Geo-environmental quality assessment in Jharia coalfield, India, using multivariate statistics and geographic information system

Abstract

A study on geo-environmental quality assessment in Jharia coalfield, India, has been attempted using multivariate statistical analysis and geographic information system (GIS) modelling techniques. Water quality index, calculated for each sample network station in the study area to assess the suitability of water for human consumption, revealed very poor to poor quality surface water and mine water. Air quality indexing indicated that there is no sample station with clean air as per the Indian standards, which indicate the hazardous air quality. Multi-criteria evaluation (MCE), a potential GIS tool, has been applied to the delineation of various degrees of stressed villages in terms of quality of life (QoL). The role of various geo-environmental parameters such as quality of groundwater, surface water, mine water and air together with village population densities has been emphasized for delineation of the environmentally stressed villages in Jharia coalfield. The integrated cluster analysis and MCE approach provide an improved means to geo-environmental quality assessment in Jharia coalfield in terms of QoL. The assessment study is aimed to be used for future coal mining, ensuring ecologically sustainable industrial development, particularly in a coalfield.

Introduction

The issues confronting today are faced with achieving desired development for economic or social reasons on one hand and safeguarding the environment and maintaining good quality of life (QoL) on the other. While emphasizing the need for developmental activities, the assimilative capacities of the geo-environmental components, i.e. air, water and land, to various types of pollution and environmental degradation are considered but with inadequate attention. The developmental activities, if haphazard and uncontrolled, lead to overuse, congestion, incompatible land use and poor living conditions. The problems of environmental pollution due to mining are becoming more and more complex (Karunakaram 1982). In Jharia coalfield, owing to unplanned mining history for several years prior to 1970 and urban sprawl and haphazard human settlement, there has been environmental degradation resulting in the deterioration of the QoL.

In the present paper, an attempt has been made to assess the QoL in reference to geo-environmental degradation due to mining in Jharia coalfield. The assessment has been made by identifying the environmentally affected areas of varying degrees, employing univariate and multivariate statistics, quality indexing and GIS overlay analysis. The study also aims to demonstrate the applicability and usefulness of GIS in geo-environmental assessment and its appropriateness for delineation of environmentally affected zones in Jharia coalfield while ensuring ecologically sustainable development.

Jharia coalfield: the study area

Jharia coalfield, located in Dhanbad district of Jharkhand state, is one of the single largest coalfields in India that has been actively associated with coal mining activities for more than a century. The study area lies in the heart of Damodar valley along the north of Damodar river. The coalfield is named after the chief mining centre, Jharia, situated in the eastern part of the coalfield. The coal basin extends for about 38 km in the east–west direction and a maximum of 18 km in the north–south direction covering an area of about 450 km². In view of large population and unhygienic condition around Jharia coal belt, active population of the Jharia coalfield faces acute shortage of clean drinking water and fresh air. The coalfield covers a semi-arid tract. Some parts of the coalfield experience drought so much so that the habitats use mine-discharged water as potable water. It is in this context that an attempt has been made by the present authors to delineate areas stressed with degraded environment, caused by coal mining activities in the coalfield.

The coalfield falls within the Survey of India topographic map numbers 73 I/1, I/2, I/5 and I/6 and is bounded within latitudes 23° 39′N and 23° 48′N and longitudes 86° 11′E and 86° 27′E covering an area of 450 km². The location map of the coalfield is shown in Fig. 1.

Fig. 1

Location map of Jharia coalfield—the study area

A brief geological description of the study area

In the study area, Precambrian basement metamorphic rocks are overlain by Talchir Formation and is followed upward by Barakar Formation which is the main coal-bearing horizon. This in turn is overlain by Barren Measures and followed upward by Raniganj Formation, which is the second coal-bearing horizon in the coalfield. A generalized regional chrono-stratigraphic succession of Jharia coalfield (Chandra 1992) is given in Table 1.

