Content area
In this study, we assessed the changes in the physical and chemical characteristics of the soil samples collected from the artisanal and small-scale limestone mining site in Sohra (Cherrapunjee), Meghalaya, by comparing them with the non-mining site. Eleven distinct soil parameters, namely pH, electrical conductivity (EC), texture (ST), moisture content (MC), bulk density (BD), total porosity (TP), water holding capacity (WHC), organic carbon (OC), total nitrogen (TN), available phosphorus (AP), and exchangeable potassium (EK), were evaluated seasonally (winter, pre-monsoon, and post-monsoon) for 2 years. The results showed that limestone mining has significantly affected the soil quality. The effect is evident by the substantial increases in EC values, sand content, and alkaline soils coupled with noticeably low concentrations of OC and TN. In addition, prominent changes were perceived in the soil MC and EK content, as well as in WHC, BD, and TP percent. Results from ANOVA revealed significant differences (p < 0.05) in mean values at different sampling seasons and sites. The multivariate statistical analysis results showed that the computed correlation coefficient (r) matrix data ranged from − 1.00 to 0.974. A strong positive correlation was highest between OC and TN (0.974), followed by OC with EK (0.828). Principal component (PC) analysis revealed two major components, PC 1 and PC 2, having eigenvalues of 6.276 and 1.747, respectively. Cumulatively, these two components explained 80.23% of the total variance. The loading factor in PC 1 is high and is attributed to OC (.974), TN (.970), and EK (.903). However, in PC 2, the loading factor is positively pooled by MC (0.894) and TP (0.765). The present study concludes that artisanal and small-scale limestone mining altered the soil’s physical and chemical properties, and these changes are likely to have a subsequent deteriorating impact on the area’s biodiversity, landscape, and natural ecosystem. Therefore, to minimize the impact and ensure sustainable soil management in the area, approaches for effective mitigation and remediation measures, including formulating steps for the conservation and enhancement of the soil’s environmental quality, are recommended.
Introduction
Mining mineral resources has provided humans with various benefits, namely fuels, metals, stones, precious stones, ores, and construction materials, essential for their sustenance and survival (Arndt et al., 2017; Calas, 2017). Most importantly, mining contributes appreciably to the country’s economic development, urbanization, modernization, and industrialization. In addition, it also provides benefits such as revenue generation, employment opportunities, and livelihood options to a large population (Chen et al., 2015; Haddaway et al., 2019; Pavolová et al., 2022). However, in addition to its positive impact, mining is one of the most environmentally degrading industries in the world. Extracting minerals through mining, including limestone extraction from the earth’s crust, has dramatically affected the local environment at various mining, processing, and utilization stages (Aleksandrova & Timofeeva, 2021; Candeias et al., 2019). Studies reported that mining has an irreversible impact on the soil’s physical and chemical characteristics. For instance, the removal of top soils in any mining process has resulted in the elimination of seed banks and rootstocks (Anju et al., 2022), modification of soil texture and structure (Feng et al., 2019; Hartati & Sudarmadji, 2016), depletion of organic matters and essential nutrients (Ahirwal & Maiti, 2016; Mensah, 2015; Punia & Bharti, 2023), inevitable increase in the percentage of sand and reduction of clay content (Chileshe et al., 2020), increase in bulk density and reduction in total porosity (Edache & Mallo, 2019; Koſodziej et al., 2016), potential influence on soil water content (Wang et al., 2017), degradation in the quality of soil (Ganapathi & Phukan, 2020; Kumar & Reddy, 2016), and overall alteration of soil physical, chemical, and microbiological properties (do Nascimento et al., 2021; Gbenga & Olumuyiwa, 2023; Lamare & Singh, 2017; Li et al., 2018; Oyinloye, 2015; Panday et al., 2019; Rahman et al., 2017).
The artisanal and small-scale mining and its impact on different aspects of the environment have been reported by various studies from Nigeria (Nasir et al., 2021; Vivan et al., 2021), Kenya (Anyona & Rop, 2022; Rumbe, 2021), South Africa (Kafu-Quvane & Mlaba, 2024), Ghana (Asare et al., 2024; Karikari et al., 2021), and Thailand (Kittipongvises, 2017). Similarly, a review of related literature also revealed that the only research work reported earlier from the limestone mining area located in Sohra, Meghalaya, focuses on the localized effect of limestone mining on the water quality (Lamare & Singh, 2015), the plant diversity (Suting et al., 2019), and quest for actinobacteria in limestone mine spoils (Syiemiong & Jha, 2019). With this regard, the scope for exploring scientific research in the area is vast. Literature also revealed that to date, no scientific work has been reported in the area concerning soil quality assessment. To develop effective soil management, mitigation, and reclamation efforts in the future, there is a need for soil characteristics information. Therefore, to fill this information gap, this study was carried out with the objective to assess the changes in the soil’s physical and chemical properties induced by artisanal and small-scale limestone mining activities.
Materials and methods
Study area
The state of Meghalaya is in the North-Eastern Region of India. It is blessed with rich and diverse natural resources, both renewable and non-renewable. Major renewable resources include water, forests, and a variety of flora and fauna. The major non-renewable resources in the state are coal, limestone, granite, uranium, kaolin, clay, glass sand, etc. According to the Indian Mineral Yearbook (2013), Meghalaya constitutes about 9% of the country’s limestone reserve. Its deposition is abundantly distributed mainly in the southern fringe of the state, extending for about 200 km from Jaintia Hills in the east, Khasi Hills, and Garo Hills in the west (Fig. 1). Tripathi et al. (1996) reported that the maximum limestone reserve in Meghalaya is in Jaintia Hills (55%), followed by Khasi Hills (38%) and Garo Hills (7%).
[See PDF for image]
Fig. 1
Map showing the distribution of limestone deposits in Meghalaya (left) and the soil sampling sites in the study area (right)
The study area is in the Sohra (also referred to as Cherrapunjee) area of the East Khasi Hills District of Meghalaya. Its geographical coordinate is between 25°06′44″ N to 25°37′12″ N latitude and 92°07′47″E to 91°25′41″E longitude. It is approximately 65 km away from the State Capital, Shillong. This region is known for receiving the highest rainfall in the world (Mawroh, 2019; Murata et al., 2007; Soja et al., 2016), with an annual rainfall ranging from 11.995 to 14.189 mm (Central Ground Water Board, 2022). The area’s topography is characterized by an undulating terrain dominated by grassland, thin topsoil, and exposed rock with a clump distribution of vegetation patches on the hill slope, which varies from place to place in size and shape.
Sampling site descriptions
In this study, two sampling sites were selected for collection of soil samples. A brief description of the sampling sites is described below. The map showing limestone geographical distribution in the state of Meghalaya, and the soil sampling sites in the study area are displayed in Fig. 1.
Limestone mining site: It is situated in the Nongthymmai locality in Mawsmai village. The geo-coordinate of the sites is 25°15′0.96″ N to 25°15′18.18″ N and 91°43′21.19″ E to 91°43′57.15″ E and is 1335 m above mean sea level. Limestone mining in this area is confined and is chiefly used to produce quicklime and edible lime. Mining is done manually with minimal use of machinery, hence, categorized as Artisanal and Small Scale mining (ASM). Limestone rocks extracted from the scree undergo manual sizing to obtain rock pieces of suitable size, which then undergo calcination processes in the traditional design vertical kilns to obtain quicklime, a final product (Fig. 2). The processed product is exported to the paper industry in the neighboring states. In addition, other minor uses of extracted limestone in the area include the construction of temporary roadbeds, house construction, and other purposes.
