Modelling spatial variation of fluoride pollutant using geospatial ...

Environ Earth Sci (2015) 74:7801–7812 DOI 10.1007/s12665-015-4563-8

THEMATIC ISSUE

Modelling spatial variation of fluoride pollutant using geospatial approach in the surrounding environment of an aluminium industries Prem Chandra Pandey1,6 • Pavan Kumar2,5 • Vandana Tomar3 • Meenu Rani4 Swati Katiyar5 • Mahendra Singh Nathawat6,7

•

Received: 3 November 2014 / Accepted: 25 April 2015 / Published online: 3 June 2015 Ó Springer-Verlag Berlin Heidelberg 2015

Abstract Contamination by any substance takes place through air, water or soil and causes serious effects on flora and fauna of the regions. Air-transmitted pollutants spread in a faraway place from emission points of the pollutant source via wind (speed and direction as causing factors). Fluoride is one the pollutant generated in aluminium industries which is harmful to human and plants in excess concentration. This study focuses on the use of spatial interpolation methods for assessment of fluoride concentration around aluminium industries. The samples were collected from different test sites in the study area to investigate the fluoride concentration level. The test sites include several locations such as industrial unit,

This article is part of a Topical Collection in Environmental Earth Sciences on ‘‘Environmental Problems and Solutions in India’’, guest edited by Tanu Jindal. & Prem Chandra Pandey [email protected]; [email protected]

river site, residential and distant villages. Then, the collected samples were used to predict the overall fluoride concentration in the entire study area. The aim of the study was to evaluate the spatial variation and presence of fluoride concentration in the surroundings of the aluminium industries. Geostatistical interpolation modelling was applied to assess the prediction of fluoride contamination for other non-sampling points using the direction and distance method of empirical Bayesian kriging (EBK) modelling. Thus, geospatial modelling was used to predict the contamination of fluoride around the study area to create environmental awareness. In particular, this study assesses the fluoride pollutant concentration which might become dangerous with slowly increasing concentration against its standard concentration, which will severely impact the human health. In overall, EBK can provide valuable

3

Haryana Institute of Public Administration, Sector -18, Gurgaon 122008, Haryana, India

Pavan Kumar [email protected]

4

Farming Systems Research, Modipuram, SiwayaJamalullapur, Meerut 250110, UP, India

Vandana Tomar [email protected]

5

Department of Remote Sensing, Banasthali University, Tonk 304022, Rajasthan, India

Meenu Rani [email protected]

6

Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India

Swati Katiyar [email protected]

7

Present Address: Department of Geography, Indira Gandhi National Open University (IGNOU), School of Sciences, Maidan Garhi 110068, New Delhi, India

Mahendra Singh Nathawat [email protected] 1

Present Address: Department of Geography, Centre for Landscape and Climate Research, University of Leicester, Leicester LE1 7RH, UK

2

Present Address: Department of Remote Sensing and GIS, Kumaun University, SSJ Campus, Almora 263601, Uttarakhand, India

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information regarding the fluoride concentration on possible level of fluoride in concern to public health. Keywords Atmospheric pollutant  Fluoride contamination  Environmental pollutant  Human health  Empirical Bayesian kriging  Prediction and modelling

Introduction Fluoride is mainly present in the earth as a free fluoride ion in water, whether natural or anthropogenic addition (IPCS 2010). Fluorire bioavailability was hardly affected by hardness of water (0–500 mg CaCo3/l) which has little effect on ionic dissociation (Jackson et al. 2002). The first study related to linkage between fluoride and human health postulates in the early 1900s with the knowledge of fluoride variable existence in the human bones and teeth (Edmunds and Smedley 2005). Several studies have assessed the possible impact fluoride on the human and plants for biological, clinical and biochemical effects (NAS 1971; Ozsvath 2009; Singh et al. 1963; Jacobson and Weinstein 1977). There were many literatures which confirmed the fluoride as atmospheric pollutants in the form of gaseous fluoride, i.e. hydrogen fluoride that are phytotoxic (Drury et al. 1980; Kumar and Rani 2011) as well as pollutant for the ground water (Singh et al. 2013a). Some researchers reported the existence of fluoride in groundwater in some parts of India (Singh et al. 2013b). While Young et al. (2011) have also shown the factors that control the fluoride contents of ground water. Gago et al. (2014) presented a case study for fluoride sorption and desorption on soils in the surrounding environment of the aluminium smelter plant that diffuse and mix with ground water. Thus, groundwater may be affected with fluoride and presence of fluoride in drinking water poses a problem for local people. This study will deal with the spread of fluoride through atmosphere from emission points to surrounding environment, and thus presented the methods here. Fluorides are emitted from smelter in gaseous form (HF) as well as ashes that are discharged into river and waterbodies. These emitted HF through the aluminium plants and thermal power plants diffuse into the atmosphere. Some studies combine biochemical analysis and physiological observations to explain the underlying mechanism associated with effects of fluoride as an air pollutant (Chang 2012). Chang confirmed that the amount of accumulated fluoride varies from 7.5 to 40.3 ppm in different plant species, although they grew in the same location and the same environmental conditions. This may be attributed to water uptake, atmospheric and proximity to the emission points. Previous studies have demonstrate about the overlook of well-defined guidelines in several regions of the world, that means the level of fluoride in drinking water exceeds this guideline value (Fawel et al. 2006). When absorbed by the

