Accepted Manuscript Application of geospatial modeling technique in delineation of fluoride contamination zones within Dwarka Basin, Birbhum, India Raju Thapa, Srimanta Gupta, D.V. Reddy PII:
S1674-9871(16)30203-1
DOI:
10.1016/j.gsf.2016.11.006
Reference:
GSF 511
To appear in:
Geoscience Frontiers
Received Date: 31 May 2016 Revised Date:
25 October 2016
Accepted Date: 4 November 2016
Please cite this article as: Thapa, R., Gupta, S., Reddy, D.V., Application of geospatial modeling technique in delineation of fluoride contamination zones within Dwarka Basin, Birbhum, India, Geoscience Frontiers (2016), doi: 10.1016/j.gsf.2016.11.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Application of geospatial modeling technique in delineation of fluoride contamination zones within Dwarka Basin, Birbhum, India Raju Thapa a, Srimanta Gupta a,*, D.V. Reddy b Department of Environmental Science, The University of Burdwan, Burdwan 713104, West
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a
Bengal, India b
CSIR–National Geophysical Research Institute, Hyderabad 500007, Telangana, India
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* Corresponding author: Email address:
[email protected] (Srimanta Gupta)
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Abstract
Dwarka River Basin is one of the fluoride affected river basin in Birbhum, West Bengal. In the present research work, various controlling factors for fluoride contamination in groundwater i.e., geology, aquifer type, groundwater table, soil, rainfall, geomorphology, drainage density, land use land cover, lineament and fault density, slope and elevation were considered to delineate the
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potential fluoride contamination zones within Dwarka River Basin in Birbhum. Assigning
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weights and ranks to various inputs factor class and their sub-class respectively was carried out on the basis of knowledge driven method. Weighted overlay analysis was carried out to generate the final potential fluoride contamination zones which are classified into two broad classes i.e.,
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‘high’ and ‘low’ and it is observed that major portion of the study area falls under low fluoride contamination category encompassing 88.61% of the total area which accounts for 759.48 km2
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and high fluoride contaminated region accounts for 11.40 % of the total study area encompassing an area of about 97.67 km2. Majority of high fluoride areas fall along the flood plain of Dwarka River Basin. Finally, for validation 197 reported points within Dwarka having fluoride in underground water are overlaid and an overall accuracy of 92.15% is observed. An accuracy of 83.21% and 84.24% is obtained for success and prediction rate curve respectively.
Keywords: Fluoride; Groundwater; Weighted Overlay Method; Potential fluoride contamination zone; Dwarka basin; India.
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1. Introduction Fluoride in groundwater is basically results from fluoride bearing minerals such as fluorite (CaF2), fluor-apatite (Ca5(PO4)3F), cryolite (Na3AlF6) etc are found in granite, pegmatite and and fluor-apatite are as follows: CaF2 + 2NaHCO3 = CaCO3 + 2 Na+ + 2F- + H2O + CO2
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Ca5(PO4)3F + 3H+ = 5 Ca2+ + 3HPO42- + F-
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granite gneisses (Deshmukh et al., 1995; Rao, 2009). Major reactions of dissolution of fluorite
Anthroprogenic sources such as agricultural application of pesticides and fertilizer, septic tank
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seepage, coal burning as well as industries can contribute fluoride in groundwater through leaching (Smith and Hodge, 1979; Deshmukh et al., 1995; Smedley et al., 2002, Jordan and Smith, 2005; Edmunds and Smedley, 2012).
In India, more than 62 million people with approximately 2.5–6 million of predominately children consume fluoride contaminated water and suffer from fluorosis (Athavale, 1999; Subba Rao, 2008; Raju et al., 2009). More than 50 thousand population in seven blocks namely Nalhati-
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I, Suri-II, Saithia, Khoyrasol, Mayureswer-I, Rampurhat-I, and Rajnagar of Birbhum are affected
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by fluorosis due to consumption of fluoride contaminated groundwater. Geophysical modeling techniques including simulation modeling, overlay methods, statistical
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methods and other methods have wide application in understanding underground water vulnerability (Tesoriero et al., 1998), delineating potential groundwater zones (Srivastav and Bhattacharya 2000; Khan and Moharana, 2002; Sankar, 2002; Magesh et al., 2012) or estimation
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of potential artificial recharge zone (Shaban et al., 2006; Chowdhury et al., 2010), but comparatively very few research work are conducted in the field for fluoride hazard zonation mapping. Implementation of various surface interpolation techniques such as inverse distance interpolation, kriging, countering etc. for extrapolation of fluoride contamination zone is very commonly employed but such interpolations technique are unable to produce reliable probable fluoride contamination zonation due to abrupt spatial variation of fluoride contamination over a limited area. So, in the present research work geological modeling of fluoride hazard zonation is carried out by incorporating various controlling factors such as geology, geomorphology, aquifer
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composition, water table, soil type, rainfall, land use /land cover (LULC), drainage density, slope, elevation, fault and lineaments density by assigning different weights on the basis of their relative importance in a spatial domain. Validation of the resultant output is carried out with the
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reported fluoride contaminated groundwater locations in the study area.
