Phytomonitoring of air pollution around brick kilns in Balochistan province Pakistan through air pollution index and metal accumulation index

Journal of Cleaner Production 229 (2019) 727e738

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Phytomonitoring of air pollution around brick kilns in Balochistan province Pakistan through air pollution index and metal accumulation index Khanoranga*, Sofia Khalid Fatima Jinnah Women University, The Mall, Rawalpindi, Pakistan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 November 2018 Received in revised form 8 April 2019 Accepted 5 May 2019 Available online 6 May 2019

Some plants species can be used as biomonitoring agents around highly polluted sites. This study was carried out to assess the sensitivity and tolerance of plants species against air pollution around brick kilns in Balochistan province, Pakistan. Plant species were collected from the study sites and were evaluated against pollution by making use of air pollution tolerance index and metal accumulation index. The susceptibility of plants to different pollutants was determined by analyzing various biochemical parameters (i.e. total chlorophyll content, ascorbic acid, pH of the leaf extract and relative water content), heavy metal(loid)s level of the investigated plant species. The pollution tolerance level of the analyzed plant samples showed that Morus alba was the most tolerant and Convolvulus arvensis, the most sensitive species to the air pollution. The metal accumulation index value was found highest for Lepidium sativum and lowest for Malcolmia africana, depicting their metal accumulation capacity. Moreover, it was found that the pollution level of all the analyzed heavy metal(loid)s was beyond the permissible limits of WHO except Mn. Keeping in view this research, it is suggested that the mentioned tolerant plant species grown in the vicinity of brick kilns may serve as sinks for pollution whereas, the sensitive ones may be used for monitoring of pollution level. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Brick kiln Biomonitoring Metal accumulation index Heavy metal(loid)s Balochistan

1. Introduction Brick kilns industries are regarded as one of the main sources of pollution in developing countries such as Pakistan, India, and Bangladesh (Ahmad et al., 2012). The operational activities are generally carried out without use of any modern technologies and contaminate the environment through several emissions more importantly gaseous and particulate forms of pollutants such as NOx, SOx, COx, heavy metals and polycyclic aromatic hydrocarbons (PAHs) (Kamal et al., 2014; Jan et al., 2014; Saha and Hosain, 2016). Plants are the main components of an ecosystem maintaining ecological balance through actively participating in the cycling of nutrients and gases (photosynthesis and respiration) and also providing large surface areas for assimilation and accumulation of different pollutants in the environment (Díaz et al., 2007). The accumulation of certain pollutants in plants may cause injuries or physiological changes in numerous ways (Braun et al., 2017). It has

* Corresponding author. E-mail address: [email protected] (Khanoranga). https://doi.org/10.1016/j.jclepro.2019.05.050 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

been reported that the sensitive species show greater injuries (i.e., Necrosis, chlorosis, stomatal damage altering the rate of respiration and transpiration, impacts on the chlorophyll content reducing photosynthetic activity, disruption of cell permeability and enzymatic reactions) due to air pollution compared to tolerant plant species (Agrawal and Agrawal, 1989; Kuddus et al., 2011; Shah et al., 2018). Biomonitroing is a cost effective and emerging technique using the responses of organisms (plants and animals) to determine the quality of their environment. Plants are widely used as bioindicators to pinpoint the possible sources of pollutants in the environments. Changes occurring in the physiology, morphology and other biochemical parameters are regarded as the biomarkers of the pollution effects. Plant species vary in their responses to air pollution exposures. Some plant species are very sensitive to pollution stress resulting in measurable and visible symptoms while certain plant species are resistant to different pollutants. These biomarkers are used to convey information on alteration in the quality of the environment (Chakrabortty and Paratkar, 2006; Elloumi et al., 2018). The sensitive species can be good biomonitor of a pollution in the contaminated environment. Similarly, the tolerant species are beneficial for the abatement of the

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contaminated environment by acting as a sink and reducing the level of certain pollutants (Lohe et al., 2015). Plants growing in the vicinity of brick kilns are considered to be vulnerable to pollutant exposure (Wahid et al., 2014). This is most likely, due to the gaseous exchange of oxides of sulphur (SOx), nitrogen (NOx) and carbon (COx), hydrogen fluoride (HF) and ozone (O3) through stomata, wet and dry deposition of particulate matter, fly ash and other pollutants which reduce the interception of incident light and result in clogging of stomata (De Camargo and Lombardi, 2018). Brick kilns industries in Pakistan are fired with low-quality fuel responsible for the release of different types of hazardous emissions in the ambient environment (Achakzai et al., 2017a,b). Generally, heavy metals contamination of plants growing in the vicinity of brick kilns is of major concern. Heavy metals such as Cd, Cr, Pb, Hg and As are the most important hazardous, pollutants degrading the quality of the environment and also imposing a threat to the health of people (Begum et al., 2015). These are released in the environment due to the incomplete combustion of coal in brick kilns (Ishaq et al., 2010). Once, these are released into the environment they move into different environmental matrices such as soil, plants, water, and air. The soil in any ecosystem acts as a sink for different pollutants. Contaminated soil, therefore, is also a threat to the vegetation growing on it (Yang et al., 2016; Sikder et al., 2016). Although, some metals are beneficial for the growth of plants such as Fe, Mn, Co, Cu and Zn and are a major constituent of several enzymes and other cellular structures (Malik et al., 2010). Moreover, the other hazardous heavy metals such as Hg, Cd, Pb, and Cr are very toxic to the plants by disrupting important enzymes structures and replacing the essential metals (Fontenele et al., 2017). Furthermore, plants may also have strong defense system (antioxidant system) providing tolerance against heavy metals, but the excess of heavy metals in plants may cause different physiological, morphological and biochemical changes in the exposed plants such as decrease in photosynthetic pigments, visible leaf injuries, stomatal clogging, early senescence, reducing permeability of cellular structure and causing restricted plant growth. In case of staple crops, it may lead to yield losses (Waseem et al., 2014; Khalid et al., 2018). Thus, the ecophysiological response of plants to the exposures of different pollutants can be used for biomonitoring of pollution level (Josephine et al., 2019). Tolerant plants species can be used as a remediation tool to protect environment indirectly lowering the risk of exposure of human to hazardous pollutants. Biomonitoring of heavy metal pollution with plants is a cost-effective method (Liu et al., 2017; Nakazato et al., 2018). Plants exposed to a pollution source absorb and accumulate environmental pollutants (heavy metals). Different plants have various tendencies of the accumulation of heavy metals thus acting like hyperaccumulators of different pollutants (Shah et al., 2014; Irshad et al., 2015). Phytomonitoring is a crucial step in studying the transfer of pollutants in the food chain (Amin and Ahmad, 2015; Van der Ent et al., 2015; Rodríguez-Bocanegra et al., 2018). In fact, the hyperaccumulator plants species are also widely reported in phytoremediation, a promising technique used in the reclamation of the contaminated soil (Cristaldi et al., 2017). A wide range of plants belonging to different families has been identified, having a great tendency of the tolerance and accumulation for numerous hazardous heavy metals (Mahdavian et al., 2017; Sarkar, 2018). These tolerant plant species are regarded as appropriate for soil stabilization and extraction of heavy metals through their roots (Reeves et al., 2018). The susceptibility of plants to air pollution can be determined by analyzing different physiological and biochemical parameters including total chlorophyll content, ascorbic acid, pH of the leaf extract and relative water content respectively. These indicator

