comparing the air quality and plant community structure, situated about six km in north direction from Jharia. 2.2 Air monitoring. The air quality was monitored for ...
NexGen Technologies for Mining and Fuel Industries ISBN 978-93-85926-40-2
Changes in vegetation community structure around Jharia coalfields, India Bhanu Pandey1, Madhoolika Agrawal1 and Siddharth Singh2 1
Department of Botany, Banaras Hindu University, Varanasi, India Environmental Management Group, Central Institute of Mining & Fuel Research (CSIR), Dhanbad, India
2
ABSTRACT: Mining events generate considerable destruction to landscape and biological communities. Plant communities get disturbed due to coal mining activities and habitats get further destroyed. This study deals into the general blueprint of modifications in vegetation characteristics of woody and herbaceous plants due to air pollutants and altered soil quality, imposed by coal mining activities. Air monitoring, soil analyses and phytosociological analyses were carried out in the vicinity of Jharia Coalfield (JCF) in the year 2011–2012. The Important Value Index (IVI) of sensitive species reduced and those of tolerant species enhanced with increasing pollution load and altered soil quality around coal mining areas. Although the species richness of woody and herbaceous plants diminished with higher pollution load, a significant number of species acclimatized to the stress induced by the coal mining activities. Woody plant community at JCF was more affected by coal mining than herbaceous community. Canonical Correspondence Analysis (CCA) revealed that structure of herbaceous community was primarily driven by soil pH, soil sulphate concentration (SO4), whereas woody layer community was influenced by sulphur dioxide (SO2) in ambient air and soil Water Holding Capacity (WHC). The differences in species diversity observed at mining areas suggested an increase in the proportion of resistant herbs and grasses showing a tendency towards a definite selection strategy of ecosystem in response to air pollution and altered soil characteristics. 1. INTRODUCTION
G
lobal industrialization and the level of human population have been closely related to the amount of worldwide energy consumption.[1] Currently energy is mostly produced by burning of fossil fuel such as coal.[2] In order to meet the energy requisite, coal production and coal mining have staggeringly increased in India, which ranks third amongst top ten coal producing countries.[3] Coal is considered as the most polluting source of energy, which creates environmental problems at various stages of its procurement from mining, transportation, stock piling, coal preparation and utilization.[4] Coal mining and active mine fires, vehicular emissions and windblown dust through unpaved
roads and overburdens are responsible for high air pollutants around coal mining areas.[5] Sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulates are the important air pollutants around coal mining areas. The damaging effects of air pollutants on vegetation have been reported widely.[6, 7] In coal mining areas, the top soil gets mixed with loosely bound overburden materials may also affect the soil properties,[8] which may affect plant communities.[9] Composition of plant communities within the limits of the area is a function of changing habitat conditions along the environmental gradients.[10] According to Gaussian response model abundance for a particular species along the environmental variable takes a form,
813
Yi = ce
( xi − u)2
2t 2
Where i = 1,.., N, Yi is the abundance of the species at site i, N is the number of sites, c is the maximum abundance of the species at the optimum u, and t is its tolerance. Different air pollutants and altered soil physico-chemical properties affect individual plants and their population via various mechanisms of action and thus influence the plant community structure, because the optimum value of environmental gradient is different for different species. Plant community structure of an area tends to become established by the selection pressures of the environment. The superimposition of severe pressures on plant community sometimes occurs to allow for feedback mechanisms to operate for selection of resistant species. There is now much concern that the stability of various plant communities has been seriously threatened by increasing concentrations of air pollutants and disturbed soil quality. Our major objectives were to determine the effects of coal mining on plant communities and test the hypothesis that combined stress of air pollution and soil contamination, affects differently the individuals of plant community. 2.
