 The PM2.5 retained on the leaves after the rainfall events was measured as follows (Fig. 1). First, the leaves of each branch sample removed from the artificial rainfall simulator were cut and placed into clean buckets containing 400 ml of distilled water. In order to make the particulate matter on the leaves immerse in the liquid better, the samples were covered and soaked for 2 h before being removed (Xie et al., 2018). To separate the PM2.5 from leaf surface and ensure an even distribution in solution, the buckets were shaken for 1 min before testing (Xie et al., 2018). We labeled 324 solution samples using tags. Then, the PM2.5 number in the solution in each bucket, representing the total PM2.5 number retained on different branches during the rainfall events, was measured using a laser particle counter SMIT-Y (Shanghai Ming Zi Jia Electronics Co. Ltd, Shanghai, China) with a measuring range of 0–120000 and a measurement error of 1%. The working principle of the particle counter is as follows: When the solution with particles passes through the counter by a transparent tube which is irradiated with lasers, then the particles will form different sizes of shadows to a resistance plate, the number of shadows can be estimated and the particle number can be obtained. Finally, the PM2.5 number retained was calculated on a per leaf area basis to ensure comparability between branches and species; this was determined from the total PM2.5 number and the total leaf area per branch. After all the samples were tested at different time points (0, 5, 10, 15, 20, and 25 min), we calculated the variations in the PM2.5 number retained on the leaves over time during each rainfall event for the different species. In addition, the net change in the PM2.5 number at different time points was compared to the PM2.5 value of control group samples.
2.4. Rainfall intensity settings The imposed rainfall intensity should be consistent with the characteristics of rainfall in the region. The conventional method of rainfall intensity classification did not apply to this experiment. The conventional rainfall intensity is the rainfall volume over a 12- or 24-h period, which does not accurately reflect the real characteristics of individual rainfall events. A study conducted on individual rainfall events in Shanghai showed that the rainfall intensity of almost 90% of rainfall events was below 10 mm/h, the 25%, 50%, and 75% percentiles of individual rainfall events are approximately 1 mm/h, 4 mm/h and 8 mm/h, respectively (Gao et al., 2012). A rainfall intensity of 1 mm/h might be too low to obtain accurate results because of the short duration (25 min) of the experiment for this study. According to the rainfall characteristics in Shanghai and the experimental requirements, we designed a gradient consisting of three rainfall intensities, i.e., 4 mm/h, 8 mm/h, and 12 mm/h. It is difficult to realize the rainfall below 16 mm/h by the traditional rainfall device. We improved the traditional rainfall device, controlled the water pressure by the frequency modulation pump, controlled the water volume by the number of sprinklers, adjusted the sprinklers angle to control the spraying range. This rainfall simulator with dimensions (Length, width and height) of 6 × 4 × 5 m (Fig. 1) located in a sealed glass house in Shanghai Jiao Tong University. The glass house isolated the pollution of the air particles from the outside and prevented the rain from being disturbed by wind. We put 40 rainfall barrels evenly under the rainfall device to test the intensity and uniformity of the rainfall in different pressure, number and setting angle of sprinklers. When we set the pressure of the water pump to 1.5 kg, the sprinkler angle to 15 degrees of elevation, opened 2, 4 and 6 sprinkler tips, we got rainfall events with average intensities of 4.2, 7.8, and 12.3 mm/h, and coefficient of variation of 0.08, 0.10, 0.12 respectively. In the new device, we could get more stable, more uniform, smaller rainfall.