parameters are computed in a single index described as air pollution tolerance index (APTI) (Esfahani et al., 2013). The evaluation of single parameters for the assessment of pollution load on the plants may not give a clear picture of the impacts of pollution. Therefore, APTI is an integrated tool for assessing the response of plants to a diverse range of pollutants through biochemical and physiological parameters. Thus, this reliable tool can be effectively utilized for identification of indicators (tolerant and sensitive) plant species to be grown in the contaminated environment (Okunlola et al., 2016; Kashyap et al., 2018). Although many research studies have been conducted in developing countries about the potential of plants for air pollution monitoring and toxic elements (Zhan et al., 2014; Alahabadi et al., 2017). Pollution monitoring in rapidly developing province of Pakistan is important for sustainable development and current study is the first assessment of brick kiln’s pollution impacts on surrounding vegetation in the study area. The current study has been designated: (1) To study the impacts of different pollutants on the foliar biochemical parameters (2) To assess the concentration of heavy metals and arsenic in the studied plants (3) To determine the tolerance of plants against brick kilns pollution through APTI and Metal accumulation index (MAI) (4) To recommend suitable species of plants for phytomonitoring of air pollution and toxic elements growing in the vicinity of brick kilns. 2. Materials and methods 2.1. Study site The brick sector of Balochistan is mainly supported by the high production of bricks from Quetta, Pishin and Mastung districts respectively. The current study was conducted in these three districts of Balochistan (Fig. 1). Sampling was conducted in Kuchlak (Quetta) where approximately 30 brick kilns were operating whereas, in Pishin and Mastung sampling was carried out in Yaroo, Saranan, and Dasht where about 50e60 and 130 brick kilns were operational. Three brick kilns in each study sites were selected for sampling. The design of brick kilns was an old bull’s trench type commonly used throughout the country. Coal, wood and rubber tires were normally used as fuel at all sites. The selection criteria of brick kilns were based on its nearness to the agricultural land and production capacity. Brick kilns were surrounded by agricultural land where wheat crops and orchards of fruits were cultivated. 2.2. Plant sampling Representative plants species both cultivated and wild were randomly selected with increasing distance of about 100 m, 300 m, and 500 m away from brick kilns in three districts (Quetta, Pishin, and Mastung) of Balochistan. In total nine plants i.e. Triticum aestivum (Wheat), Chenopodium album (Goosefoot), Medicago sativa (Alfalfa), Lepidium sativum (Garden cress), Prunus armeniaca (Apricot), Convolvulus arvensis (Bindweed), Elaeagnus angustifolia (Russian olive), Vitis vinifera (Wine grape), Punica granatum (Pomegranate) were collected in Quetta whereas total of five plants namely, Triticum aestivum, Peganum harmala (Wild rue), Lepidium sativum, Morus alba (White mulberry), Malcolmia africana (African mustard) were collected in Pishin and Mastung. Samples were collected at the end of April 2017 and analysis was completed in May 2017. Samples were collected in triplicates i.e. three leaves samples from each plant species at each site. The collected samples of plants were stored in ice container and then immediately brought to the laboratory for analysis. After taking fresh weight plants were washed with distilled water to remove soil and other dirt particles. Some plant samples were kept refrigerated until further analysis while others were oven dried. The refrigerated

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738

729

Fig. 1. Map of the study area.

plant samples were investigated for different biochemical parameters (total chlorophyll content, ascorbic acid, relative water content, and pH of leaf extract) whereas dried samples were investigated for the presence of heavy metals according to standard methods. For identification purpose the collected plant samples were pressed, dried and pasted on standard herbarium sheets. The plant specimens were then identified by expert taxonomist at Botany Department of the University of Balochistan according to Flora of Pakistan. The identified plant specimens were deposited to the herbarium of University of Balochsiatn (Shuaib et al., 2019). To minimize the chance of variation all samples were collected on the same day.

2.3.2. Relative water content Relative water content was determined by following the method used by Singh (1977). Leaves of each plants species were weighed immediately after sampling. The leaves were immersed into water and kept overnight, after which they were blotted to dry and weighed again to get turbid weight. The turbid leaves were dried in an oven at 70  C for 24 h to get the constant dry weight. Relative water content was determined by applying following formula:

Relative water content ð%Þ

¼

FW  DW  100 TW  DW

(4)

Whereas; FW Fresh weight, DW Dry weight and TW Turbid weight. 2.3. Biochemical analysis 2.3.1. Total chlorophyll content Total chlorophyll content in leaves of selected plant species was estimated using standard method used by Singh et al. (1991). 0.1 g of leaf material was suspended in 80% prepared acetone solution and small amount of sand or magnesium carbonate was also added to get better suspension. Leaf extract was centrifuged at 3000 rpm for about 3 min. Absorbance of leaf extract was measured through UV spectrophotometer at wavelengths of 663 nm, and 645 nm. The estimated chlorophyll content was calculated through Arnon (1949) equations.

Chl a ðmg=gÞ ¼ ½ð12:7  A663Þ  ð2:6  A645Þ 

ml acetone mg leaf tissue

(1)

Chl b ðmg=gÞ ¼ ½ð22:9  A645Þ  ð4:68  A663Þ 

ml acetone mg leaf tissue

Total chlorophyll content (mg/g) ¼ Chl aþ Chl b

2.3.3. pH of leaf extract For determination of pH, 5 g of sample was homogenized with 50 ml of distilled water. pH of prepared suspension was measured using digital pH meter (Kuddus et al., 2011). 2.3.4. Ascorbic acid Ascorbic acid was determined by titration method. 2 g of leaf sample was weighed and then quickly homogenized with 5% metaphosphoric acid. The sample was crushed with mortar pestle until a slurry type mixture was formed. The mixture was filtered with Whatman filter paper. 10 ml of aliquot was taken from the mixture and titrated against dichloroindophenol (DCIP) dye solution (Reiss, 1993). 2.3.5. Air pollution tolerance index APTI was calculated by computing all the four parameters in a single equation;

AðT þ PÞ þ R 10

(2)

APTI ¼

(3)

Whereas, A (Ascorbic acid) T (Total chlorophyll content) P (pH of leaf

(5)

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the concentration of NO2 ranged between 11.38 and 298.48 ppb (Quetta), 18.15e112.95 ppb (Pishin) and 91.28e492.15 ppb (Mastung) respectively. The concentration of the NO2 was found above the permissible limits of WHO (21 ppb) (Ahmad and Aziz, 2013).

extract) R (Relative water content). 2.4. Method of digestion 0.5 g of crushed plant powder sample was taken in beaker and 12 ml (9 ml HCL þ 3 ml HNO3) of aqua regia was added. Beaker was covered with watch glass and heated on hot plate at 80  C for 2 h. After which, the suspension was allowed to cool and filtered through Whatman No: 42 filter paper. Leaching was done with dionized water and the volume was made up to 50 ml. The digested plant samples were analyzed for heavy metal(loid)s (Bigdeli and Seilsepour 2008). As and Hg were assessed using AAS coupled to hydride generation and cold vapor (220 Spectra AA, Varian) while the rest of heavy metals were assessed through Flame atomic absorption spectrophotometer (FAAS, AA-7000 Shimadzu). MAI was used to assess the heavy metal(loid)s accumulation efficiencies of the plants using the standard formula;

MAI ¼


X n 1 IJ N J¼1

(6)

Where N shows the total number of heavy metals studied and IJ is sub-index of J gained by dividing the metal concentration by its
rska-Socha et al., 2017). standard deviation (Hu et al., 2014; Nadgo 2.5. Data precision and accuracy Each sample was analyzed in triplicate and mean of the triplicate was used for further data interpretation. Reagent blanks were also included with samples in each step of the experiment. The accuracy of Atomic absorption spectrophotometer was assessed through the standards of all metals prepared through the dilution (1000 mg/L) of the certified reference solution (Merck) of corresponding metal with double deionized water and working standards were analyzed after every 10 samples. All the chemicals and reagent were of analytical grade. The limit of detection (LOD) and limit of quantification (LOQ) were determined through triplicates of 10 method blanks digested in same way as the plant samples and values were calculated through standard formulas (Radwan and Salama, 2006). LOD values for Cd, Cr, Pb, Cu, Mn, Zn, Ni, Co, Fe, As and Hg were 0.02, 0.02, 0.05, 0.1, 0.17, 0.06, 0.13, 0.09, 0.66, 0.01, 0.02 mg/kg respectively. LOQ values for Cd, Cr, Pb, Cu, Mn, Zn, Ni, Co, Fe, As and Hg were 0.06, 0.05, 0.14, 0.31, 0.45, 0.14, 0.34, 0.26, 1.95, 0.04, 0.05 mg/kg respectively. The validation of analytical method was carried out through a recovery study, by spiking samples with known concentration of standards of heavy metal(loid)s (Tang et al., 2018). The spiked samples were analyzed in the same way as like actual samples. The % average recoveries achieved for Cd, Cr, Pb, Cu, Mn, Zn, Ni, Co, Fe, As and Hg were 93, 86, 87, 90, 90, 96, 96, 97, 94, 88, 86% respectively. 2.6. Statistical analysis For each plant samples mean was calculated from triplicates samples and presented with the standard deviation in tables. Mean of variance for investigated biochemical parameters with respect to distance, study sites and plant species was determined through One-way ANOVA using Microsoft Excel 2013. 3. Results and discussion The current study investigates the air quality through two important criteria pollutants viz. NO2 and CO at the study area. The concentration of CO was found below the detection limit whereas;