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soil and sediments, the fluoride concentration level is considered safe when it varies from \10 to 3700 ppm with a mean of 430 ppm (WHO 2011). According to several scientific researches, fluoride is a health risk at any level to human beings (ATSDR 2003). There are standards that define the concentration level of fluoride in air, water and soil (ATSDR 2003). The environmental levels in ambient air range 1.0–7.5 lg m-3. There are many solutions for the removal of fluorine/ fluoride fumes from the air and water with scientific technologies. The fluoride can be removed from the water using activated alumina prepared from pseudo-boehmite by absorption methods (Leyva-Ramos et al. 2008). The wet scrubbers and electrostatic precipitators help in reducing fluoride emissions from the chimney into the atmosphere (Freeman et al. 1993). Besides, preventing fluoride emissions, it also helps in the removal of various pollutants such as SO2, aluminium oxides and carbon particles. Various methods were used in the industries to reduce or minimize the emission of pollutant in the atmosphere (Cheremisinoff 1993). Researcher confirmed that the fluoride is nearly ubiquitous trace air pollutant which is detectable in most of the substances irrespective of location, type emission/source points (Hodge and Smith 1977). Martin and Jones (1971) confirm that human inhales measurable traces of fluoride in the body every day irrespective of the location and regardless where he lives. The amount of traceable fluoride in the body accounts for 0.001–0.004 mg of fluoride per day through the air. It may be more with drinking water that ranges from 0.1 ppm to almost 4 ppm and occasionally higher than this value. Here, comes the role of this study that is carried out at the place where gaseous fluoride is emitted into the atmosphere every day from the smelter and pot room of the industries. This gaseous fluoride may accumulate in the air and water, which can be used by humans and plants surrounding the region every day (Kumar and Rani 2011; Pandey et al. 2014). It may cause more inhalation and ingestion of fluoride through air and water in the body. So, one can imagine a situation where a considerable amount of this emitted pollutant disperses every day in the atmosphere. Thus, fluoride is one of the atmospheric pollutants emitted from aluminium industries. The smelter and pot rooms were the main work area from where fluoride is emitted either into the atmosphere or soils as residues. These industries were bound to include electrostatic precipitators and dry or wet scrubbers to minimize the emission of fluoride into the atmosphere (Freeman et al. 1993; Cheremisinoff 1993). This study is the continuation of previous studies illustrating the impact of fluoride on plants (Kumar and Rani 2011. Earlier, we have analysed the impact of fluoride on the human health (Pandey et al. 2014)

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using selective electrode method (Frant and Ross 1966) in the surrounding environment of aluminium smelter. Gaseous fluoride (HF) is highly phytotoxic causing harm and injury to plants of surrounding environment of aluminium industries. People are prone to dental disease and plants are more affected to necrosis, yellowing and then death (Kumar and Rani 2011; Pandey et al. 2014). Therefore, it is necessary to know the fluoride concentration and have knowledge about the possible impact of fluoride on human health as well as on plants. The importance of this study is to assess spatial variation of fluoride over the study region and also to assess at non-sampling locations. The spatial variation of fluoride is modelled through EBK as raster output from sampling sites and they provide a distribution of values for each location in the study area. To achieve the objective, fluoride concentration is sampled and modelled for range using the Bayesian empirical kriging (EBK) interpolation. Previous study has shown the hazardous effects of radiation or radioactive materials and predicted for upcoming impact with distance and directions in Chernobyl, Russia (Yablokov et al. 2010; Nesterenko 2002). They have assessed the harmful effects of radiation as well as predicted the spreading of harmful radioactive materials. Though, fluoride is not so hazardous and harmful in instant ways, but it has detrimental effects that gradually destroy and produce harmful effects to human and plants (Kumar and Rani 2011; Pandey et al. 2014). Thus, this makes it mandatory to consider the effects, concentration and range of the fluoride at emission points that can spread to surrounding places or regions affecting plant and human health.