MATERIAL AND METHODS
2.1 Study area The present study was executed around Jharia coalfield (JCF) (latitudes 23° 39’ to 23° 48’ N, longitudes 86° 11’ to 86° 27’ E, 222 m above mean sea level), located in Dhanbad district of Jharkhand state during 2011 and 2012 (Figure 1). JCF is the most exploited coalfields and
represents the largest coal reserves in India. A reference site, Central Institute of Mining and Fuel Research (CIMFR) (latitudes 23° 47’ to 23° 49’ N, longitudes 86° 23’ to 86° 25’ E, 172 m above mean sea level), was also selected for comparing the air quality and plant community structure, situated about six km in north direction from Jharia. 2.2 Air monitoring The air quality was monitored for particulate matter (PM10) (High volume method), sulphur dioxide (SO2)[11] and nitrogen dioxide (NO2)[12] once in a fortnight at each site for eight hours during the complete study period. The methods in detail are given in Pandey et al.[4] 2.3 Soil analysis From each site, soil samples were collected every three month for two consecutive years from 2011. Sites for soil sampling were the same where air monitoring was done. Three replicate samples of soil were collected from each selected site. The soil from three replicate sites were mixed together to form a composite soil sample and three replicates were used for various analyses. Soil samples were analyzed for pH, total nitrogen (N), available phosphorus (P) and sulphate (S) contents. Soil pH was determined by the methods described by Maiti.[13] Total nitrogen in soil was determined using Gerhardt Automatic N Analyzer (Model KB8S, Frankfurt, Germany). Available phosphorous was extracted following Olsen et al.[14] and estimated by the method of Dickman and Bray.[15] Available sulphur in soil was estimated following Williams and Steinberg.[16] 2.4 Phytosociological analysis
Fig. 1. Location of the study site.
Plant diversity analysis of the flora was conducted at the time of peak canopy biomass during 2011. The sites for plant diversity analyses were the same where air monitoring and soil analyses were done. At both site, 25 sub sites were stabilized. 1 m × 1 m (herbaceous layer) and 20 m × 20 m (woody layer) size quadrats were used for sampling the plant 814
community. Presence and absence of species, number of the individual of species and their basal area in each quadrat were recorded. Plant species were identified through Flora of Bihar, India.[17] Important Value Index (IVI) (by relative frequency, relative density, relative dominance), species diversity (Shannon–Wiener index), β diversity and species richness were calculated. The basal area of the species was taken as an index of dominance for tree species.
in these mines[19] while, combustion processes, including vehicle exhaust, coal, oil and natural gas, with some emissions during blasting may be the reason of high NO2 concentration. PM10 concentration exceeded the prescribed limit of NAAQS (100 µg m–3)[18] at JCF. The sources of fugitive dust at coal mining areas include overburden and top soil removal, drilling and blasting, mineral haulage, mechanical handling operations, minerals stockpiles and site restoration.[20]
2.5 Statistical analysis The quantitative changes observed for different parameters were tested for the level of significance within sites using Student’s t test. In order to reduce multicollinearity of variables, PCA was conducted by the varimax rotated factor matrix method, based on the orthogonal rotation criterion with Kaiser normalization.To best explore the available data, we conducted the multivariate statistical analysis, CCA using XLSTAT-Pro (Version 2013.1, Addinsoft, Inc., Brooklyn, NY, USA) software for Windows, which can relate the quantitative changes of plant community to environmental gradient directly. For CCA, the five subplots were considered as separate site (10 sites). Three major environmental variables after reduction of multicollinearity were included. The statistical significance (at the 5% level) of relationships between species data (species density) and environmental variables were assessed using the Monte Carlo test on 999 random permutetions to test the null hypothesis that plant community were unrelated to environmental variables. 3.
RESULTS AND DISCUSSION
The average concentrations of PM10, SO2 and NO2 varied significantly between mining and non mining sites and higher concentrations at JCF was recorded compared to CIMFR. The concentration of SO2 and NO2 at JCF and CIMFR were well within the prescribed limit of National Ambient Air Quality Standards (NAAQS) India (80 µg m–3)[18] (Figure 2). High SO2 at JCF may be ascribed to active mine fires
Fig. 2. Site-wise variations in ambient air concentration of gaseous (SO2 and NO2), particulate (PM10) pollutants and of soil properties at coal mining (JCF) and reference site (CIMFR).