Table 1 Generalized stratigraphic succession of Jharia coalfield (after Chandra 1992)

Data collection

In the present study, a part of the groundwater data has been collected through various field visits undertaken by the authors to the collieries and nearby localities of Jharia coalfield while remaining data have been collected from various sources. The analysis data of geochemical constituents in respect of groundwater, surface water and population density of Jharia coalfield have been collected from the Department of Environmental Engineering, Bharat Coking Coal Ltd, Koyla Nagar, Dhanbad. The surface water samples at sampling points 14, 20, 21, 22, 28, 32A, 38, 68, 93, 138 and 144 are collected from rivers and the rest are from local ponds. The analysis data of constituents of mine water and air of Jharia coalfield were collected from the Center of Mining Environment, Indian School of Mines. Samples of mine water were collected from areas of coal production in the mines where water gets accumulated and eventually pumped out to the surface.

Integrated approach to geo-environmental quality assessment

The methodology adopted in the present study integrates multivariate statistics, quality indexing and GIS-based overlay analysis of thematic maps. Cluster analysis (a multivariate statistical tool), also called segmentation analysis (or taxonomy analysis), aims to identify homogeneous subgroups in a population. In other words, cluster analysis identifies a set of groups which minimize within-group variation and maximize between-group variation. Cluster analysis is of two types based on the method of clustering, viz. hierarchical clustering and k-means clustering. In hierarchical clustering, a linking procedure of forming clusters is selected in terms of similarity measures, which determine how many clusters best suit the data. Hierarchical clustering is appropriate for smaller samples (typically < 250). To perform hierarchical clustering, one needs to specify the measure of similarity for generating all possible clusters. The clusters are nested rather than being mutually exclusive, i.e. larger clusters are created at later stages containing smaller clusters that are created at earlier stages of agglomeration. On the other hand, in k-means clustering, the number of clusters is specified in advance and then calculated as to how to assign cases to the k clusters. The technique is very computer-intensive and is therefore preferred sometimes when data sets are very large (> 1,000). Quality indices of water and air have been calculated at sampling points following a two-step approach: first, the calculation of quality rating for each of the quality parameters and second, a summation of these sub-indices in the overall index.

The thematic maps considered in the present study comprised that of groundwater, surface water, mine water and air quality. An integration of these maps provides a combined influence of various geochemical constituents of water and air at sample locations derived through quality indexing. Population density map when overlaid on the derived integrated water and air quality map aided in the delineation of varying degrees of affected population zones owing to environmental degradation in the coalfield due to mining. The overlay analysis has been carried out employing multi-criteria evaluation (MCE) procedure.

Multi-criteria evaluation technique primarily concerns providing an index of evaluation through combination of information using several criteria. In case of Boolean criteria (constraints), the solution usually lies in the union (logical OR) or intersection (logical AND) of conditions. However, for continuous factors, a weighted linear combination technique (Voogd 1983) is preferred. As the criteria are measured at different scales, they are standardized and transformed such that all thematic maps are positively correlated with suitability. In this technique, assigning of weights is the most difficult aspect, for which the most commonly used technique is the pair-wise comparison based on certain combination rules, viz. conjunction, disjunction and independence.

Multivariate statistical studies of water

Multivariate data can be defined as an observational unit characterized by several variables. An example of data appropriate for multivariate analysis is the chemical quality of water, which depends on factors like composition of host rock, slope of ground, movement of water, etc. Chemical characteristics of water play a vital role with respect to potable, agricultural and industrial purposes. Cluster analysis is one statistical tool to group similar pairs of correlation in a large symmetric matrix. It reduces a large data set into groups with similar characteristics. It provides logical and pair-by-pair comparison between various chemical constituents. The results of cluster analysis can be presented in a two-dimensional hierarchical diagram, on which the natural breaks between the groups become obvious. An observer can pick up groups at any desired level of similarity or dissimilarity (Miller and Kahn 1962; Parks 1966; Koch and Link 1970; Till 1974; Rao 2003).

Statistical treatment of data

To gain an understanding on the population parameters of various geochemical constituents of groundwater, surface water and mine water, the geochemical constituents have been treated for univariate statistical analyses, the results of which are provided in Tables 2, 3 and 4 for groundwater, surface water and mine water, respectively. The study revealed wide variations of values in respect of all the geochemical constituents of ground, surface and mine water except for pH. The percent variation around mean for pH of these waters is relatively low, i.e. 2.34, 2.24 and 1.91, respectively. The low value of percent variation around mean in respect of pH for the three types of water indicates consistency in the pH values of sampling points. The skewness of all three types of water is observed to be positive with a relative dominance of lower values of geochemical constituents except for pH of groundwater and mine water.