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production of aluminium with the capacity of 20,000 tonnes per annum using one pot line and one smelter. Now, from 2000 onwards, industry has increased production to 700,000 tonnes per annum of the alumina refinery and a 345,000 tonne per annum of the aluminium smelter with 4 pot line and two smelters. Hindalco Company handles the very beginning of the aluminium production to finalizing the finished products like bauxite mining, alumina refining, aluminium smelting to downstream rolling and extrusions. The production of the aluminium starts with bauxite ore as a raw material. This bauxite with other input materials such as carbon, caustic soda, lime, coke and pitch are processed on container line and smelter through various processes to produce aluminium products. Besides the useful aluminium production, the industries also emit the fluoride pollutant (refer Fig. 2a). Figure 2b presents a simple flowchart of the process and thereafter the steps taken for the study. The fluorides are emitted in the form of fly ash, fumes and gaseous fluorine (HF) through smelter and pot line/pot room into the atmospheres. The emitted gas from pot line or smelter is having very high temperature and of fumes of fluorine, whose removal efficiency was not traceable (EPA 1976). Sometimes, very high technology is used by industrial management like dry/wet scrubber or electrostatic precipitators (Cheremisinoff 1993). Thus, in spite of high technology adopted, there is frequently emission of the fluoride in the atmosphere, an inevitable part of aluminium production.

Data and methodology used Study area The study site is located at Renukoot town of Sonbhadra district, Uttar Pradesh, India as shown in Fig. 1. The spatial location of the study area is situated between 82°450 – 83°150 E and 23°550 –24°180 N (see Fig. 1). This region has fluoride emission source of aluminium industries and considered as best place to carry out the study related to spatial variation of fluoride concentration. For this, several locations were selected for collecting samples to analyse the fluoride at that specific points. Therefore, the study site provides a better platform to collect samples for fluoride measurements, estimation, and spatial variation prediction in surrounding environment of industries. HINDALCO, a flagship company of the Aditya Birla Group is the largest integrated aluminium plant in Asia located at Renukoot city, Dudhi block at Sonbhadra, India. The Hindalco Industries has been set up in the Renukoot, Uttar Pradesh, India in the year 1962 (ABMC 2013). The initiation of the industrial installation considers the

The schematic methodology adopted in the present study is shown in Fig. 2b. Several locations were selected, including the emission/source points and non-emission points, for sample collections (as shown in Fig. 3). GPS instrument was used to record and capture the spatial information of the point locations in latitude and longitude. The spatial locations of the recorded points are transferred and imported as shape file to the computer and processed in the lab for further analysis and performing spatial interpolation using the Arc GIS software at GIS environment. The field id of the shape file stores the location id, location name, measured fluoride concentration at each point in the laboratory. The selection of locations to collect samples was according to the range of industries with respect to fluoride emissions related to proximity and distance to industries. Previous studies were conducted on the workers of industries and residents (not workers/residential areas) at different locations (Pandey et al. 2014) and plants of the surrounding environment (Kumar and Rani 2011), that confirmed that fluoride has

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Fig. 1 Location map of Study area at Renukoot, Sonbhadra District, India

adverse impact on the human dental health and plants conditions. The selection of study site thus represents favourable condition for performing spatial analysis to know spatial variation of the fluoride concentration. This spatial assessment of fluoride will provide the fluoride concentration at any place represented by the raster datasets. The importance of conducting the spatial variation assessment stands for the fact that it is not possible to collect samples from each and every location over a large region. Therefore, for assessment of any pollutant, samples can be collected from several locations covering the entire regions, and then use spatial interpolation to find the spatial variation in the region. The gaseous pollutants are not restricted to the emission points only but they do spread to other surrounding place (non-emission points) through atmosphere. The problems were identified with

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the aim to measure the fluoride concentration over different emission and non-emission points. Laboratory investigations The field-collected samples include the leaf of the plants, soil samples, and urine and serum of the humans. These samples were analysed in the laboratory for the estimation of the fluoride concentration. The sample site for collection of leaves and soil includes locations near emission points in and around industries and non-emission points away from the industries. These locations include residential places, river, and wasteland regions (as discussed earlier). The measured fluoride concentrations are shown in Table 1. Three samples were collected at each point and average is taken during the analysis performed in study.