3.1. Total chlorophyll content Chlorophyll content is regarded as the index of the productivity as well as it is suggestive of the photosynthetic activity, growth, and biomass (Ninave et al., 2001). It is very sensitive to the exposure of pollutants and most liable to damage by pollution load (Areington and Varghese, 2017). It is also reported, that chlorophyll content varies with the type of species, the age of leaf and pollution load as well as with several other biotic and abiotic factor (Prajapati and Tripathi, 2008). The results of the current study showed that marked variation was determined in the chlorophyll content of the studied plants with increasing distance from the brick kilns (Table 1). Some decrease in chlorophyll content was observed with increasing distance from source but it was not statistically significant (p > 0.05) except Quetta (Table 2). The most probable reasons for this might be the fallout of the pollutants some distance away from the sources depending upon the speed and direction of the wind. These results also confirmed that the impacts of pollution on the environment can be felt away from the sources (Laghari et al., 2015; Achakzai et al., 2017a,b). Similarly, significant (p < 0.05) variations in the chlorophyll content among the studied plants and study sites were also found, signifying the fact that plants vary in the chlorophyll contents as reported by several other authors (Klumpp et al., 2000; Thawale et al., 2011). Variation in the chlorophyll content in the studied plants might be due to the increased pollution level near the source. The decreased in chlorophyll content near the source might be due to the released of different types of emission (Gaseous and particulate forms of pollutants) due to the combustion of coal and other operational activities of brick kilns (Gupta and Narayan, 2010; Adrees et al., 2016). The aerial deposition of particulate matter and other fly ash on the leaf surfaces blocks stomata and thus, disturbing the gaseous exchange for photosynthesis, especially in sensitive plant species (Sarma et al., 2017). The results of the current study are in line with several other studies that report the impacts of pollution stress on the

Table 1 The impacts of brick kilns pollution on the total chlorophyll content (mg/g) of the studied plants in three districts of Balochistan. Name of plants Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

100 m

300 m

500 m

1.68 ± 1.56 3.23 ± 2.61 4.89 ± 1.02 4.41 ± 2.32 1.99 ± 1.80 0.87 ± 0.91 5.94 ± 1.53 1.32 ± 1.35 3.69 ± 2.06

2.39.15 3.06 ± 0.28 4.28 ± 1.06 3.50 ± 0.87 1.59 ± 0.32 0.87 ± 0.06 5.57 ± 1.44 1.96 ± 0.93 3.13 ± 0.78

3.58 ± 0.72 4.76 ± 1.5 6.56 ± 0.59 6.46 ± 0.32 3.49 ± 0.58 2.23 ± 0.55 7.85 ± 0.14 2.92 ± 0.09 5.69 ± 0.46

0.89 ± 0.68 1.90 ± 0.57 1.21 ± 0.90 3.71 ± 1.02 2.89 ± 0.39

1.49 ± 1.14 2.29 ± 0.76 2.72 ± 1.28 3.72 ± 1.19 3.03 ± 0.81

1.95 ± 1.105 2.72 ± 0.64 2.04 ± 0.75 4.19 ± 1.49 3.29 ± 0.86

0.99 ± 0.76 1.92 ± 0.59 1.17 ± 0.97 4.01 ± 0.94 3.56 ± 1.40

2.16 ± 0.18 3.62 ± 0.97 2.94 ± 0.08 5.65 ± 1.09 4.54 ± 0.76

2.89 ± 0.75 4.13 ± 1.63 3.11 ± 1.55 5.87 ± 1.18 4.54 ± 2.19

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738 Table 2 Result of One Way ANOVA signifying variation in studied parameter with increasing distance from the source and among the studied plant species at the study sites.

Table 3 The impacts of brick kilns pollution on the ascorbic acid content (mg/g) of the studied plants in three districts of Balochistan.

Variable

Study site

Distance

Plantspecies

Name of plants

Total chlorophyll content (mg/g)

Quetta Pishin Mastung 0.001* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.000*

0.05* 0.51 0.11

0.000* 0.001* 0.01*

0.98 0.81 0.36

0.04* 0.03* 0.04*

0.00* 0.31 0.22

0.01* 0.000* 0.000*

0.95 0.58 0.40

0.001* 0.01* 0.04*

0.83 0.81 0.28

0.03* 0.000* 0.000*

Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

Study sites Ascorbic acid (mg/g)

Study sites pH of the leaf extract

Study sites Relative water content (%)

Study sites APTI

Study sites *p is significant at the level of 0.05.

chlorophyll content of the investigated plants (Gupta et al., 2016;
r et al., 2018). Molna 3.2. Ascorbic acid Ascorbic acid plays a significant role in the defense mechanism of plant and is regarded as strong reducing agent (Hussain et al., 2017). It helps in protecting chloroplast of the plants from inactivation due to the exposure to air pollutants like SO2. The concentration of ascorbic acid is directly proportional to its reducing power while the reducing property of the ascorbic acid is pH dependent. High pH favors the conversion of hexose sugar into ascorbic acid (Tanee and Albert, 2013). In the current study a positive correlation (r ¼ 0.98) was determined between ascorbic acid and pH of extract (data are not presented here). Ascorbic acid also helps in other physiological processes of the cell such as cell division, cell wall formation, and defense mechanism. It is a natural antioxidant and reduces the effects of the pollution exposure. The results of the current study illustrated that ascorbic acid showed an inconsistent trend of variations with the increasing distance from the source (Table 3) and One-way ANOVA also confirmed that variations were statistically insignificant (p > 0.05) in all three sites with respect to distance from the source (Table 2). Ascorbic acid is generally produced in greater amounts in plants during stress conditions. In the current study, plants showed significant (p < 0.05) differences in the ascorbic acid content confirming the fact that plants vary in response to different pollutants. High level of the ascorbic acid signifies the tolerance of plants against pollution exposure. The results of the study are in agreement with other authors (Dwivedi and Tripathi, 2007; Ogunrotimi et al., 2017). Variations in the level of ascorbic acid among the study sites and among different plant species were also significant (p < 0.05) as shown in (Table 2). 3.3. pH of leaf extracts pH of the leaf extract is an important parameter which can help in determining the susceptibility of plants to the pollution exposure i.e. SO2 and NOx (Rahul and Jain, 2014). pH of the leaf extract is generally lower in the presence of the acidic pollutants and will be low in the sensitive species upon exposure to air pollutants. In the

731

100 m

300 m

500 m

2.42 ± 0.34 3.28 ± 1.32 7.66 ± 3.63 5.55 ± 0.38 2.41 ± 0.82 1.23 ± 1.7 10.72 ± 2.52 1.47 ± 0.88 4.57 ± 1.18

2.73 ± 0.56 3.21 ± 0.87 2.27 ± 0.60 3.47 ± 1.96 1.63 ± 0.81 3.84 ± 2.91 4.22 ± 2.13 1.96 ± 1.69 3.50 ± 1.4

3.19 ± 2.05 4.2 ± 2.09 3.81 ± 1.84 4.74 ± 1.90 2.42 ± 0.72 3.62 ± 1.48 3.4 ± 1.99 4.07 ± 1.79 4.20 ± 1.39

1.77 ± 1.28 5.89 ± 2.79 2.20 ± 0.97 8.99 ± 1.83 7.70 ± 2.55

2.52 ± 0.31 5.6 ± 1.94 3.06 ± 1.45 11.12 ± 1.5 8.56 ± 1.94

3.33 ± 2.93 5.43 ± 0.23 4.34 ± 40 12.36 ± 2.97 8.29 ± 2.51

5.70 ± 2.72 9.99 ± 3.77 5.14 ± 2.16 15.9 ± 1.21 12.73 ± 3.14

2.68 ± 0.67 6.08 ± 3.21 3.63 ± 1.06 11.23 ± 6.55 8.33 ± 3.53

5.70 ± 2.5 9.99 ± 3.8 5.14 ± 0.38 15.9 ± 6.06 12.73 ± 5.28

presence of the acidic pollutants, the pH of the cell sap become acidic which restricts the conversion of hexose sugar to ascorbic acid, which is helpful in the defense mechanism of the plants (Agbaire and Esiefarienrhe, 2009). Similarly, acidic pH increases the stomatal sensitivity to pollution thus altering important physiological processes (i.e. rate of respiration and transpiration) of plants. Additionally, pH also regulates several other physiological processes of the plants i.e. numerous enzymatic and photosynthetic activity is dependent upon the specific range of pH. The findings from the current study showed an inconsistent trend of variation in leaf extract pH with the increasing distance from the source (Table 4). Generally, the pH was found lower near the source (100 m) in most studied plants in all three sites. The output of oneway ANOVA (Table 2) illustrated that variations with distance were statistically insignificant (p > 0.05) except in Quetta whereas variations in the pH of the leaf extracts of plants among the study sites

