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GEOLOGY, ECOLOGY, AND LANDSCAPES 2020, VOL. 4, NO. 2, 140–150 INWASCON https://doi.org/10.1080/24749508.2019.1608407 RESEARCH ARTICLE a,b a C R Paramasivam and Siddan Anbazhagan a b Centre for Geoinformatics and Planetary Studies, Periyar University, Salem, India; Department of Geology, Alagappa University, Karaikudi, India ABSTRACT ARTICLE HISTORY Received 10 September 2018 The opencast mining operations have periodical eﬀects in surrounding landscape and it is impor- Accepted 13 April 2019 tant to monitor the quality of the soil. The pre- and post-mining activities inﬂuence the soil quality due to removal of vegetation and topsoil cover. The low-grade magnesite ores and dumping of KEYWORDS waste materials are the root causes of contamination of soil. The samples collected from the depth Soil quality; magnesite mine; of 15 cm on the surface of soil with diﬀerent physical (soil texture and soil moisture) and chemical physicochemical; cluster (pH, organic carbon, and nitrogen) parameters areanalyzedtostudy theeﬀectofopencastmining analysis; correlation on the surrounding soil. Nine soil samples collected from three magnesite mine sites include agriculture land in close proximity. The result of this analysis predicts the sample soils deﬁciency of nutrients contents like nitrogen, phosphorus, potassium, and calcium. The soil analysis comprises chemical parameters such as pH, lime status, texture, electrical conductivity, available macronu- trients nitrogen (N), phosphorous (P), potassium (K), and micronutrients such as Fe, Mn, Zn, and Cu percentageswhich areusedtodetermine thecondition andquality ofthesoil. TheSTATISTICA software (ver.8) was used for analysis of Pearson’s correlation with linear relationship of soil samples and rows and columns of data matrix on cluster analysis. Introduction Topsoil management plays an important role in reclamation plan to prevent nutrient losses. Removal Soil deﬁned as a thin layer of the Earth’s crust that serves of soil and rock overburden in the magnesite-mining as a natural medium for the growth of plants. It is an area causes loss of topsoil and exposes the parent unconsolidated mineral matter inﬂuenced by the genetic material. Excavation results in the removal of fertile and the environmental factors of the parent material, topsoil and thus generating a huge amount of spoil or climate, organisms, and topography prevalent over overburden generally in the form of gravel, coarse a period of time (Masto et al., 2015; McKenzie, 2013). sand, fragmented rock pieces, etc., which deteriorates Soil provides a reservoir of nutrients required for the the aesthetic beauty of the proximate landscape vegetation, but not necessarily at optimum levels of (Arvind Kumar Rai, Biswajit Paul, & Singh, 2011; immediate availability to plants. Soil testing refers to the Lad & Samant, 2013; Lamare & Singh, 2014). physicochemical analysis parameters and a scientiﬁc Soil disturbance and associated compaction results means for quick characterization of the fertility status in conditions conducive for erosion (Arvind Kumar and predicting the nutrient requirement for crop produc- Rai et al., 2011; Sheoran, Sheoran, & Poonia, 2010). tion. The purpose of soil analysis is to assess the adequacy, Soil removal from the mining area alters many nat- surplus, or deﬁciency of available nutrients for vegetation ural soil characteristics, reduces its biodiversity and growth and to monitor the changes brought about by the the productivity of agriculture. The methods of mining practices. Mining activity distorts the natural mining process cause soil loss due to digging of environment, particularly the cultivatable land and the strip mines and an open-pit mine requires removal natural forest area (Debasis, 2014; Lad & Samant, 2015). of plants and soil from the surface of the ground. This