Non-mining site: It is in Maw-Ki-Syiem and is approximately 1.5 to 2 km from the limestone mining site. Its geo-coordinates are 25°16′23.53″ N to 25°16′35.01″ N and 91°43′27.96″ E to 91°43′33.07″ E, and it is 1237 m above mean sea level. The area is characterized by undulating hills dominated by open grassland, and this site was selected as a control/reference point because the area is devoid of mining activities, coupled with minimal human interference.
[See PDF for image]
Fig. 2
Artisanal and small-scale mining of limestone in Sohra, Meghalaya
Sampling period
This study was conducted for 2 years, i.e., 2013 and 2014. In each year, soil sampling was carried out seasonally, i.e., during winter (WIN), pre-monsoon (PRM), and post-monsoon (POM) seasons.
Soil sampling procedure
To ensure that samples represent the area, random soil sampling was carried out for both sampling sites following the standard procedure (Allen et al., 1974; Maiti, 2003). In each site, 25–30 replicates of soil samples were collected at a 0–15 cm depth using a soil corer. The collected bulk samples were passed through a 2 mm sieve to remove leaves, roots, rocks, and pebbles. The soils were then properly mixed in a clean polythene sheet, and ultimately, a composite soil sample was yielded following the coning and quartering method. The composite samples were packed in an air-tight polythene bag and transported to the laboratory. In the laboratory, the freshly collected samples were used to determine soil pH, electrical conductivity, and moisture content. Then, the soil samples were air-dried, after which soil samples were grounded and passed through the 0.2 mm sieve. The dried composite soil samples were used for analysis of other selected soil physical and chemical parameters.
Parameters studied and laboratory analysis
Soil pH and EC were determined in soil and distilled water suspension (1:2.5) using Deluxe pH-101 m and Conductivity-601 m, respectively. ST was determined by adopting the hydrometer method (Allen et al., 1974). Then, based on the percentage of sand, silt, and clay, its texture classes were determined using the USDA texture triangle diagram. The percentage of MC, BD, and WHC in soil samples was estimated using the gravimetric method (Maiti, 2003), laboratory method for disturbed soil (Gupta, 2005), and Keen Box methods (Maiti, 2003), respectively. TP was calculated using particle densities (2.65 gm/cm3) and soil bulk density values (Allen et al., 1974). The OC content was determined by adopting the Walkley and Black Rapid Titration Method (Maiti, 2003). TN was determined by the Kjeldahl Method using the Nitrogen Analyser-Pelican Kelplus Model: Classic DX (VA). The molybdenum blue method was adopted to determine AP (Allen et al., 1974) using Systronics UV–VIS Spectrophometer-118, and 1N ammonium acetate extract solution was used to estimate EK (Jackson, 1973) using Microprocessor Flame Photometer (ESICO) Model 1381.
Statistical analysis
Using Microsoft Excel 2007, the mean () and standard deviation data of the eleven soil physical and chemical parameters were computed seasonally from limestone mining sites and non-mining sites for 2 years. To comprehend whether the data were statistically significant or not between different sites, seasons, and years of sampling, a three-way analysis of variance (ANOVA, p < 0.05) was performed (Jagadamma et al., 2008; Nikodemus et al., 2020) using SPSS software Version 22. The multivariate statistical analysis, such as a Karl Pearson correlation coefficient matrix, was employed to determine the closeness of association among the ten soil variables (excluding soil texture) studied. The principal component analysis (PCA) was employed to determine the number of components having eigenvalue > 1, from the measured weight of the variables, and the correlation of each variable with the components extracted. However, PCA was applied only after quantifying the Kaiser–Meyer–Olkin (KMO) sampling adequacy test and Bartlett’s test of sphericity test (Firdous et al., 2016; Khan et al., 2022; Rahman et al., 2017).
Results and discussion
Physical and chemical characteristics
The analytical results of the ten soil physical and chemical parameters (excluding soil texture) obtained throughout the study are expressed in means ± standard deviations and presented in Table 1, together with the statistical ANOVA results.
Table 1. Analytical data (mean ± standard deviation) and ANOVA results of the ten soil physical and chemical parameters studied (p > 0.05, insignificant in bold)
Soil parameters | Location | Analytical data | ANOVA test | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Year and seasons | |||||||||||||
2013 | 2014 | Significant (p < 0.05) | |||||||||||
WIN | PRM | POM | WIN | PRM | POM | Year | Seasons | Sites | Year *seasons | Year *sites | Year *seasons; *sites | ||
MC (%) | Non-mining site | 4.51 ± 0.05 | 23.47 ± 0.16 | 18.32 ± 0.07 | 4.21 ± 0.10 | 26.42 ± 0.22 | 4.19 ± 0.03 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Mining site | 2.09 ± 0.08 | 21.83 ± 0.17 | 9.14 ± 0.04 | 1.84 ± 0.20 | 8.64 ± 0.27 | 2.60 ± 0.09 | |||||||
pH | Non-mining site | 5.9 ± 0.06 | 5.9 ± 0.06 | 5.3 ± 0.06 | 5.7 ± 0.02 | 5.8 ± 0.06 | 5.5 ± 0.09 | 0.214 | 0.000 | 0.000 | 0.078 | 0.845 | 0.000 |
Mining site | 8.0 ± 0.10 | 7.5 ± 0.10 | 7.8 ± 0.15 | 7.9 ± 0.13 | 7.7 ± 0.13 | 7.6 ± 0.07 | |||||||
EC (dS/m) | Non-mining site | 0.021 ± 0.001 | 0.016 ± 0.002 | 0.012 ± 0.001 | 0.025 ± 0.002 | 0.018 ± 0.0005 | 0.014 ± 0.0003 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Mining site | 0.740 ± 0.030 | 0.315 ± 0.007 | 0.267 ± 0.004 | 0.846 ± 0.030 | 0.400 ± 0.009 | 0.583 ± 0.008 | |||||||
BD (g/ml) | Non-mining site | 1.40 ± 0.0118 | 1.25 ± 0.034 | 1.35 ± 0.008 | 1.26 ± 0.016 | 1.06 ± 0.024 | 1.48 ± 0.006 | 0.400 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Mining site | 1.42 ± 0.018 | 1.49 ± 0.007 | 1.57 ± 0.013 | 1.57 ± 0.013 | 1.40 ± 0.011 | 1.68 ± 0.01 | |||||||
TP (%) | Non-mining site | 47.00 ± 0.44 | 52.85 ± 1.28 | 48.98 ± 0.31 | 52.38 ± 0.62 | 59.88 ± 0.92 | 43.99 ± 0.22 | 0.462 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Mining site | 46.55 ± 0.67 | 43.89 ± 0.28 | 40.81 ± 0.50 | 40.76 ± 0.50 | 47.29 ± 0.42 | 36.71 ± 0.38 | |||||||
WHC (%) | Non-mining site | 37.9 ± 0.605 | 52.8 ± 2.13 | 42.7 ± 1.45 | 46.8 ± 0.65 | 60.0 ± 1.39 | 38.6 ± 0.93 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Mining site | 31.09 ± 0.94 | 27.26 ± 1.04 | 26.46 ± 0.88 | 26.06 ± 0.99 | 29.44 ± 1.07 | 27.12 ± 0.91 | |||||||
OC (%) | Non-mining site | 2.03 ± 0.09 | 2.10 ± 0.06 | 2.96 ± 0.22 | 2.89 ± 0.09 | 1.98 ± 0.15 | 2.04 ± 0.15 | 0.652 | 0.000 | 0.000 | 0.000 | 0.331 | 0.004 |
Mining site | 0.41 ± 0.10 | 0.21 ± 0.06 | 0.31 ± 0.12 | 0.51 ± 0.09 | 0.35 ± 0.14 | 0.44 ± 0.12 | |||||||
TN (%) | Non-mining site | 0.243 ± 0.02 | 0.210 ± 0.01 | 0.285 ± 0.008 | 0.280 ± 0.01 | 0.191 ± 0.02 | 0.215 ± 0.02 | 0.002 | 0.000 | 0.000 | 0.020 | 0.778 | 0.007 |
Mining site | 0.079 ± 0.016 | 0.023 ± 0.016 | 0.033 ± 0.021 | 0.033 ± 0.008 | 0.023 ± 0.016 | 0.019 ± 0.008 | |||||||
