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Screening potential plant species for arresting particulates in Jharia coalfield, India


Mining and related activities cause severe degradation of ambient air quality. A study of particulate matter (PM) across transportation, mining and control (C) sites for dust attenuation capacity (DAC) in selected tree species were carried out in Jharia coalfield (JCF) to estimate the menace of dust pollution and also to measure air pollution tolerance index (APTI). Results indicated that the maximum value of PM10 and PM2.5 ranged from 54 to 174 and 29 to 78 μg m− 3 respectively across all the sites. The maximum values occurred in transportation and the minimum at C for both the particulates. Mining and transportation resulted in an increase in PM10 values by 161 and 200% and PM2.5 values by 100 and 136% respectively as compared to those in C. The mean concentration of PM10 and PM2.5 across the sites exceeded the permissible limit of 100 and 60 μg m− 3 respectively. Transportation was worse than mining due to the high proportion of hazardous fine (PM2.5) particulates. DAC indicated that Tectona grandis (TG) captured maximum dust (2.15 mg cm− 2) with 85% and Peltophorum inerme (PI) the minimum (0.15 mg cm− 2) with 5% efficiency. The trend for DAC showed TG > Ficus glomerata (FG) > Psidium guajava (PG) > Ficus benghalensis (FB) > Ficus religiosa (FR) > Alstonia scholaris (AS) > Aegle marmelos (AM) > Gmelina arborea (GA) > Dalbergia sissoo (DS) > Syzyzium cumini (SC) > Azadirachta indica (AI) > Terminalia arjuna (TA) > Mangifera indica (MI) > Albizia lebbeck (AL) > PI in descending order. APTI based on pH, total chlorophyll, ascorbic acid and relative water content indicated maximum values for TG (17) with 90% and minimum for PI (10) with 57% of the total and is a measure of the sustainability of plants in JCF. The descending order for APTI was TG > PG > FG > FR > FB > AI > MI > SC > DS > GA > AM > AS > AL > TA > PI. Thus, TG is the most suitable and PI the least. Stomatal density is negatively related to DAC and positively related to APTI. DAC therefore, cannot be attributed to a single factor but a mix of complex factors such as morphological and anatomical characteristics of the leaf, particle size, species type, metabolism, location, meteorology and stress conditions. Based on the findings a greenbelt design was proposed to improve the air quality of the mining and transportation areas.


India is the world’s fastest-growing economy with a population of 1.3 billion and is the fourth-largest producer of coal. High-grade coking coal in India is mainly located in Jharia and Raniganj coalfield and is the most prolific coal mining area in our country. Traditionally, coal mining was considered one of the most polluting industries having a significant environmental impact if proper management strategies are not adopted. Historically, mining was executed with the main object of mineral beneficiation with little or no consideration towards the environment, local community or holistic development. Mining activity is the major contributor to air pollution apart from other industrial activities of this region along with transportation activity, poor road condition, coke oven plants and other industries.

The coal-burning releases harmful gases as well as particulate matter and its transportation with mine wastes has been recognized as the prime source of suspended particulate matter (SPM) with the dominance of < 10 μ size. Hazardous level of air pollution has been found in coal-burning areas. Mining depletes vegetation causing extended soil erosion altering the microbial ecosystem. Coal mining also affects the local, regional and global environment through the release of coalbed methane, which is twenty-three times as powerful as carbon dioxide as a greenhouse gas [1]. Coal mining thus badly impacts air quality and is one of the most potent sources of multi-faceted environmental pollution, hence requires efficient mine planning, design and an equally effective scientific environmental management with the mine closure plan. The best practice will always set the limits on the procedure to be followed keeping cost-benefit in mind.

The capacity of plants to check air pollution is well recognized and cited in the standard literature on numerous occasions [2,3,4,5]. Foliar uptake [5,6,7] of metal and metalloid particulates is reported by many authors [8,9,10,11]. Several studies have attempted on the arrest of air pollutants by avenue plants [6, 12,13,14,15]. Plantation of evergreen and deciduous trees with high tolerance capacities for effectively mitigating the air quality by abrasion, sedimentation, adsorption and absorption of pollutants is the desired option. Screening of plant species with proper spacing for controlling fine (< 2.5 μ) dust [5, 6, 15, 16] in vulnerable areas can thus be selected. Dust attenuation capacity (DAC) is attributed to its outer morphology and inner metabolism [17]. A greenbelt development with the screened plant species would arrest and reduce the pollutant dispersion.

Catalysts with suitable metal combinations can also be used to arrest air-borne particulates through adsorption and absorption. The catalyst made from 75% waste aluminum foil was reported by Osman et al. [18, 19] having enhanced gas burning efficiency leading to reduction of vehicle pollutant emission. Photocatalytic layer coating in the form of titanium dioxide (TiO2) has been known to reduce particulate matter of < 10 μ (PM10) and < 2.5 μ (PM2.5) size by 30% [18] due to UV irradiation of carbonaceous compounds present in ambient particulates with the release of CO2 resulting in 92% reduction in total carbon [19]. More recent research with other catalysts is targeted in this area with a wide application scenario in the future.