2. Study area
Dwarka River is a west to east flowing river in Birbhum district of West Bengal. Dwarka River
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Basin encompasses areas in Dumka in Jharkhand, Birbhum and Murshidabad in West Bengal. Within Birbhum the river basin lies between N 23°58’12.115’’ to N 24°14’14.99” in latitude and
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E 87°28’39.46” to E 87°56’24.98” longitude encompassing a total basin area of about 858 km2. It borders Jharkhand state in the western side and Murshidabad District of West Bengal in the eastern side. In north and south direction it is landlocked by different blocks of Birbhum district (Fig. 1). 2.1 Hydrological framework
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Dwarka River Basin is mainly dominated by Archean hard rocks, middle to upper Jurassic Rajmahal traps, sub-recent laterites and recent to sub-recent alluvium and Gondwana
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sedimentaries. In the western region Jurassic hard massive rock of Rajmahal trap (basaltic) are exposed. In south–western region Archean rocks consisting of granite-gneiss with enclave of
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metamorphites, amphibolite and hornblende schist are exposed. Pegmatite and Gondwana supergroup consisting of Raniganj and Barakar formation are also present as patches. Almost entire eastern region consists of older alluvium and hard clay. Recent alluvium is mainly
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confined along the course of Dwarka River (Fig. 3a). In Dwarka basin groundwater occurs under both water table condition and confined condition in the near-surface aquifer and deeper aquifer respectively. The main repository of groundwater is formed from the weathered zone of underlaid hard rock. In western part of district bed rocks occurs closer to the surface with depth varying from 20 to 80 meter below ground level (m b.g.l.) whereas in the eastern part the bed rocks are very deeper which are overlaid by huge thickness of alluvial sediments. Major portion of the alluvium covered areas form uplands. The gradient of
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water table varies from 5–6 m/km in the east, to 16.5-25 m/km in the central part, to 12.5 m/km in the west. In eastern boundaries of the study area higher groundwater potentialities prevails. Pedologically, the study area shows red sandy and red loamy soil type in western part which is
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followed in east by laterites covering entire central and eastern regions. From climatic condition point of view sub-humid climate prevails in the study area with rainfall varying from 110 mm to 1400 mm with majority of rainfall in the monsoon season (about 80%). 3. Materials and methods
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Basin boundary was demarcated with the help of Survey of India toposheet (No. 72P08, 72P12, 72P16, 73M05, &3M09 and 73M13) and ArcGIS 10.1 software. Information such as existing
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spatial map along with remote sensing data was gathered from different government department and government official websites. Published map such as land use land cover, rainfall, soil type, and slope were collected from NATMO district planning map series 2004, Geology and geomorphology from District resource map (Survey of India), fault and lineament map from Bhuvan official portal (http://bhuvan.nrsc.gov.in) of National Remote Sensing Centre (NRSC) and hydrogeological map from CGWB report, 1985. Elevation map and drainage density map
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were prepared from DEM data collected from CartoDEM with resolution of 2.5m from Bhuvan
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website. Different thematic maps were prepared using ArcGIS software, Google Earth Pro, ILWIS and Surfer 13. The methodology adopted for present study is represented in Fig. 2.