AP (%) | Non-mining site | 1.733 ± 0.05 | 3.261 ± 0.58 | 9.706 ± 1.89 | 8.422 ± 1.19 | 4.725 ± 1.55 | 3.586 ± 0.93 | 0.055 | 0.000 | 0.016 | 0.000 | 0.978 | 0.987 |
Mining site | 2.167 ± 0.13 | 3.872 ± 0.79 | 11.267 ± 0.66 | 9.808 ± 1.37 | 5.681 ± 0.51 | 3.794 ± 0.29 | |||||||
EK (mg/g) | Non-mining site | 0.530 ± 0.048 | 0.492 ± 0.019 | 0.507 ± 0.003 | 0.783 ± 0.14 | 0.320 ± 0.009 | 0.500 ± 0.015 | 0.643 | 0.000 | 0.000 | 0.000 | 0.259 | 0.398 |
Mining site | 0.375 ± 0.015 | 0.163 ± 0.006 | 0.267 ± 0.029 | 0.365 ± 0.08 | 0.138 ± 0.003 | 0.270 ± 0.022 | |||||||
WINwinter, PRMpre monsoon, POMpost monsoon, and 0.000 indicates statistically significant (ANOVA, p < 0.05)
Soil texture (ST)
The texture of soil at the non-mining site was loamy sand and sandy soil at the mining site. The percentage of sand at non-mining sites was comparatively lower than at the mining sites. However, silt and clay content was higher at the non-mining site than at the mining site (Table 2). This result is supported by Shilla’s (2015) findings, which state that Sohra soil varied from sandy soil to sandy loam texture. Similarly, disturbed soil from the limestone mines in Mussoorie hills, Dehradun (Dadhwal, 1987), and Sirmaur district of Himachal Pradesh (Hanief et al., 2007) are reported to have a high percentage of sand and a low percentage of silt and clay. Analysis of variance (p < 0.05) showed that ST varied significantly between different sites, indicating statistical significance. The undulating terrain in the non-mining area is dominated by grassland and exposed rocks. Seasonal heavy rainfall and high slopes in the area could be significantly attributed to the erosion of fine silt and clay particles, thereby increasing the percentage of sand in the non-mining area. However, the low percentage of silt and clay with a high content of sand at the mining site may be attributed chiefly to the limestone mining activities and the lack of vegetation growth in the area.
Table 2. Percentage of soil particle distribution and its texture classes in Sohra, Meghalaya
Soil texture (%) | Sampling sites | |
|---|---|---|
Mining (%) | Non-mining (%) | |
Sand | 95.26 (± 1.46) | 87.65 (± 1.33) |
Silt | 3.31 (± 1.11) | 9.61 (± 1.40) |
Clay | 1.43 (± 0.36) | 2.74 (± 0.42) |
Texture class | Sandy* | Loamy Sand* |
*indicates that soil texture is statistically significant between sites (ANOVA, p < 0.05)
Moisture content (SMC)
Results indicated that the percentage of MC at the non-mining site was relatively higher than at the limestone mining site (Table 1). Throughout the study period, MC was maximum during pre-monsoon and minimum during the winter season. The increase in the percentage of MC is attributed to the influence of rainfall the area received. A study reported that excess runoff infiltrates down into the ground and is trapped in the soil through capillaries (Mohapatra & Goswami, 2012), which in turn favors progressive growth of vegetation, mainly grass species, and further modify the soil ecosystem at the non-mining area. On the contrary, the lower percentage of MC at the mining site is attributed chiefly to the low moisture retention capacity of mine soil because of the presence of large-size stone material, sandy soil texture, low organic matter, and lesser vegetation growth (Dutta & Agrawal, 2002). Additionally, Yaseen et al. (2015) stated that MC tends to fluctuate from time to time and is never constant. ANOVA (p < 0.05) results revealed that the mean MC values show significant variations between different sampling years, seasons, and sites.
pH
The pH of the soil from the non-mining site was found to be moderately acidic (Table 1). Similar, findings have been reported in Sohra soil by Nath (2013) and Shilla (2015). In addition, Meghalaya soils in general are acidic (Swer et al., 2011; Government of Meghalaya, 2019, 2024). This can be attributed to high precipitations, as it induced leaching of cations like calcium (Ca2+), magnesium (Mg2+), and potassium (K+) over time coupled with sandy soil texture and negligible input of OC (Banerjee et al., 2004; Johnson & Zhang, 2002). On the contrary, soil samples from the limestone mining site are slightly alkaline. Similar findings have also been documented in limestone mine soils from Mussoorie, Dehradun (Dadhwal, 2003), Coimbatore, Tamil Nadu (Ravichandran et al., 2009) in India, and Oyo in Nigeria (Etim & Adie, 2012). The primary cause of the pH variation is calcium carbonate (the main constituent of limestone) which reacts with water to produce alkalinity. Following the ANOVA results, the mean pH values differed significantly (p < 0.05) between the sampling seasons and sites but not between years (p > 0.05) nor between years with seasons and sites.
Electrical conductivity (EC)
A remarkable difference in the level of the EC was noticed in soil samples collected from the mining site compared to that of the non-mining site (Table 1). At the mining site, the level of EC in soils is significantly high, with the highest seasonal variation recorded during winter and the lowest during the pre-monsoon season. Similarly, soil collected from limestone mines in Sirmaur district, Himachal Pradesh, was also found to exhibit relatively high EC values (Hanief et al., 2007). The significantly high level of EC at the mining site and a notable temporal variation are chiefly attributed to the disintegration of calcium carbonate, which is the main constituent of limestone when dissolved with water and accelerates electrical conductivity in the soil. This finding is supported by Rai et al. (2011) and Lamare and Singh (2015). The reduction in EC value during pre-monsoon and post-monsoon corresponds to dilution of the ions with surface runoff. On the contrary, the non-mining site exhibits relatively lower EC values indicating the absence of contamination. From the ANOVA results, it is clear that the mean value of EC exhibits statistically significant differences (p < 0.05) between different years, seasons, and sites.
Bulk density (BD)
Throughout the study period, the soil BD from the mining site increased to a certain extent when compared to that of the non-mining site (Table 1). Mining activities are reported to have a positive effect on the soil BD (Shrestha & Lal, 2011; Shrestha et al., 2005). The present study shows that BD was maximum at the mining site and during the post-monsoon season. An increase in the content of sand and a low level of organic matter in the soil will subsequently lead to a high BD level in the soil. Singh et al. (2001) and Hanief et al. (2007) also reported similar findings. Soils with high BD values inhibit the growth of plant roots and soil microorganisms (Andrews et al., 2004; Grossman et al., 2001). However, soil from the non-mining site recorded lower BD values. Sohra surface soils are reported to have a bulk density ranging from 1.01 to 1.24 g/m3 (Shilla, 2015). Statistically significant differences (p < 0.05) in BD mean values exist at different sampling seasons and sites, but there is no significant difference (p > 0.05) between years.