The present study emphasizes on the vegetative remediation of ambient particulates having a sustainable approach keeping long term accruing benefits for people’s health of Jharia coalfield (JCF) region. Current task is an attempt to estimate the dust emission due to coal mining activities, road transportation and its impact on ambient air quality along with DAC and tolerance index of selected plant species to put forth a cost-effective management plan in the form of a greenbelt designed to improve the air quality of JCF region. Previous studies [20, 21] made in JCF have pointed to the development of a green belt but no one elaborated screening of plant species for sustainability based on DAC and air pollution tolerance index (APTI) based on principles of ecological engineering. This study will bridge such gap and will make way for effective air pollution control of the region.

Materials and methods

The ambient air monitoring stations were selected following guidelines of the Central Pollution Control Board [22], India, based on the micrometeorological conditions of the area and the mining practices, methods of working, locality, openness and especially resource availability. Location sites are shown in Table 1. Method and instruments used are shown in Table 2. The approach was to see that mining activities and locations covered loading, drilling, blasting, transport and haul road, barren overburden dumps, stockyard, coal handling plant, workshop and open mine face.

Table 1 Details of air sampling locations in Jharia coalfield
Table 2 Methodology and instrument used for air quality analysis

Study site

The study sites are situated in Dhanbad district, the Jharkhand state of India between 23° 04′ to 23° 5′ N longitude 86° 2′ to 86° 3′ E latitude (Fig. 1 and Table 1). The selected sites comprised of coal mines dominated by opencast operations [Jeenagora (MA), Amjhar (MB) and Aamtal (MC)], transportation [Barwa Road (BR)] and a control site [CSIR-CIMFR (C)]. The drainage pattern of the area is controlled by Damodar river flowing west to east along the southern periphery and its tributaries viz. Jamunia, Khudia, Katri, Ekra, Tisra, Chatkari rivers. Soil cover is the alluvial type with low organic matter and impoverished nutrients. A coal-bearing Gondwana superstratum lies beneath this soil cover. The area is mostly surrounded by the Archean rocks with fine to medium-grained buff-colored sandstone, greyish to greenish micaceous shale, coal seams and siltstones.

Fig. 1
figure 1

Location map of the study area

The selection of the different study sites was based on contribution to the dust load such as transportation and mining in particular and to devise effective control measures to curtail finer dust. These sites represent their respective categories in perfect measure so that the methodology adopted to estimate and devise dust control mechanism takes all relevant variables in mind. The steps taken and procedure adopted are based on the Environmental Protection Agency (USA) guidelines followed by the Government of India [22, 23] and are reproducible. However, variations may occur based on mining methods and climatic factors.

The area is having a tropical climate and is characterized by a very hot summer and cold winter. The temperature in the summer months (March to June) varies from the lowest minimum of 15 °C (March) to the highest maximum of 46 °C (June), in the colder months (November to February) from the lowest minimum of 8 °C to the highest maximum of 35 °C and in the rainy season (July to October), the temperature varies from 16 to 36 °C. Relative humidity (RH) is high on rainy days (94%) in July and low in October (36%). Thunderstorms usually occur in June and July accompanied by a temporary fall in temperature. The area receives annual precipitation of about 1000–1200 mm, out of which 75–80% occurs from June to September with a smaller amount during winter.


The vegetation is tropical dry deciduous dominated by woody savanna with high biodiversity mainly due to topographic and edaphic factors. Soil is usually red loam to lateritic. Rainfall occurs from June to October with a maximum of 1200 mm during July–August. Shorea robusta is the dominating tree of JCF region. Other major trees are Butea monosperma, Aegle marmelos (AM), Azadirachta indica (AI), Dalbergia sissoo (DS), Ficus benghalensis (FB), Ficus glomerata (FG), Ficus religiosa (FR), Mangifera indica (MI), Psidium guajava (PG), Cassia fistula, Syzygium cumini (SC), Alstonia scholaris (AS), Tectona grandis (TG), Albizia lebbeck (AL), Gmelina arborea (GA), Peltophorum inerme (PI), Terminalia arjuna (TA), Tamarindus indica, Holarrhena antidysenterica, Madhuca indica and Anthocephalus cadamba. Heteropogon contortus, Saccharum spontaneum, Cynodon dactylon, Cymbopogon and Bothrichloa are the frequently occurring grass species. Fifteen species were selected for dust attenuation studies. An additional file shows morphological and anatomical characteristics of the selected tree species [see Additional file 1].


The study sites were selected and divided into three categories based on pollution load, e.g., transportation and mining with a control site for PM10 and PM2.5 sampling. Monitoring was undertaken at different study sites during pre-monsoon (March–June) and post-monsoon (Nov-Feb) seasons, due to intensive excavation activities at coal mines.