2.7. Data analysis and statistics Data analysis was conducted using the SPSS PASW 18.0 program (SPSS Inc., Chicago, USA) and R for Windows 3.3.3 (Bell Laboratories Lucent Technologies; Mount Laurel, New Jersey, USA). 6 tree species under 3 different rainfall intensities constitute the 18 PM2.5 wash off processes. The patterns of washoff processes were analyzed using descriptive statistics and are presented as line graphs. The 18 processes that exhibited common patterns were classified by hierarchical cluster analysis, with the net changes of PM2.5 number at 5, 10, 15, 20, and 25 min compared to 0 min respectively. The washoff processes of the subsequent groups were simulated using polynomial regression based on the average values. Analysis of variance (ANOVA) was used to determine the significant factors influencing the PM2.5 washoff processes. The data were first tested for normality using the one-sample Kolmogorov–Smirnov test. 3. Results
2.5. Artificial rainfall simulation experiments
3.1. General characteristics of washoff processes
Exposure to the SiO2 particles ceased, and three branch samples from each group were randomly selected as the control group samples, recorded as having experienced 0 min of rain fall washoff; the 15 remaining branch samples were moved to an artificial rainfall simulator. The water used for simulating rainfall was distilled water, which contained 0.1 × 103 number of PM2.5 per cubic centimeter. Since the rainfall simulation test start, three of the samples were randomly selected as a group and taken out in every 5 min, and the whole experiment would last 25 min. That means every group of branches suffered from rain time is different, with the duration of 5, 10, 15, 20, 25 min respectively. After measuring PM2.5 number maintained on the leaf surface of all branch samples, the change processes of PM2.5 number on leaves surface over time in rain events would be shown. Such an experiment will be carried out under three different rainfall intensities (4, 8 and 12 mm/h).
The changes in the PM2.5 numbers retained by the six species under the three different rainfall intensities (4, 8, and 12 mm/h) are shown in Fig. 2. The washoff processes are complex and changed under different rainfall intensities and species. In addition, the cycles and patterns differed between processes. The maximum/minimum (Max/Min) value indicates variation of PM2.5 amounts and the average value represents the ability of leaves to retain PM2.5 during rain events (Table 2). The max/min can eliminate the influence of dimension and magnitude of variables, making different data comparable. A greater the max/min ratio indicates a greater variation of variables. The Max/Min values of PM2.5 retained by the six species ranged from 3.4 to 23.4, indicating that rain had a noticeable impact on PM2.5 retention. The ranking of Max/Min values was Lotus Magnolia (23.4) > Common Boxwood (20.6) > Dawn Redwood (8.5) > Dragon Juniper (4.8) > Cedrus Deodara (4.5) > Japanese 4
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 2. PM2.5 retention on leaves over time under different rainfall intensities. The uncertainty bars represent standard deviations of results from replicate experiments on replicate foliage samples.
trees PM2.5 number at 5, 10, 15, 20, and 25 min compared to that at 0 min respectively. The members of the groups are presented in Table 3. Groups I, II, III and IV had 9, 5, 2, and 2 members, respectively (Table 3). Almost all the washoff processes of the broad-leaved trees belonged to Group I except those of Japanese Maple and Common Boxwood under rainfall intensities of 8 mm/h and 12 mm/h, respectively. Group II contained 2 processes of Dawn Redwood, 2 processes of Cedrus Deodara and 1 process of Japanese Maple. Group III contained 2 processes of Dragon Juniper under rainfall intensities of 8 and 12 mm/ h. Group IV contained 1 processes of Common Boxwood under a rainfall intensity of 12 mm/h and 1 processes of Cedrus Deodara under a rainfall intensity of 4 mm/h. An analysis of these processes alongside branch characteristics (Table 1) indicated that the processes of broadleaved trees with large leaves and simple crowns always belonged to Group I. The processes of Group II may be found in species with small leaves and complex crowns under high rainfall intensity. The processes of Group III consisted of Dragon Juniper (with an extremely complex crown) under high rainfall intensity. The processes of group IV may occur in trees with small leaves but under different rainfall conditions.
Table 2 Characteristics of PM2.5 retention determined by leaf area during rain events. Life forms
Broad-leaved Coniferous
Species
Lotus Magnolia Japanese Maple Common Boxwood Dawn Redwood Dragon Juniper Cedrus Deodara
Number of PM2.5 (103n/cm2) Mean
Max
Min
max/min
4.1 33.0 6.4 12.6 14.7 40.