Table 4 The impacts of brick kilns pollution on the pH of the leaf extracts of the studied plants in three districts of Balochistan. Name of plants Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

100 m

300 m

500 m

5.93 ± 0.72 7.03 ± 0.20 6.07 ± 0.27 6.46 ± 0.60 5.9 ± 0.50 5.8 ± 0.46 6.63 ± 0.23 5.83 ± 0.98 6.13 ± 1.09

6.73 ± 0.35 6.43 ± 0.35 6.5 ± 0.72 6.8 ± 0.45 6.3 ± 0.40 7.23 ± 0.43 6.96 ± 0.60 6.76 ± 0.76 6.9 ± 0.20

6.43 ± 0.35 6.76 ± 0.35 6.96 ± 0.72 7.13 ± 0.45 6.46 ± 0.40 6.40 ± 0.44 6.50 ± 0.60 6.63 ± 0.76 6.70 ± 0.20

6.20 ± 0.15 6.60 ± 0.25 6.30 ± 0.25 7.41 ± 0.43 7.03 ± 0.15

6.40 ± 0.10 6.91 ± 0.10 6.50 ± 0.15 7.63 ± 0.17 7.20 ± 0.10

6.8 ± 0.25 7.2 ± 0.29 6.8 ± 0.12 7.8 ± 0.23 7.4 ± 0.35

6.57 ± 0.35 6.8 ± 0.85 6.13 ± 0.32 7.50 ± 0.52 7.40 ± 0.26

6.63 ± 0.23 6.93 ± 0.15 6.60 ± 0.2 7.60 ± 0.2 7.27 ± 0.23

7.03 ± 0.06 7.47 ± 0.38 7.10 ± 0.17 8 ± 0.36 7.60 ± 0.43

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were statistically significant (p < 0.05). Besides this, plants also showed significant (p < 0.05) differences in the pH of leaf extract illustrating the fact that response to pollutants varies with the types of plants i.e. acidic pH is the indication of the sensitivity of the plant upon exposure to pollution (Lakshmi et al., 2009; Rai and Panda, 2014). The findings are in agreement with several other studies reporting acidic pH of the leaf extracts near the pollution source and some also reported an inconsistent trend of variations with distance away from the source (Thawale et al., 2011). 3.4. Relative water content It is another crucial parameter determining the susceptibility of plants to the pollution exposure (Kaur and Nagpal, 2017; Sharma et al., 2017). The results of the current study demonstrate that relative water content of studied plants showed an inconsistent trend of variation with increasing distance from the source (Table 5). The results of one-way ANOVA (Table 2) also showed that variations in the increasing distance were statistically insignificant (p > 0.05). Similarly, plants exhibited significant (p < 0.05) variations in the relative water content and variations in the level of relative water content of plants among the study sites were also statistically significant (p < 0.05). It may be due to the differences among plants intolerance against air pollution. Increased level of pollution increased the permeability of cell and dissolved nutrients increasing the risk of early senescence (Sinha et al., 2017). The high relative water content in the presence of high pollution is the indication of greater tolerance of plants. The findings of the current study are supported by other studies (Seyyednejad et al., 2017; Ogbonna et al., 2017). 3.5. Air pollution tolerance index APTI is a reliable method for screening the susceptibility of different plants upon exposure to same or different pollutants
rska-Socha et al., 2017). The method is very simple and easy (Nadgo to adopt during field condition without acquiring large environmental monitoring setups (Maity et al., 2017). The result of the current study demonstrated that all the studied plants in three sites showed an inconsistent trend of variations with respect to

Table 5 The impacts of brick kilns pollution on the relative water content (%) of the studied plants in three districts of Balochistan. Name of plants Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

100 m

300 m

500 m

58.61 ± 4.69 66.35 ± 8.07 79.24 ± 9.77 74.93 ± 5.39 47.5 ± 6.55 42.87 ± 11.13 84.69 ± 7.55 46.61 ± 11.02 69.25 ± 2.62

63.17 ± 7.78 64.43 ± 4.75 69.98 ± 7.08 77.14 ± 10.57 53.59 ± 11.77 47.39 ± 12.64 79.82 ± 3.54 46.37 ± 11.68 70.60 ± 4.17

61.84 ± 6.37 64.5 ± 4.86 77.99 ± 9.33 74.29 ± 5.75 54.50 ± 13.12 47.71 ± 13.44 83.63 ± 6.61 50.76 ± 13.96 71.92 ± 5.77

56.67 ± 7.50 66.26 ± 1.02 61.05 ± 10.60 80.22 ± 7.90 76.04 ± 3.44

61.08 ± 5.63 65.04 ± 7.74 68.61 ± 11.94 80.01 ± 9.78 74.02 ± 7.79

65.67 ± 5.05 74.88 ± 3.45 69.12 ± 5.80 79.92 ± 9.69 76.82 ± 6.87

57.61 ± 8.74 68.94 ± 3.82 61.05 ± 13.24 84.72 ± 3.18 82.89 ± 6.33

68.27 ± 7.89 78.10 ± 2.72 67.65 ± 1.13 86.24 ± 1.96 81.67 ± 2.39

73.02 ± 10.11 80.72 ± 5.06 74.36 ± 9.21 85.38 ± 2.92 81.67 ± 1.96

increasing distance from the source. Achakzai et al. (2017a,b) also reported similar result, where the APTI showed an inconsistent trend of variations with increasing distance from the source. Results of the study also revealed that plants exhibited marked demarcation with respect to their susceptibility to a similar source of pollution (Table 6). The trend of APTI of plants collected around brick kilns in Quetta was Elaeagnus angustifolia (21.75) > Medicago sativa (17.59) > Lepidium sativum (14.21) > Punica granatum (12.53) > Chenopodium album (10.38) > Triticum aestivum (8.66) > Prunus armeniaca (8.41) > Vitis vinifera (7.58) > Convolvulus arvensis (6.02). Furthermore, the trend of variation of air pollution tolerance indices at Pishin and Mastung were Morus alba (19.07) > Malcolmia africana (15.13) > Peganum harmala (12.12) > Lepidium sativum (9.76) > Triticum aestivum (8.64) and Morus alba (26.82) > Malcolmia africana (21.30) > Peganum harmala (16.53) > Lepidium sativum (11.37) > Triticum aestivum (11.27) respectively. According to the classification of Agrawal et al. (1991), plants are classified into three different categories based on the air pollution tolerance indices i.e. APTI values less than 16 are classified as sensitive whereas, those having APTI above than 16 are regarded as tolerant species. The current study places Elaeagnus angustifolia, Medicago sativa, Morus alba, Malcolmia africana and Peganum harmala in tolerant species whereas, Convolvulus arvensis was found to be the most sensitive species among all the studied plants (Table 7). It is also evident from the results that plants exhibited considerable variation in tolerance upon exposure to different pollutants. It might be due to the difference in their biochemical parameters (Rai and Panda, 2014). Conclusively, the tolerant plant species, identified through the current study could be employed as means of pollution abatement in the highly polluted areas whereas, the sensitive species can be recommended as a bio-indicator tool for the assessment of pollution level (Bharti et al., 2018). The tolerant plant species reduces significant amount of different types of pollutants in the air thus acting like scavengers for pollutants in the environment. Similarly, the sensitive plants acts as detective of pollution. They show visible symptoms of air pollution long before their effects are observed in the other organisms (Seyyednejad et al., 2011; Jain et al., 2019). The results of such biomonitoring studies are handy for a landscaper to identify the most suitable species to be grown in the

Table 6 Variations in APTI of plants collected around brick kilns in three districts of Balochistan. Name of plants Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

100 m

300 m

500 m

7.84 ± 0.71 9.81 ± 2.10 17.41 ± 2.120 11.52 ± 5.20 8.17 ± 1.73 5.35 ± 0.63 21.10 ± 3.59 6.04 ± 0.59 11.08 ± 1.66

8.07 ± 3.33 10.74 ± 4.96 17.35 ± 1.03 15.7 ± 2.80 8.51 ± 0.82 6.5 ± 0.23 21.84 ± 1.21 8.86 ± 2.24 13.48 ± 1.65

10.06 ± 1.74 10.57 ± 1.38 18.01 ± 5.51 15.41 ± 3.84 8.56 ± 2.28 6.23 ± 1.61 21.4 ± 6.84 7.84 ± 2.21 13.03 ± 2.34