Air monitoring for PM10 and PM2.5 was done for 24 h on 15-d intervals at each site and concentration were expressed as μg m− 3. The mean value for dust was calculated for 24 h based on the National Ambient Air Quality Standard [23], India. The PM10 and PM2.5 were sampled by ambient air sampler (model APM550, Envirotech Instruments, India). The concentration of particulates was determined gravimetrically [23] (Table 2). The concentration of SPM (μg m− 3) is computed by measuring the mass of collected particles and the volume of air sampled.

Dust deposition

Ten replicates of the fully-grown leaf of the selected plants were collected randomly in the early morning from branches at a height of 2–4 m and were transferred quickly to the laboratory in polythene bag within 24 h and kept in the ice-box for further analysis. The amount of dust was calculated by taking the initial and final weight of the beaker in which unwashed and washed leaf samples were kept. It was calculated by using the Eq. (1) [14].

$$ W=\frac{\mathrm{W}2-\mathrm{W}1}{\mathrm{A}} $$

where, W = Dust deposition (g cm− 2), W1 = Weight of beaker without dust in g, W2 = Weight of the beaker with dust in g, A = Total leaf area in cm2.

Dust deposition was calculated yearly for attenuation capacity estimation under natural conditions.

Leaf area

Conventional graphical methods were used for calculating the leaf area. Fresh leaf was placed on the graph paper and the outer boundary was drawn with a sharp pencil. The blocks were counted to calculate the surface area of the leaf. Utmost care was taken to count smaller blocks.

pH, total chlorophyll, ascorbic acid and relative water content

A total of 100 g of the fresh leaf was crushed to paste, mixed with 10 mL of distilled water and centrifuged for 15 min. The supernatant was taken out and shaken well to measure pH by Orion meter. To estimate the chlorophyll, 0.1 g of the fresh leaf of the desired species was taken in 10 mL of 80% acetone and kept for 24 h at 4 °C. It was mixed uniformly and centrifuged at 6000 g for 15 min. Optical density was measured at 480, 510, 645 and 663 nm. Chlorophyll a and b were calculated using the formula described by Maclachlan and Zalik [24]. To estimate the ascorbic acid, 0.5 g of a fresh leaf was homogenized in a 20 mL extracting solution (5 g oxalic acid + 0.75 EDTA-Na2 in 1000 mL of deionized water) in an ice bath for 30 s. The homogenate samples were centrifuged at 6000 g for 15 min. 5 mL of 2,6-dichlorophenol-indophenol was poured into 1 mL of leaf extract, shaken vigorously. Optical density was measured at 520 nm by Spectronic20D spectrophotometer. 1 mL of 1% ascorbic acid was added to discolor the solution and absorbance taken again at 540 nm. 1% ascorbic acid (aqueous) was used to obtain the calibration curve [25]. For determination of Relative Water Content (RWC), fresh weight (Wf) of the leaf sample was taken and kept in water for 24 h for saturation. The turgid weight (Wt) was also taken. The leaf sample was again put into the oven at sufficient temperature to get dried completely. The dried leaf was again reweighed (Wd). The RWC was determined using Eq. (2) [26].

$$ RWC=\frac{\mathrm{Wf}-\mathrm{Wd}}{\mathrm{Wt}-\mathrm{Wd}} $$

Stomatal density

The study was performed by an optical microscope (Leica DM300) with eyepiece and objective lens (OL) having a magnification of 10x and 40x respectively with a field view of 0.045 mm diameter. The stomatal count was performed manually corresponding to the 40x OL area (πr2) for stomatal density (SD) calculation.


Determination of APTI was done based on the formula given by Singh and Rao [27].

$$ APTI=\frac{\mathrm{AA}\ \left(\mathrm{TC}+\mathrm{P}\right)+\mathrm{RWC}}{10} $$

where, AA = Ascorbic acid; TC = Total chlorophyll; P = pH; and RWC = Relative water content.

Statistical analysis

SPSS (Ver 16.0) was used to generate dendrograms and Pearson correlation for LA, P, TC, RWC, DAC and APTI.

Results and discussion

PM10 and PM2.5

Analytical data for PM10 and PM2.5 are shown in Table 3. The maximum value of PM10 and PM2.5 ranged from 54 to 174 and 29 to 78 μg m− 3 respectively across all the sites. The maximum values occurred in BR and the minimum was at C for both the particulates. Mining and transportation resulted in an increase in PM10 values by 161 and 200% and PM2.5 values by 100 and 136%, respectively, as compared to those in C. The mean concentration of PM10 and PM2.5 across the sites exceeded the permissible limit of 100 and 60 μg m− 3 [23]. Higher concentrations of PM10 in mining areas can be attributed to operational strip-mining activities, up and downwind direction. The mean values of PM2.5 revealed that transportation sites had a higher percentage of finer particulates and were worse than mining due to heavy automobile exhaust emission and adjacent burning of coal for fuel use. Therefore, mining cannot be blamed singularly for fine particulate emission. Mean value below the permissible limit for MC (52 μg m− 3) can be attributed to downwind location from the mine and plantations of native dust arresting trees near the site. Air Quality Index remains largely unhealthy (151 to 200) to severe (201 to 300) categories with a minimum in summer and highest in winter months [28].