11.7 67.4 14.4 28.2 26.6 68.2
0.5 19.7 0.7 3.3 5.6 15.1
23.4 3.4 20.6 8.5 4.8 4.5
Maple (3.4). The ranking of the average values was Cedrus Deodara (40.3 × 103/cm2) > Japanese Maple (33.0 × 103/cm2) > Dragon Juniper (14.7 × 103/cm2) > Dawn Redwood (12.6 × 103/ cm2) > Common Boxwood (6.4 × 103/cm2) > Lotus Magnolia (4.1 × 103/cm2). These results indicate that there were considerable variations in the PM2.5 numbers retained by the trees at different time points and under different rainfall intensities. The rankings showed that rainfall had a greater effect on the ability of broad-leaved trees to retain PM2.5 compared with conifers, and trees with more complex structures and smaller leaves could better retain particles and had more stable PM2.5 retention during rainfall events. The washoff processes exhibited different cycles and patterns under different species and rainfall intensities (Fig. 2). The PM2.5 washoff processes of broad-leaved trees generally had two peaks, while there was only one or no peak for coniferous trees. This means that the PM2.5 washoff processes of coniferous trees may be more stable than those of broad-leaved species. In general, the processes under a rainfall intensity of 12 mm/h exhibited larger fluctuation ranges and smaller cycles than those under rainfall intensities of 4 and 8 mm/h. This indicates that the PM2.5 washoff processes are more variable under higher rainfall.
3.3. Fitted models of washoff processes The net change in PM2.5 and the average of each group at different time points were calculated. To interpret the PM2.5 washoff processes more clearly and accurately, the relationships of net change in PM2.5 over time were simulated using polynomial regressions (Fig. 4). The features between the four models were notably different, and all of the R2 values were > 0.94, indicating that the PM2.5 washoff processes were clustered into appropriate groups and that all models fitted well. All the processes were well fitted by quartic polynomial functions. They might follow a bimodal curve (y= 0.51x 4 + 27.05x3 449.99x2 + 2396.4 x+ 24.89), unimodal curve 7.63x 3 + 23.64x2 + 1123.2 x+ 134.92 ), continually-rising (y= 0.19x 4 371.15x2 + 2962.9 x 30.22 ) or a Ucurve (y= 0.33x 4 + 19.39x3 5319.3 x 36.08) shaped curve (y= 0.90x 4 50.54x3 + 926.28x2 under different conditions. The processes of Group I were bimodal distributions, indicating that PM2.5 retention changed easily and rapidly over a cycle of 10–15 min. The process of Group II presented a unimodal curve over a cycle of 25 min; the PM2.5 retained by leaves increased initially and
3.2. Clusters of washoff processes The washoff processes shown in Fig. 2 were clustered into four groups using the hierarchical cluster method (Fig. 3). The variables used to develop cluster discrimination were the net changes of different 5
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 3. Hierarchical cluster plot of PM2.5 washoff processes for different tree species and rainfall intensities. The numbers in parenthesis in the figure are the values of the applied rainfall rate in mm/h.
showed that the net changes were normally distributed (P = 0.068). Then, ANOVAs were performed to determine the significant factors that influenced the net change of PM2.5 on leaves during rainfall events. The results are shown in Table 4. The effects of species (P < 0.001), species × rainfall (P = 0.029), species × exposure time (P < 0.001), and species × rainfall × exposure time (P = 0.002) were significant for PM2.5. The results indicate that species, rainfall and exposure time could significantly influence PM2.5 retention on leaves and the effect of tree species on particle retention was greater than that of rainfall intensity and exposure time. The PM2.5 retained by trees during rainfall events is a comprehensive result of multiple factors, and the key factor is the characteristic of the species.
Table 3 The clustered members of PM2.5 washoff processes with common patterns, explored using hierarchical cluster analysis. Serial numbers
Species
Rainfall (mm/h)
Clusters
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Lotus Magnolia Lotus Magnolia Lotus Magnolia Japanese Maple Japanese Maple Japanese Maple Common Boxwood Common Boxwood Common Boxwood Dawn Redwood Dawn Redwood Dawn Redwood Dragon Juniper Dragon Juniper Dragon Juniper Cedrus Deodara Cedrus Deodara Cedrus Deodara
4 8 12 4 8 12 4 8 12 4 8 12 4 8 12 4 8 12
I I I I II I I I IV II I II I III III IV II II
4. Discussion This study presented the PM2.5 washoff processes from leaves under small rainfalls (smaller than rainstorms) with particulate matter, supplementing the theoretical relationship between particulate matter and leaves. In this study, four groups (I, II, III and IV) of washoff processes (bimodal, unimodal, continually-rising and U-shaped curves, respectively) were categorized,. All processes were well-fitted by quartic polynomial functions, although they followed different patterns. The processes of Group I were bimodal distributions, indicating PM2.5 retention changed easily and rapidly with an accumulation-removal cycle of 10–15 min. The processes of broad-leaved trees with large leaves and simple crowns always belonged to Group I. Broadleaved trees have large leaves and simple structures, resulting in highly efficient interception of rainwater and consequently, a short cycle of water accumulation and dripping (Muzylo et al., 2009). The process of Group II was presented as a unimodal curve over a period of 25 min; the PM2.5 retained by leaves increased initially and then decreased, reaching a maximum value in 10–15 min. Most of the processes of Group II occurred in trees with small leaves and complex crowns under high rainfall intensity. The processes of Group II had longer cycles than those of Group I. This may have occurred because of the lower rainfall interception efficiency of small leaves, and the extra water storage capacity associated with the complex crown and leaves could prolong the cycles. The third type of washoff process showed a continuous increase with time and occurred mainly under high rainfall intensity and in trees
Note: I: bimodal curve; II: unimodal curve; III: continually-rising curve; IV: Ushaped curve.