Soil pH, N and P were higher at CIMFR compared to coal mining site (JCF) while, S was lower at CIMFR than JCF (Figure 2). Significant variations were observed for all soil physico-chemical parameters between the sites. After coal mining, the soil surface layers become contaminated with pyrite (FeS2) that later oxidizes to sulphuric acid when exposed to oxygen and water, results in high levels of soil acidification, leaving low soil pH.[21] Increase in soil sulphur (10 to 100 times) and low pH near coal mining area at Lisbon,
815
Columbiana have also been reported.[22] The lower N content in soil of JCF as compared to CIMFR may be due to lack of adequate amount of mineralisable organic nitrogen, lower mineralization and nitrification rates, reduced vegetation cover and lack of microbial activity.[13] In acidic soil, phosphorous forms insoluble Fe and Al phosphate[23] so, its available form reduced in soil of JCF. Low inorganic N and low available P near the site of stripe mining have been reported at Ohio, USA.[24] The polluted sites, JCF was species poor compared to reference site CIMFR (Table 1). Table 1.
Achyranthes aspera, Convolvulous alsinoides, Dichanthium annulatum, Eclipta alba and Solanum nigrum were most sensitive and present at CIMFR and completely absent from the coal mining areas (Table 1). Eragrostis cynosurides and Setaria glauca are polluphilic species as these were only present at the polluted sites (JCF). Ageratum conyzoides, Chrysopogon aciculatus, Evolvulus alsinoides, Parthenium hysterophorus and Tephrosia purpurea also showed lower IVI at CIMFR compared to JCF and therefore are also tolerant to coal mining activities (Table 1).
Importance value index of different herbaceous species present at different study sites Importance value index (IVI)
Plant species
CIMFR
Achyranthus aspera L.
6.7
JCF 0.0
Ageratum conyzoides L.
10.5
9.5
Alternanthera paranychiodes St. Hil.
63.6
27.2
6.2
11.6
Amaranthus spinosus L. Boerahaavia diffusa L. Cassia alata L. Chrysopogon aciculatus (Retz.) Trin. Convolvulus alsinoides L.
6.8
8.2
20.2
56.0
7.1
0.0
6.2
0.0
45.0
41.1
Cyperus rotundus L
7.7
30.9
Desmodium trifolium DC.
8.8
28.8
Dichanthium annulatum Forssk..
7.6
0.0
Digitaria sanguinalis (L.) Scop.
7.0
13.2
Eclipta alba (L.) Hassk.
7.0
0.0
Cynodon dactylon (L.) Pers.
Eragrostis cynosurids (Retz.) P. Beauv.
0.0
0.0
13.9
0.0
Heteropogan contortus (L.) P. Beauv.
7.3
11.6
Osimum basilicum L.
6.1
4.6
Parthinium hysterophorus (L.) Sp. PL.
7.0
0.0
Setaria glauca (L.) P. Beauv.
0.0
15.2
Sida cordifolia L.
7.7
32.5
Solanum nigrum L.
7.7
0.0
Solanum xanthocarpum Schradt. and Wendl.
5.9
4.3
Sphaeanthus indicus L.
5.8
5.1
28.4
0.0
Evolvulus alsinoides L.
Tephrosia purpurea (L.) Pers. Syn.
816
A. scholaris, B. lanzan and F. tomentosa are described as native species in the studied area.[25] Their lower IVI suggests their sensitivity to altered air and soil conditions, due to excessive coal mining activities in JCF. Species such as F. beghalensis, F. religiosa, P. guajava, M. indica, A. indica, A. marmelos and C. citriodora showing highest IVI at polluted sites are resistant (Table 2). The total number of herbaceous species was 23 and 15 at CIMFR and JCF, respectively. Alternanthera paronychioides showed highest IVI followed by Cynodon dactylon, Tephrosia purpurea and Cassia allata at CIMFR while highest IVI at JCF was represented by Cassia allata (Table 1). The number of woody species Table 2.
was 22 and 16 at CIMFR and JCF, respectively (Table 2). Tree density and canopy cover were higher at CIMFR compared to JCF. Butea monosperma was the most dominant species followed by Ficus benghalensis, Ficus religiosa and Psidium guajava at JCF. These four species contributed 50.2% of the total importance value at JCF (Table 2). At CIMFR, Ficus benghalensis, Ficus religiosa, Acacia catechu and Syzygium cuminii were dominant species accounting for 33.4% of the total importance value. Even if some species showed resistance but the numbers of species participated in community structure as dominants or co-dominants were higher at non mining area compared to coal mining sites. This result suggests that a large
Importance value index of different woody species present at different study sites Importance value index (IVI)
Plant species
CIMFR
JCF
Acacia arabica (Lam.)