Table 3 PM10 and PM2.5 across different study sites

The air quality highly depends on meteorology such as wind velocity and direction. The predominant wind direction during sampling was S to N in winter, SE to NW in the summer season, respectively. The area is characterized by small to large mine voids, dumps, rivulets, tributaries and small hillocks with the average wind speed of 4–5 m s− 1 and the wind circulating within the area. These characteristics do not allow dust-laden wind to escape and the particulate deposition occurs within the area. However, few sites do not have such obstructions leading to smooth wind flow taking particulates away. Temperature and moisture greatly impact the process of inversion. It is highest (> 80%) during Dec-Jan. and lowest (< 17%) during May–June due to low (7–20 °C) and high (34–43 °C) temperature, respectively. Rainfall impact is negligible on the particulate deposition during winter and summer months due to minimal rainfall (100–400 mm). The sky is clear and thunderstorms are occasional during both the seasons. The concentration of dispersed particulates of an area declines with the increasing distance from the mining area towards the NE, the annual predominant wind direction. The SPM concentrations decreased with increasing distance from the emission source. Wind direction alters from time to time and accountable for the scattering of air pollutants to an unidentified location. The trend of PM10 and PM2.5 are indicated in Fig. 2a and b across study sites.

Fig. 2
figure 2

Particulate matter across different study sites (a) PM10 (b) PM2.5

Screening plant species for dust attenuation


The characteristics of the ambient particulate and plant leaf form an important criterion of DAC evaluation [29]. These include both solid and liquid such as mist, soot, dust, salt particles, spores, microbes which are very minute (< 10 μm) and forms a significant part [8] of airborne dust. Their morphology and chemical properties depend on their origin. They consist of secondary aerosol (gas to particle), combustion products and settled metal and organic vapors [8]. These tiny particulates interact with polycyclic aromatic hydrocarbons, toxic metals, persistent free radicals to form tertiary aerosols. Gravimetric process of wet and dry nature is followed to remove particulates from the environment. They settle within a week. Thus, their time effects are varied and site-specific. The entry point of an air pollutant is the leaf cuticle and stomata [8, 9]. Further reactions within the plant cell are interrelated depending on particle properties, plant metabolism and external environment. They can either be sequestered, detoxified or yield CO2 and H2O after metabolic processes.

Leaf morphology and anatomy

Plants can play a significant role in mitigating airborne particulates and their removal capacities vary with the plant traits [3, 16, 17] (see Additional file 1). Tree crown morphology is one of the most important factors in arresting particulates along with crown density, leaf type and pattern. Venation pattern plays an important role in arresting the particulate matter. Thus, particulate deposition cannot be attributed to a single factor but is a complex mix of factors such as leaf characteristics [2,3,4,5, 16, 17, 29,30,31,32,33,34,35] such as hair, wax, foliage, leaf area, venation patterns, grooves and trichomes (see Additional file 1), particle size [12, 32, 34, 35], among others. Large trees are considered to arrest fewer particulates as compared to shrubs with foliar leaves close to the source [12]. Similarly, dense plantation with porous and large leaf surface area arrests a greater number of particulates [12]. Thus, the choice of vegetation (tall, short, dense, sparse etc.) affects dust deposition. Emphasizing on PM2.5 being most hazardous [11] due to toxicity [10, 11] and fineness, reports indicate conical leaves with 60% DAC compared to expanded leaves of only 47% [8, 17]. Broad-leaved, foliar [36] structures such as grooves, glands and trichomes when exposed captured PM2.5 the most during foliar growth [11, 16]. Complex factors were contributing to particle arrest but are significantly plant-specific [16].

Thus, phenology and biodiversity would increase PM2.5 capture [16]. Further, it is estimated that a single mature plant of these species will yield timber worth millions. Particulates cause a decrease in stomatal activity [7, 37] and chlorophyll content [37] and thus photosynthetic rate [7, 37]. Fully matured plant ecosystem sequesters atmospheric carbon and increases biodiversity to harvest valuable woody trees.


The removal of ambient dust can be successfully performed based on plant behavior, particle properties and climate. All the selected plant species (evergreen or deciduous) belonging to different plant families were evaluated for DAC yearly. The highest efficiency of 85% was observed for TG with DAC of 2.15 mg cm− 2 and the lowest for PI with 5% and DAC of 0.15 mg cm− 2. This can be attributed to the rough, hairy trichomes with glandular deposits at the adaxial surface of the leaf and shorter petiole suitable for particulate capture in TG while the leaf of PI is compound, glabrous with no prominent hair and glandular wax. Moreover, there is a minimum abrasive mechanism to check wind speed helping gravity settling because of biparipinnate leaf which is smaller and divided (Fig. 3). It is to be noted that broad hairy leaf with a slight sticky wax can retain fine particulates for a long time [2, 30, 36]. A similar observation was seen in AM and all the species of Ficus due to waxy-glabrous leaf. DAC of PG, AS, GA and TA can be attributed to medium-size leaf with a hairy and rough surface with a significant wax deposit. DAC of DS, AL and MI is lower due to the small size and smooth leaf surface. Leaf of SC and AL arrested fewer particulates due to the glabrous surface with minimum roughness. The exposed upper surface of the leaf obstructs wind flow creating abrasion helping sedimentation and precipitation for particulate deposit. The trend of plant DAC followed the descending order: TG > FG > PG > FB > FR > AS > AM > GA > DS > SC > AI > TA > MI > AL > PI. Details have been described and depicted in Fig. 4, Table 4 and Additional file 1.