Table 2 Statistical parameters of chemical constituents of groundwater

Table 3 Statistical parameters of chemical constituents of surface water

  

Table 4 Statistical parameters of chemical constituents of mine water

Cluster analysis has been carried out to substantiate the geo-interpretation of hydrogeochemical data. Cluster analysis has been useful in studying the similar pair groups of chemical constituents of water. The values of chemical constituents were subjected to hierarchical cluster analysis. Based on the indices of correlation coefficients, similar pair groups of chemical constituents have been linked and then the next most similar pair groups and so on, until all the chemical constituents have been clustered in a dendrogram by averaging method (Davis 1973; 1986).

Groundwater

A 16  ×  16 matrix of correlation coefficients was computed to perform cluster analysis (Table 5). Correlation matrices of various stages of clustering were then obtained. In the first step of the cluster analysis, mutually highest correlation coefficients were identified from the initial linear correlation matrix. Next, similar highest correlation coefficients of chemical constituents in respect of pair groups such as Mg²⁺–total hardness (TH) and Fe²⁺–Zn²⁺ were clustered. The new correlation coefficients between Mg²⁺–TH and Fe²⁺–Zn²⁺ clusters and independent constituents were recalculated by averaging method as given by Davis (1973, 1986). Remaining correlation coefficients of individual constituents were retained. Cu²⁺ and Fe²⁺–Zn²⁺ were then clustered considering Fe²⁺–Zn²⁺ as a paired group. In this step, Na⁺–Ca²⁺ were clustered. New correlation coefficients among Mg²⁺–TH, Na⁺–Ca²⁺ and Fe²⁺–Zn²⁺–Cu²⁺ clusters and independent constituents were recalculated. In the subsequent step, electrical conductivity (EC)–Mg²⁺–TH and Cu²⁺–Fe²⁺–Zn²⁺–CO ²⁻₃ were clustered. Next, TDS was clustered with Cu²⁺–Fe²⁺–Zn²⁺–CO ²⁻₃ , and Na⁺–Ca²⁺ with Mg²⁺–TH. Finally, the two clusters, viz. TDS–Cu²⁺–Fe²⁺–Zn²⁺–CO ²⁻₃ –NO ⁻₃ –HCO ⁻₃ and pH–EC–TH–Mg²⁺–Na⁺–Ca²⁺–K⁺–Cl⁻–SO ²⁻₄ were linked (Table 6). The resulting dendrogram is displayed in Fig. 2. The analysis indicates two broad types of groundwater, viz.

$$\begin{aligned} \hbox{Type I}:&\quad \hbox{Mg--TH--EC--Ca}^{2+}\hbox{--Na}^{+}\hbox{--K}^{+}\hbox{--Cl}^{-}\hbox{--pH--SO}_{4}^{2-}.\\ \hbox{Type II}:&\quad \hbox{Cu}^{2+}\hbox{--Fe}^{2+}\hbox{--Zn}^{2+} \hbox{--CO}_{3}^{-}\hbox{--TDS--NO}_{3}^{-}\hbox{--HCO}_{3}^{-}. \end{aligned}$$