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shows the different tree/shrub leaves collected from the sampling sites. First of all, leaves were collected from all sampling location points and taken to the laboratory for further analysis. About 50 gm of leaves from each sample site was placed in the oven at 62 °C for 24 h. Thereafter, 2–4 g of dried sample was taken in platinum dish and sodium carbonate–lithium carbonate solutions were added to it. This resulting mixture of dried sample and chemical solutions was evaporated on a hot plate and again ignited in the electric furnace at 700 °C for about 1-h duration. Then, this ash product is washed in crucible with 25 ml of distilled water and placed in a beaker. It is again washed in crucible with 35 ml of perchloric acid (HClO4) and poured in the same beaker placed earlier. The washing of solution was performed with distillation using steam distillation unit. Thereafter, approximately 250 ml of this distilled solution was collected in a beaker, and 5 ml of citrate buffer was mixed with this 250 ml solution to make it 250 ml by volume. Now, 10 ml of solution from the 250 ml volume was taken in a small beaker and mixed with total ionic strength anionic buffer (TISAB) for fluoride analysis. Thereafter, fluoride values were read from this solution (TISAB and 10 ml mixed solution) using the expandable ion analyser. Since the ash frequently results in refractory fluorides, the solubility of the residue is ensured by fusing with alkali carbonate or hydroxide. At each sampling site, three samples were collected and their average was taken after the lab analysis. Following fusion, the melt is dissolved, and the fluoride separated for determination.   V f ¼R  ; ð1Þ W where f fluoride concentration, R final reading of expandable ion analyser (mg/l), V final volume, i.e. 250 ml (in ml), W weight of the leaf sample in gram (2–4 g). Geospatial technique

Fig. 2 a Schematic diagram of manufacturing of aluminium and pollutant emissions such as fluoride gas, scraps and measurement actions and b general methodology and concept of the research undertaken in this study

Laboratory investigations of fluoride in plant samples The study aims to evaluate the spatial variation of fluoride in the region, therefore, sample collected from the tree leaves serves the purpose as trees are more prone to and affected by the gaseous fluoride than the soils. Table 2

Collecting samples from each and every location are exhaustive, expensive and time consuming, so spatial interpolation techniques are used to produce prediction for nonsampled locations from the samples taken at different chosen locations. Thus, the selection of EBK, a geostatistical method, is based on the fact that it allows accurate prediction of data with minimal interactive modelling than other spatial interpolation methods. Thus, main purpose of EBK is to provide powerful approaches for investigating fluoride sample data with effective interpolation technique and statistical outputs. The Geo-statistical Analyst extension of Arc GIS 10 software is used to determine and assess the spatial interpolation of the collected fluoride samples. It uses the EBK geostatistical model which is different from

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Fig. 3 Sample collection points showing industrial area, residential area and the outer region

the classical kriging methods using estimated semivariogram with account for the errors. In EBK, estimation of interpolation is based on the several semivariogram models instead of a single semivariogram model by classical interpolation methods (Gribov and Krivoruchko 2012). The basis of spatial interpolation methods depends upon the needs of the functions of the distance between collecting observations and locations for which interpolation needs to be performed. The two main classes of interpolation are deterministic interpolated and probabilistic based on statistical interpolation (Krivoruchko 2011). Predefined functions are based on the deterministic interpolation methods. Deterministic models assign values to collected sample sites based on surrounding measured values, and the resulting smoothness of the surface was the output of specified mathematical formulas (ESRI 2011). EBK relies on statistical models that include autocorrelation and provides the capability of producing a prediction surface, along with some measure of the certainty or accuracy of the predictions (ESRI 2011). In this predictor interpolation, it quantifies the uncertainty associated with the interpolated values for the site that requires interpolation from observed or collected sample points. The measured fluoride

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concentration dispersed strategically at points, and assigned predicted values to other desired location using interpolation methods. It is very complicated or expensive to collect samples from each location of defined study area while visiting the sites. Thus, interpolation methods work efficiently to know the concentration of fluoride over other points or a region which is not visited or measured. Thus, it predicts the concentration for surface values at that place and produces surface values, whether samples were collected or not at the points.