Fig. 3
figure 3

Leaf characteristics of plant species (a) T. grandis (b) P. inerme

Fig. 4
figure 4

DAC of trees in JCF, India

Table 4 List of DAC of plant species for greenbelt development in JCF

Out of fifteen species selected for the study in JCF, eleven species showed a declining trend in SD while four species showed an increasing trend (Fig. 5). The highest decline of 59% in SD (number cm− 2) was observed in AM and the lowest in TA with 4% with respect to C. Some species showed an increase in SD from 17% in FB to 125% in SC with respect to C. This variation in the SD can be attributed to frequent rainfall occurring in the region during sampling which lowers dust deposition on the leaf surface along with the negligible ionic activity of fine particulates responsible for dissolution of the cell-wall. The SD of TG is much higher (1.51 × 106 cm− 2) than PI (0.69 × 106 cm− 2) in JCF (Table 5). Thus, higher SD indicates the sustainability due to high metabolic pliability [38] of the species which can be seen by relatively lower DAC and higher APTI values (Figs. 5 and 6).

Fig. 5
figure 5

Variation of SD with DAC

Table 5 SD (106) and DAC in JCF
Fig. 6
figure 6

Variation of SD with APTI

The screening process for calculating APTI is depicted in the flow chart (Fig. 7). APTI of various plant species (Fig. 8) was calculated based on P, TC, AA and RWC [39].

Fig. 7
figure 7

Screening of plant species for assessing APTI

Fig. 8
figure 8

APTI of selected plant species

LA is highest for TG with 690.5 and lowest for AL with 5.5 cm2 respectively. The plant leaf is known for its important contribution in regulatory processes such as photosynthesis and transpiration. It acts as a receptor for particulate and gaseous pollutants. Fine and coarse air particulates along with the gaseous pollutants such as SOx, NOx, SO2, CO and O3 enter epidermis and stomata of the leaf to reach plant cell altering metabolic process affecting flowering, reproduction leading to a reduction in leaf size and number with enhanced senescence. Observed P is negatively related to the DAC in JCF as the P of coal dust particles is mostly acidic [21]. Highest P was found for PI (7.0) and lowest for TG (5.4) having DAC of 0.15 and 2.15 mg cm− 2 respectively. This can be attributed to dissolution of deposited particulates into the cell sap of the plant leaf which is acidic [21] and gases such as SOx and NOx [14]. TC shows a positive relationship with the DAC and APTI. The maximum value of 2.9 mg g− 1 was found in TG having DAC of 2.15 mg cm− 2 and APTI of 16.8 and minimum in PI with 1.7 mg g− 1 having DAC of 0.15 mg cm− 2 and APTI of 10.1. Ascorbic acid (also known as citric acid) has been known to inhibit the formation of toxic elements due to its antioxidant properties. It is present in higher amount in growing plants and substitutes requirement of water in stress conditions regulating biological and physiological processes such as cell division [14]. Formation of H2O2, O2 and OH radicals due to atmospheric SO2 is effectively checked by ascorbic acid present in chloroplast. It also protects cell wall damage due to oxidation O2 to O3 [40]. A positive relationship is seen between the level of ascorbic acid with DAC and APTI as reported by many authors [13, 14]. Highest AA of 8.4 mg g− 1 was found in TG having DAC of 2.15 mg cm− 2 and APTI of 16.8 and lowest of 4.0 mg g− 1 in PI having DAC of 0.15 mg cm− 2 and APTI of 10.1. Water is essential for all the metabolic activities of the plant. RWC maintains balance during high-stress conditions created due to increased transpiration rate. Higher levels of RWC indicates plant survivability to meet adverse climatic conditions. A positive relation is seen between RWC, DAC and APTI. It varies from a minimum of 67.4% in AS with DAC of 1.23 mg cm− 2 and APTI of 12.5 to maximum of 99.6% in TG with DAC of 2.15 mg cm− 2 and APTI of 16.8. Detailed interactions between all the components are shown in Table 6 and Figs. 7, 9 and 10.