Total porosity (TP)
TP in soil generally increases with decreased BD. Throughout the study, a relatively higher percentage of TP was observed in soils collected from the non-mining site when compared to that from the mining site. Seasonally, the percentage of TP was maximum during pre-monsoon and minimum during post-monsoon seasons (Table 1). Literature mentions that the porosity of sandy soil ranges typically from 35 to 50%, whereas finer soil ranges from 40 to 60% (Carter & Gregorich, 2008). Statistical results show that there is a significant difference (p < 0.05) in TP mean values between different sampling seasons and sites, but no significant interaction with different years (p > 0.05).
Water holding capacity (WHC)
When the percentage of WHC between the study sites is compared, it was found that the percentage at the non-mining site was slightly higher than at the mining site. Seasonally, the maximum WHC percent was observed during the pre-monsoon season of 2014, and the lowest in the winter season of 2013 (Table 1). This is supported by the findings reported by Shilla (2015). The leading causes for the elevated level of WHC at the non-mining site are attributed chiefly to the wetting of the soils by rainfall, soil texture, particle size percentage, and higher organic matter (Donahue et al., 1990) because soils with these properties provide a strong affinity towards water. However, WHC at the mining site is lower and is significantly attributed to high sand percentage and low soil organic matter. Evrendilek et al. (2004) findings also supported these results. The ANOVA (p < 0.05) results show that mean WHC values are statistically significant in different sampling years, seasons, and sites.
Organic carbon (OC)
Data displayed in Table 1 shows that the non-mining site exhibits significantly higher OC content than that from the mining site. Saxena (1987) and Jaiswal (2006) specified that soil having OC content less than 0.2% is designated as poor quality soil. However, if the content is more than 0.80%, the soil is said to have high OC indicating excellent/fertile soil. Therefore, based on the estimation, the non-mining site is said to have high OC; this may be attributed to the establishment and decomposition of the vegetative material, chiefly grass species, in the area, which subsequently led to the increase in OC in soils. On the contrary, due to the lack of vegetation growth in the limestone mining site, OC content in soil tends to be minimal. Previous study also found that OC content in limestone mine soils possesses low levels of nutrients (Singh & Jamaluddin, 2010). In the same context, Hanief et al. (2007) and Etim and Adie (2012) also reported that soil samples collected from the limestone quarry in the Sirmaur district, Himachal Pradesh, and Oyo, Nigeria, are poor quality soil, as they possess relatively low OC content. Statistically, there are significant differences (p < 0.05) in the OC mean values in different sampling seasons and sites (ANOVA, p < 0.05), but not between years (p > 0.05) nor between year association with sites.
Total nitrogen (TN)
Throughout the study period, the concentration of TN in soils from limestone mining sites was relatively low when compared to that from the non-mining site (Table 1). Hanief et al. (2007) also found that limestone mine soils in Himachal Pradesh contain significantly low TN content. This is attributed chiefly to the lack of plant growth, insignificant content of plant biomass, and high sand at the mining site. Singh and Jamaluddin (2010) also support this finding. On the contrary, the relatively high concentration of TN at non-mining sites is influenced chiefly by the presence of vegetative growth and organic matter in the soil. Statistically, the ANOVA results showed that mean TN values varied significantly (p < 0.05) between different years, seasons, and sites but no significant interaction between year and site association (p > 0.05).
Available phosphorus (AP)
The present investigation revealed that the concentration of AP in soils was slightly higher at the mining site than at the non-mining site. However, overall variations between the study sites were found minimal (Table 1). Phosphate solubility and availability in soils are significantly controlled by either sorption–desorption or precipitation-dissolution reactions (Holtan et al., 1988). In addition, soil organic matter, pH, and cation exchange capacity greatly influence its availability in soils (Smithson, 1999). At the non-mining sites, the recorded concentration of phosphorus could be attributed to acidic soil (Rai et al., 2011; Yaseen et al., 2015) and high moisture content because, under such conditions, phosphorus tends to get liberated from the decomposed organic matter or through mineralization (Tate, 1984; Jaggi et al., 2005). On the contrary, in alkaline soil, particularly in mineral soils, the level of availability of phosphorus is low because of phosphate sorption by a variety of soil elements (Lajtha et al., 1999). In addition, with the increase in pH, phosphorus transforms into a more complex insoluble form of phosphorus (Dean & Rubins, 1947; Olsen, 1953). However, the concentration of AP at the mining sites could be due to the excess amount of calcium carbonate (limestone’s main constituent) because under alkaline conditions its solubility is negligible, therefore acting as a sink for phosphorus adsorption and precipitation of Ca-phosphate (Hopkins & Ellsworth, 2005; Naeem et al., 2013). Results of ANOVA (p < 0.05) show that there is a significant difference (p < 0.05) in mean AP values in different seasons and sites but no significant interaction between years (p > 0.05) nor with means of year association with sites and seasons.
Exchangeable potassium (EK)
The concentration of EK was slightly higher in soil samples collected from the non-mining site than from the mining site. Throughout the study period, the concentration of EK was found lowest during the pre-monsoon and the highest in the winter season (Table 1). The variations in the EK content in the study sites could be attributed to leaching, which is strongly correlated to the amount of clay, organic matter (Rao & Srinivas, 2017; Sparks & Huang, 1985), soil pH, and texture (Fotyma, 2007). Leaching of EK is insignificant on clayey soil (Raheb & Heidari, 2012) but is often a problem on sandy soils (Sparks & Huang, 1985). On the contrary, the reduction in the EK content could be attributed to absorption by plants (Johnston, 2003). From the ANOVA results, it is indicated that the mean EK values show statistically significant differences (p < 0.05) in different seasons and sites but no significant interaction between sampling years (p > 0.05) nor with mean of year association with sites and seasons.
Correlation matrix analysis
The computed correlation matrix coefficient (r) of the ten soil physical and chemical parameters (excluding soil texture) ranged from − 1.00 to 0.974, and the data output is presented in Table 3. From the matrix data, 45 pairs of data output were generated, of which 32 pairs are statistically correlated (15 pairs are positively correlated, and 17 are negatively correlated).