information is needed for optimum retention of Mining industry aﬀects the agricultural land area and natural vegetation to avoid transferring of undesirable induces human settlement pattern, thereby causing levels of nutrients into the environment and further to disruption of social relations (Debasis, 2014; Arvind ensure asuitablenutrientcontentin thesoil. Regularsoil Kumar Rai et al., 2011; McKenzie, 2013). analysis for every 3–5 years period is a vital part of good Mohapatra and Goswami (2012)and Arvind Kumar soil management practice (McKenzie, 2013). Recently, Rai et al. (2011) assessed the soil characteristics in open- Savitha, Ramamoorthy, and Sudhakaran (2018)have cast coal mining region under diﬀerent monsoon found invasive plants with their unique species-speciﬁc conditions to ﬁnd the soil texture, moisture, pH, organic trait and the changes invaded soil environment from carbon, nitrogen, phosphorous, and potassium. Ghose surrounding native soil characters. (2004) has studied the process of opencast mining CONTACT C R Paramasivam firstname.lastname@example.org Centre for Geoinformatics and Planetary Studies, Periyar University, Salem, India © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 141 activities and changes in physicochemical and the micro- correlate the physicochemical parameters of the soil sam- biological properties of soil in Eastern Coal Limited ples to infer the macro- and micronutrient deﬁciency in (ECL) coalﬁelds. the deﬁned locations that caused by mining activity and Masto et al. (2015) have assessed the soil quality as tend to support vegetation in mining regions by improv- one of the key parameters for the evaluation of envir- ing the soil fertility. onmental contamination in the coal mining area. High level of potential acidity (low pH) severely restricts the productivity of mine soils (Sang Yong, Material and methods 2018). Thereclamation is theprocess to restore the Location and geology of the study area ecological integrity of disturbed mines land areas. It includes the management of physical, chemical, and The magnesite ore mineral deposit is predominant in biological disturbances of soil fertility, microbial Salem and spread over about 17-sq.km area and the community, and various soil nutrient cycles (Arvind total reserves estimated at 44 million tones of the ore Kumar Rai et al., 2011; Lad & Samant, 2013;Sheoran deposits. Magnesite vein deposit occurs at 7 km north- et al., 2010;SSM, 2009; Sudhakaran, Ramamoorthy, west of Salem as white color ore, located in the part of Savitha, & Balamurugan, 2018;Tola et al., 2017). southern part of peninsula (GSI, 2006). Geologically the It is diﬃcult to achieve successful soil analysis without region is bounded by ultramaﬁc terrain of Archean per- proper site selection, sample collection, and preparation iod. The ultramaﬁc intrusive of the chalk hills is invaded (SSM 2009;Zaware, 2014). The present study revealed into preexisting country rocks that comprises foliated that magnesite-mining activity is responsible for the biotite gneisses, migmatite, magnetite quartzite, eclogite, alteration of physicochemical properties of soils in the hornblende gneisses, charnockite, and pegmatites. The mines area and the surrounding agricultural area soils, ultramaﬁc rocks include dunite, peridotite, and shonki- which implies an adverse eﬀect on the quality of the soil. nite and are important as they are the major contributors These opencast mining procedures result in the loss of to the trace element budget of soil. These two ultramaﬁc major and minor nutrients. Soil quality status in and rocks occur as intrusive and gneissic rocks separate those around the opencast mines is degraded day by day due formation(He et al., 2015). A number of shear zones are to the removal of topsoil and it is vital to improve micro- associated with the magnesite deposits. These shear zones