Table 6 APTI of selected plant species in JCF
Fig. 9
figure 9

Relative changes in LA, P, TC, AA, RWC, SD, DAC and APTI of selected plants

Fig. 10
figure 10

Hierarchical dendrogram cluster for (a) Selected trees and (b) LA, P, TC, AA, RWC, DAC and APTI

The APTI was calculated based on Singh et al. [39]. Highest tolerance index was found for TG (16.8) with 90% and the lowest for PI (10.1) with 57% and is a measure of sustainability in JCF. The sequence in descending order was TG > PG > FG > FR > FB > AI > MI > SC > DS > GA > AM > AS > AL > TA > PI. The species can be a labeled indicator by their tolerance or sensitivity. It can either act as a storehouse for the air pollutants or may die [13, 14].

Hierarchical dendrogram analysis amongst plant species indicates the development of three clusters with first group consisting of FR, GA, AS, MI, AM, FB, AL, PI, DS, AI, TA, FG and PG. The second group consists of a single species of SC which is closely related to the first group. The third group consists of TG which is distantly related to both the above groups (Fig. 10a).

Hierarchical dendrogram analysis amongst plant characteristics indicates the development of four clusters with first group consisting of TC, DAC, P, AA and APTI which is more closely related with SD. Both groups are closely related to RWC. All the above three groups are more distantly related to LA (Fig. 10b). Thus, DAC and APTI may vary distantly with LA (Table 6 and Fig. 10).

Greenbelt design

The study area showed the massive dispersion of air dust pollutant by coal mining and transportation activities in the form of PM10 and PM2.5 which can be reduced significantly by the above plants to enhance the air quality of the mining areas. A green belt development around the mining sites and transportation with suitable plant species would reduce the pollutant dispersion to a greater extent [5]. Particulate attenuation by the trees can easily be estimated by using various mathematical models [6]. Transpiration is a valuable physiological process for assimilation and removal of fine particulates from the ambient environment.

To reduce the impact of air pollution, systematic and scientific plantation has to be done on both sides of the haul road and approach road of mining and transportation road with vegetation cover comprising of herbs (grasses), shrubs and trees of 6 to 40 m in height at a distance of 5–6 m (from the edge) on both sides of the transporting road (Fig. 11). The accurate size of the plantation depends on road width, number of moving vehicles, lane numbers and the speed limits of the plying vehicles. Huge quantities of CO2 are also sequestered by the afforested plants, thus improving the air quality both in terms of dust and gaseous pollutants. Green belt development attenuates environmental pollution and provides fruits, nuts, timber, fuel-wood and fodder. It also helps to achieve the targeted 33% forest cover in the long run. Further, it provides ecosystem services like nutrient cycling, dwelling for the wildlife, browse for animals and sustainable tourism. It also replenishes groundwater raising water table. The planted trees in large area help in raising rainfall.

Fig. 11
figure 11

Green belt design along the mine road

Statistical analysis

Pearson correlation (two-tailed) was studied at p < 0.05 level of significance for all the characteristics (Table 7). It indicats that TC, AA, RWC, DAC, APTI and SD are negatively related with P. TC and DAC is not significantly related to SD at this level. All other parameters are positively related.

Table 7 Pearson correlation* amongst different plant characteristics


The results of the present study indicate that opencast coal mining emits a huge amount of particulate matter into the atmospheric environment due to various operations such as drilling, blasting, loading, excavation, transportation and coal washing activities. The study showed that all the sampling sites were polluted with respect to the standard limit of PM10 and PM2.5 except C. DAC was highest for TG (2.15 mg cm− 2) and lowest for PI (0.15 mg cm− 2). The decreasing order for DAC was TG > FG > PG > FB > FR > AS > AM > GA > DS > SC > AI > TA > MI > AL > PI. APTI based on pH, TC, AA and RWC was highest for TG (16.8) and lowest for PI (10.1) amongst the selected tree species. The descending order for APTI was TG > PG > FG > FR > FB > AI > MI > SC > DS > GA > AM > AS > AL > TA > PI. The SD behaved negatively to DAC and positively to APTI. Thus, TG is best suitable and PI the least in JCF. Attributes of particulate attenuation, therefore, is a function of many factors such as particle size, species type, metabolism, location, meteorology, nutrient stress, micromorphological and anatomical properties of the leaf and cannot be assigned to a single factor. The plant can act as a very efficient biofilter to arrest particulates. Based on the results, a greenbelt has been designed to improve the air quality of the mining and transportation areas.

Availability of data and materials

All data generated or analyzed during this study are included in the article and its Additional file 1.


  1. Ojeifo E, Abaa K, Orsulak M, Pamidimukkala PK, Sircar S, Sharma S, et al. Coalbed methane: recovery and utilization in North Western San Juan, Colorado. State College: Penn State University; 2010.

    Google Scholar 

  2. Barima YSS, Angaman DM, N'gouran KP, Koffi NA, Bi FZT, Samson R. Involvement of leaf characteristics and wettability in retaining air particulate matter from tropical plant species. Environ Eng Res. 2016;21:121–31.