6.95 ± 1.65 11.69 ± 2.52 7.68 ± 0.56 18.09 ± 2.26 15.23 ± 2.37

8.12 ± 3.61 11.63 ± 0.18 9.82 ± 3.19 20.45 ± 4.66 16.05 ± 2.81

10.84 ± 3.61 13.02 ± 0.18 11.78 ± 3.19 18.65 ± 4.66 14.1 ± 2.81

11.22 ± 4.31 7.93 ± 0.64 8.65 ± 2.41 20.25 ± 1.48 18.45 ± 4.07

19.19 ± 3.37 12.94 ± 2.50 12.73 ± 0.28 30.11 ± 7.27 22.73 ± 4.17

19.19 ± 3.37 12.94 ± 2.5 12.73 ± 0.28 30.11 ± 7.27 22.73 ± 4.17

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738 Table 7 Classification of the studied plants according to APTI values. Name of plants Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

APTI

Category

8.66 10.38 17.59 14.21 8.41 6.03 21.75 7.58 12.53

Sensitive Sensitive Intermediate Sensitive Sensitive Sensitive Intermediate Sensitive Sensitive

8.64 12.12 9.76 19.07 15.13

Sensitive Sensitive Sensitive Intermediate Sensitive

11.27 16.53 11.37 26.82 21.30

Sensitive Sensitive Sensitive Intermediate Intermediate

contaminated environment for amelioration purposes. It is also worth noting that combining a variety of parameters for the estimation of pollution level through plants is more realistic and reliable approach rather than assuming the level of pollution through single parameter result (Okunlola et al., 2016; Sen et al., 2017). APTI is widely used by other researchers as a significant tool for a landscaper for selecting and identifying the most suitable species to be grown in the urban environment as mean of mitigating pollution due to rapid growth in urbanization and industrial sectors (Zhang et al., 2016; GHassanen et al., 2016). The results of One-way ANOVA showed that variation with increasing distance from the source was statistically insignificant (p > 0.05) at three sites (Table 2) whereas, variations in the ATPI among the studied plants and study sites were statistically significant (p < 0.05). 3.6. Heavy metal(loid)s content in the studied plants The concentration of all studied heavy metal(loid)s (except Mn) was found to be beyond the permissible limits of WHO in the majority of the plants at the study sites (Tables 8e10). In the view of current study, inconsistent trend of variation with distance was recorded and the variations where found insignificant (p > 0.05) except Cd (p ¼ 0.002), Cr (0.001) and As (0.01) in Mastung and Fe (0.001) in Quetta, respectively. Substantially, significant (p < 0.05) variations in the heavy metal(loid)s concentration with respect to site and plant species were also recorded (Table 12). Some plants showed high uptake for one particular metal whereas, the same plant showed lower accumulation tendency for other heavy metals. Similar, results were also reported by Begum et al. (2015), they found the similar trend of variations of heavy metals by different plants collected in the vicinity of brick kilns of Fetah Jang, Pakistan. A large number of factors controls the uptake of heavy metals and arsenic in plants i.e. soil physiochemical properties, climatic conditions and plants genotypes (Trebolazabala et al., 2017). The probable sources of the heavy metals and arsenic in the study area might be due to the combustion of coal used in the baking of bricks in brick kilns and phosphate fertilizers being used in agricultural activities (Proshad et al., 2017; Ravankhah et al., 2017). The variations in the heavy metals and arsenic content of plants with increasing distance from the source might be attributed to the fallout of pollutants in various forms (gaseous and particulate

733

matter) some distance away from the source depending upon metrological conditions (Wind speed and direction, humidity and temperature). It is obvious that the impacts of pollution source on the environment can be felt far away from the source (Achakzai et al., 2017a,b). Some other studies have also reported the high concentration of heavy metals in plants and soil around brick kilns (Pandhija and Rai, 2009; Ishaq et al., 2010; Ismail et al., 2012). 3.6.1. Metal accumulation index Plants in contaminated environment accumulate different metals simultaneously. MAI was used to assess the metals accumulation efficiencies of plants growing in the proximity of brick kilns. The results of the current study are summarized in Table 11. According to the current study, Lepidium sativum exhibited the highest MAI value whereas Malcolmia africana showed the lowest metal accumulation capacity. Moreover, the trend of MAI for studied metals in plant species collected around brick kilns in Quetta was Lepidium sativum (118.08) > Chenopodium album (108.83) > Elaeagnus angustifolia (98.77) > Medicago sativa (74.87) > Prunus armeniaca (60.87) > Convolvulus arvensis (57.58) > Vitis vinifera (57.47) > Punica granatum (56.19) > Triticum aestivum (48.94). The MAI trend exhibited by plant species collected around brick kilns in Pishin and Mastung followed, Lepidium sativum (113.81) > Malcolmia africana (61.61) > Triticum aestivum (60.70) > Peganum harmala (50.43) > Morus alba (44.38) and Morus alba (70.06) > Lepidium sativum (51.8) > Peganum Harmala (50.35) > Triticum aestivum (47.86) > Malcolmia africana (37.83) respectively. The results of the current study are in agreement with
rska-Socha et al. (2017), they studied Melandrium album and Nadgo Robinia pseudoacacia for heavy metals bimonitoring. The present study suggests that Lepidium sativum can grow in soil contaminated with heavy metal(loid)s in the vicinity of brick kilns. Furthermore, plants also varied in the uptake of heavy metal(loid)s and spatial variations were also found among plants growing at different sites. It might be due to differences in the metal uptake efficiency of plants and variation in the pollution load at different sites. Plants exhibiting a higher value of MAI can be used as a barrier between contaminated and uncontaminated environment. Plants with higher MAI values have good accumulation capacities and also regarded as tolerant species. The greater the MAI, the more efficient is that plant to be grown in the heavy metal(loid)s contaminated areas. Indigenous plants with high MAI values can be used in polluted areas as nature based solution to combat pollution. Plants with greater MAI are regarded as tolerant species and can act as
rskasink for heavy metal(loid)s pollution (Jamil et al., 2009; Nadgo Socha et al., 2016). The metal accumulation tendencies of the studied plants were greater than the earlier studies (Liu et al., 2007; Khan et al., 2011; Monfared et al., 2013). 4. Conclusion Overall results indicate that all the studied parameters showed significant (p < 0.05) variations among the plant species, and study sites while variations in the level of studied parameters with respect to distance from the source (brick kilns) were found insignificant (p > 0.05) except Cd, Cr, As and Fe. APTI can be used as an effective tool for phytomonitoring of pollution level. It was calculated through four biochemical parameters namely, total chlorophyll content, ascorbic acid, pH of the leaf extract, and relative water content. Consequently, the results showed that plants respond differentially to the pollution. Hence, Morus alba (26.82), Elaeagnus angustifolia (21.75), Malcolmia africana (21.30), Medicago sativa (17.59) and Peganum harmala (16.53) were found to be the most tolerant species whereas, Convolvulus arvensis (6.02) was found to be the most sensitive species. The most tolerant species

734

Table 8 Heavy metal(loid)s concentration (mg/kg)± STDEV in the studied plant species collected around brick kilns in Quetta. Distance

Cd

Cr

Pb

Cu

Mn

Zn

Ni

Co

Fe

As

Hg

Triticum aestivum

100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m —————————

3.65 ± 1.134 10.74 ± 1.77 4.77 ± 3.38 2.92 ± 1.73 3.04 ± 1.52 3.41 ± 2.02 5.28 ± 2.85 5.41 ± 1.75 2.8 ± 1.80 6 ± 1.67 7.39 ± 2.54 6.92 ± 1.65 7.89 ± 2.72 5.71 ± 4.72 7.26 ± 4.94 7.41 ± 2.15 7.03 ± 1.67 6.86 ± 1.67 5.06 ± 1.35 4.65 ± 2.97 5.7 ± 1.90 4.61 ± 1.32 7.78 ± 1.88 7.73 ± 9.12 8.57 ± 3.81 5.95 ± 4.73 8.25 ± 6.09 2.8e10.74

18.74 ± 14.87 18.06 ± 6.41 30.4 ± 10.89 30.79 ± 1.96 29.41 ± 16.44 20.61 ± 15.79 22.86 ± 17.52 20.14 ± 10.75 35.75 ± 13.91 27.24 ± 15.55 31.21 ± 12.90 27.97 ± 13.38 16.33 ± 18.38 26.47 ± 23.64 17.71 ± 6.59 22.62 ± 7.62 14.67 ± 3.68 13.95 ± 7.11 16.4 ± 6.42 15.21 ± 3.63 13.23 ± 13.46 14.48 ± 14.51 9.52 ± 10.8 18.93 ± 14 15.53 ± 12.40 12.81 ± 9.48 8.66 ± 6.45 8.66e35.75