and macronutrients to save soil quality (Ghose, 2004). conﬁrm the several trends of the belt and seem to control The anthropogenic activities inﬂuence physicochem- the veins of the magnesite deposit. Shear zones vary ical changes. Transport, waste disposal, industrialization, in width and are traversing. The ultramaﬁcrocks social, and agricultural activities have adverse eﬀect on show general parallelism with the foliation of adjoining environmental pollution and the ecosystem (Oumenskou gneissic (GSI 2006; Satyanarayanan, Eswaramoorthi, et al., 2018;Sudhakaranetal., 2018). Subramanian, & Periakali, 2016). Further, the study strengthens with the statistical The climatic condition prevailing in and around mag- approach of Pearson’s correlation that provided the nesite-mining region is generally hot and dry. The cli- direction and the strength of linear relationship mate in mining area is dry and moderate with between the two variables with coeﬃcient matrices. temperature ranges from 23 to 38°C. The average annual Two-way-joining cluster analysis is used for grouping rainfall varies from 800 to 1600 mm. The climate during objects of Q-mode and R-mode of the same data to January and February is generally pleasant; the dry sum- reorganize the rows and columns of the data mer begins in March, with the year’shighest tempera- matrix. It’s built with the combination of cases (sample tures reaching in May. The weather continues to be more sites) and variables (chemical parameters) to make the temperate in June and July, and in August, it changes to discrete patterns of clusters (Andrew et al., 2018; cloudy. The northeast monsoon contributes rainfall dur- Chung et al., 2016; Oumenskou et al., 2018; Tellen & ing September–December. Most of the magnesite mines Yerima, 2018; Venkatramanan, Chung, Ramkumar, are surrounded by agricultural activities (Anbazhagan & Gnanachandrasamy, & Kim, 2015). Paramasivam, 2016). The regional scale includes trees The aim of this paper is to study the impact of mining such as acacia bushes, neem, pungun, ailanthus, nelli, activity over the agriculture soil found in and around the bamboo, casuarina, and tamarind. mining site. Speciﬁcally, the results obtained from phy- The major class of soil group “Vertisol soil” found in siochemical parameters of the soil samples report that the the study area is classiﬁed into ﬁvetypesas(a)redsoil,(b) soil fertility deteriorated in the mine’swaste dump found black soil, (c) brown soil, (d) alluvial soil, and (e) mixed nearby agriculture land. Further, the statistical approach soil. The basis of parent material, texture, permeability, such as Pearson’s correlation method is applied to soil and alkalinity soils is grouped as series under these cate- fertility indices to provide a scientiﬁc basis for monitoring gories. However, the mining region under investigation the management of agriculture soil fertility. Therefore, mostly contains red and brown soils (Figure 1)due to the objective of this study is to analyze the soil quality of weathering of ultramaﬁc and gneissic country rocks the samples collected from diﬀerent locations and to (Geological Survey of India (GSI), 2006). 142 C. R. PARAMASIVAM AND S. ANBAZHAGAN Figure 1. Diﬀerent soil types in magnesite-mining regions. Alﬁsols soils matured with an alluvial subsurface Survey Manual (SSM), 2009). These soil samples are (argillic) horizon. The study area covers the underneath collected in the month of May 2016 (Figure 2). portion containing alﬁsols and occupies an area of The appropriate geographic locations of sample 64.22 sq.km. Soil order entisol includes soils of slight collection are measured through handheld GPS and recent development of metagenetic process. (Garmin Oregon 550) instrument. The soil sample Further, the study area includes NW portion and used for laboratory analysis must be representative these soils occupy an