    Article  Google Scholar 

  3. Zhang ZM, Liu JK, Wu YN, Yan GX, Zhu LJ, Yu XX. Multi-scale comparison of the fine particle removal capacity of urban forests and wetlands. Sci Rep-UK. 2017;7:46214.

    Article  Google Scholar 

  4. Zhang WK, Zhang Z, Meng H, Zhang T. How does leaf surface micromorphology of different trees impact their ability to capture particulate matter? Forests. 2018;9:681.

    Article  Google Scholar 

  5. Bottalico F, Chirici G, Giannetti F, De Marco A, Nocentini S, Paoletti E, et al. Air pollution removal by green infrastructures and urban forests in the city of Florence. Agric Agric Sci Proc. 2016;8:243–51.

    Google Scholar 

  6. APR J, Monks PS, Leigh RJ. Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale. Atmos Environ. 2016;147:1–10.

    Article  Google Scholar 

  7. Li YF, Wang YJ, Wang B, Wang YQ, Yu WQ. The response of plant photosynthesis and stomatal conductance to fine particulate matter (PM2.5) based on leaf factors analyzing. J Plant Biol. 2019;62:120–8.

    Article  Google Scholar 

  8. Agarwal P, Sarkar M, Chakraborty B, Banerjee T. Phytoremediation of air pollutants: prospects and challenges. In: Pandey VC, Bauddh K, editors. Phytomanagement of polluted sites. Amsterdam: Amsterdam Elsevier; 2018. p. 221–41.

    Chapter  Google Scholar 

  9. Lv JT, Christie P, Zhang SZ. Uptake, translocation, and transformation of metal-based nanoparticles in plants: recent advances and methodological challenges. Environ Sci-Nano. 2019;6:41–59.

    Article  Google Scholar 

  10. Sgrigna G, Baldacchini C, Esposito R, Calandrelli R, Tiwary A, Calfapietra C. Characterization of leaf-level particulate matter for an industrial city using electron microscopy and X-ray microanalysis. Sci Total Environ. 2016;548:91–9.

    Article  Google Scholar 

  11. Sanchez-Lopez AS, Carrillo-Gonzalez R, Gonzalez-Chavez MDA, Rosas-Saito GH, Vangronsveld J. Phytobarriers: plants capture particles containing potentially toxic elements originating from mine tailings in semiarid regions. Environ Pollut. 2015;205:33–42.

    Article  Google Scholar 

  12. Gawronski SW, Gawronska H, Lomnicki S, Sӕbo A, Vangronsveld J. Plants in air phytoremediation. In: Cuypers A, Vangronsveld J, editors. Advances in botanical research. Cambridge: Academic Press; 2017. p. 319–46.

    Google Scholar 

  13. Rai PK, Panda LLS. Leaf dust deposition and its impact on biochemical aspect of some roadside plants of Aizwal, Mizoram, North East India. Int Res J Env Sci. 2014;3:14–9.

    Google Scholar 

  14. Rai PK, Panda LLS. Dust capturing potential and air pollution tolerance index (APTI) of some road side tree vegetation in Aizawl, Mizoram, India: an indo-Burma hot spot region. Air Qual Atmos Hlth. 2014;7:93–101.

    Article  Google Scholar 

  15. Beckett KP, Freer-Smith PH, Taylor G. The capture of particulate pollution by trees at five contrasting urban sites. Arboricult J. 2000;24:209–30.

    Article  Google Scholar 

  16. Chen LX, Liu CM, Zhang L, Zou R, Zhang ZQ. Variation in tree species ability to capture and retain airborne fine particulate matter (PM2.5). Sci Rep-UK. 2017;7:3206.

    Article  Google Scholar 

  17. Sæbø A, Popek R, Nawrot B, Hanslin HM, Gawronska H, Gawronski SW. Plant species differences in particulate matter accumulation on leaf surfaces. Sci Total Environ. 2012;427:347–54.

    Article  Google Scholar 

  18. Osman AI, Abu-Dahrieh JK, McLaren M, Laffir F, Nockemann P, Rooney D. A facile green synthetic route for the preparation of highly active γ-Al2O3 from aluminum foil waste. Sci Rep-UK. 2017;7:3593.

    Article  Google Scholar 

  19. Osman AI, Abu-Dahrieh JK, McLaren M, Laffir F, Rooney DW. Characterisation of robust combustion catalyst from aluminium foil waste. Chemistry Select. 2018;3:1545–50.

    Google Scholar 

  20. Jain S, Bhattacharya T, Chakraborty S. Comparison of plant tolerance towards air pollution of rural, urban and mine sites of Jharkhand: a biochemical approach to identify air pollutant sink. In: Kalamdhad AS, Singh J, Dhamodharan K, editors. Advances in waste management. Singapore: Springer; 2019. p. 123–42.

    Chapter  Google Scholar 

  21. Chaulya SK, Kumar A, Mandal K, Tripathi N, Singh RS, Mishra PK, et al. Assessment of coal mine road dust properties for controlling air pollution. Int J Environ Prot. 2011;1:1–7.