then decreased, reaching a maximum value between 10 and 15 min. The change in PM2.5 in Groups I and II exhibited a cycle that combined the processes of accumulation and attenuation, similar to the accumulation and dissipation processes of rainfall on leaves. The process of Group III was a continuous increase, indicating that the PM2.5 on leaves accumulated continuously throughout the experiment. The process of Group IV was an initial sharp decrease followed by an increase, i.e., a U-shape. Under this scenario, PM2.5 on the leaf surfaces decreased in the preliminary stage and then increased. The rainfall interception efficiency or storage capacity was very low in the preliminary stage, but then increased after the leaves became wet. 3.4. Significant factor test The normality tests for the net change in the PM2.5 on leaves were conducted using the one-sample Kolmogorov–Smirnov test, which 6
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
Fig. 4. Fitted models of the net change in PM2.5 over time for the four different groups identified by hierarchical cluster analysis. The uncertainty bars represent standard deviations of the PM2.5 net change from the same group.
the two species might interact, improving the water storage and more PM2.5 might be inputted with stemflow. The PM2.5 washoff curves under small rain events (smaller than rainstorm) in this study provides a potential way to optimize the estimation of the ability of plants to purify particulate matter. The washoff processes of PM2.5 in this study were different from those in previous research which follows negative exponential curves (Mcdowell et al., 1984; Pullman, 2008). One of the reasons might be that additional PM2.5 in rainfall and stemflow can be intercepted (Muzylo et al., 2009), whereas this was not considered in the chemical element washoff processes in previous studies. The other reason might be the different rainfall intensities. A rainfall intensity that is too heavy might result in very short washoff cycles and smooth curves, while the fluctuation of water interception and dripping may be more obvious under lower rainfall intensities. The PM2.5 washoff processes in this study is common in reality due to the frequent occurrence of small rainfall events (smaller than rainstorms). However, only negative exponential washoff curves had been considered in existing models when estimating the particulate matter reduction ability of plants (Schaubroeck et al., 2014), which needs to be optimized. Broad-leaved trees probably have a better PM2.5 washoff efficiency than conifers during rains. In this study, we found smaller average values and larger max/min values of PM2.5 retained on broad-leaved tree species than on coniferous species. This indicates that amount of retained particles on leaves of broadleaf species is lower and its variance is higher, which means particles on leaves of broadleaf species can be washed out in rains more easily than that on conifers. In general, there were more peaks in the PM2.5 washoff processes of broad-leaved trees than those of coniferous trees over the duration of the experiment. These results show that there are more fluctuating processes on the PM2.5 retention of broad-leaved tree species compared with coniferous species in rainfall. The trees with a more complex structure and smaller leaves could better retain particles and had more stable PM2.5 washoff processes during rainfall events (Beckett et al., 2000). The cycles of PM2.5 accumulation and removal on broad-leaved trees could be shorter than those of conifers, because the accumulation and removal processes of particulate matter on leaves is highly correlated with that of rainwater on leaves when raining (Schaubroeck et al., 2014). The larger
Table 4 ANOVA results of between-subject effects on the net change in PM2.5 on leaves during rain events. Source
Corrected Model Intercept Species Rainfall Exposure time Species × rainfall Species × exposure time Rainfall × exposure time Species × rainfall × exposure time Error Total Corrected Total R2 Adjusted R2
DF
89 1 5 2 4 10 20 8 40 180 270 269
Test for PM2.5 net-change F
P
2.54 60.50 10.38 1.09 0.16 2.07 3.14 1.44 1.92
< 0.001 < 0.001 < 0.001 0.340 0.957 0.029 < 0.001 0.182 0.002
0.557 0.338
with extremely complex crowns (e.g., Dragon Juniper). The underlying reason might be similar to that for Group II, i.e., the extremely complex crown resulted in cycles even longer than the experimental duration of 25 min. The process of Group III indicated a positive accumulation with a continuous increase, indicating that PM2.5 accumulation exceeded the amount that dripped from the leaves throughout the experiment. A large number of particles on the surface of complex branches might be inputted to leaf surface with stemflow, which helps the PM2.5 accumulation. The process of Group IV was an initial sharp decrease followed by an increase, i.e., a U-shape. This kind of process occurred under the scenario where there was a short storage duration in the preliminary stage that lengthened after the leaves became wet. In the preliminary stage, Common Boxwood intercepted water, which subsequently dripped off rapidly under a rainfall intensity of 12 mm/h, and the PM2.5 retained by Cedrus Deodara decreased rapidly due to the extremely low water storage capacity of the needle leaves under a rainfall intensity of 4 mm/h. After wetting, however, the complex structure and leaves of 7