area of 58.57 sq.km. Vertisol of the ﬁeld from where it was taken and the soil soils are typically dark colored and have high percen- substances were analyzed (Table 1). Soil maps follow tage of clay-dominated minerals (Murthy, 1988). the distribution of soil taxonomic units and provide Vertisol soil and their intergrades, with some inclusions descriptive summaries of the main properties of the of entisols, are found in the hills and pediments. The soils (Dan Pennock et al, 2006). Vertisol soils are predominantly found to be in the NE The methodology adapted in the present study of soil portion of the study area and cover 36.47 sq.km. The quality assessment includes physicochemical parameters part of mines and waste dump area covers 15.6 sq.km such as N, P, K, pH, and electrical conductivity (EC), Fe, and categorized under miscellaneous group of soils. Zn, Mn, and Cu using standard procedure (Figure 3). The statistical analysis brings out a clear correlation with the physicochemical parameters of the soil that aid in Soil sampling predicting the fertility of the sampling locations. The requirement and procedure for obtaining soil Totally, nine random soil samples were collected to samples vary according to the purpose of sampling. represent the magnesite-mining area and nearby agri- The random sampling method is adapted to collect the cultural land. First, remove the surface litter at the samples from the active mining area, adjacent agriculture sampling spot and tend to drive the shovel to a plough land, and mine waste dump adjacent in mines (Figure 4). depth of 15 cm and draw the soil (McKenzie, 2013;Soil GEOLOGY, ECOLOGY, AND LANDSCAPES 143 Figure 2. Soil sample locations of study area in and around magnesite-mining region. Table 1. Soil sample locations. Sample no. Latitude and longitude Location and category S1 N 11°43′25.999″ and E 78°08′55.582″ MSL 344 m Adjacent to Dalmia mine (Chettichavadi) Agriculture land S2 N 11°44′35.100″ and E 78°09′23.706″ MSL 350 m Adjacent to TANMAG mine (Chettichavadi) Agriculture land S3 N 11°44′35.100″ and E 78°09′23.706″ MSL 350 m Adjacent to SAIL mine (Senkaradu) Agriculture land S4 N 11°41′49.185″ and E 78°06′34.700″ MSL 325 m SAIL keel board mine Gully erosion S5 N 11°43′27.081″ and E 78°08′14.546″ MSL 362 m Dalmia mine (bottom road MT block) Open pit S6 N 11°41′43.275″ and E 78°06′33.785″ MSL 325 m SAIL keel board mine Waste dump leached S7 N 11°44′02.122″ and E 78°07′44.273″ MSL 349 m SAIL mine (Red Hills extension) Nearby magnesite vein S8 N 11°43′16.179″ and E 78°08′10.592″ MSL 383 m Dalmia mine (Top hill MT block) Topsoil S9 N 11°45′10.148″ and E 78°09′23.456″ MSL 370 m TANMAG mine (Mine entrance) Topsoil The soil samples S1, S2, and S3 collected from agricultural ﬁeld by judicious selection of sampling points (Dan land and S4 collected from the waste dump gully eroded Pennock & Braidek, 2006). The concept of the limit pointatSAIL(Keelboard)mine.Theopenpitsoilsample of quantiﬁcation is that the measurements reported (S5) is collected from an existing pit on the road to the level for high standard used for the quantiﬁcation Dalmia mines (MT block). The soil sample S6 is collected and not mere detection. Chemical analysis is con- from waste dump leached zone soil at SAIL (Keel board) ducted in a commercial laboratory to determine phy- mine. The soil sample with traces of ore body is collected sicochemical parameters of soil samples collected in from the area adjacent to magnesite vein area at SAIL and around the magnesite-mining region. Soil diﬀers (Red Hills) mine. The topsoil samples S8 and S9 are from the parent material in the morphological, phy- collected from Dalmia and TANMAG mines. sical, chemical, and biological properties. In addition, soils diﬀer among themselves depending on the dif- ferent genetic identity factors (INM, 2011; APHA, Analysis of physicochemical properties of soil 