    Article  Google Scholar 

  22. CPCB. National Ambient air Quality Status and Trends-2011. Central Pollution Control Board: New Delhi; 2014.

    Google Scholar 

  23. CPCB. National Ambient air Quality Standards in India. Central Pollution Control Board: New Delhi; 2015.

    Google Scholar 

  24. Maclachlan S, Zalik S. Plastid structure, chlorophyll concentration and free amino acid composition of a chlorophyll mutant of barley. Can J Bot. 1963;41:1053–62.

    Article  Google Scholar 

  25. Keller T, Schwager H. Air pollution and ascorbic acid. Eur J Forest Pathol. 1977;7:338–50.

    Article  Google Scholar 

  26. Weatherley PE. Studies in the water relations of the cotton plant. I. the field measurement of water deficits in leaves. New Phytol. 1950;49:81–97.

    Article  Google Scholar 

  27. Singh SK, Rao DN. Evaluation of plants for their tolerance to air pollution. In: Symposium on Air Pollution Control. IIT, New Delhi; 1983. p. 23–5.

  28. CPCB. National air quality index. Central Pollution Control Board: New Delhi; 2019.

    Google Scholar 

  29. Terzaghi E, Wild E, Zacchello G, Cerabolini BEL, Jones KC, Di Guardo A. Forest Filter Effect: role of leaves in capturing/releasing air particulate matter and its associated PAHs. Atmos Environ. 2013;74:378–84.

    Article  Google Scholar 

  30. Burkhardt J, Zinsmeister D, Grantz DA, Vidic S, Sutton MA, Hunsche M, et al. Camouflaged as degraded wax: hygroscopic aerosols contribute to leaf desiccation, tree mortality, and forest decline. Environ Res Lett. 2018;13:085001.

    Article  Google Scholar 

  31. Chen XP, Zhou ZX, Teng MJ, Wang PC, Zhou L. Accumulation of three different sizes of particulate matter on plant leaf surfaces: effect on leaf traits. Arch Biol Sci. 2015;67:1257–67.

    Article  Google Scholar 

  32. Dzierżanowski K, Popek R, Gawrońska H, Sæbø A, Gawroński SW. Deposition of particulate matter of different size fractions on leaf surfaces and in waxes of urban forest species. Int J Phytoremediat. 2011;13:1037–46.

    Article  Google Scholar 

  33. Weerakkody U, Dover JW, Mitchell P, Reiling K. Quantification of the traffic-generated particulate matter capture by plant species in a living wall and evaluation of the important leaf characteristics. Sci Total Environ. 2018;635:1012–24.

    Article  Google Scholar 

  34. Liu JQ, Cao ZG, Zou SY, Liu HH, Hai X, Wang SH, et al. An investigation of the leaf retention capacity, efficiency and mechanism for atmospheric particulate matter of five greening tree species in Beijing, China. Sci Total Environ. 2018;616:417–26.

    Article  Google Scholar 

  35. Janhall S. Review on urban vegetation and particle air pollution– Deposition and dispersion. Atmos Environ. 2015;105:130–7.

    Article  Google Scholar 

  36. Popek R, Gawrońska H, Wrochna M, Gawrońska SW, Sæbø A. Particulate matter on the foliage of 13 woody species: deposition on surfaces and phytostabilisation in waxes – a 3-year study. Int J Phytoremediat. 2013;15:245–56.

    Article  Google Scholar 

  37. Przybysz A, Popek R, Gawronska H, Grab K, Loskot K, Wrochna M, et al. Efficiency of photosynthetic apparatus of plants grown in sites differing in level of particulate matter. Acta Sci Pol-Hortoru. 2014;13:17–30.

    Google Scholar 

  38. Ha J, Martinez H. Influence of environmental pollution on leaf properties of urban trees in China and USA: a comparative study using stomatal density. Asian J Plant Sci Res. 2018;8:1–7.

    Article  Google Scholar 

  39. Singh SK, Rao DN, Agrawal M, Pandey J, Narayan D. Air pollution tolerance index of plants. J Environ Manag. 1991;32:45–55.

    Article  Google Scholar 

  40. Roshchina VV, Roshchina VD. Ozone and plant cell. Dordrecht: Springer; 2003.

    Book  Google Scholar 

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Authors are thankful to Director, CSIR-CIMFR, Barwa Road, Dhanbad, India for his valuable guidance and granting permission to publish this paper.


This work was supported by CSIR-CIMFR, Barwa Road, Dhanbad-826015, India.

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Mr. RK Singh helped acquire field and laboratory data, Dr. RS Singh and Dr. D Pal in preparing the manuscript, Dr. KKK Singh and Dr. PK Singh in editing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shailendra K. Singh.

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Additional file 1: Table S1.

Morphological and anatomical characteristics of the leaf.

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Singh, S.K., Singh, R.K., Singh, R.S. et al. Screening potential plant species for arresting particulates in Jharia coalfield, India. Sustain Environ Res 29, 37 (2019).

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