10.6
0.0
Adina cordifolia Hook. F.
11.7
10.7
6.7
0.0
Artocarpus heterophyllus Lam. Azadirachta indica A. Juss. Albizia lebbeck (L.) Aegle marmelos (L.) Correa.
8.9
16.0
16.0
20.0
7.7
12.3
Alstonia scholaris (L.) R. Br.
12.9
0.0
Acacia catechu Willd.
20.1
12.5
Bombax ceiba L.
15.2
10.2
Borassus flabellifer L.
0.0
0.0
16.4
0.0
Butea monosperma (Lam.) Taub.
2.7
45.9
Corymbia citriodora (Hook.)
0.0
11.2
Buchanania lanzan Spreng.
Delonix regia (Boj.) Raf.
17.7
0.0
Ficus benghalensis L.
31.7
38.1
Ficus racemosa L.
12.5
10.5
Ficus religiosa L.
29.3
36.4
Ficus tomentosa L.
5.0
0.0
12.8
10.4
Holoptelea integrifolia (Roxb.) Madhuca indica J. F. Gmel.
9.1
14.0
Psidium guajava L.
15.8
30.1
Syzygium cuminii (L.) Skeels.
19.2
10.9
Saccopetalum tomentosum (Roxb.)
12.7
10.6
5.1
0.0
Terminalia arjuna L
817
number of species acclimatize to stress conditions due to coal mining activities. As variations in environmental factors stumulate the modifying abilities of organisms, only those species which were adapted to new conditions or those which become accustomed to the new conditions, participate in the community formation.[26] The species susceptible to the air pollution and altered soil properties are eliminated and more tolerant species become widespread.[27] Air pollutants and altered soil properties in the area due to coal mining activities modified the response of the vegetation either by acting synergistically or antagonistically. The Shannon-Wiener diversity index was high at CIMFR as compared to JCF both for the herbaceous and woody vegetation (Table 3). β-diversity was 2.02 and 1.70 for herbaceous vegetation, while 3.11 and 2.44 for woody layer at CIMFR and JCF, respectively. The high concentration of dominance and low species richness were recorded at the polluted site i.e. JCF (Table 3). Shannon-Wiener diversity index suggests that the coal mining sites are suffered due to heavy pollution load and the most sensitive species have disappeared leaving only tolerant species represented by a large number of individuals. The stability of the community is related to the species diversity, the higher the value of the diversity index, the greater will be the stability of community structure and function. Species diversity and concentration of dominance are inversely related. The plant community is heterogeneous at CIMFR because of low pollution load at this site, favouring the growth, survival and regeneration of natural
vegetation and new arrivals. However, the vegetation closest to coal mining subsequently lost sensitive plant species, which created a niche that became available to opportunistic and more tolerant species, which became abundant at polluted sites. Species richness was 6.13 and 4.09 for herbaceous vegetation and 8.97 and 6.59 for woody layer at CIMFR and JCF. Evenness for herbaceous species was high at JCF. Evenness for tree species was high at CIMFR followed by JCF (Table 3). In the present study, A. paronychioides and C. dactylon were the most dominant species of the herbaceous community at both the sites (Table 1). A. paronychioides is not a native of the area, but become very prevalent.