Results The purpose of this study is to look at the spatial variation of fluoride concentration at the surrounding environment of aluminium industries. The results obtained from the laboratory reveal same pattern of fluoride concentration at sites far from the emission source. It provided the general idea of concentration being highest at the source points and lowest at far distant places. With these, one can directly assume that workers are exposed to a dangerous situation than the outside person, but they are too exposed to the harmful impact of gaseous fluoride due to spatial

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Table 1 Measurement value and predicted values for the different locations with errors when power values a = 1.0, a = 1.5 and a = 2.0 Fluorine concentration

When power, a = 1.0

When power, a = 1.5

When power, a = 2.0

Measured at different locations (mg l-1)-lab methods

Predicted

Predicted

Predicted

Error

Error

1

Plant-2 colony

16

22.35294

23.09639

7.096386

23.824786

7.824786

2

Hi-tech carbon plant

26.3

24.70363

-1.59638

26.5212

0.221196

27.698096

1.398096

3 4

Plant-2 Pot room-2

28.4 23

23.37278 21.37711

-5.02722 -1.62289

24.61562 22.47791

25.247463 23.626226

-3.15254 0.626226

5

Hindalco colony

16

20.56279

21.167232

5.167232

6

Plant-1

33.4

21.78803

7

Railway colony

11

22.11584

8

Pipari village

15

20.63775

9

Po-room 1

31

19.01376

0

Near WTP

14

21.18309

21.1828

7.182797

21.114972

7.114972

11

Near STP

25

23.55262

-1.44738

25.47913

0.479132

26.816053

1.816053

12

Scrapyard

22

19.00257

-2.99743

18.16857

13

Near railway colony

11.1

20.66831

9.56831

20.33382

14

Hospital

8

20.26028

12.26028

19.59083

11.59083

15

River side

8

21.30395

13.30395

21.83664

13.83664

16

Near village Pipari

12

19.082

7.082

18.73906

6.739058

18.396179

6.396179

17

Temple site

14.2

18.83539

4.635393

18.59758

4.397575

18.296716

4.096716

18 19

Waste land Near guest house

16.2 17.1

19.72078 21.86503

3.520776 4.765029

19.11732 22.48627

2.917319 5.386271

18.446219 23.070405

2.246219 5.970405

20

River Side_1

8

21.30083

13.30083

21.40692

21

Near Murdhawa village

11.3

21.36174

10.06174

20.90332

22

Near Murdhawa village

10.7

22.57954

11.87954

22.7899

23

Near Rihand dam

15.3

20.10915

4.80915

19.44108

4.14108

18.727405

24

Murdhawa colony

17

21.14664

4.146637

20.53269

3.53269

19.874472

2.874472

25

Near Padmini hotel

13.3

19.2704

5.970398

19.07402

5.774024

18.899237

5.599237

26

Near Hindalco colony

16

22.08857

6.088568

22.58817

6.588167

23.043098

7.043098

Average predicted values

16.90

21.13

4.23

21.35

4.45

21.46

4.56

7.02

21.35

6.82

21.46

6.83

Average error value (absolute values)

dispersion and atmospheric transmission. However, using the geospatial interpolation, sometimes place far from emission/source points may have higher fluoride concentration than the corresponding sampling points due to coupling effects due to two emission sources. Laboratory results The results of fluoride analysis performed in laboratory are shown in Table 1. This measured fluoride concentration is found to be maximum nearby emission/source points in the industrial area. These fluoride concentrations were found to be minimum nearby the river area and places far away from the source points. The maximum fluoride concentration was found to be 33 mg l-1 and minimum was found to be 8 mg l-1 for the collected sample points.