36.2 ± 13.11 37.25 ± 8.76 41.28 ± 8.11 30.77 ± 15.54 30.66 ± 7.01 29.69 ± 8.24 25.67 ± 7.96 29.52 ± 19.09 30.41 ± 7.85 28.47 ± 5.25 26.78 ± 5.54 25.06 ± 12.32 27.03 ± 9.94 29.17 ± 15.37 38.4 ± 3.78 28.49 ± 9.75 20.82 ± 10.72 19.88 ± 6.24 15.61 ± 2.37 21.28 ± 6.72 28.34 ± 9.01 26.89 ± 8.48 37.44 ± 9.38 25.51 ± 5.03 17.21 ± 2.57 25.84 ± 13.53 22.54 ± 12.57 15.61e41.28

9.58 ± 4.7 27.39 ± 5.83 5.54 ± 2.90 2.93 ± 1.54 5.29 ± 1.39 3.64 ± 1.14 21.38 ± 5.19 5.28 ± 0.63 17.86 ± 6.27 29.19 ± 39.47 14.82 ± 12.75 15.56 ± 18.85 9.62 ± 7.2 9.12 ± 6.77 13.46 ± 4.78 7.70 ± 0.62 10.9 ± 5.34 18.16 ± 7.07 7.36 ± 0.03 15.06 ± 11.24 11.04 ± 6.25 8.51 ± 1.15 5.45 ± 2.06 5.69 ± 2.64 10.41 ± 3.83 16.02 ± 4.84 20.56 ± 8.09 2.93e29.19

59.98 ± 47.81 112.28 ± 20.13 92.71 ± 20.13 80.26 ± 6.29 59.87 ± 36.80 44.1 ± 36.80 49.96 ± 13.03 71.13 ± 44.34 149.77 ± 44.34 197.28 ± 28.88 99.78 ± 13.46 83.42 ± 13.46 109.10 ± 0.33 171.65 ± 14.92 52.27 ± 14.92 143.9 ± 42.79 80.01 ± 29.74 68.74 ± 29.74 98.04 ± 73.44 106.11 ± 41.41 85.92 ± 41.41 86.58 ± 35.44 73.31 ± 29.10 70.88 ± 29.10 68.97 ± 27.26 80.18 ± 63.27 126.18 ± 63.27 44.1e197.28

31.93 ± 10.1 21.59 ± 10.54 49.37 ± 11.75 44.81 ± 7.33 34.28 ± 11.74 62.21 ± 0.56 45.41 ± 32.49 40.31 ± 3.37 35.45 ± 12.94 38.02 ± 5.27 69.01 ± 36.29 55.66 ± 30.54 54.06 ± 24.81 43.9 ± 1.54 54.35 ± 12.42 52.35 ± 26.39 32.04 ± 7.30 49.87 ± 27.92 48.37 ± 13.19 49.2 ± 11.67 50.57 ± 28.36 60.02 ± 28.93 52.87 ± 26.03 43.44 ± 5.52 36.01 ± 5.9 56.09 ± 23.06 56.81 ± 23.11 21.59.69.01

57.23 ± 27.444 43.15 ± 5 34.97 ± 13.55 50.18 ± 11 51.71 ± 35.16 36.56 ± 17.3 63.11 ± 7.19 68.2 ± 17.38 80.4 ± 15.22 81.79 ± 21.16 42.75 ± 12.09 63.34 ± 25.01 66.03 ± 6.72 62.15 ± 6.62 55.72 ± 7.01 6.92 ± 6 13.7 ± 3.96 12.37 ± 4.08 9.86 ± 7.31 12.28 ± 7.93 25.91 ± 11.86 56.31 ± 32.83 38.94 ± 8.14 35.57 ± 7.58 51.9 ± 17.57 71.53 ± 8.33 53.47 ± 14.58 6.92e81.79

2.5 ± 0.55 6.12 ± 2.84 2.95 ± 0.80 4.32 ± 4.42 4.68 ± 2.89 2.79 ± 0.97 5.9 ± 3.53 6.89 ± 4.26 3.95 ± 0.51 4.28 ± 3.3 3.79 ± 0.88 4.79 ± 1.68 10 ± 8.17 7.44 ± 4.08 6.44 ± 1.22 4.07 ± 2.3 4.64 ± 3.34 7.03 ± 3.37 5.24 ± 3.36 5.16 ± 2.03 3.73 ± 0.92 4.43 ± 1.1 5.97 ± 3.15 4.98 ± 1.3 9.79 ± 1.03 11.18 ± 2.61 9.7 ± 4.23 2.5e11.18

1105.63 ± 110.42 533.66 ± 138.78 975.45 ± 400.84 773.28 ± 146.79 772.63 ± 227.52 661.97 ± 217.89 862.34 ± 148.64 523.17 ± 171.54 1119.33 ± 280.20 789.98 ± 52.21 755.06 ± 135.24 834.06 ± 28.29 413.99 ± 96.30 1019.82 ± 356.55 523.93 ± 205.76 1257.53 ± 502.1 617.39 ± 85.56 887.88 ± 32.46 816.08 ± 75 797.99 ± 184.32 616.35 ± 134.06 622.22 ± 551.55 504.26 ± 91.20 688.21 ± 362.78 1042.8 ± 234.6 806.84 ± 355.83 894.38 ± 212.65 413.99e1257.53

1.96 ± 0.41 2.14 ± 0.14 1.71 ± 0.1 1.49 ± 0.40 1.82 ± 0.10 1.35 ± 0.16 1.26 ± 0.21 1.35 ± 0.14 1.28 ± 0.312 1.7 ± 0.25 1.78 ± 0.14 1.71 ± 0.17 1.11 ± 0.44 1.18 ± 0.32 1.43 ± 0.32 2.37 ± 0.42 1.98 ± 0.53 0.86 ± 01.7 1.32 ± 1.52 4.59 ± 1.14 3.9 ± 0.91 4.14 ± 0.42 2.64 ± 1.84 2.69 ± 1.07 3.75 ± 0.10 1.91 ± 0.25 1.61 ± 0.42 0.86e4.59

3.42 ± 0.52 2.28 ± 0.21 3.12 ± 0.10 1.031 ± 0.13 2.07 ± 0.05 1.51 ± 0.09 1.09 ± 0.06 1.72 ± 0.10 1.24 ± 0.25 0.75 ± 0.06 0.87 ± 0.13 1.93 ± 0.28 2.75 ± 0.22 2.31 ± 0.18 3.35 ± 0.37 2.61 ± 0.94 1.93 ± 0.12 1.57 ± 0.87 1.47 ± 0.89 1.56 ± 0.66 2.43 ± 0.07 1.17 ± 0.66 1.35 ± 0.75 1.61 ± 0.78 0.99 ± 0.01 0.65 ± 0.05 0.97 ± 0.08 0.65e3.42

Chenopodium album

Medicago sativa

Lepidium sativum

Prunus armeniaca

Convolvulus arvensis

Elaeagnus angustifolia

Vitis vinifera

Punica granatum

Range

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738

Name of plants

Table 9 Heavy metal(loid) s concentration (mg/kg)± STDEV in the studied plant species collected around brick kilns in Pishin. Distance

Cd

Cr

Pb

Cu

Mn

Zn

Ni

Co

Fe

As

Hg

Triticum aestivum

100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m ———

8.64 ± 3.73 11.17 ± 9.10 5.65 ± 2.99 10.85 ± 2.74 12.85 ± 12.70 15.77 ± 19.53 17.8 ± 20.89 23.76 ± 33.74 26.38 ± 39.51 7.26 ± 4.76 12.36 ± 12.99 13.01 ± 18.36 5.65 ± 4.90 19.24 ± 22.99 19.02 ± 23.03 5.65e26.38

18.09 ± 5.23 20.62 ± 7.81 35.57 ± 13.28 23.87 ± 17.91 22.8 ± 8.50 14.84 ± 2.74 18.59 ± 9.72 12.91 ± 7.44 27.7 ± 21.00 20.15 ± 3.39 32.56 ± 12.73 29.99 ± 17.76 21.49 ± 8.03 32.66 ± 11.03 22.98 ± 8.32 12.91e35.57

42.49 ± 12.75 25.88 ± 7.59 23.98 ± 17.65 25.24 ± 6.22 28.01 ± 9.76 19.79 ± 11.62 29.38 ± 6.70 20.61 ± 9.39 17.29 ± 14.26 8.08 ± 0.65 16.81 ± 18.65 11.19 ± 6.84 29.05 ± 0.61 7.93 ± 2.35 7.64 ± 0.44 7.64e42.49