Atmospheric Environment 214 (2019) 116844
C. Xie, et al.
leaves of the broad-leaved trees had higher water interception efficiency, while the more complex structure of conifers improved the water storage capacity (Li et al., 2016). Consequently, broad-leaved trees might have more rapid dripping than conifers and a more changeable PM2.5 balance. The PM2.5 washoff efficiency of broadleaved trees is higher than that of conifers, which is opposite to the PM2.5 deposition efficiency (Janhäll, 2015). This mean that both the adsorption capacity and washoff ability of particulate matter need to be considered when screening trees of particulate matter purification. Higher rainfall intensity can shorten the cycle of PM2.5 accumulation and removal on leaves, resulting in the particulate matter on the leaf surface being effectively washed away. We found that the number of particulate matter on leaf surface change over time under a rainfall intensity of 12 mm/h exhibited larger fluctuation ranges and smaller cycles of accumulation and removal on leaves than those under rainfall intensities of 4 and 8 mm/h. In previous studies, the washoff processes followed negative exponential curves under rainstorm intensities (≥16 mm/h) (Mcdowell et al., 1984; Pullman, 2008). This indicates that the cycle of PM2.5 accumulation changed to dripping shortened with increasing rainfall intensity, which is closely related to the water balance on leaves (Schaubroeck et al., 2014). In rainless areas, the enrichment of particulate matter on leaf surface has a great impact on plant photosynthesis, respiration, disease and other physiological functions (Grote et al., 2016). If it is necessary, spraying plants with rainfall intensity over 16 mm/h can effectively wash out the particulate matter on leaf surfaces and reduce the impact of particulate matter on plant physiology.
Because of the complex and continuous PM2.5 exchange process between stem and leaf surface, we can not separate the net PM2.5 removal from leaf in rain event. In the next stage of work, we will consider the PM2.5 removal from stem and leaf surface of a branch synthetically, so as to calculate the net PM2.5 removal of plants in rain event. In addition, a comprehensive evaluation of the ability of vegetation to reduce PM based on washoff efficiency and deposition effects needs to be conducted. However, this study provides a basis for calculating the PM2.5 removal from plant surface and assessing the ability of plant to purify particulate matter.