Table 5 Correlation matrix of first stage for groundwater

Table 6 Correlation matrix of final stage for groundwater

Fig. 2

Dendrogram for cluster analysis of groundwater

Surface water

An 11  ×  11 matrix of correlation coefficients has been computed to perform cluster analysis of surface water (Table 7). Correlation matrices of various stages of clustering were then obtained. In the first step, as in the case of groundwater, mutually highest correlation coefficients were identified from the initial linear correlation matrix. Next, similar highest correlation coefficients of chemical constituents in respect of pair groups such as Mg²⁺–pH and Na⁺–K⁺ were clustered. The new correlation coefficients between Mg²⁺–pH and Na⁺–K⁺ clusters and independent constituents were recalculated by averaging method. Remaining correlation coefficients of individual constituents were retained. Cl⁻ and Na⁺–K⁺ were then clustered considering Na⁺–K⁺ as a combined element. In this step, EC–SO ²⁻₄ were clustered. New correlation coefficients among Mg²⁺–pH, EC–SO ²⁻₄ and Na⁺–K⁺–Cl⁻ clusters and independent constituents were recalculated. Finally, the two clusters, viz. TDS–pH–Mg²⁺–EC–SO ²⁻₄ –Na⁺–K⁺–Cl⁻–Ca²⁺ and Fe²⁺–Mn²⁺ were linked (Table 8). The resulting dendrogram for surface water is shown in Fig. 3. The cluster analysis indicates two broad types of surface water in the study area, viz.

$$\begin{aligned} \hbox{Type I}:&\quad \hbox{TDS--pH--Mg}^{2+}\hbox{--EC--SO}_{4}^{2-}\hbox{--Na}^{+}\hbox{--K}^{+}\hbox{--Cl}^{-}\hbox{--Ca}^{2+}\;\hbox{or EC type water}.\\ \hbox{Type II}:&\quad \hbox{Fe}^{2+}\hbox{--Mn}^{2+}\;\hbox{or iron type water}. \end{aligned}$$

The type I water includes two subtypes of water, viz.

$$\begin{aligned} \hbox{Subtype I}:& \quad\hbox{EC--SO}_{4}^{2-}\hbox{--Na}^{+}\hbox{--K}^{+}\hbox{--Cl}^{-}. \\ \hbox{Subtype II}:& \quad\hbox{pH--Mg}^{2+}. \end{aligned}$$

Table 7 Correlation matrix of first stage for surface water

Table 8 Correlation matrix of final stage for surface water

Fig. 3

Dendrogram for cluster analysis of surface water

Mine water

To perform cluster analysis of mine water, a 13  ×  13 matrix of correlation coefficients was computed (Table 9). Correlation matrices of various stages of clustering were then generated. In the first step, as in the cases of groundwater and surface water, mutually highest correlation coefficients were identified from the initial linear correlation matrix. Next, similar highest correlation coefficients of chemical constituents in respect of pair groups of TH–SO ²⁻₄ , Mn²⁺–NO ⁻₃ and Zn²⁺–Na+ were clustered. The new correlation coefficients between TH–SO ²⁻₄ , Mn²⁺–NO ⁻₃ and Zn⁺–Na⁺ clusters and independent constituents were recalculated. Remaining correlation coefficients of individual constituents were retained. TDS and TH–SO ²⁻₄ were then clustered considering TH–SO ²⁻₄ as a paired group. Finally, the two clusters, viz. pH–Cl⁻–TDS–TH–SO ²⁻₄ –Fe²⁺–Zn²⁺–Na⁺–K⁺ and EC–Cu²⁺–Mn–NO ⁻₃ were linked (Table 10). The resulting dendrogram is shown in Fig. 4. The cluster analysis for mine water also indicates two broad types of mine water, viz.

$$\begin{aligned} \hbox{Type I}:& \quad\hbox{pH--Cl}^{-}\hbox{--TDS--TH--SO}_{4}^{2-}\hbox{--Fe}^{2+}\hbox{--Zn}^{2+}\hbox{--Na}^{+}\hbox{--K}^{+}. \\ \hbox{Type II}:& \quad\hbox{EC--Cu}^{2+}\hbox{--Mn}^{2+}\hbox{--NO}_{3}^{-}. \end{aligned}$$

Table 9 Correlation matrix of first stage for mine water

Table 10 Correlation matrix of final stage for mine water

Fig. 4

Dendrogram for cluster analysis of mine water

Water quality studies

Coal mining requires large amount of water for various purposes including dust control, fire protection and coal washing. The average use of water in coal mining varies from 63 to 120 l per metric tonne in underground mining and about 17 l per tonne for surface mining (Hinawi 1981). In addition to this, 33 l of water per tonne is required for waste disposal both in surface and underground mining. When this water drains through a large area of the mine, it carries with it soluble minerals that may be present either in the coal or associated rocks, thus causing severe degradation of water quality. Coal processing also causes serious water pollution. The black water produced through coal washing is a potential pollutant if it is discharged into streams without treatment (Krishnamurthy 2004; Chadwick et al. 1987).