[28] Evenness of community increased, whereas species richness declined at the sites having higher pollution load. Evidently species richness had greater influence on species diversity than evenness observed in this study. Principal component analysis was performed to the environmental variable (air and soil parameters) for identification of variables with maximum variance and to reduce the multicollinearity in the variable assessed. Principal Component (PC) loadings for air pollutants and soil characteristics for the year 2011 with corresponding eigen values, variances and loading factors are given in Table 4. Three PCs with eigen values greater than 1.0 were extracted with 82.2% cumulative variance for studied areas (Table 4). The relationships between the principal component and the variables are indicated by the factor loadings. All the components have distinct characteristics, and hence assigned
Table 3. Shannon-Wiener index, β-diversity, evenness, species richness, and concentration of dominance of the herbaceous and woody species at different study sites Indexes
Herbaceous vegetation
Woody vegetation
CIMFR
JCF
CIMFR
JCF
Shannon-Wiener index
2.60
2.19
2.97
2.54
β-diversity
2.02
1.70
3.11
2.44
Evenness
1.91
1.86
2.21
2.11
Species richness
6.13
4.09
8.97
6.59
Concentration of dominance
0.13
0.15
0.06
0.10
818
Table 4. Rotated principal components loading for air pollutants and soil properties in Jharia coalfields Variables
PC1
PC2
PC3
SO2
0.866
–0.191
–0.182
NO2
0.784
–0.224
–0.331
PM10
0.783
–0.345
–0.191
Soil pH
–0.321
0.257
0.893
Total Nitrogen
–0.089
0.871
0.279
Available Phosphorus
–0.499
0.764
0.142
Soil SO4
0.533
–0.66
–0.079
Eigen value
2.622
2.05
1.081
% Variance
37.462
29.291
15.448
on the basis of marker species. High loadings for SO2, NO2 and PM10 indicated that the first component was in the group of air pollutants and SO2 showed the marker variable of the first component. The PC2 was for soil nutrient identified by high loadings of Nitrogen and Phosphorus. The PC3 with 15.49% total variance showed highest loadings for soil pH and this component was identified as the component for soil acidity. Highly loaded variables of component of air pollutants, component of soil nutrient and component of soil acidity were utilized to perform CCA. Within the CCA eigenvector analysis, we observed that most of the inertia was carried by the first axis. We obtained 89.12% and 89.97% of the total inertia of first axis for herbaceous and tree species, respectively. Therefore, the relationships between the species and the variables were analyzed by twodimensional CCA map. The synthetic gradient extracted by CCA ordination diagram consists the points showing species and classes for qualitative environmental variables, and arrows for quantitative environmental variables as components obtained by PCA (Figures 3 and 4). The eigen values for two axes were 0.117 and 0.056 for herbaceous vegetation, and 0.232 and 0.026 for woody layer. The relative importance of a environmental variables is depicted in the CCA ordination diagram by the length of their corresponding lines.[29]
Fig. 3. Ordination of the herbaceous species by canonical correspondence analysis. Aa: Achyranthes aspera; Ac: Ageratum conyzoides; Ap: Alternanthera paronychioides; As: Amaranthus spinosus; Bod: Boerhaavia diffusa; Ca: Cassia alata; Cha: Chrysopogon aciculatus; Coa: Convolvulus alsinoides; Cd: Cynodon dactylon; Cr: Cyperus rotundus; Dt: Desmodium trifolium; Dia: Dichanthium annulatum; Ds: Digitaria sanguinalis; Ea: Eclipta alba; Ec: Eragrostis cynosuroides; Eva: Evolvulus alsinoides; Hc: Heteropogon contortus; Ob: Ocimum basilicum; Ph: Parthenium hystero-phorus; Sg: Setaria glauca; Sc: Sida cordifolia; Solanum nigrum; Sn Sx: Solanum xanthocarpum; Si: Sphaeranthus indicus; Tp: Tephrosia purpurea.