1999; Lad & Samant, 2015). The soil analysis results samples include the testing of chemical parameters such as pH, lime status, texture, EC, nitrogen, phosphorous, The soil survey represents the association between potassium, Fe, Mn, Zn, and Cu (Table 2). soil classes and landscape units established in the 144 C. R. PARAMASIVAM AND S. ANBAZHAGAN Figure 3. Methodology ﬂowchart in soil quality assessment. Figure 4. Field collection of soil samples (S1–S9). Accuracy is a measure of how close an analytical be freely handled and grouping of categories textural result is to its true value. It has two components, bias classes. Sandy loam soils are compressed; they hold and precision (Swyngedouw & Lessard, 2006). The their shape but break apart easily. physicochemical parameters analysis of pH, lime, EC: EC measured by a digital conductivity meter texture, EC, macro- (N, P, and K), and micronutri- (ATC-975-C, India). ents (Fe, Mn, Zn, and Cu) is followed by APHA and Nitrogen: Nitrogen content of soil estimated by the INM standard methods (Integrated Nutrient per sulfate oxidation method. Management (INM), 2011; APHA, 1999). Phosphate: Available phosphorous of soil was pH: The pH of the soil suspension measured by determined by using ammonium molybdate solu- a digital pH meter (Eutech-356C, India). tion and was measured in a spectrophotometer at Lime status: The calcium hydroxide titration 690 nm. method to determine lime status. Potassium: Available potassium in soil extracted Texture: In the soil-testing laboratory, soils divided with neutral ammonium acetate of molarity value 1. into broad textural classes for which no speciﬁc equip- This considered as plant available K in the soils and ment. Cast formed by squeezing moist soil in hand can being estimated with the help of ﬂame photometer. GEOLOGY, ECOLOGY, AND LANDSCAPES 145 Table 2. Physicochemical properties of soil samples. Soil samples Parameters S1 S2 S3 S4 S5 S6 S7 S8 S9 Limits* pH 7.1 8.3 7.8 8.2 7.7 8.1 8.1 7.8 8.2 6–11 Lime status Absent High Absent Absent Absent Moderate Moderate Absent Moderate – Texture Sandy Sandy Sandy Sandy Sandy Sandy Sandy Sandy Sandy – loam loam loam loam loam loam loam loam loam EC 0.6 0.2 0.1 0.1 0.4 0.1 0.08 0.1 0.3 <1 Nitrogen (kg/acre) 63 71 77 67 63 64 53 57 63 >100 Phosphorous (kg/acre) 25 2.6 10.8 1.4 2.0 0.6 1.2 1.2 3.0 >10 Potassium (kg/acre) 168 16 131 28 42 33 25 33 250 150–300 Fe (%) 4.75 3.17 3.0 1.17 2.08 1.08 1.08 2.83 2.25 >4% Mn (%) 0.92 1.42 1.81 0.62 1.0 1.19 0.38 1.46 1.31 >4% Zn (%) 0.42 1.22 0.46 0.86 0.86 0.26 0.08 0.2 0.82 >4% Cu (%) 1.43 0.91 1.62 0.3 0.23 0.23 0.23 0.42 0.75 >4% *Source: Department of Agriculture & Cooperation, Ministry of Agriculture, Government of India, 2011. All the values of macronutrients expressed as kg/acre The ﬁndings of pH,EC,lime status,texture,and soil. available macro- and micronutrients are used to validate the fertility of the soil samples. The pH of soil samples Heavy metals: Heavy metals like Fe, Mn, Zn, and Cu were analyzed following the standard methods of varies for each location but it is neutral as shown in atomic absorption spectrophotometer. The values are Figure 5. The soil sample collected in the surrounding oftheminealsoshows similarity in pH values.The expressed as percentage in soil. presence of calcium carbonate in nine samples diﬀers with each other and is categorized as absent, moderate, Results and discussion and high. It is absent in the mining area and its adjacent area (samples S1, S3, S4, S5, and S8), whereas the mining Physicochemical analysis dump area and the mine vein area have moderate pre- The pH and EC conditions of soil samples S1–S9 repre- sence (samples S6 and S7) and agriculture land adjacent sent the quality of the soil. While the micronutrients such to mine sites has high content of lime (sample S2). The as Fe, Mn, Zn, and Cu percentages of S1 soil sample texture