Water pollution can be controlled if adequate care is taken to ensure that the discharged water from the mines and associated industries does not carry chemical compounds. Chemical analysis provides a better understanding of the chemical aspect of water. This can also permit planning for necessary treatment that may be required to cope up with future changes in the quality of water.

Water quality index

Water quality and its suitability for drinking purpose can be examined by determining its quality index. Water quality index (WQI) is defined as a technique of rating that provides the composite influence of individual water quality parameter on the overall quality of water. It is calculated from the point of view of human consumption. The standards for drinking purpose as recommended by ISI (Rao 1997) have been considered for calculation of WQI. The weights for various water quality parameters are assumed to be inversely proportional to the recommended standards (Table 11) for the corresponding parameters (Rao 1997; Mishra and Patel 2001; Naik and Purohit 2001; Rao et al. 2002; Mahanta et al. 2004). The formulation for weight calculation is given by the expression:

$$ {w}_{i}= {k}/{s}_{i}, $$

where w _i is the unit weight for the ith parameter; s _i the recommended standard for parameter and i  =  1, 2, 3, ..., 16; and k the constant of proportionality (Kumar 2002).

Table 11 Chemical parameters with corresponding Indian standards (for drinking purpose)

The calculation involves the following steps:

• (a) First, the calculation of the quality rating for each of the water quality parameters

• (b) Second, a summation of these sub-indices in the overall index

Individual quality rating is given by the expression:

$$ {q}_{i} = 100{v}_{i}/{s}_{i}, $$

where v _i is the measured value of the ith parameter in groundwater sample under consideration and s _i the standard or permissible limit for the ith parameter.

The WQI is then calculated as follows:

$$ \hbox{WQI} = {\sum\limits_{i = 1}^n {(q_{i} } }w_{i})/{\sum\limits_{i = 1}^n {w_{i} } }. $$

Indian standards as per ISI for drinking water together with its corresponding status categories of WQI (Rao 1997) are given in Tables 11 and 12, respectively. The sample network stations for groundwater, surface water and mine water are shown in Figs. 5, 6 and 7. Water quality indices calculated individually for sampling points in respect of groundwater, surface water and mine water along with water status are given in Tables 13, 14 and 15.

Table 12 Status categories of WQI

Fig. 5

Geological map of Jharia coalfield with groundwater sample network stations

Fig. 6

Geological map of Jharia coalfield with surface water sample network stations

Fig. 7

Geological map of Jharia coalfield with mine water sample network stations

Table 13 Calculated water quality index (WQI) for individual groundwater sampling station

Table 14 Calculated water quality index (WQI) for individual surface water samples

Table 15 Calculated water quality index (WQI) for individual mine water samples

Air quality studies

Air pollution may be defined as the presence of one or more contaminants or combination in the outdoor atmosphere in such quantities and of duration which may be or tend to be injurious to human beings or other living creatures or plants or to the atmosphere itself.

Air pollution in mining area is very typical. There are many sources of air pollution in mining complexes, viz. open cast mines, underground mines, thermal power plants, washery, diesel engines, light and heavy vehicles. Main pollutants released due to mining are mostly gaseous or particulate. Particulate are non-gaseous substances consisting of dust, other solid vapour bubbles and liquid floating in the air that almost act as gaseous molecules in many respects.

The effects of air pollutants on the environment depends on the chemical and physical properties of pollutants as well as the dilution and dispersion capabilities of the prevalent ambient meteorological condition in the area. Pollution dilution in the atmosphere may be considered as a function of their density, atmospheric stability conditions and wind speed (Frangi 1996).

Air pollutants can be classified into two broad groups—primary and secondary air pollutants.

Primary pollutants

These are the pollutants emitted into the atmosphere directly from identifiable sources and are found in the atmosphere in the same chemical form as that at the time of emission from the source. Finer particles (less than 100 μm in diameter), coarse particles (greater than 100 μm in diameter), sulphur compounds, oxides of nitrogen, carbon monoxide, halogen compounds, organic and radioactive compounds are some of the primary pollutants.