5. Conclusion
Appendix A. Supplementary data
In general, the PM2.5 washoff processes were associated with the constant exchange processes between accumulation and removal. Replacement processes also occurred, i.e., where additional PM2.5 in rain would be intercepted, and initial particles would drip off. Therefore, relative to the initial value, the amount of particles on the leaves was not necessarily reduced during rainfall events. The cycles of the exchanges could be short for leaves with a high water interception efficiency and small water storage capacity, or long otherwise. However, if the water interception efficiency is extremely high and the water storage capacity is quite small, the process might begin to decline. The shorter the exchange cycle, the higher the efficiency with which PM2.5 is washed off from leaves. Broad-leaved trees with short cycles might have a higher efficiency to transport PM2.5 from leaves to the soil compared with conifers, which is opposite to the PM2.5 deposition efficiency. In this respect, broad-leaved trees have advantages in reducing particulate matter. Rainfall intensity could change the water interception efficiency, and the concentration of particulate matter in rainwater and stemflow could change the particles that accumulate on the leaves. A comprehensive evaluation of the ability of vegetation to reduce PM2.5 should be carried out based on washoff efficiency and the deposition effect, according to the local precipitation conditions and species characteristics. Due to the significant impact of tree species on the process of PM deposition and washoff, appropriate tree species selection will have a positive effect on particulate matter reduction. There were some limitations of this study, and interesting topics should be expanded in future work. The duration of the experiment in this study may have been too short, and some processes were not completely observed. Future experiments can be considered to extend the experimental time. In addition, we only conducted the experiments using rainfall containing 0.1 × 103 PM2.5 per cubic centimeter and only one type (SiO2) and size (2.5 μm) of PM. The effect of different concentrations of PM2.5 in rainfall on washoff processes should be studied further. However, we found that species was the most important factor that affected PM2.5 washoff processes, and in-depth studies on the influence of species characteristics could be developed in further work.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116844.
Declaration of interest statement Here within enclosed is our manuscript for consideration to be published in Atmospheric Environment. The authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere. The submission is original and that all authors are aware of the submission and agree to its publication. Acknowledgments This work was funded by the Projects in the National Key Research and Development Program of China (2016YFC0502703) All authors would like to give thanks to Bingxin Ma for his help on designing wind tunnel. We would like to thank our team workers Dan Chen, Zhedong Wang, Size Cai, Yunqiang Xia, Wen Pang for their hard work on the experiment and data collection.
References Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8 (2), 166–175. https://doi.org/10.1007/s13181-011-0203-1. Beckett, K.P., Freer-Smith, P.H., Taylor, G., 2000. Particulate pollution capture by urban trees: effect of species and wind speed. Glob. Chang. Biol. 6, 995–1003. https://doi. org/10.1046/j.1365-2486.2000.00376.x. Beckett, K.P., Freer-Smith, P.H., Taylor, G., 1998. Urban woodlands: their role in reducing the effects of particulate pollution. Environ. Pollut. 99 (3), 347–360. https://doi.org/ 10.1016/S0269-7491(98)00016-5. Chen, J., Yu, X., Bi, H., Fu, Y., 2017. Indoor simulations reveal differences among plant species in capturing particulate matter. PLoS One 12 (5), e0177539. https://doi.org/ 10.1371/journal.pone.0177539. Cohen, M.L., Steinmetz, W.D., 1986. Foliar washoff of pesticides by rainfall. Environ. Sci. Technol. 20 (5), 521–523. https://doi.org/10.1021/es00147a014. Dang, A., Silburn, M., Craig, I., Shaw, M., Foley, J., 2016. Washoff of residual Photosystem II herbicides from sugarcane trash under a rainfall simulator. J. Agric. Food Chem. 64 (20), 3967–3974. https://doi.org/10.1021/acs.jafc.5b04717. Emmanouil, C., Drositi, E., Vasilatou, V., Diapouli, E., Krikonis, K., Eleftheriadis, K., Kungolos, A., 2017. Study on particulate matter air pollution, source origin, and human health risk based of PM10 metal content in Volos City, Greece. Toxicol. Environ. Chem. 99 (4), 691–709. https://doi.org/10.1080/02772248.2016.1242005. Gao, Y., Fang, G.L., Hu, L., Xu, L.J., 2012. An analysis of characteristics of rainfalls with short and long durations in Shanghai. In: 2012 International Symposium on Urban Stormwater Management, pp. 153–158. Gromke, C., Buccolieri, R., Sabatino, S.D., Ruck, B., 2008. Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations – evaluation of CFD data with experimental data. Atmos. Environ. 42 (37), 8640–8650. https://doi.org/10.1016/j.atmosenv.2008.08.019. Grote, R., Samson, R., Alonso, R., Amorim, J.H., Cariñanos, P., Churkina, G., Fares, S., Thiec, D.L., Niinemets, Ü., Mikkelsen, T.N., 2016. Functional traits of urban trees: air pollution mitigation potential. Front. Ecol. Environ. 14 (10), 543–550. https://doi. org/10.1002/fee.1426. Holder, C.D., 2012. The relationship between leaf hydrophobicity, water droplet retention, and leaf angle of common species in a semi-arid region of the western United States. Agric. For. Meteorol. 152, 11–16. https://doi.org/10.1016/j.agrformet.2011. 08.005. Holder, C.D., 2013. Effects of leaf hydrophobicity and water droplet retention on canopy storage capacity. Ecohydrology 6 (3), 483–490. https://doi.org/10.1002/eco.1278. Janhäll, S., 2015. Review on urban vegetation and particle air pollution – deposition and dispersion. Atmos. Environ. 105, 130–137. https://doi.org/10.1016/j.atmosenv.