condition of all nine soil samples exhibits sandy range from low to suﬃcient level and the remaining loam, which is very essential for the plantation/revegeta- shows a low index in percentage, the percentage of tion on those soils. The EC for nine samples varies to each macronutrients present in the samples depicts a high other, but all in limits of less than 1 concentration and so diﬀerence in their results due to the selection of locations it is termed as good condition soil as shown in Figure 6. for acquiring soil samples. For example, in the soil sam- Based on these parameters, it is inferred that these soils ples S1 and S3, the value of N shows low, whereas the aﬀord plant growth (Arvind Kumar Rai et al., 2011). values of P and K show high per kg/acre. In the case of Theanalysis of thesoil samples (S4–S8) collected in sample soils such as S2, S4, S5, S6, S7, S8, and S9, it the mining region infers that it is deﬁcient in the required reveals a low index value of macronutrients. Owing to nutrient parameters of the soil-like potassium, phosphor- mining activities, the topsoil is excavated, and as a result, ous, and nitrogen as shown in Figure 7.The soil samples the soil loses its property of vegetation growth (Arvind were analyzed for the study of micronutrients presence Kumar Rai et al., 2011). (percentage),such asFe,Mn, Zn,and Cu, soasto Figure 5. Histogram distribution of pH conditions in diﬀerent soil samples. 146 C. R. PARAMASIVAM AND S. ANBAZHAGAN Figure 6. Histogram distribution of EC conditions in diﬀerent soil samples. Figure 7. Macronutrients (N–P–K) concentrations in diﬀerent soil samples. quantify the soils. It will be varying according to the Statistical analysis terrain and the climatic condition. The presence of Fe Statistical analysis of ﬁeld-collected soil samples uses percentage in sample S1 shows suﬃcient but all the STATISTICA software (ver.8). Each soil property was remaining soil samples are low in percentage. The pre- assessed in terms of descriptive statistics like mean, sence of Mn percentage varies with each soil sample but is median, mode, standard deviation, correlation limited to low percentage. The presence of Zn percentage matrix, and cluster analysis which strengthen the also varies with each soil sample. Except for soil sample ﬁndings of geostatistical parameters analysis (Chung S2, which has suﬃcient percentage, remaining all samples et al., 2016; Venkatramanan et al., 2015). show a low percentage. The presence of Cu percentage in soil samples S1 and S3 is suﬃcient, whereas the remaining samples show a low percentage as shown in Figure 8. Descriptive statistics for physicochemical parameters Hence, deﬁciencies of micronutrients in the soils are of soil reported to aﬀect the chances of vegetation growth The analysis of the ﬁeld-collected soil samples is done (Arvind Kumar Rai et al., 2011). Thus, this shortfall of through the study of physicochemical parameters micronutrients is adjusted by adding the essential nutri- (pH, EC, N, P, K, Fe, Mn, Cu, and Zn) using the ents to the soil either naturally or by artiﬁcial fertilizers. conventional statistics as shown in Table 3. Standard Figure 8. Micronutrients (Fe, Mn, Zn, and Cu) concentration percentage in diﬀerent soil samples. GEOLOGY, ECOLOGY, AND LANDSCAPES 147 Table 3. Descriptive statistics for physicochemical parameters commonly has limited supply of these macronutri- of soil. ents. Further, the study of soil samples infers the Parameters Mean Median Minimum Maximum Std. dev. following factors: pH 7.91111 8.10000 7.00000 8.3000 0.40139 EC 0.22000 0.10000 0.08000 0.6000 0.18055 ● Nitrogen has good correlation with micronutri- N (kg/acre) 64.22222 63.00000 53.00000 77.0000 7.06714 P (kg/acre) 5.31111 2.00000 0.60000 25.0000 8.00569 ents such as Zn, Cu, and Mn. K (kg/acre) 80.66667 33.00000 16.00000 250.0000 82.86133 ● Phosphorous has good correlation