Secondary pollutants

Secondary pollutants are produced in the air by the interaction among two or more primary pollutants or by the reaction with normal atmospheric constituents, with or without photoactivation. Ozone, formaldehyde, photochemical smog are some of the secondary pollutants.

In the air sampling procedure, prevailing wind direction has been taken care of. Because the present study aims at emphasizing an integrated approach to the application of multivariate statistics and GIS for geo-environmental quality assessment in Jharia coalfield, combined impacts of water and air pollution on QoL in the coalfield due to mining have been thought appropriate to aid in the delineation of environmentally stressed areas despite less number of air sampling stations.

Air quality index

The air quality standards for different pollutants differ widely from country to country. There is a necessity to describe the air quality based on the cumulative effect of all the pollutants, as the synergistic effect of all the pollutants are more severe than the effect of the individual pollutant. Based on this assumption, seven categories have been identified to describe the ambient air quality index (AQI). The National Ambient Air Quality Standards and the classification of AQI (Gogoi 1999) are shown in Tables 16 and 17.

Table 16 National Ambient Air Quality Standards

Table 17 Classification of air quality index (ISI 1975)

In arriving at AQI of an area, an important assumption that all the pollutant parameters are of equal importance is made. If there are ‘n’ air quality parameters, P _i  =  (1, 2, 3, ..., n), the air quality rating (Q _i ) is given by the following expression:

$$ {Q}_{i} = ({V}_{i} \times 100)/V_{\rm si}, $$

where V _i is the observed value of parameter Q _i and V _si the standard value recommended for the parameter. If ‘n’ is the number of pollutant parameters considered for air quality, then n numbers of quality ratings are obtained for the observed values. The geometric mean of these individual air quality ratings is designated as AQI for that particular station.

In the present study, individual quality ratings (Q _i ) of all the air pollutant parameters in respect of 13 sample stations have been calculated using the above-mentioned procedure. Based on these individual quality rating values, air quality indexing of the network stations has been carried out that resulted in categorization and determination of air status of all stations as per National Ambient Air Quality Standards. The result of air quality indexing of Jharia coalfield is given in Table 18. The sample network stations for air are shown in Fig. 8.

Table 18 Calculated air quality index (AQI) for individual air sampling locations

Fig. 8

Geological map of Jharia coalfield with air sample network stations

Geo-environmental assessment using GIS

The GIS has functional capabilities of data capture, input, manipulation, transformation, visualization, combination, query, analysis, modelling and output. In the present study, various thematic maps, namely, groundwater quality, surface water quality, mine water quality and air quality have been generated. Population density map of the study area has been generated from the census report of Dhanbad district. These maps have been digitized, edited, topology built and polygonized in SPANS Topographer software and subsequently thematic information layers have been generated for GIS overlay analysis.

Various thematic information layers that have been generated in GIS using various data input include:

• 1. Groundwater quality map

• 2. Surface water quality map

• 3. Mine water quality map

• 4. Air quality map

• 5. Population density map

In the present paper, GIS analyses have been carried out using SPANS software with kriging as an interpolation technique. The underlying mathematical concept of geostatistical interpolation technique known by the name ‘kriging’ is based on ‘theory of regionalized variable’ (ReV) developed by Matheron in 1960. According to this theory, a variable distributed in space and/or time exhibiting some degree of spatial correlation can be considered a regionalized variable. Thus, geochemical constituents of water and air considered in the present study for quality indexing and delineation of environmentally impacted zones are distributed in space and exhibit spatial correlation. Because of this, geostatistical kriging technique is considered to be the appropriate interpolation technique for providing best, linear and unbiased interpolated values at unsampled grids with minimum variance. As mining of coal mostly concentrated in the northern periphery of Jharia coalfield with seam occurring in Barakar Formation, most sample network stations are distributed within this limit. The interpolation was constrained in the coalfield with sample network stations located at irregular intervals. However, it may be mentioned that interpolation results may be further refined with addition of more sample networks covering the entire coalfield in a regular grid interval.