6.352938

Error

4.562787 -11.612 11.11584 5.637747 -11.9862 7.183091

20.86339 22.97517 22.93302 20.5121 18.80451

-3.78438 -0.52209 4.863392 -10.4248 11.93302 5.512096 -12.1955

-3.83143 9.233816

13.40692 9.603318 12.0899

23.990128

-9.40987

23.546394

12.54639

20.403098 18.799977

17.29127 19.993918

5.403098 -12.2

-4.70873 8.893918

18.717678

10.71768

22.317423

14.31742

21.373073

13.37307

20.34665

9.04665

22.931853

12.23185 3.427405

Modelling fluoride contamination using empirical Bayesian kriging method The collected samples from several test sites were used to investigate the fluoride concentration using the interpolation methods. The fluoride concentration is measured at the sampling test site and known to us. These are random sampling sites that provide the measured fluoride concentration, but what about the remaining sites? Using the measured fluoride concentration samples at sampling test sites, fluoride concentration variation is predicted as raster output. Spatial interpolation techniques are employed to evaluate the spatial variation in fluoride concentration level at those non-sampling locations. Thus, geospatial interpolation techniques play an important role in providing the predictable concentration. It determined the fluoride

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Table 2 Different tree or shrub leaves sampled during the study time period No.

Location

Botanical name

1

Plant-2 colony

Cynadon dactylon

2

Hi-tech carbon plant

Lantana camra

3

Plant-2

Azadirachta indica

4

Pot room-2

Mangifera indica

5

Hindalco colony

Azadiracha indica

6

Plant-1

Mangifera indica

7 8

Railway colony Pipari village

Ficus religiosa Cynadon dactylon

9

Po-room 1

Solanum tuberosum

10

Near WTP

Mangifera indica

11

Near STP

Zizyphus mauritiana

12

Scrapyard

Ficus religiosa

13

Near railway colony

Azadirachta indica

14

Hospital

Azadirachta indica

15

River side

Lantana camra

16

Near village Pipari

Zea mays

17

Temple site

Triticum aestivum

18

Wasteland

Mangifera indica

19

Near guest house

Cynodon dactylon

20

River Side_1

Cynodon dactylon

21

Near Murdhawa village

Zea mays

22 23

Near Murdhawa village Near Rihand dam

Triticum aestivum Zizyphus mauritiana

24

Murdhawa colony

Tamarindus indica

25

Near Padmini hotel

Zea mays

26

Near Hindalco colony

Triticum aestivum

concentration level for entire study sites resulting in a raster output, instead of single point. These raster datasets allow to predict the fluoride concentration for each 2 9 2 m2 of the area. These will provide the information regarding the places from non-sampling points, and near sample managed sites. The measurement of fluoride content at selected sites was modelled using geostatistical methods to predict the spatial variation of fluoride following the emission from the industrial activities. Voronoi diagrams are useful and found its applications in various fields such as ecology, hydrology, architect, computational geometry. Voronoi maps are generated using the samples based on distance to points in a specific subset of the plane. It assigns values to each location within a polygon, which are closer to the sample point of that particular polygon. Thus, all points within a single cell are equidistant to the nearest two (or more) sampling points. A local statistics was used to generate the mean and standard deviation of the sample points for each polygon, and using this mean and standard deviation of neighbour polygons a colour map is produced to illustrate the areas of low or

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higher fluoride concentration as shown in Fig. 4. Figure 4a shows that the Voronoi map generated from the mean of fluoride samples, while Fig. 4b shows the standard deviation of the fluoride samples for polygons for every location. Darker colour represents higher fluoride values and light-coloured polygon represents the low fluoride values at that regions. Both Voronoi maps (mean statistics and standard deviation) demonstrate that the fluoride is maximally affecting the industrial region where the emission of the fluoride is taking place as compared to its surroundings. But, mean Voronoi map shows higher concentration as compared to standard deviation Voronoi map. Though they cover somewhat different amount of regions, higher level of fluoride was seen near industrial processing units. The surrounding regions, such as residential areas and far away villages, have minimum fluoride concentration, but they were not spared by harmful effects of fluoride on health and plants. Figure 5a, b shows the covariance and semivariogram clouds that represent the spatial autocorrelation between the measured fluoride samples. In the covariance and semivariogram clouds, those points which are close to each other are more alike and have the same concentration. So each point in Fig. 5a, b represents a pair of locations of fluoride sampling test sites. Covariance and semivariogram values are the difference squared between the values of each pair of locations, are plotted on the y-axis relative to the distance separating each pair of measurements on the xaxis. This step is required and tested to avoid and find out any erroneous data present in samples or not. The measured

Fig. 4 Voronoi Map generated using the mean and standard deviation values of the collected fluoride samples