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3.1 Preparation of thematic layers
Initially eleven factors that are supposed to have influence on fluoride contamination of
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groundwater water were integrated for delineating fluoride contamination zonation mapping in the study area. In ArcGIS environment different maps such as geology, geomorphology, soil, slope, aquifer, land use land cover, water table, rainfall were extracted with polygon shapefile and latter raster map were produced from them. Elevation, drainage density and fault-lineament were extracted from CartoDEM. 3.2 Assigning weights and ranks through knowledge driven method 3.2.1 Background knowledge of different factors
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Fluoride bearing minerals such as fluorite, cryolite and fluor-apatite are commonly found in granitic rocks (Deshmukh et al., 1995, Rao, 2009) which can contaminate groundwater with fluoride through water-rock interaction. High fluoride concentration along the Dwarka River flood plain has also been reported (PHED report, 2007; Mondal et al., 2014). Under favorable
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Eh–pH condition laterite soil which is good absorbent of fluoride can release fluoride in groundwater. Adsorption is favoured by slightly acidic condition of lateritic soil. Wenzel and Blun (1992) noted that, while minimum mobility occurs at pH 6.0–6.5, it is increased at pH < 6 as a result of the formation of [AlF]2+ and [AlF]+ complexes in solution. In the lateritic soil,
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adsorption of fluoride is favoured strongly by the presence of freshly precipitated Fe(OH)3 or Al(OH)3. High pH and low Eh condition in the soil (due to long-term cultivations and application
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of organic manure) give rise to favorable condition for fluoride release in groundwater. Weathering of granitic rock with fluoride bearing minerals result in red loamy soil with high fluoride concentration. Intensive use of pesticides and phosphate fertilizers in cultivation field causes high fluoride concentration in groundwater due to leaching (Kabata-Pendias and Pendias, 1984). Sodicity of soil increases with prolonged irrigation of agricultural field which can increase the fluoride content of groundwater (Brindha et al., 2010) due to change of Ca-
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dominated groundwater to Na-dominated one. Fluoride is a component in most soil. The total concentration of fluoride varies from 20 to 500 mg/kg in regions lacking natural fluoride and
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phosphate deposits, the concentration can be several mg/g in areas bearing fluoride deposits (Kabata-Pendias and Pendias, 1984). In soil, fluoride transportation and transformation is
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influenced by pH, water hardness, presence of clays which act as excellent ion-exchange materials and presence of calcium and aluminum complexes. Physical and chemical factors
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along with favorable conditions for fluoride release are represented in Table 1. Fluoride leaches out under specific condition and does not leach out readily. Percolating water from soil surface can dissolve the fluoride content in soil and carry it to the underground water reserve. Areas with gentle slope and elevation allows more percolation of water from surface, promote higher waterrock interaction (Apambire et al., 1997; Edmunds and Smedley, 2005; Magesh et al., 2011a; Magesh et al., 2011b; Waikar and Nilawar, 2014). The probability of fluoride in groundwater is much higher along the fault and lineaments (Kundu et al., 2001; Kim and Jeong, 2005). Regions with lesser rainfall can trigger salt formation (NaCl) which later reaches to groundwater stock with the percolating water (Jacks et al., 2005, Mondal et al., 2013) and makes Na-HCO3 type
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groundwater which is responsible for greater mobility of fluoride contamination. Higher rainfall results in dilution of groundwater with fresh rainwater (Genxu and Guodong, 2001). Hence, it will decrease fluoride concentration. Drainage density is inversely proportional to permeability of water (Magesh et al., 2012). The less permeable a rock is, the less the infiltration of rainfall,
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which conversely tends to be concentrated in surface runoff. Higher runoff eventually leads to higher dilution of fluoride. Alternatively less infiltration of rainfall may give rise to less leaching of fluoride to the groundwater system.
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3.2.2 Assigning weights and rank
Based on knowledge driven method, expert opinion subjective weights of factor classes were
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carried out. Weights and ranks were assigned on the basis of published information on sources and favorable condition for fluoride contamination of groundwater. On the basis of their relative importance or role played all the potential influential factors were assigned ranks ranging from 01 to 10 where rank 10 indicates the maximum influence and rank 01 indicates the least influence of the factor on fluoride contamination of ground water. Weights to sub-classes were assigned with reference to the rank of the concern class with maximum weights value equals to
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3.3 Weighted overlay method
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the weight of the respective class (Table 2).
Weighted overlay method has been extensively used in groundwater related studies by various
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researchers (Shaban et al., 2006; Kumar and Kumar, 2010; Mayilvaganan et al., 2011; Riad et al., 2011a; Riad et al., 2011b; Dabral et al., 2013; Sajikumar and Pulikkottil, 2013; Mitra and Acharya, 2014; Dinesan et al., 2015; Senanayake et al., 2015). After assigning appropriate ranks
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and weights to different input factors and their respective sub-classes respectively, weighted overlay method (WOM) was adopted to delineate potential fluoride contamination zone (FC) using the following Eq. 1. FC =
-------------------------------------------------------------------------------------- (1)
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Where, A represent the rank of the factor class, B represent weight of the factor sub-class, xth (x = 1, 2, 3 ………. m) represent the factor maps and yth (y = 1, 2, 3 ………. n) represent the factor class.