Groundwater quality map

The groundwater quality map of the study area represents the influence area delineation of various status categories of groundwater, viz. very poor, poor, good and very good. The status categories are standardized with the numerical values 1, 2, 3 and 4, respectively (Sarkar 2003). This layer is generated by plotting the network stations of groundwater with the value assigned for the status and digitized as a point layer and rasterized in the SPANS Topographer software. This function generates output in the quadtree data format. The map generated indicates very poor status of groundwater in the northern part of the coalfield and very good status of groundwater in the southern part (Fig. 9).

Fig. 9

Groundwater quality map of Jharia coalfield

Surface water quality map

This map delineates areas with four status categories of surface water such as very poor, poor, good and very good. These categories of water have also been assigned a value of 1, 2, 3 and 4, respectively. The sample network stations with the status value are digitized as a point layer and rasterized in the SPANS Topographer software. The surface water quality map indicates very poor surface water over the entire study area whereas a few areas in the northeastern part are with very good surface water (Fig. 10).

Fig. 10

Surface water quality map of Jharia coalfield

Mine water quality map

The mine water quality map delineates areas with four status categories of mine water. The sample network stations were digitized as a point layer with the status value ranging from 1 to 4 and were rasterized in the SPANS Topographer software. Most of the mine water in the southern part is of poor quality whereas a few areas in the northeastern part of the coalfield are of very poor quality. There are some pockets of very good mine water in the northern part while remaining areas are with good mine water quality (Fig. 11).

Fig. 11

Mine water quality map of Jharia coalfield

Air quality map

This map represents the air quality variation throughout the coalfield. The status categories of air such as severely polluted, highly polluted and polluted were given a value of 1, 2, and 3, respectively, as there is no location with clean air in the study area. The sample network stations with these status values were digitized as point layer rasterized in the SPANS Topographer software. This map suggests severely polluted air in the northern part, polluted air in the southern part and heavily polluted air in the southeastern part of the coalfield (Fig. 12).

Fig. 12

Air quality map of Jharia coalfield

Population density map

The population density map of the study area represents the delineation of villages with various categories of population density, viz. very low, low, moderate, high and very high. The population density ranges with density class are shown in Table 19. The village boundaries with respective population density values are digitized as polygon layer and rasterized in the SPANS Topographer software. This density map suggests that the villages Godhur, Bhatdih, Kenduadih and Amlabad are with very high population density, village Loyabad with moderate density, village Putki with low density and remaining villages with very low population density (Fig. 13).

Table 19 Population density classification

Fig. 13

Population density map of Jharia coalfield

Overlay analysis

In the present study, the choice among a set of environmental impact zones is based upon multiple criteria such as groundwater quality, surface water quality, mine water quality, air quality and population density of villages. This process is commonly known as MCE (Sarkar et al. 2001). For multi-criteria modelling, first, identifying the quadtrees used in the analysis creates a template. Then, a matrix is created using the parameters to be overlaid. If a base map has been assigned to the study area, this function will confine the analysis to the data falling within the basemap.

Overlay analysis of water quality

The water quality of the study area is influenced by groundwater, surface water and mine water. To represent the overall water quality of the area the three types of water, viz. ground water, surface water and mine water are overlaid using the multi-criteria modelling. First, a matrix has been generated for the suitability mapping of water quality using the status category values as shown in Table 20.

Table 20 Status category for overlay analysis of water quality

The water quality map suggests very good water in the northeastern part, very poor water in a few areas in the northern part and eastern part, good water in the central part and poor water in the southern part of the study area (Fig. 14).

Fig. 14

Overall water quality map of Jharia coalfield

Overlay analysis of water and air qualities

This analysis has been carried out to delineate areas influenced by integrated effect of air quality and water quality. For this analysis, a matrix has been created using the air quality and water quality status categories (Table 21).

Table 21 Status categories for overlay analysis of water and air qualities

As there is no sample location with clean air, the very good status category is not taken into consideration. This analysis between air and water qualities suggests that most of the areas are influenced by integrated poor air and water qualities. In the south central part, a few areas are with good environment and a few areas with very poor quality of environment in terms of air and water qualities (Fig. 15).