CCA analysis revealed that structures of herbaceous and woody layer community were mainly driven by air pollutants and soil nutrients, whereas soil acidity plays small role in plant community. For herbaceous and woody layer, the first axis showed strong positive
819
correlation with soil nutrients and strong negative correlation with soil acidity (Figures 3 and 4). The second axis showed only strong soil nutrients.
near the first axis and with high resistant to air emission and consequent altered soil qualities around coal mines area (Figure 3). F. religiosa, F. benghalensis, P. guajava, B. monosperma, C. citriodora, M. indica and T. arjuna found to be resistant species as their density at coal mining areas were high. F. religiosa, F. benghalensis, P. guajava and B. monosperma showed very close relations towards air pollutants. A. scholaris, B. lanzan and F. tomentosa seemed to be sensitive to coal mining emissions. The other sensitive species such as A. heterophyllus, S. cumini and A. cordifolia showed very close relation to soil nutrients (Figure 4). 4.
Fig. 4. Ordination of the woody species by canonical correspondence analysis. Aa: Acacia Arabica; Ac: Adina cordifolia; Ah: Artocarpus heterophyllus; Ai: Azadirachta indica; Al: Albizia lebbeck; Am: Aegle marmelos; As: Alstonia scholaris; Aca: Acacia catechu; Bc: Bombax ceiba; Bf: Borassus flabellifer; Bl: Buchanania lanzan; Bm: Butea monosperma; Cc: Corymbia citriodora; Dr: Delonix regia; Fb: Ficus benghalensis; Fra: Ficus racemosa; Fr: Ficus religiosa; Ft: Ficus tomentosa; Hi: Holoptelea integrifolia; Mi: Madhuca indica; Pg: Psidium guajava; Sc: Syzygium cuminii; St: Saccopetalum tomentosum; Ta: Terminalia arjuna.
Setaria glauca, Amaranthus spinosus, Cassia alata, Digitaria sanguinalis, Desmodium trifolium and Sida cordifolia were common where air pollutants were high while soil nutrients and soil pH were low. These species seem to be resistant to coal mine emission and low nutrients. While A. aspera, C. alsinoides, D. annulatum, E. alsinoides and T. purpurea, common at sites where soil nutrient was high and air pollutants were lower and sensitive to coal mine emissions. Abundance of A. aspera, C. alsinoides and D. annulatum was lower at all study sites, as these species are found distant to both axes in CCA biplot (Figure 3). B. diffusa, C. rotundus, D. trifolium, H. contortus, S. xanthocarpum and S. indicus were found
CONCLUSION
Distinct changes in the herbaceous and woody plant communities were recorded at coal mining areas. The changes in species diversity between mining and non mining reference indicated an increase in the proportion of resistant herbs and grasses, showing a tendency towards a definite selection strategy of ecosystem in response to air pollutants, soil nutrients and altered soil acidity. The principal component and canonical correspondence analyses suggested that air pollutants, soil nutrients and soil pH were major factors governing the community structure at the coal mining areas. The study suggested that due to stresses in air and soil there was a changes and development of resistance in herbaceous plants. Since trees are good in absorbing air pollutants and mitigating the climate of the area, efforts should be taken to conserve the tree species still existing in the area and more plantations of resistant species should be attempted to reduce the air pollution level in the area leading to health benefit to local communities. ACKNOWLEDGEMENTS We thank, Head, Department of Botany, Coordinator, CAS and DST FIST, Banaras Hindu University and Director, Central Institute of Mining and Fuel Research, Dhanbad for providing all the necessary laboratory facilities
820