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locations that are close to each other have small values (on y-axis), and as the distance between the two measured locations increases, the semivariogram/covariance value (yaxis) increases. At certain distance on the x-axis, these cloud points flatten suggesting that by this point forwards pairs of measured sites separated by this distance are not correlated. Thus, the covariance and semivariogram cloud and spatial surface show that there are no erroneous samples present in the data, thereafter, spatial interpolation is performed to assess the fluoride concentration over the region. Figure 6a, b represents represent the spatial map of difference squared and distance between the each measured pair of fluoride sampling sites, thus showing the spatial relationship between the sampling sites. After ensuring that no erroneous data were present in the samples spatial variation of fluoride is performed using EBK. When the power value of the interpolation methods accounts for different values, its increasing patterns with increments are dependent on fractional Brownian motion. When a is 1.0, a correlation model estimates linearly the scores and predicts the distance and directions from the collected sample points. Figure 7a illustrates the prediction and simulation interpolation surface result when a is 1.0 that shows the moderate trend and random noises.

However, when a is 1.5 and a is 2.0, it shows nearly noiseless large-scale sample data variation than previous surface results of the interpolation, i.e. when a is 1.0. Thus, Fig. 7 shows simulated spatial surface of fluoride concentration using different collected samples with three different power values of a (1.0, 1.5 and 2.0). Spatial interpolation for predicting the standard errors provides a better perception of fluoride contamination or concentration over distance and direction. EBK model generated the predicted fluoride values (Fig. 7a–f) and associated prediction standard errors (Figure d–f) for a = 1.0, a = 1.5 and a = 2.0, respectively. The predicted fluoride values and associated errors are illustrated in Fig. 8a–f and values are presented in Table 1. RMS values obtained were 7.96, 7.90 and 7.87 for fluoride concentration, when power values were a = 1.0, a = 1.5 and a = 2.0, respectively, using EBK model in and around emission and non-emission points. Figure 7d–f shows scatterplot of the measured fluoride values, predicted fluoride values and predicted error values for a 1.0, 1.5 and 2.0 as shown in Table 1. The error values of a 1.0 range from -11.9862 to 13.30395, and a 1.5 error values range from as low as -12.1955 to as high as 13.83664. Similarly, the low values for the a 2.0 comes to

Fig. 5 a The covariance surface and b semivariogram values showing the autocorrelation for the pairs of points against the measured fluoride values estimated from EBK model (y-axis

difference squared between the values of each pair of fluoride sampling locations and x-axis—relative distance between each measured pair of fluoride sampling site)

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Fig. 6 Spatial covariance surface and semivariogram surface showing the spatial map of difference squared and distance between the each measured pair of fluoride sampling sites

(a) Power Value, α= 1.0

(b) Power Value, α= 1.5

Predicted Fluoride values, when α=1.0

30

30

Errors

5 0 -5

10 5 0

0

10

20

30

40

Errors

-5

0

10

20

30

40

5 0

0

10

20

30

40

-10

-15

-15

Error

10

-5

-10

-10

15

Values

10

Predicted Values

25 20

15

Values

Values

15

30

Predicted values

20

Predicted Values

20

Predicted Fluoride values, when α 2.0

Predicted Fluoride values, when α=1.5

25

25

(c) Power Value, α= 2.0

Measured Fluoride concentration values

Measured Fluoride concentration values

(d) Power Value, α= 1.0

(e) Power Value, α= 1.5

-15

Measured Fluoride concentration values

(f) Power Value, α= 2.0

Fig. 7 a–c A simple illustration to show the spatial distribution of accumulated fluoride concentration using semivariogram models (when power values of 1.0, 1.5 and 2.0). d–f Graphs generated using

the measured fluoride values present the predicted fluoride values and errors, power values of 1.0, 1.5 and 2.0, respectively

be -12.2 and high as 14.31742 (refer to Table 1 for these values in corresponding a values). The average predicted errors for a = 1. 0 is 4.23, a = 1.5 was 4.45 and a = 2.0 was 4.56 but the absolute average predicted errors for a = 1. 0 is 7.02, a = 1.5 was 6.82 and a = 2.0 was 6.83. Thus, an optimal alpha value to perform the spatial variation of the pollutant level can be considered at a -1.5.