Table 3. Correlation matrix coefficient of 10 soil physical and chemical parameters studied
Variables | MC | pH | EC | BD | TP | WHC | OC | TN | AP | EK |
|---|---|---|---|---|---|---|---|---|---|---|
MC | 1 | |||||||||
pH | − 0.324 | 1 | ||||||||
EC | − 0.477** | 0.875** | 1 | |||||||
BD | − 0.628** | 0.605** | 0.603** | 1 | ||||||
TP | 0.625** | − 0.602** | − 0.601** | − 1.000** | 1 | |||||
WHC | 0.583** | − 0.791** | − 0.717** | − 0.901** | 0.901** | 1 | ||||
OC | 0.218 | − 0.938** | − 0.760** | − 0.637** | 0.635** | 0.782** | 1 | |||
TN | 0.200 | − 0.937** | − 0.785** | − 0.629** | 0.628** | 0.760** | 0.974** | 1 | ||
AP | − 0.069 | 0.084 | 0.061 | 0.132 | − 0.136 | − 0.143 | 0.035 | − 0.060 | 1 | |
EK | − 0.193 | − 0.686** | − 0.475** | − 0.356* | 0.357* | 0.505** | 0.828** | 0.821** | 0.081 | 1 |
**and*indicate a significant correlation between the various physicochemical parameters at the 0.01 and 0.05 levels, respectively (2−tailed)
MCmoisture content, ECelectrical conductivity, BDbulk density, TPtotal porosity, WHCwater holding capacity, OCorganic carbon, TKtotal nitrogen, APavailable phosphorus, EKexchangeable potassium
The most substantial positive correlation variables with p < 0.01 have been found for OC with TN (0.974), followed by TP with WHC (0.901), pH with EC (0.875), OC with EK (0.828), TN with EK (0.0821), and WHC with OC (0.782), and TN (0.760). Other soil variables (parameters) showing a significant positive correlation were observed between TP with OC (0.635), TN (0.628), and MC (0.625) and between BD with pH (0.605) and EC (0.603) at a 1% level of significance. On the contrary, the highest negative correlation was between BD and TP (r = − 1.000, p < 0.01), followed by soil pH with OC (− 0.938) and TN (− 0.937), and between WHC and BD (− 0.901). There was no significant statistical correlation (p > 0.05) between AP and the rest of the studied variables.
Principal component analysis (PCA)
In this study, the SPSS computed Kaiser–Meyer–Olkin (KMO) test value is 0.756, and Bartlett’s test of sphericity was significant (p < 0.001). This indicates that the sampling adequacy in the study was acceptable, and the correlation between variables was significant enough for computing the PCA technique. A higher degree of adequacy in test samples is best postulated when KMO values are greater than 0.5 and close to 1.0 (Kaiser & Rice, 1974). In addition, the principal components having eigenvalues of more than one are considered significant (Howladar et al., 2017; Shrestha & Kazama, 2007).
Table 4 shows that ten eigenvalues are produced from the studied soil variables when the PCA technique is applied. However, only two major principal components, PC 1 and PC 2, exhibit eigenvalues greater than 1. The PCA results also revealed that these two components (PCs) cumulatively explained 80.23% of the total variance. The computed eigenvalues of the first and second components are 6.276 and 1.747, respectively, which subsequently account for 62.76% and 17.47%, respectively, of the percentage of variance of the parameters studied.
Table 4. Computed eigenvalues and total variance of the studied variable
Component | Eigenvalues | Proportion of variance | Cumulative % |
|---|---|---|---|
1 | 6.276 | 62.76 | 62.76 |
2 | 1.747 | 17.47 | 80.23 |
3 | 0.974 | 9.74 | 89.97 |
4 | 0.601 | 6.01 | 95.98 |
5 | 0.172 | 1.72 | 97.70 |
6 | 0.11 | 1.10 | 98.80 |
7 | 0.085 | 0.85 | 99.65 |
8 | 0.021 | 0.21 | 99.86 |
9 | 0.014 | 0.14 | 100.00 |
10 | 8.43E-05 | 0.00 | 100.00 |
Extraction method: principal component analysis
In addition, Table 5 displayed the loading factor values obtained from the Varimax rotation method, showing that the highest factor loading in all studied variables was with OC (0.974). The interpretation of PCA results is said to be excellent when values of factor loadings exceeding 0.7 are taken into consideration (Hu et al., 2013; Nowak, 1998). In the factor loading results, the first component was strongly and positively loaded with OC, TN, and EK variables. On the other hand, the negative factor loading is related to EC and soil pH variables. A similar association in the first component, particularly between the soil organic carbon and nitrogen, has also been reported by Belkhiri and Narany (2015) and Rahman et al. (2017). Our findings indicate that the chemical parameters of soil chiefly influence the first principal component. This could be due to the degradation in the soil variables (low nutrients and high level of contaminations) that are attributed chiefly to anthropogenic activities, particularly limestone mining activities. On the other hand, the second component was positively loaded with MC (0.894), TP (0.765), and WHC (0.643). However, BD was negatively loaded. In the second component, factor loading was found to influence and contribute mainly to soil physical parameters, which could be stimulated by the level of water retention, compactness, and nutrients linked in the soil.
Table 5. Factor loading of 10 soil physical and chemical parameters in PC 1 and PC 2 (significant loading in bold)
Variables | Rotating component | |
|---|---|---|
Component 1 | Component 2 | |
OC | 0.974 | 0.144 |
TN | 0.970 | 0.146 |
pH | − 0.917 | − 0.243 |
EK | 0.903 | − 0.255 |
EC | − 0.752 | − 0.394 |
WHC | 0.712 | 0.643 |
MC | 0.066 | 0.894 |
BD | − 0.549 | − 0.766 |
TP | 0.548 | 0.765 |
AP | 0.058 | − 0.300 |
Extraction method: principal component analysis
Rotation method: Varimax with Kaiser Normalization
MCmoisture content, ECelectrical conductivity, BDbulk density, TPtotal porosity, WHCwater holding capacity, OCorganic carbon, TKtotal nitrogen, APavailable phosphorus, EKexchangeable potassium
Conclusions
Based on the information from this study, artisanal and small-scale limestone mining in Sohra, Meghalaya, altered the physical and chemical properties of soils. These changes will likely have subsequent spatial and temporal deteriorating impacts on the area’s flora, fauna, soil microbes, landscape, and natural ecosystem. The effect of continuous mining is evident in the substantial increases in EC values, sand content, and alkaline soils coupled with noticeably low OC and TN concentrations. In addition, prominent changes were perceived in the soil MC and EK content, as well as in the percentage of WHC, BD, and TP. Results from ANOVA revealed that the mean values of all studied parameters vary significantly (p < 0.05) between different sampling seasons and sites. The computed correlation coefficient (r) matrix data ranged from − 1.00 to 0.974. The strongest positive correlation was highest between soil OC and TN (0.974), followed by OC with EK (0.828). A significant correlation among different soil parameters was noticeable, indicating the associations between parameters. It also signifies that an increase or decrease in the values of parameters will affect the corresponding parameter values as well. Principal component (PC) analysis results revealed two major components, PC 1 and PC 2, having eigenvalues of 6.276 and 1.747, respectively. Cumulatively, these two components explained 80.23% of the total variance of the parameters studied. The highest positive loading factor variables were OC (0.974), followed by TN (0.970), EK (0.903) in PC 1, MC (0.894), and TP (0.765) in PC 2. Our study underscores only the physical and chemical characteristics of affected mine soils. However, options for future research avenues in line with soil biological, hydrological, geological, biodiversity, and ecological services, among other areas could be dynamic and imperative. Therefore, to ensure the best sustainable land use and restrict further depletion of soil quality in the future, we recommend the development of initiatives for implementing effective mitigation and remediation measures, including formulating steps for the conservation and enhancement of the soil environmental quality, coupled with sensitization of the community and other stakeholders.
Author contribution
This research paper is a subset of Dr. R E Lamare Ph.D. work, and Prof. O P Singh is Dr. R E Lamare Ph.D. Supervisor. The first author collected the soil samples, analyzed the samples and data, interpreted the results, and wrote the manuscript. The second author supervised and provided valuable input and corrections to the manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval
All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.