8
Atmospheric Environment 214 (2019) 116844
C. Xie, et al. 2015.01.052. Jin, S., Guo, J., Wheeler, S., Kan, L., Che, S., 2014. Evaluation of impacts of trees on PM2.5 dispersion in urban streets. Atmos. Environ. 99, 277–287. https://doi.org/10. 1016/j.atmosenv.2014.10.002. Kim, K.H., Jahan, S.A., Kabir, E., 2013. A review on human health perspective of air pollution with respect to allergies and asthma. Environ. Int. 59, 41–52. https://doi. org/10.1016/j.envint.2013.05.007. Li, X., Xiao, Q., Niu, J., Dymond, S., Doorn, N.S.V., Yu, X., Xie, B., Lv, X., Zhang, K., Li, J., 2016. Process-based rainfall interception by small trees in Northern China: the effect of rainfall traits and crown structure characteristics. Agric. For. Meteorol. 218–219, 65–73. https://doi.org/10.1016/j.agrformet.2015.11.017. Mcdowell, L.L., Willis, G.H., Southwick, L.M., Smith, S., 1984. Methyl parathion and EPN washoff from cotton plants by simulated rainfall. Environ. Sci. Technol. 18, 423–427. https://doi.org/10.1021/es00124a006. Muzylo, A., Llorens, P., Valente, F., Keizer, J.J., Domingo, F., Gash, J.H.C., 2009. A review of rainfall interception modelling. J. Hydrol 370, 191–206. https://doi.org/10.1016/ j.jhydrol.2009.02.058. Nowak, D.J., Hirabayashi, S., Bodine, A., Hoehn, R., 2013. Modeled PM 2.5 removal by trees in ten U.S. cities and associated health effects. Environ. Pollut. 178, 395–402. https://doi.org/10.1016/j.envpol.2013.03.050. Oprea, M., Dunea, D., Liu, H.Y., 2017. Development of a knowledge based system for analyzing particulate matter air pollution effects on human health. Environ. Eng. Manag. J. 16 (3), 669–676. https://doi.org/10.30638/eemj.2017.068. Peachey, C.J., Sinnett, D., Wilkinson, M., Morgan, G.W., Freer-Smith, P.H., Hutchings, T.R., 2009. Deposition and solubility of airborne metals to four plant species grown at varying distances from two heavily trafficked roads in London. Environ. Pollut. 157, 2291–2299. https://doi.org/10.1016/j.envpol.2009.03.032. Petroff, A., Mailliat, A., Amielh, M., Anselmet, F., 2008a. Aerosol dry deposition on vegetative canopies. Part II: a new modelling approach and applications. Atmos. Environ. 42 (16), 3654–3683. https://doi.org/10.1016/j.atmosenv.2007.12.060. Petroff, A., Mailliat, A., Amielh, M., Anselmet, F., 2008b. Aerosol dry deposition on vegetative canopies. Part I: review of present knowledge. Atmos. Environ. 42 (16), 3625–3653. https://doi.org/10.1016/j.atmosenv.2007.09.043. Popek, R., Gawrońska, H., Wrochna, M., Gawroński, S.W., Sæbø, A., 2013. Particulate matter on foliage of 13 woody species: deposition on surfaces and phytostabilisation in waxes – a 3-year study. Int. J. Phytoremediation 15, 245–256. https://doi.org/10. 1080/15226514.2012.694498. Potter, C.S., Ragsdale, H.L., 1991. Dry deposition washoff from forest tree leaves by experimental acid rainfall. Atmos. Environ. 25, 341–349. https://doi.org/10.1016/ 0960-1686(91)90305-Q. Power, A.L., Worsley, A.T., Booth, C., 2009. Magneto-biomonitoring of intra-urban spatial variations of particulate matter using tree leaves. Environ. Geochem. Hlth. 31, 315–325. https://doi.org/10.1007/s10653-008-9217-2. Pražnikar, Z., Pražnikar, J., 2012. The effects of particulate matter air pollution on respiratory health and on the cardiovascular system. Slovenian J. Public Health 51, 190–199. https://doi.org/10.2478/v10152-012-0022-z. Pullman, M., 2008. Conifer PM2.5 Deposition and Resuspension in Wind and Rain Events.