Fig. 15

Combined water and air quality map of Jharia coalfield

Environmental impact zonation

Environmental impact zonation map delineates areas into different environmental impact zones such as high impact, moderate impact and low impact zones. A matrix has been generated for this analysis using the parameters of air quality, water quality and population density (Table 22). Air quality indices of all individual sampling stations yielded either ‘polluted’, ‘severely polluted’ or ‘heavily polluted’ status class. The influence zones of the air sampling points when overlaid with water quality indices yielded no status category as ‘very good’.

Table 22 Status categories for environmental impact zonation

Population density has also been taken as a parameter because the population of the area also has impacts on the total geo-environment. This final output in terms of environmental impact zonation map suggests that most of the areas of the coalfield fall in the moderate impact zone. Some of the villages in the southern and south central part of the area fall in the high impact zone and a few villages in the central part of the coalfield fall in the low impact zone (Fig. 16).

Fig. 16

Environmental impact zonation map of Jharia coalfield

Results and discussions

Unplanned spurt in human activities due to mining affect the geo-environment of the mining areas. While carrying out developmental activities, the assimilative capacities of the geo-environmental components, i.e. air, water and land, to pollution should also be paid due weightage. A geo-environmental development program needs a large volume of multidisciplinary data from various sources. In Jharia coalfield, unplanned mining history prior to 1970 and urban sprawl resulted in damage to the quality of water and air (Ghosh and Ghosh 1991). In the present study, geo-environmental components, i.e. water and air qualities, of Jharia coalfield have been evaluated and assessed. Multivariate statistical studies have been carried out for groundwater, surface water and mine water of the area. In the case of groundwater, basically two main interrelated types of chemical constituents are responsible for the hydrogeochemical variability in the study area. Based on the dendrogram, the study area has been inferred to constitute two types of groundwater. Cluster analysis for surface water of the study area indicates two broad types of surface water, viz. EC type and iron type. The EC type water includes two subtypes. The dendrogram obtained from cluster analysis reveals two distinct types of mine water in the study area.

While WQI calculated for groundwater revealed good to very good quality index, that calculated for suitability of surface water and mine water for human consumption indicates poor to very poor status of water for drinking purpose. Air quality index is calculated to classify the air quality of the study area into different status categories, viz. very clean, clean, fairly clean, moderately polluted, polluted, heavily polluted and severely polluted. There is no sample station with clean air quality, which indicates the hazardous degradation of air environment in the study area.

The thematic information layers generated in GIS provide spatial distribution of various status categories of water and air in the study area. To generate the water quality map, first the groundwater quality, surface water quality and mine water quality maps are generated and overlaid using MCE. The water quality map suggests that in most areas the water is of poor quality. Few areas in the northeastern part of the coalfield are having very good quality water whereas the central part of the coalfield has good quality water. In the eastern and northwestern part of the coalfield, there are a few areas with very poor quality water.

The air quality map suggests that the southern part of the coalfield is covered by polluted air, whereas the air in the northern part of the coalfield is severely polluted. Air in the southeastern part of the coalfield is heavily polluted. Matrix analysis between air and water qualities has been carried out to delineate areas with combined influence of various status categories of water and air qualities. This analysis shows that most of the areas in the coalfield are in poor condition if the two variables, air and water, are considered together. A few areas in the central Jharia coalfield are of good condition while some parts in northwestern and eastern part are of very poor condition.

Identification of highly stressed villages in terms of QoL as per the evaluation of geo-environmental quality assessment plan has been carried out using the MCE. Integrated air and water quality has been overlaid on population density map of the coalfield to delineate villages with high impact, moderate impact or low impact of environmental degradation. Villages namely Loyabad, Godhur, Kenduadih, Amlabad and Bhatdih have been determined to be highly stressed, villages namely Jirampur, Ghanuadih, Kendidih, Fatepur, Bharakuta, Bagdigi, Chotakakirabad, Aralgoria, Putki, Sealgudri, Sohnidih, Tundi, Gobindpur are low stressed while remaining villages are moderately stressed.

Maximum areal extent of the coalfield thus falls in the moderate environmental impact zone, which indicates the degradation of geo-environment in the study area. To maintain the ecologically sustainable industrial development, an attempt has to be made to take suitable measures to overcome hazards associated with appropriate development of the area.