Conflict of interest
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
Ahirwal, J; Maiti, SK. Assessment of soil properties of different land uses generated due to surface coal mining activities in tropical Sal (Shorea robusta) forest, India. CATENA; 2016; 140, pp. 155-163.1:CAS:528:DC%2BC28XitFGgsr4%3D [DOI: https://dx.doi.org/10.1016/j.catena.2016.01.028]
Aleksandrova, AY; Timofeeva, SS. Impact of mining of common minerals on the environment and public health. In IOP Conference Series Earth and Environmental Science; 2021; 848, 012136. [DOI: https://dx.doi.org/10.1088/1755-1315/848/1/012136]
Allen, SE; Grimshaw, HH; Parkinson, JA; Christopher, Q. Chemical analysis of ecological materials; 1974; Blackwell Scientific Publications Oxford:
Andrews, SS; Karlen, DL; Cambardella, CA. The soil management assessment framework: A quantitative soil quality evaluation method. Soil Science Society of American Journal; 2004; 68, pp. 1942-1962. [DOI: https://dx.doi.org/10.2136/sssaj2004.1945]
Anju, MV; Warrier, RR; Kunhikannan, C. Significance of soil seed bank in forest vegetation-A review. Seeds; 2022; 1,
Anyona, S., & Rop, B. (2022). Environmental impacts of artisanal and small-scale mining in Taita Taveta County. In Proceedings of the Sustainable Research and Innovation Conference, May 6-8, 2015 (pp. 228–241).
Arndt, NT; Fontbote, L; Hedenquist, JW; Kesler, SE; Thompson, JFH; Wood, DG. The future global mineral resources. Global Perspectives; 2017; 6,
Asare, D; Ansong, M; Asante, WA; Kyereh, B. Impact of different illegal artisanal small-scale mining techniques on soil properties in a major mining landscape in Ghana. Environmental Challenges; 2024; 17, 101008.1:CAS:528:DC%2BB2cXhvFymsrnF [DOI: https://dx.doi.org/10.1016/j.envc.2024.101008]
Banerjee, SK; Mishra, TK; Singh, AK; Jain, A. Impact of plantation on ecosystem development in disturbed coalmine overburden spoils. Journal of Tropical Forest Science; 2004; 16,
Belkhiri, L; Narany, TS. Using multivariate statistical analysis, geostatistical techniques and structural equation modeling to identify spatial variability of groundwater quality. Water Resources Management; 2015; 29, pp. 2073-2089. [DOI: https://dx.doi.org/10.1007/s11269-015-0929-7]
Calas, G. Mineral resources and sustainable development. Elements an International Magazine of Mineralogy, Geochemistry, and Petrology; 2017; 13,
Candeias, C., Ávila, P., Coelho, P., & Teixeira, J.P. (2019). Mining activities: Health impacts. Encyclopedia of Environmental Health, 416–435. https://doi.org/10.1016/B978-0-12-409548-9.11056-5
Carter, MR; Gregorich, EG. Soil sampling and methods of analysis; 2008; 2 Canadian Society of Soil Science:
Central Ground Water Board. (2022). Dynamic groundwater resources, Meghalaya. Technical Report: Series A. Department of Water Resources, River Development & Ganga Rejuvenation, Ministry of Jal Shakti, Government of India (pp. 1–34).
Chen, QS; Yu, WJ; Zhang, YF. Considerations on the strengthening mineral resources reserve in China. China Mining Magazine; 2015; 5,
Chileshe, MN; Syampungani, S; Festin, ES et al. Physico-chemical characteristics and heavy metal concentrations of copper mine wastes in Zambia: Implications for pollution risk and restoration. Journal of Forest Research.; 2020; 31, pp. 1283-1293.1:CAS:528:DC%2BC1MXnsFCkurg%3D [DOI: https://dx.doi.org/10.1007/s11676-019-00921-0]
Dadhwal, KS. Kumar, A. Characterization and eco-restoration of limestone mine spoils of outer Himalaya. Dimensions of Environmental Threats; 2003; Daya Publishing House: pp. 119-127.
Dadhwal, K. S. (1987). Characterization and evaluation of some techniques for stabilization of mine spoil/debris under Mussoorie hills of Uttar Pradesh. In Ph.D. Thesis, Agra University.
Dean, LA; Rubin, EJ. Anion exchange in soils. Exchangeable phosphorous and anion exchange capacity. Soil Science; 1947; 63, pp. 374-387. [DOI: https://dx.doi.org/10.1097/00010694-194705000-00005]
do Nascimento, ARVJ; Cunha, GKG; do Nascimento, CWA et al. Assessing soil quality and heavy metal contamination on scheelite mining sites in a tropical semi-arid setting. Water Air Soil Pollution; 2021; 232, 375.1:CAS:528:DC%2BB3MXhvFKlu7rN [DOI: https://dx.doi.org/10.1007/s11270-021-05299-6]
Donahue, R. L., Miller, R. W., & Shickluna, J. C. (1990). Soils: An introduction to soils and plant growth (5th ed., p. 234) Prentice-Hall.
Dutta, RK; Agrawal, M. Effect of tree plantations on the soil characteristics and microbial activity of coal mine spoil land. Tropical Ecology; 2002; 43,
Edache, LO; Mallo, IIY. Impact of limestone mining and cement production on bulk density, porosity and moisture content of soils in Yandev, Gboko, Benue State, Nigeria. Journal of Environmental Science; 2019; 1, pp. 1-11.
Etim, EU; Adie, GU. Assessment of toxic heavy metal loading in topsoil samples within the vicinity of a limestone quarry in South Western Nigeria. African Journal of Environmental Science and Technology; 2012; 6,
Evrendilek, F; Celik, I; Kilic, S. Changes in soil organic carbon and other physical soil properties along adjacent Mediterranean forest, grassland, and cropland ecosystems in Turkey. Journal of Arid Environments; 2004; 59, pp. 743-752. [DOI: https://dx.doi.org/10.1016/j.jaridenv.2004.03.002]
Feng, Y; Wang, J; Bai, Z; Reading, L. Effects of surface coal mining and land reclamation on soil properties: A review. Earth-Science Reviews; 2019; 191, pp. 12-25.1:CAS:528:DC%2BC1MXjs1Oltrc%3D [DOI: https://dx.doi.org/10.1016/j.earscirev.2019.02.015]
Firdous, S; Behum, S; Yasim, A. Assessment of soil quality parameters using multivariate analysis in the Rawal Lake watershed. Environment Monitoring and Assessment; 2016; 188, 533.1:CAS:528:DC%2BC28XhsVSls73I [DOI: https://dx.doi.org/10.1007/s10661-016-5527-5]
Fotyma, M. Content of potassium in different forms in the soils of southeast Poland. Polish Journal of Soil Science; 2007; 40,
Ganapathi, H., & Phukan, M. (2020). Environmental hazards of limestone mining and adaptive practices for environment management plan. In: R. Singh., P. Shukla., & P. Singh (eds.), Environmental Processes and Management. Water Science and Technology Library, 91. Springer, Cham. https://doi.org/10.1007/978-3-030-38152-3_8
Gbenga, A; Olumuyiwa, AO. Evaluation of the impacts of limestone mining on soil and water quality in Okpella, Edo State. Nigeria. IRE Journals; 2023; 7,
Government of Meghalaya. (2019). Survey and mapping the extent and distribution of soil acidity in agricultural lands of Meghalaya by using geospatial techniques and soil health card, office of the research officer, district and local research station and laboratories (pp. 1–60).
Government of Meghalaya. (2024). Department of Agriculture and farmer’ welfare website. Retrieved September 09, 2024, from https://megagriculture.gov.in/PUBLIC/agri_scenario_soil.aspx
Grossman, RB; Harms, DS; Seybold, CA; Herrick, JE. Coupling use-dependent and use-invariant data for soil quality evaluation in the United States. Journal of Soil and Water Conservation; 2001; 56, pp. 63-68.