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4. Results and discussion 4.1 Geological, geomorphological and hydrogeological parameters
Geology plays a vital role in distribution, occurrence and quality of groundwater. Geological units release minerals in groundwater through water rock interaction. Geologically, whole study
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area is categorized into eight different sub-classes namely, laterite and lateritic soil encompassing approximately 302 km2, hard clay impregnated with caliche nodule with 298 km2,
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Granite-gneiss with enclave of metamorphites with 96 km2, Rajmahal trap (basalt) with 79 km2, alternating bands of sand, silt and clay with 73 km2, Gondwana super group with 9.35 km2, Unclassified metamorphosis with 0.8 km2 and Pegmatite with 0.4 km2 (Fig. 3a). Aquifer carries out the function of absorbing water, storage and transmission of the same. Longer residences time of groundwater in aquifer media increase the chances of contamination chances.
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Based on aquifer material the river basin is classified into four different classes i.e., Gneisses and associated rock, Older alluvium and laterite etc thick discontinuous aquifer, older alluvium fairly
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thick down to about 450 m b.g.l with regionally extensive confined or unconfined aquifers and Basalt with intericapptan clay encompassing an area of 63.49 km2, 288 km2, 481 km2 and 24.69
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km2 respectively (Fig. 3b).
The water table in the study area is expressed as metres above mean sea level (m a.s.l.) and was
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broadly classed into four categories less than 25m in eastern part of study area followed by 25– 40 m towards west, which is followed by 40–65 m, and greater than 65 m in the extreme western part of the study area (Fig. 3c). In the study area study of the soil type reveals that major portion is covered by laterite soil covering about 679 km2, followed by red loamy soil covering about 124 km2 and the rest 54 km2 area is covered by red sandy soil (Fig. 3d). The rainfall distribution in the study area was grouped into four classes. Region receiving rainfall less than 1100 mm rainfall were categorized as ‘very low’ and cover an area of 81.65 km 2, ‘low’ areas receiving 1200 to 1300 mm precipitation with an area of 208 km2, ‘medium’ class
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receiving rainfall between 1300–1400 mm over 192 km2 and high above 1400 mm encompassing an area of 374 km2 (Fig. 4a). Geomorphology plays an essential role in distribution of various landforms, structural fractures
groundwater related studies (Machiwal et al., 2011).
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and earth structures. It gives information about the underlying geology and is very important for In the study area old floodplain
encompasses an area of 88.8 km2, older alluvial fan covers 477.85 km2, Pediment–pediplain complex covers an area of 258.24 km2, anthropogenic terrain covers approximately 21 km2, 12 km2 are coved by moderately dissected hills and valleys and younger alluvial plain covers 0.02
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km2 (Fig. 4b).
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Land use land cover affects the rate of infiltration and surface runoff (Waikar and Nilawar, 2014). In the study area cultivated land encompasses major portion of the study area approximately 709 km2, rural settlement are present in patches covering an area of 71 km2, 36 km2 is covered by forest area, urban settlement is 5.38 km2, water bodies accounts for 13 km2 and uncultivable land accounts for 25 km2 (Fig. 4c).
Drainage density plays a very crucial role in groundwater availability and contamination.
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Drainage density is inversely related and Permeability (Magesh et al., 2012). Drainage density is
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reclassified into four categories i.e., very low (0–0.267 km/km2) with 56.64 km2, low (0.267– 0.44 km/km2) region covers 199 km2, medium region covers (0.44–0.61 km/km2) with 284.5
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km2 and high region (0.61–1.02 km/km2) accounts for 316 km2 of the total study area (Fig. 4d). Surface runoff and rate of infiltration is influenced by the slope of the surface. Flatter slope can facilitate higher groundwater recharge whereas steeper slope increases runoff and reduces
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infiltration. In the study area lower slope area (0°–4°) covers an area of 579 km2, medium slope (4°–10°) encompasses an area of 245 km2 and high zone (10°–39°) cover an area of 33 km2 (Fig. 5a). At lower topography storage of water tends to be more in comparison to the higher topography (Ramu, 2014). 268 km2 area falls under 42 m elevation, 176.5 km2 area falls within 42 to 62 m elevation and 62 to 118 m elevation covers 102 km2 (Fig. 5b). Faults and lineaments influence the occurrence of groundwater. Concentration of fluoride in groundwater is generally higher in high fault and lineament densities regions. High region (0.36 – 0.69 km/km2) encompasses an area of 276 km2, medium (0.18-0.36 km/km2) covers an area of
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286.79 km2, low (0.065- 0.185 km/km2) 214.68 km2 and 80 km2 area falls under very low region (0 – 0.065 km/km2) (Fig. 5c). 4.2 Potential fluoride contamination zonation map
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With the use of remote sensing and GIS techniques potential fluoride contamination zonation
FC=
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map was generated with the integration of different input maps using Weighted Overlay method.