13.47 ± 5.44 18.69 ± 0.60 14.69 ± 3.74 8.33 ± 0.45 34.41 ± 6.06 21.96 ± 5.32 13.19 ± 3.68 22.89 ± 6.04 28.4 ± 15.84 20.89 ± 7.11 18.21 ± 5.60 25.08 ± 7.22 35.38 ± 6.17 6.45 ± 1.03 4.55 ± 1.96 4.55e35.38

90.04 ± 4.95 54.49 ± 34.35 64.17 ± 19.31 43.3 ± 40.85 54.56 ± 40.28 36.55 ± 29.12 67.88 ± 15.11 74.9 ± 31.86 61.98 ± 35.67 46.49 ± 43.88 58.72 ± 27.10 39.55 ± 2.84 68.09 ± 18.78 80.92 ± 30.82 85.66 ± 27.23 36.55e90.04

60.9 ± 22.72 48.69 ± 12.08 54.69 ± 24.56 33.8 ± 3.86 35.34 ± 11.71 50.44 ± 15.96 63.86 ± 42.36 41.78 ± 2.28 34.56 ± 13.25 49.33 ± 23.61 39.57 ± 2.84 47.43 ± 33.13 42.29 ± 38.63 26.88 ± 4.55 34.02 ± 14.64 26.88e63.86

24.79 ± 20.27 21.44 ± 8.50 15.82 ± 3.1 49.62 ± 36.32 32.18 ± 0.25 28.5 ± 17.19 48.93 ± 42.32 36.81 ± 39.26 47.94 ± 34.39 45.76 ± 1.83 51.95 ± 18.65 46.95 ± 18.17 44.86 ± 21.93 14.56 ± 3.23 50.54 ± 41.31 14.56e51.95

15.99 ± 13.04 5.7 ± 3.93 5.48 ± 1.92 19.16 ± 23.36 19.85 ± 23.91 14.97 ± 19.15 16.49 ± 19.61 18.35 ± 21.04 32.73 ± 43 20.64 ± 26.79 17.06 ± 13.56 10.17 ± 10.35 21.38 ± 29.32 4.97 ± 3.29 3.21 ± 1.16 3.21e32.73

795.61 ± 1.69 1319.45 ± 5.03 1122.94 ± 5.4 1155.9 ± 5.56 1200.63 ± 4.12 1596.19 ± 8.27 1294.83 ± 3.17 1396.83 ± 4.75 1092.89 ± 5.71 473.46 ± 3.81 493.53 ± 2.96 293.69 ± 2.33 686.31 ± 1.89 613.23 ± 2.61 917.06 ± 4.27 293.69e1596.19

1.59 ± 0.07 1.27 ± 0.39 1.02 ± 0.44 0.59 ± 0.18 0.76 ± 0.38 1.21 ± 0.43 1.65 ± 0.35 1.21 ± 0.40 0.94 ± 0.34 0.71 ± 0.52 1.48 ± 0.41 1.56 ± 0.20 0.82 ± 0.05 0.72 ± 0.34 2.15 ± 1.65 0.59e2.15

1.89 ± 0.80 2.47 ± 0.45 2.63 ± 0.45 1.61 ± 0.78 1.17 ± 0.17 1.3 ± 0.17 1.85 ± 0.31 1.98 ± 0.89 3.52 ± 0.89 1.75 ± 1.10 1.79 ± 0.19 1.97 ± 0.19 1.35 ± 1 1.59 ± 0.51 1.77 ± 0.51 1.17e3.52

Peganum harmala

Lepidium sativum

Morus alba

Malcolmia africana

Range

Table 10 Heavy metal(loid)s concentration (mg/kg) ± STDEV in the studied plant species collected around brick kilns in Mastung. Name of plant

Distance

Cd

Cr

Pb

Cu

Mn

Zn

Ni

Co

Fe

As

Hg

Triticum aestivum

100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m 100 m 300 m 500 m ——

12.32 ± 0.88 22.58 ± 19.96 9.02 ± 6.64 13.91 ± 3.02 14.55 ± 4.25 6.36 ± 4.53 14.79 ± 3.97 13.18 ± 8.72 2.76 ± 0.54 15.01 ± 4.07 12.35 ± 4.13 7.42 ± 0.53 15.59 ± 2.56 15.59 ± 3.55 7.78 ± 2.18 2.78e22.58

22.1 ± 3.64 33.19 ± 11.47 26.81 ± 8.34 20.2 ± 5.18 14.39 ± 6.44 25.9 ± 1.24 32.96 ± 4.45 28.23 ± 7.92 31.94 ± 9.97 30.89 ± 2.89 32.22 ± 9.33 43.94 ± 11.75 45.26 ± 31.09 47.41 ± 5.25 58.37 ± 2.52 14.39e58.37

29.05 ± 9.61 14.59 ± 5.49 7.64 ± 0.44 32.49 ± 24.14 40.89 ± 4.63 43.69 ± 28.42 32.24 ± 7.35 32.05 ± 6.21 8.3 ± 1.38 53.12 ± 14.83 42.3 ± 21.65 35.32 ± 9.04 42.43 ± 4.96 25.38 ± 6.30 9.57 ± 1.26 7.64e53.69

34.98 ± 5.80 13.28 ± 5.10 12.14 ± 5.05 20.58 ± 0.57 33.34 ± 9.97 18.32 ± 0.39 34.01 ± 6.93 32.93 ± 11.93 33.28 ± 10.25 68.69 ± 24.32 37.48 ± 13.89 21.12 ± 8.71 57.83 ± 30.84 53.74 ± 24.39 37.81 ± 18.15 12.14e68.69

68.09 ± 18.78 80.92 ± 30.82 85.66 ± 27.23 199.22 ± 85.78 214.2 ± 104.63 264.64 ± 141.67 31.95 ± 20.94 235.79 ± 145.32 80.7 ± 68.64 94.77 ± 14.15 139.99 ± 50.49 84.28 ± 15.24 45.85 ± 6.64 119.84 ± 148.64 127.8 ± 103.10 31.95e264.64

29.29 ± 20.93 46.38 ± 31.34 27.68 ± 12.94 50.72 ± 28.33 50.28 ± 39.15 41.85 ± 38.57 39.8 ± 13.09 56.9 ± 25.17 68.65 ± 38.02 62.6 ± 6.29 40.33 ± 45.82 67.3 ± 13.02 42.38 ± 21.30 44.25 ± 32.79 39.74 ± 2.32 27.68e68.65

44.86 ± 21.93 14.56 ± 3.23 50.54 ± 41.31 42.84 ± 8.79 70.98 ± 14.71 70.17 ± 8.88 57.53 ± 26.09 57.14 ± 26.06 54.17 ± 34.70 40.38 ± 7.08 51.82 ± 14.56 37.33 ± 13.59 66.15 ± 6.98 62.62 ± 30.93 61.26 ± 28.27 14.56e70.98

28.05 ± 23.58 21.63 ± 10.61 9.55 ± 5.13 27.76 ± 20.05 24.11 ± 20.95 15.65 ± 22.40 23.65 ± 20.04 24.41 ± 18.07 17.95 ± 27.60 27.91 ± 21.73 29.92 ± 19.30 21.8 ± 24.98 35.81 ± 9.09 27.47 ± 15.22 41.76 ± 5.44 9.55e41.76

686.31 ± 189.54 613.23 ± 260.70 917.06 ± 426.99 2052.11 ± 1014.08 2841.13 ± 1302.05 2443.6 ± 740.03 1982.1 ± 828.82 1469.72 ± 214.57 1430.61 ± 252.17 1079.4 ± 120.98 1801.3 ± 754.03 1908.73 ± 700.53 2724.29 ± 1345.14 1096.2 ± 183.86 1608.68 ± 43.31 613e2841.13

0.82 ± 0.05 0.72 ± 0.34 2.15 ± 1.65 3.41 ± 1.73 0.91 ± 0.44 0.88 ± 0.06 1.04 ± 0.50 1.34 ± 0.32 1.41 ± 0.10 1.35 ± 0.31 1.36 ± 0.69 1.40 ± 0.51 1.24 ± 0.21 1.23 ± 0.56 0.70 ± 0.19 0.70e3.41

0.75 ± 0.07 2.13 ± 0.19 1.64 ± 0.30 1.04 ± 0.32 0.79 ± 0.19 1.35 ± 0.15 1.83 ± 0.14 1.76 ± 0.40 1.50 ± 0.10 1.42 ± 0.72 2.03 ± 0.64 2.01 ± 0.46 2.34 ± 0.41 1.93 ± 0.89 2.36 ± 0.59 0.75e2.36

Peganum harmala

Lepidium sativum

Morus alba

Malcolmia africana

Range

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Plant

735

736

Table 11 MAI of the studied plants in three districts of Balochistan. Name of plants