with EC, K, Fe (%) 2.37889 2.25000 1.08000 4.7500 1.21341 Zn (%) 0.57556 0.46000 0.08000 1.2200 0.38089 and Fe. Cu (%) 0.68000 0.42000 0.23000 1.6200 0.53891 ● Potassium has good correlation with EC, P, and Mn (%) 1.12333 1.19000 0.38000 1.8100 0.44365 Cu. ● Micronutrient Fe has good correlation with EC, deviation is the measure of dispersion of a set of data P, and Cu. Similarly, the Cu has good correla- from its mean (Tola et al., 2017). It measures the tion with P, K, Fe, and Mn. The Mn has good absolute variability of a distribution and higher dis- correlation with N and Cu. persion or variability. The greater is the standard deviation value, the greater will be the magnitude of Thus, the correlation study predicts about the soil the deviation of the value obtained from their mean. deﬁcits and fertility factors to support the improvisa- For example, the major nutrients of K have minimum tion process of soil quality. value as 16, and maximum value as 250 and the standard deviation denoted in the descriptive statis- Cluster analysis tical analysis value is 82.86. Hence, the magnitude of The results of the cluster analysis are shown in a tree variability change is high when compared to other diagram with single linkage by a dendrogram (Figures 9 parameters of soil samples as shown in Table 3. and 10), which lists all of the samples and indicates at what level of similarity any two clusters are joined. The x-axis is the measure of the similarity or distance at Correlation matrix which clusters join and diﬀerent programs use diﬀerent The presence or absence of a pathogen in relation to soil measures on this axis (Venkatramanan et al., 2015). chemistry was determined through independent distri- In the dendrogram, it was shown that the macro- bution (T-test), P >0.05 considered as signiﬁcant. nutrients (N, P, and K), micronutrients (Fe, Mn, Zn, The Pearson’s correlation coeﬃcient matrices for and Cu), and physical characters (EC and pH) were the analyzed parameters are presented in Table 4. found in chain cluster measurements. In the soil Correlation coeﬃcients less than 0.5 are not considered samples, macronutrient cluster was in the linkage as they are not at signiﬁcant levels. The interrelationship distance of >200. In the small cluster, linkage with of diﬀerent parameters is useful in studying the associa- micronutrients was in the linkage distance of 15 tion of soil parameters (Andrew et al., 2018; Tellen & (Figure 9 and Table 5). Yerima, 2018;Chung et al., 2016). In the dendrogram shown, the soil sampling stations The correlation matrix shows that there is a good (Figure 10) S1 and S3 are the most similar, joined to relationship between the physicochemical constituents form the ﬁrst cluster, and followed by the second cluster presence in the soil as shown as bold in Table 4. Though S9 associated with S1 and S3 in the linkage distance of the parameter of pH is not in good correlation with the 85. The S2 and S7 cluster has link with S3 and S9 cluster other parameters, it aﬀects the availability of fertilizer with the linkage distance of 90. A small cluster is formed nutrients. In addition, pH is not an indication of ferti- of S2 and S7 with linkage clusters of S4, S8, S7, and S5. lity, but still soil EC has good correlation with other soil parameters such as P, K, and Fe, which is the measure of the amount of salts in the soil. Conclusions The macronutrients such as nitrogen (N), phos- phorous (P), and potassium (K) play a key role in the The nine soil samples were collected from randomly healthy growth of the vegetation. The study area chosen locations but limited to magnesite mines and Table 4. Correlation statistics for physicochemical parameters of soil. Parameters pH EC N P K Fe Zn Cu Mn pH 1.00 −0.73 0.07 −0.85 −0.31 −0.71 0.32 −0.48 0.00 EC −0.73 1.00 −0.03 0.71 0.51 0.67 0.24 0.37 −0.09 N 0.07 −0.03 1.00 0.22 0.16 0.28 0.54 