Gupta, PK. Methods in environmental analysis water, soil and air; 2005; Agrobios Publication:
Haddaway, NR; Cooke, SJ; Lesser, P et al. Evidence of the impacts of metal mining and the effectiveness of mining mitigation measures on social-ecological systems in Arctic and boreal regions: A systematic map protocol. Environmental Evidence; 2019; 8, 9. [DOI: https://dx.doi.org/10.1186/s13750-019-0152-8]
Hanief, SM; Thakur, SD; Gupta, B. Vegetal profile of natural plant succession and artificially revegetated limestone mines of Himachal Pradesh. India. Journal of Tropical Forestry; 2007; 23,
Hartati, W; Sudarmadji, T. Relationship between soil texture and soil organic matter content on mined-out lands in Berau, East Kalimantan. Indonesia. Nusantara Bioscience; 2016; 8,
Holtan, H., Kamp-Nielsen, L., Stuanes, A.O. (1988). Phosphorus in soil, water and sediment: An overview. In: G. Persson, M. Jansson, (eds.), Phosphorus in Freshwater Ecosystems. Developments in Hydrobiology, 48. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3109-1_3
Hopkins, B; Ellsworth, J. Phosphorus availability with alkaline/calcareous soil. In Western Nutrient Management Conference; 2005; 6,
Howladar, MF; Numanbakth, MAA; Faruque, MO. An application of water quality index (WQI) and multivariate statistics to evaluate the water quality around Maddhapara Granite Mining Industrial Area, Dinajpur. Bangladesh. Environment System Research; 2017; 6, 13. [DOI: https://dx.doi.org/10.1186/s40068-017-0090-9]
Hu, Y; Liu, X; Bai, J; Shih, K; Zeng, EY; Cheng, H. Assessing heavy metal pollution in the surface soils of a region that had undergone three decades of intense industrialization and urbanization. Journal of Environmental Science and Pollution Research; 2013; 20, pp. 6150-6159.1:CAS:528:DC%2BC3sXhtFClsLbE [DOI: https://dx.doi.org/10.1007/s11356-013-1668-z]
Indian Minerals Yearbook, Indian Bureau of Mines, Ministry of Mines, Government of India. (2013). (Part-1: General Reviews) (52nd ed.).
Jackson, MK. Soil chemical analysis; 1973; Prentice Hall Inc.:
Jagadamma, S; Lal, R; Hoeft, RG; Nafziger, ED; Adee, EA. Nitrogen fertilization and cropping system impacts on soil properties and their relationship to crop yield in the central Corn Belt, USA. Soil & Tillage Research; 2008; 98, pp. 120-129. [DOI: https://dx.doi.org/10.1016/j.still.2007.10.008]
Jaggi, RC; Aulakh, MS; Sharma, R. Impacts of elemental S applied under various temperature and moisture regimes on pH and available P in acidic, neutral and alkaline soils. Biology and Fertility of Soils; 2005; 41, pp. 52-58.1:CAS:528:DC%2BD2cXhtVOqs73L [DOI: https://dx.doi.org/10.1007/s00374-004-0792-9]
Jaiswal, P. C. (2006). Soil, plant, and water analysis. Kalyani publishers.
Johnson, G.V., & Zhang, H. (2002). Cause and effect of soil acidity. Oklahoma Cooperative Extension Service. Retrieved on September 11, 2024, from http://osufacts.okstate.edu
Johnston, AE. Understanding potassium and its use in agriculture; 2003; Brussels, Belgium, European fertilizer manufacturers' association:
Kafu-Quvane, B; Mlaba, S. Assessing the impact of quarrying as an environmental ethic crisis: A case study of limestone mining in a rural community. International Journal of Environmental Research and Public Health.; 2024; 21,
Kaiser, HF; Rice, J. Little jiffy, mark IV. Educational and Psychological Measurement; 1974; 34, pp. 111-117. [DOI: https://dx.doi.org/10.1177/001316447403400115]
Karikari, AY; Duah, AA; Akurugu, BA et al. Assessing the impacts of artisanal mining on the quality of south-western Rivers System in Ghana. Environmental Monitoring and Assessment; 2021; 193, 715.1:CAS:528:DC%2BB3MXit1CltLbO [DOI: https://dx.doi.org/10.1007/s10661-021-09515-y]
Khan, J; Singh, R; Upreti, P; Yadav, RK. Geo-statistical assessment of soil quality and identification of heavy metal contamination using integrated GIS and Multivariate statistical analysis in industrial region of western India. Environmental Technology and Innovative; 2022; 28, 102646.1:CAS:528:DC%2BB38XhsFSgsLfP [DOI: https://dx.doi.org/10.1016/j.eti.2022.102646]
Kittipongvises, S. Assessment of environmental impacts of limestone quarrying operations in Thailand. Environmental and Climate Technologies; 2017; 20,
Koſodziej, B; Bryk, M; Sſowiſska-Jurkiewicz, A; Otremba, K; Gilewska, M. Soil physical properties of agriculturally reclaimed area after lignite mine: A case study from central Poland. Soil and Tillage Research; 2016; 163, pp. 54-63. [DOI: https://dx.doi.org/10.1016/j.still.2016.05.001]
Kumar, GS; Reddy, AN. Application of remote sensing to assess environmental impact of limestone mining in the Ariyalur district of Tamilnadu. India. Journal of Geomatics; 2016; 10,
Lajtha, K., Driscoll, C. T., Jarrel, W. M., & Elliot, E. T. (1999). Soil phosphorus: Characterization and total element analysis. In G. P. Robertson, D. C. Coleman, C. S. Bledsoe, & P. Sollins (Eds.), Standard soil methods for long-term ecological research (2, pp. 115–142). Oxford University Press.
Lamare, RE; Singh, OP. Localized effect of artisanal and small-scale mining of limestone mining on water quality in Meghalaya. India. Pollution Research; 2015; 32,
Lamare, RE; Singh, OP. Changes in soil quality in limestone mining area of Meghalaya. India. Nature Environment and Pollution Technology.; 2017; 16,
Li, J; Xin, Z; Yan, J; Li, H; Chen, J; Ding, G. Physicochemical and microbiological assessment of soil quality on a chronosequence of a mine reclamation site. European Journal of Soil Science; 2018; [DOI: https://dx.doi.org/10.1111/ejss.12714]
Maiti, S. K. (2003). Handbook methods in environmental studies: Air, soil, and overburden analysis (Vol 2). ABD Publishers.
Mawroh, B. Water scarcity in Sohra (Cherrapunjee): A paradox. Transient; 2019; 8, pp. 40-49.
Mensah, AK. Role of re-vegetation in restoring fertility of degraded mined soils in Ghana: A review. International Journal of Biodiversity and Conservation; 2015; 7,
Mohapatra, H; Goswami, S. Impact of coal mining on soil characteristics around Ib River coalfield, Orissa. India. Journal of Environmental Biology; 2012; 33,
Murata, F; Hayashi, T; Matsumoto, J. Rainfall on the Meghalaya plateau in northeastern India-One of the rainiest places in the world. Natural Hazards; 2007; 42, pp. 391-399. [DOI: https://dx.doi.org/10.1007/s11069-006-9084-z]
Naeem, A; Akhtar, M; Ahmad, W. Optimizing available phosphorus in calcareous soils fertilized with diammonium phosphate and phosphoric acid using Freundlich adsorption isotherm. The Scientific World Journal; 2013; 1, 680257. [DOI: https://dx.doi.org/10.1155/2013/680257]
Nasir, BL; Umar, N; Dahiru, MK. Assessment of the environmental impact of artisanal and small-scale miners in Bakin Ayeni Community of Kokona Local Government Area of Nasarawa State. Nigeria African Scholar Journal of Environmental Design and Construction Management.; 2021; 22,
Nath, D. (2013). Role of soil microorganisms in C and P dynamics during recovery of degraded terrestrial ecosystems of Cheerapunji Plateau. In Thesis: Department Of Botany, North-Eastern Hill University.