http://hdl.handle.net/1813/11239. Qian, J., Ferro, A.R., 2008. Resuspension of dust particles in a chamber and associated environmental factors. Aerosol Sci. Technol. 42 (7), 566–578. https://doi.org/10. 1080/02786820802220274. Räsänen, J.V., Holopainen, T., Joutsensaari, J., Ndam, C., Pasanen, P., Rinnan, Å., Kivimäenpää, M., 2013. Effects of species-specific leaf characteristics and reduced water availability on fine particle capture efficiency of trees. Environ. Pollut. 183, 64–70. https://doi.org/10.1016/j.envpol.2013.05.015. Reddy, K.N., Locke, M.A., 2015. Imazaquin spray retention, foliar washoff and runoff losses under simulated rainfall. Pest Manag. Sci. 48, 179–187. https://doi.org/10. 1002/(SICI)1096-9063(199610)48:2%3c179::AID-PS457%3e3.0.CO;2-M. 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, 347–354. https://doi.org/10.1016/j.scitotenv.2012.03.084. Salmond, J.A., Marc, T., Sotiris, V., Katherine, A., Andrew, C., Matthias, D., Dirks, K.N., Clare, H., Shanon, L., Helen, M., 2016. Health and climate related ecosystem services provided by street trees in the urban environment. Environ. Health-Glob. 15 (Suppl. l), 36. https://doi.org/10.1186/s12940-016-0103-6. Schaubroeck, T., Deckmyn, G., Neirynck, J., Staelens, J., Adriaenssens, S., Dewulf, J., Muys, B., Verheyen, K., 2014. Multilayered modeling of particulate matter removal by a growing forest over time, from plant surface deposition to washoff via rainfall. Environ. Sci. Technol. 48 (18), 10785–10794. https://doi.org/10.1021/es5019724. Song, Y., Maher, B.A., Li, F., Wang, X., Sun, X., Zhang, H., 2015. Particulate matter deposited on leaf of five evergreen species in Beijing, China: source identification and size distribution. Atmos. Environ. 105, 53–60. https://doi.org/10.1016/j.atmosenv. 2015.01.032. Wang, H., Shi, H., Wang, Y., 2015. Effects of weather, time, and pollution level on the amount of particulate matter deposited on leaves of ligustrum lucidum. Sci. World J 935942. 2015. https://doi.org/10.1155/2015/935942. WHO(World Health Organization), 2018. World Health Organization Releases New Global Air Pollution Data. Geneva, Switzerland. http://www.ccacoalition.org/en/ news/world-health-organization-releases-new-global-air-pollution-data. Willis, G.H., Mcdowell, L.L., Southwick, L.M., Smith, S., 1994. Azinphosmethyl and fenvalerate washoff from cotton plants as a function of time between application and initial rainfall. Arch. Environ. Contam. Toxicol. 27, 115–120. https://doi.org/10. 1007/bf00203896. Wu, S., Deng, F., Wei, H., Huang, J., Wang, X., Hao, Y., Zheng, C., Qin, Y., Lv, H., Shima, M., 2014. Association of cardiopulmonary health effects with source-appointed ambient fine particulate in Beijing, China: a combined analysis from the Healthy Volunteer Natural Relocation (HVNR) study. Environ. Sci. Technol. 48, 3438–3448. https://doi.org/10.1021/es404778w. Xie, C., Kan, L., Guo, J., Jin, S., Li, Z., Chen, D., Li, X., Che, S., 2018. A dynamic processes study of PM retention by trees under different wind conditions. Environ. Pollut. 233, 315–322. https://doi.org/10.1016/j.envpol.2017.10.073. Xie, Y., Dai, H., Dong, H., Hanaoka, T., Masui, T., 2016. Economic impacts from PM2.5 pollution-related health effects in China: a provincial-level analysis. Environ. Sci. Technol. 50, 4836–4843. https://doi.org/10.1016/j.envint.2018.03.030.
9