0.63 0.62 P −0.85 0.71 0.22 1.00 0.52 0.82 −0.12 0.79 0.08 K −0.31 0.51 0.16 0.52 1.00 0.44 0.06 0.58 0.28 Fe −0.71 0.67 0.28 0.82 0.44 1.00 0.13 0.80 0.43 Zn 0.32 0.24 0.54 −0.12 0.06 0.13 1.00 0.10 0.17 Cu −0.48 0.37 0.63 0.79 0.58 0.80 0.10 1.00 0.54 Mn 0.00 −0.09 0.62 0.08 0.28 0.43 0.17 0.54 1.00 P > 0.05 148 C. R. PARAMASIVAM AND S. ANBAZHAGAN Figure 9. Dendrogram (R mode) of physicochemical cluster analysis of soils. Figure 10. Dendrogram (Q mode) of physicochemical cluster analysis of soils. interpretation of macronutrients such as N, P, and Table 5. Factor analysis for physicochemical variables. K reveals that the soil quality degrades due to the leaching Parameters Factor 1 Factor 2 pH −0.914645 0.197509 of low-grade waste magnesite ore that is being dumped EC 0.821542 −0.047711 along the mine’s site. However, the heavy metals in N 0.043393 0.913997 micronutrients like Fe, Zn, Cu, and Mn are present in P 0.953631 0.104024 K 0.588700 0.284112 thesamplesoils whichare foundtoberelativelyinsignif- Fe 0.848131 0.356068 icant duetothewastedumpresiduethat arecontami- Zn −0.148557 0.613199 Cu 0.691383 0.624106 nated in those soil samples. The keen observation of the Mn 0.091992 0.791182 pH value exceeding index value 7 indicates alkalinity of Expl. Var. 3.997239 2.486492 Prp. Totl 0.444138 0.276277 thesamplesoils.Thedumpingofthe mine waste is Bold value indicate very good correlation planned with accordance to the periodical physicochem- ical aspects of the mining region to ascertain the growth adjacent magnesite-mining area. The study of physico- of plants. This will ensure an eco-friendly environment in chemical parameters of soil samples reveals that the the mining region and thus strengthening the adjacent spread of mining region may cause hindrance in agricul- agricultural ﬁelds. Perhaps, the soil proﬁle shows that the tural activity through mines waste dump. The surface and the subsurface characteristic and qualities, GEOLOGY, ECOLOGY, AND LANDSCAPES 149 namely, depth, texture, structure, conditions of soil References moisture, and poor drainage relationship, will directly Anbazhagan, S., & Paramasivam, C. R. (2016). Statistical aﬀect the growth of the plants. correlation between Land Surface Temperature (LST) The geostatistical analysis methods like correlation and Vegetation Index (NDVI) using Multi-Temporal matrix show the magnitude of variability changes in Landsat TM data. International Journal of Advanced Earth Sciences and Engineering (IJAEE), 5(1), 333–346. the soil’s physicochemical parameters with maximum Andrew,T.N.,Hicks,L.C.,Adan,J.Q. C.,Norma, S., signiﬁcance in soils. The correlation of EC with P, Erland, B.,&Patrick,M.(2018). Nutrient limitations to N with Cu, P with EC, K with Cu, Fe with P, Zn with bacterial and fungal growth during cellulose decomposition N, Cu with Fe, and Mn with N, respectively, showed in tropical forest soils. Biololgy and Fertility of Soils, 54, their associability characters of soil parameters. 219–228. Eﬀective treatment combined with monitoring is APHA. (1999). Standard methods for the examination of water and wastewater (19th ed., p. 1–541) Washington: required to meet the portability of soils in and around American Public Health Association. magnesite-mining regions. These steps are contribu- Arvind Kumar Rai, Biswajit Paul, & Singh, G. (2011). ted to ﬁnd variation in the pattern of nutrient dis- A study on physicochemical properties of overburden tribution in the soils of the study region. dump materials from selected coal mining areas of Jharia The dendrogram of soils (R mode and Q mode) is coalﬁelds, Jharkhand, India. 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Geology Ecology and Landscapes – Taylor & Francis
Published: Apr 2, 2020
Keywords: Soil quality; magnesite mine; physicochemical; cluster analysis; correlation
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