The importance of using EBK techniques is to provide areas with considerable higher or lower level of fluoride concentration. Predicted values and standard error surfaces for fluoride concentration in ppm in surrounding environment of aluminium industries were modelled and maps were generated using EBK. As illustrated in Fig. 7a–c, people residing or inhabiting or spending time in the areas

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shown in dark red, light red and orange would potentially be at risk from impact of high level of fluoride concentration. However, based on this study, future study can include more data sampling from plants, soils and humans at the same time period to provide much improved prediction to fluoride concentration in the region. Thus, output maps will provide the spatial variation in fluoride level so that environmental agency can take required steps accordingly. The level of fluoride concentration is highest at the emission/source points and lowest at the outer ends of the study area. The level of fluoride concentration is gradually decreasing with distance from emission/source points. The level of fluoride concentration is on the border line of the danger level, however, it accumulates with time and becomes dangerous for the human beings, animals and plants. These fluorides were absorbed by plants and become affected by the excess fluoride concentration in internal parts. These fluorides can cause several diseases related to bones, teeth and others which spread or came through air, drinking water and soils (WHO 2000). With the above results, it is clearly inferred that the level of fluoride is becoming less with distance from source points and according to the values from measured points at different level in different alpha values. These results inferred that despite low concentration, fluoride is still spreading to far places from emission/source points. From the above results, it can be inferred that the concentration of the fluoride is maximum in the pot line and working place as compared to the surrounding industries. It can be concluded that, the overall industrial region is most affected by fluoride emission as compared to outer residential area.

Discussion and conclusion Fluoride emission is one of the major problems in and around industrial areas that require either minimization or emission prevention. Thus, geostatistical analysis was performed using EBK model for analysing fluoride samples collected from several parts including pot lines, processing plants, near river, residential areas, dam points and village area far away from industry. Previous studies reported the effect of the fluoride on flora using the electrolysis methods by collecting samples from several locations in Hindalco region (Kumar and Rani 2011). The authors have shown that the fluoride is causing adverse impact on the flora of the region. However, they have not used to summarize the results with the region from where they have not obtained a sample. The results were restricted to the several sampling sites only, with the fluoride concentration level and their impact. In previous study, Pandey et al. (2014) conducted

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research on the samples collected from workers and residents of the region for fluoride concentration estimation. They demonstrated that fluoride level is having an adverse impact on the health and dental condition of the local inhabitants. However, EBK method of interpolation overcomes this limitation and can assess the fluoride concentration of all parts using geostatistical methods and generate a raster output with predicted fluoride levels. Thus, when EBK model was applied to the samples for fluoride analysis, it predicts the probability of contamination in and around the industries. It also provides the estimated average and total contamination by fluoride in these specified surrounding areas. It provides the spatial variation of fluoride concentration over a region that can further help in taking precautionary actions by residents to avoid the dangerous effect of fluoride. EBK utilizes a semivariogram method which is the function of distance and direction separating different point locations. Thus, in this way, EBK quantifies the spatial dependence in the collected sample points. Thus, the present study provides the straightforward outcomes for the spatial variation of fluoride concentration and proves that EBK is most robust method of data interpolation as stated by Krivoruchko (2011). Fluoride emissions cannot be ignored by industrial administration, environmental groups and governmental authorities, because it is dangerous in long-term emission to plants, animals and human beings. If the fluoride concentration is causing such harmful effects to plants (Kumar and Rani 2011) and human beings (Pandey et al. 2014), one can imagine the situation in long term. Study also reported and confirmed that the level of fluoride contamination spreads with distance and direction, and concentration is differing with respect to source or sample points. The finding of study presented that the high fluoride concentration level is found near industrial regions than residential areas, near emission points than non-emission points and decreases with distance from emission sources. The fluoride concentration may be high due to coupled effect or combined effects from two or more emission sources. Therefore, it is slow and detrimental effect of fluoride that needs an attention from environmental and management of the industries. These problems are complex in nature; decision models should be in a manner particular to provide exact predictions in surrounding residential areas and in particular to health of growing children and adults. Acknowledgments Authors were grateful to Environmental cell team, Hindalco industries, Renukoot, UP, India for providing the assistance and help to understand the process of aluminium manufacturing from alumina. Authors are thankful to Environmental cell staffs for providing all necessary support during the field

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7812 sampling and lab analysis. Thanks are also due to Dr. A. K Susheela for providing the valuable suggestion and guidance.

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