during the research work. Financial assistance in the form of a research project (EE/39) funded by Ministry of coal, Government of India, New Delhi is also acknowledged. REFERENCES [1] Al-mulali, U., “Exploring the bi-directional long run relationship between energy consumption and life quality”, Renew. Sust. Energ. Rev., 54, pp. 824–837, 2016. [2] Connor, L.H., “Energy futures, state planning policies and coal mine contests in rural New South Wales”, Energy Policy, 2016. [3] World Coal Association, 2015. “Coal Facts” 2011, http://www.worldcoal.org/resources/coal statistics. Accessed in April 2016. [4] Pandey, B., Agrawal, M. and Singh, S., “Assessment of air pollution around coal mining area: Emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis”, Atmos. Poll. Res., 5(1), pp. 79–86, 2014. [5] Pandey, B., Agrawal, M. and Singh, S., “Coal mining activities change plant community structure due to air pollution and soil degradation.” Ecotoxicology, 23(8), pp. 1474–1483, 2014. [6] Grantz, D.A., Garner, J.H.B. and Johnson, D.W., “Ecological effects of particulate matter”, Environ. Int. 29(2), 213–239, 2003. [7] Singh, A. and Agrawal, M., “Acid rain and its ecological consequences”, J. Environ. Biol., 29, 15–24, 2008. [8] Rai, A.K., Paul, B. and Singh, G., “Assessment of top soil quality in the vicinity of subsided area in Jharia coalfield, Dhanbad, Jharkhand”. Report and opinion, 2, 9, 2010. [9] Hernandez, A.J. and Pastor, J. “Relationship between plant biodiversity and heavy metal bioavailability in grasslands overlying an abandoned mine” Environ. Geochem. Hlth., 30:127–133, 2008. [10] Bello, F.D., Lavorel, S., Lavergne, S., Albert, C.H., Boulangeat, I., Mazel, F. and Thuiller, W., “Hierarchical effects of environmental filters on the functional structure of plant communities: a case study in the French Alps”, Ecography, 36:393–402, 2012. [11] West, W. and Gaeke, G.C., “Fixation of sulphur dioxide on disulfitomercurate II and subsequent colorimetric estimation”, Anal Chem, 28:1816– 1819, 1956
[12] Jacobs, M.B. and Hochheiser, S., “Continuous sampling and ultra-micro-determination of nitrogen dioxide in air”, Anal Chem, 30:426–28, 1958. [13] Maiti, S.K., “Ecorestoration of the coalmine degraded lands” Springer, India, 2013. [14] Olsen, S.R., Cole, C.V. and Watanable, F.S., Estimation of available phosphorus in soil by extraction with sodium bicarbonate. USDA circular 939, 1954. [15] Dickman, S.R. and Bray, R.H. “Replacement of adsorbed phosphate from kaolinite by fluoride”, Soil Sci., 52:263–274, 1941. [16] Williams, C.H. and Steinbergs, A., “Soil sulphur fractions as chemical indices of available sulphur in some Australian soils”, Crop Pasture Sci., 10:340–352, 1959. [17] Singh, N.P., Mudgal, V., Khanna, K.K., Srivastava, S.C., Sahoo, A.K., Bandyopadhyay, S., Aziz, N., Das, M., Bhattacharya, R.P. and Hajra, P.K., “Flora of Bihar—Analysis”, Botanical Survey of India, Ministry of Environment and Forests, 2001. [18] CPCB (Central Pollution Control Board), National Ambient Air Quality Standards (NAAQS), Gazette Notification, New Delhi, 2009. [19] Roy, D., Gautam, S., Singh, P., Singh, G., Das, B.K. and Patra, A.K., “Carbonaceous species and physicochemical characteristics of PM10 in coal mine fire area—a case study” Air Qual. Atmos. Hlth. 9(4), pp. 429–437, 2016. [20] Gautam, S., Patra, A.K., Sahu, S.P. and Hitch, M., “Particulate matter pollution in opencast coal mining areas: a threat to human health and environment” Int. J. Min Reclam. Environ., pp. 1–18, 2016. [21] Quadros, P.D. de, Zhalnina, K., DavisRichardson, A.G., Drew, J.C., Menezes, F.B., Flávio, A.D.O and Triplett, E.W., “Coal mining practices reduce the microbial biomass, richness and diversity of soil”, Appl. Soil Ecol, 98, pp. 195–203, 2016. [22] Liu, R. and R. Lal R., “An Empirical Study on improving quality of coal-mining refuse for revegetation using amendments”, J. Sustain Dev., 6:44, 2013. [23] dos Santos, J. Valentim, W. de Melo Rangel, A.A. Guimaraes, P.M.D. Jaramillo, M. Rufini, L.M. Marra, M.V. Lopez, M.A.P. da Silva, C.R.F.S. Soares, F.M. and de Souza Moreira, “Soil biological attributes in arsenic-contaminated gold mining sites after revegetation”, Ecotoxicology, 22: 1526–1537, 2013.
821