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FC represents the potential fluoride contamination zonation map, the subscript letter ‘x’ and ‘y’ represents factor maps and factor class respectively, G is geology, A is aquifer, GW is groundwater table, S is soil, R is Rainfall, GM is geomorphology, LULC is Land Use Land Cover, DD is drainage density L is lineament, Sl is slope, E is elevation and FL is fault and lineament.
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The potential fluoride contamination zone was categorized into two classes ‘high’ and ‘low’ (Fig. 6). In the study area it is observed that the high potential contamination zones occupy an
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area of 97.67 km2 which accounts for 11.40 % of the total area whereas 88.61 % of the total area accounting for 759.48 km2 falls under low potential zone. High potential fluoride contamination
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zones are observed mainly in extreme south–western part of the study area where Archean basement rock such as granite gneiss is exposed and thereby groundwater has a greater chance of interaction with fluoride bearing minerals present in the rock type. High zones are also observed
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along the floodplain of Dwarka River and in small patches in eastern boundary of the study area. In the older flood plain of Dwarka River, presence of intercalated clay lenses within aquifer is the major source of fluoride in groundwater (Mondal et al., 2014). This zeolitic clay may be the alteration product of tuff which occurs as inter-traps during Rajmahal volcanism. The occurrences of high levels of fluoride associated with volcanic tuff have been extensively reported worldwide (Cronin, 2003; Delmelle et al., 2007; Ruggieri et al., 2010, 2012). 5. Validation of model
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For validation of the potential fluoride contamination zonation map, two different methods were performed. 5.1 Superimposition of fluoride points
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The model output map was superimposed with reported data of high fluoride concentration (above 1.5 mg/L) within the study area (PHED report 2007, Mondal et al., 2014) (Supplementary Table S1). A total of 197 reported data within the study area were brought into ArcGIS environment where the points were broadly categorized into two categories namely ‘high’ having
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fluoride concentration greater than 1.5 mg/L and ‘low’ representing points having fluoride concentration less than 1.5 mg/L (Fig. 5d). The reported fluoride concentration point layers were
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superimposed over the final potential fluoride contamination map where an overall accuracy of 92.15% was achieved. Thus the reported data highly validate the final output map obtained. 5.2 Model validation with success rate and prediction rate
Success rate and prediction rate curves (Chung and Fabbri, 1999) were employed for validation of final resultant potential fluoride contamination zonation map. A total of 197 fluoride points (Supplementary Table S1) were considered for preparation of testing and training data set which
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are required in preparation of susceptibility map. There is no mathematical formula for selection
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of test and training data sets (Xu et al., 2012). For validation, final resultant potential fluoride contamination zonation map is compared with the spatial distribution of the known fluoride zones. For calculation of success rate and prediction rate curves, susceptibility index values were
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sorted in descending order and categorization of ordered cell values into 100 equal classes with cumulative intervals of 1%. Calculation of area under a curve (AUC) can be accessed for the
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accuracy of the prediction (Lee and Sambath, 2006; Vijith et al., 2009). The calculated AUC for success rate and prediction rate curves is represented in Fig. 7. An accuracy of 83.21% and 84.24% for success and prediction rate curve respectively was obtained where 100% means perfect prediction accuracy. 6. Conclusions Integrated application of remote sensing and GIS can be very useful for understanding the potential fluoride contamination zones. From the present research it can be concluded that the use of remote sensing and GIS for studying potential fluoride contamination zone generate a
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basic preliminary ideas of the probable contamination site of groundwater with fluoride. This map can be a useful tool for planners, engineers, strategic planners, decision makers for exploration of groundwater and government officials for assessment of water quality, development and management of groundwater. A thorough investigation in the micro-scale can
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be very useful for generation of detail and accurate map of the study area. Acknowledgements
The authors are very grateful to the DST (project No. SB/ES-687/2013 dated 11.11.2014), India
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for assisting with the financial support. The authors would also like to thank Central Ground Water Board, Survey of India, and Geological Survey of India for their help and support.