Cd

Cr

Pb

Cu

Mn

Zn

Ni

Co

Fe

As

Hg

MAI

6.39 ± 3.81 3.12 ± 0.25 4.49 ± 1.47 6.77 ± 0.71 6.96 ± 1.12 7.10 ± 0.28 5.14 ± 0.53 6.71 ± 1.82 7.59 ± 1.43

22.40 ± 6.93 26.94 ± 5.53 26.25 ± 8.34 28.81 ± 2.11 20.17 ± 5.5 17.08 ± 4.81 14.95 ± 1.60 14.31 ± 4.71 12.34 ± 3.46

38.24 ± 2.68 30.37 ± 0.59 28.53 ± 2.53 26.77 ± 1.70 31.53 ± 6.04 23.06 ± 4.72 21.74 ± 6.38 29.946 ± 6.53 21.86 ± 4.36

14.17 ± 11.63 3.96 ± 1.21 14.84 ± 8.46 19.86 ± 8.09 10.73 ± 2.37 12.25 ± 5.36 11.15 ± 3.85 6.55 ± 1.70 15.66 ± 5.08

88.32 ± 26.42 61.70 ± 17.70 90.29 ± 52.59 126.82 ± 61.56 111 ± 59.71 97.55 ± 40.53 96.69 ± 10.16 76.92 ± 8.45 91.77 ± 30.32

34.29 ± 14.04 47.1 ± 14.10 40.39 ± 4.98 54.23 ± 25.55 50.77 ± 5.95 44.75 ± 11.07 49.38 ± 1.12 52.11 ± 8.32 49.64 ± 11.81

45.11 ± 11.26 46.15 ± 8.34 70.57 ± 8.89 62.63 ± 19.53 61.30 ± 5.21 10.99 ± 3.59 16.01 ± 8.65 43.60 ± 11.14 58.96 ± 10.91

3.86 ± 1.97 3.93 ± 1 5.58 ± 1.5 4.29 ± 0.5 7.96 ± 1.84 5.25 ± 1.57 4.71 ± 0.85 5.13 ± 0.78 10.22 ± 0.83

871.58 ± 299.8 735.10 ± 64.08 834.95 ± 299.02 793.03 ± 39.59 652.58 ± 322.75 920.94 ± 321.35 743.47 ± 110.46 604.90 ± 93.19 914.67 ± 119.28

1.93 ± 0.21 1.55 ± 0.24 1.30 ± 0.05 1.73 ± 0.05 1.24 ± 0.17 1.73 ± 0.78 3.27 ± 1.72 3.16 ± 0.85 2.42 ± 1.16

2.94 ± 0.59 1.54 ± 0.52 1.35 ± 0.32 1.18 ± 0.65 2.80 ± 0.52 2.04 ± 0.53 1.82 ± 0.53 1.38 ± 0.22 0.87 ± 0.19

48.94 108.83 74.86 118.08 60.87 57.58 98.77 57.467 56.19

8.48 ± 2.76 13.15 ± 2.48 22.65 ± 4.40 10.88 ± 3.15 14.64 ± 7.78

24.76 ± 9.45 20.50 ± 4.94 19.73 ± 7.46 27.57 ± 6.55 25.71 ± 6.07

30.78 ± 10.18 24.35 ± 4.28 22.42 ± 6.25 12.03 ± 4.43 14.87 ± 12.28

15.62 ± 2.73 21.57 ± 13.04 21.49 ± 7.70 21.40 ± 3.46 15.45 ± 17.28

69.57 ± 18.38 44.80 ± 9.10 68.25 ± 6.47 48.25 ± 9.71 78.23 ± 9.09

54.76 ± 6.11 39.86 ± 9.20 46.73 ± 15.27 45.44 ± 5.18 34.39 ± 7.71

20.68 ± 4.53 36.77 ± 11.28 44.56 ± 6.73 48.22 ± 3.28 36.65 ± 19.34

9.06 ± 6 17.99 ± 2.64 22.52 ± 8.88 15.96 ± 5.32 9.85 ± 10.02

1079.33 ± 264.63 1317.57 ± 242.322 1261.52 ± 154.69 420.23 ± 110.04 738.87 ± 158.59

1.29 ± 0.28 0.85 ± 0.33 1.27 ± 0.36 1.25 ± 0.47 1.23 ± 0.80

2.33 ± 0.39 1.36 ± 0.23 2.45 ± 0.93 1.84 ± 0.12 1.57 ± 0.21

47.86 50.35 51.28 70.06 37.83

14.64 ± 7.07 11.61 ± 4.56 10.24 ± 6.53 11.59 ± 3.85 12.97 ± 4.54

27.66 ± 5.15 20.16 ± 5.76 31.07 ± 2.45 35.69 ± 7.18 50.35 ± 7.03

17.09 ± 10.92 39.02 ± 5.83 24.2 ± 13.77 43.58 ± 8.97 25.8 ± 16.43

20.13 ± 12.87 24.08 ± 8.10 33.41 ± 0.55 42.43 ± 24.17 49.79 ± 10.58

78.23 ± 9.09 226.02 ± 34.28 116.15 ± 106.44 106.35 ± 29.61 97.83 ± 45.19

34.45 ± 10.36 47.62 ± 4.99 55.11 ± 14.51 56.75 ± 14.41 42.12 ± 2.26

36.65 ± 19.34 61.33 ± 16.01 56.28 ± 11.84 43.18 ± 17.64 63.34 ± 12.53

19.74 ± 9.39 22.51 ± 6.21 22 ± 3.53 26.54 ± 4.23 35.01 ± 7.18

738.87 ± 158.58 2445.60 ± 394.52 1627.48 ± 307.74 1596.48 ± 451.01 1809.72 ± 832.46

1.23 ± 0.80 1.73 ± 1.45 1.26 ± 0.20 1.37 ± 0.03 1.06 ± 0.31

1.51 ± 0.70 1.06 ± 0.28 1.70 ± 0.18 1.82 ± 0.35 2.21 ± 0.25

60.70 50.45 113.81 44.38 61.61

Variable Study site

0.64 0.49 0.36

0.42 0.24 0.89

0.76 0.57 0.32

0.99 0.11 0.2

0.61 0.21 0.17

0.95 0.42 0.001*

0.78 0.25 0.002*

Distance

0.002* 0.03* 0.000*

0.02* 0.001* 0.003*

0.000* 0.01* 0.03*

0.000* 0.0001* 0.02*

0.000* 0.01* 0.03*

0.000* 0.000* 0.01*

0.001* 0.04* 0.000*

Plant species

0.002* 0.000* 0.004*

0.01* 0.001* 0.003*

0.77 0.51 0.01*

0.002* 0.001* 0.001*

0.001* 0.86 0.96

0.65 0.32 0.65

Table 12 Result of One Way ANOVA signifying variation in studied heavy metal (liod)s with increasing distance from the source, among the plant species and the study sites.

Cd

Study sites Cr

Study sites Pb

Study sites Cu

Study sites Mn

Study sites Zn

Study sites Co

Study sites Fe

Study sites As

Study sites Hg

Quetta Pishin Mastung 0.04* Quetta Pishin Mastung 0.01* Quetta Pishin Mastung 0.01* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.01* Quetta Pishin Mastung 0.05* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.002* Quetta Pishin Mastung 0.000* Quetta Pishin Mastung 0.01*

Study sites

*p is significant at the level of 0.05.

can be used as a sink for pollutants whereas; the sensitive species according to APTI values can be regarded as biomarkers of contamination in the certain area. The results of the study also showed that plant varied in metal accumulation capacity. Lepidium sativum (118.08) displayed the highest MAI whereas; Malcolmia africana (37.83) showed the lowest metal accumulation capacity. Plant showing highest MAI can be used as tolerant species whereas those with low metal accumulation index regarded as sensitive species. The sensitive metal species can be used as an indicator of soil pollution with heavy metals while the tolerant species can be used for the reclamation of soil pollution with heavy metals acting like scavengers of pollution. With the increased risk of pollution on the human health, these tolerant species are highly recommended to be grown near the pollution sources.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not for profit sectors.

Declaration of interest

None.

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738

Quetta Triticum aestivum Chenopodium album Medicago sativa Lepidium sativum Prunus armeniaca Convolvulus arvensis Elaeagnus angustifolia Vitis vinifera Punica granatum Pishin Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana Mastung Triticum aestivum Peganum harmala Lepidium sativum Morus alba Malcolmia africana

Khanoranga, S. Khalid / Journal of Cleaner Production 229 (2019) 727e738

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