Nikodemus, O; Kaupe, D; Kukuļs, I; Brūmelis, G; Kasparinskis, R; Dauškane, I; Treimane, A. Effects of afforestation of agricultural land with grey alder (Alnus incana (L.) Moench) on soil chemical properties, comparing two contrasting soil groups. Forest Ecosystems; 2020; 7, pp. 1-10. [DOI: https://dx.doi.org/10.1186/s40663-020-00253-0]
Nowak, B. Contents and relationship of elements in human hair for a non-industrialized population in Poland. Journal of Science and Total Environment; 1998; 209,
Olsen, SR. Inorganic phosphorous in alkaline and calcareous soils. Agronomy; 1953; 4, pp. 81-122.
Oyinloye, MA. Environmental pollution and health risks of residents living near Ewekoro Cement Factory, Ewekoro, Nigeria. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering; 2015; 9,
Panday, B; Mukherjee, A; Agrawal, M; Singh, S. Assessment of seasonal and site-specific variations in soil physical, chemical and biological properties around opencast coal mines. Pedosphere; 2019; 29,
Pavolová, H; Čulkovám, K; Šimková, Z; Seňová, A; Kudelas, D. Contribution of mining industry in chosen EU countries to the sustainability issues. Sustainability.; 2022; 14,
Punia, A; Bharti, R. Loss of soil organic matter in the mining landscape and its implication to climate change. Arab Journal of Geoscience; 2023; 16, 86.1:CAS:528:DC%2BB3sXpt1yjsQ%3D%3D [DOI: https://dx.doi.org/10.1007/s12517-023-11177-8]
Raheb, A; Heidari, A. Effects of clay mineralogy and physico-chemical properties on potassium availability under soil aquic conditions. Journal of Soil Science and Plant Nutrition; 2012; 12,
Rahman, MM; Howladar, MF; Faruque, MO. Assessment of soil quality for agriculture purposes around the Barapukuria coal mining industrial area, Bangladesh: Insights from chemical and multivariate statistical analysis. Environment Systems Research; 2017; 6, 24. [DOI: https://dx.doi.org/10.1186/s40068-017-0101-x]
Rai, AK; Paul, B; Singh, G. A study on physical chemical properties of overburden dumps materials from selected coal mining areas of Jharia coalfields, Jharkhand. India. International Journal of Environmental Science; 2011; 1,
Rao, CS; Srinivas, K. Potassium dynamics and role of non-exchangeable potassium in crop nutrition. Indian Journal of Fertilizers; 2017; 13,
Ravichandran, S; Gayathri, V; Negarajan, R. A study on the environmental impact of Madukkarai Limestone Mine Coimbatore District Tamil Nadu South India. Proceeding on Curtin Sarawak 1st Symposium on Geology; 2009; Curtin University of Technology Sarawak Malaysia: pp. 37-47.
Rumbe, O.G. (2021). An assessment of the effects of artisanal mining in Tabaka, Kisii County, Kenya (Doctoral dissertation, University of Nairobi). Retrieved 22, 2024 from http://erepository.uonbi.ac.ke/handle/11295/155767
Saxena, M. M. (1987). Environmental analysis water, soil and air, Agro Botanical Publication India.
Shilla, U. (2015). Impact of fire and grazing on structure and function of grassland ecosystem of Cherrapunjee. In Ph.D. Thesis: Department Of Environmental Studies, North-Eastern Hill University.
Shrestha, G, Stahl P. D., & Ingram, L. (2005). Influence of reclamation management practices on soil bulk density and infiltration rates on surface coal mine lands in Wyoming. In: National Meeting of the American Society of Mining and Reclamation (pp. 1042–1056). ASMR Lexington.
Shrestha, S; Kazama, F. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin. Japan Environmental Modelling Software; 2007; 22,
Shrestha, RK; Lal, R. Changes in physical and chemical properties of soil after surface mining and reclamation. Geoderma; 2011; 161,
Singh, AK; Jamaluddin, A. Role of microbial inoculants on growth and establishment of plantation and natural regeneration in limestone mined spoils. World Journal of Agricultural Sciences; 2010; 6,
Singh, SS; Tiwari, SC; Dhar, MS. Evaluation of soil degradation using physiochemical, biochemical, and biological parameters in humid tropics of Arunachal Pradesh. India. Annals of Forestry; 2001; 9,
Smithson, P. Special issue on phosphorus availability, uptake and cycling in tropical agroforestry. Agroforestry Forum.; 1999; 9,
Soja, R., Juszczyk, M. & Nowakowska, J. (2016). Rainfall structure for Cherrapunjee and Mawsynram in Northeast India. In: R. Singh, P. Prokop (eds.), Environmental Geography of South Asia. Advances in Geographical and Environmental Sciences. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55741-8_8
Sparks, DL; Huang, PM. Munson, RD. Physical chemistry of soil potassium. Potassium in agriculture; 1985; American Society of Agronomy: pp. 201-276.
Suting, EG; Nongkynrih, CJ; Dkhar, GD. Study of limestone mine spoilage affecting the diversity of naturally occurring plant species of Mawsmai, (Meghalaya) India. World Journal of Pharmacy and Pharmaceutical Science; 2019; 8,
Swer, H; Dkhar, MS; Kayang, H. Fungal population and diversity in organically amended agricultural soils of Meghalaya, India. Journal of Organic System; 2011; 6, pp. 1177-4258.
Syiemiong, D; Jha, DK. Search for plant growth promoting actinobacteria from a limestone mining spoil soil in Meghalaya. Research Journal of Life Science, Bioinformatics, Pharmaceuticals and Chemical Science.; 2019; 5,
Tate, K. R. (1984). The biological transformation of P in soil. In J. Tinsley, J. F. Darbyshire (Eds.), Biological Processes and Soil Fertility. Developments in Plant and Soil Sciences (Vol 11). Springer, Dordrecht. https://doi.org/10.1007/978-94-009-6101-2_22
Tripathi, R. S., Pandey, H. N., & Tiwari, B. K. (1996). State of Environment of Meghalaya. North-Eastern Hill University.
Vivan, EL; Ali, AY; Obasi, MT; Emmanuel, JN; Daloeng, HM; Giwa, CY; Yakubu, MT. Effect of artisanal mining on groundwater quality in Antang District, Jema’al Local Government Area of Kaduna State, Nigeria. Bayero Journal of Pure and Applied Sciences; 2021; 13,
Wang, Y; Bian, Z; Lei, S et al. Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model. Journal of Arid Land; 2017; 9, pp. 712-726. [DOI: https://dx.doi.org/10.1007/s40333-017-0065-8]
Yaseen, S; Pal, A; Singh, S; Skinder, BM. Soil quality of agricultural fields in the vicinity of selected mining areas of Raniganj Coalfield India. Journal of Environmental and Analytical Toxicology; 2015; 5, 269. [DOI: https://dx.doi.org/10.4172/2161-0525.1000269]
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.