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Figure captions Figure 1. Location map of study area, Dwarka River Basin in Birbhum, West Bengal, India.
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Figure 2. Flow chart of the methodology followed for delineating fluoride contamination zonation map in the study area. Figure 3. (a) Geology map of the study area; (b) Aquifer map of the study area; (c) Groundwater
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depth map of the study area; (d) Soil map of the study area. Figure 4. (a) Rainfall map of the study area; (b) Geomorphology map of the study area; (c) Land use map of the study area; (d) Drainage density map of the study area.
Figure 5. (a) Slope map of the study area; (b) Elevation map of the study area; (c) Fault and
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lineament density map of the study area; (d) Reported fluoride in groundwater within Dwarka
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River Basin in Birbhum.
Figure 6. Potential fluoride contamination zonation map of Dwarka River Basin within Birbhum, West Bengal, India.
Figure 7. Success rate and prediction rate curves of potential fluoride contamination zonation map.
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Table 1. List of various physical and chemical parameters with favorable condition for fluoride release.
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Table 2. Ranks and weights assigned to various input factors. Supplementary data
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Table S1. Reported fluoride concentration in Dwarka River Basin within Birbhum District, West Bengal, India.
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pH
pH > 6-8
Eh
Moderate to strong reducing condition
Na+ , K+
Weathering of Na and K bearing feldspar, Muscovite, and biotite turns the water Na-HCO3 type
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parameters
Ca2+
Antipathic relation with F due to precipitation of CaF2
Cl-, CO32-, HCO3-
High carbonate bearing water has alkaline nature, which favours the
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stability and mobility of F- ions in groundwater.
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Table 2 Ranks and weights assigned to various input factors. Sl. No 1
Factor
Rank
Geology
10
Factor classes Granite
gneiss
with
Weightage enclaves
of
Aquifer
09
Alternating band of sand, silt and clay
09
Laterite and lateritic soils
07
07
Rajmahal trap (Basalt)
06
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Hard clay impregnated with caliche nodule
Gondwana Super group
05
Pegmatite
02
Unclassified metamorphics
01
(Gneisses and associated rock)
09
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10
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metamorphosis
(Older alluvium and laterite etc) Also thick but discontinuous aquifer with zeolitic clay lenses, Gondwana rock, tertiary rock of Md.
08
Bazar area, also areas with bed rocks at
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shallow depth (Older
alluvium)
Fairly
thick
with
regionally extensive confined or unconfined
08
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aquifers down to down to about 450 m.b.q.l
3
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(Basalt with interic
Ground
apptan clay)
08
water table
4
Soil
06
07
25 to 40 m
08
40 to 65 m
07
> 65 m
06
< 25 m
02
Red loamy
07
Red sandy
04
Laterite
02
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6
Factor
Rank
Rainfall
06
Geomorph
05
ology
Factor classes Very low (below 1100mm)
06
Medium (1200-1300 mm)
05
High (1300-1400 mm)
02
Very high (above 1400 mm)
01
Old floodplain
05
Older alluvial fan
04
Anthroprogenic terrain
Use
04
Younger Alluvial Plain
01
Cultivated land
04
Land
Rural settlement
03
Cover
Forest area
02
(LULC)
Urban settlement
02
Water bodies
01
11
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Slope
03
Elevation
Lineament
03
03
02
01
Very low
03
Low
02
Medium
01
High
01
0 – 4.14
03
4.14 – 10.12
02
10.12 - 39.18
01
< 35 m
03
35 to 50 m
02
50 to 70 m
01
> 70 m
01
high
02
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Drainage density
9
02
02
Uncultivable land
8
03
Moderately disected hills and valleys
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Pediment-pediplain complex
7
Weightage
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Sl. No
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Factor
Rank
Weightage
medium
01
low
01
very low
01
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Factor classes
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Highlights Spatial modeling of fluoride hazard zone is proposed. Geological, geomorphological and hydrological inputs are given to drive the model. An output is generated by weighted overlay method. Fruitful validation by reported data.
