Utilization of spatial interpolation technique to compare metal accumulation pattern by lichens in plain and mountainous regions of North Central India

37 Based on the physicochemical characteristics, metals emitted from the source (both natural and 38 anthropogenic) contributes towards spatial continuity at regional scale. Apart from intrinsic properties of metals, 39 meteorological conditions and topography of the region is also known to contribute towards the spatial continuity. In 40 the present study comparative spatial assessment of twelve metals in lichen Phaeophyscia hispidula collected from 41 mountains and plains of north western India was carried out with the help of indicator kriging method. In plains and 42 mountains the total metal concentration varies between 25.38 429.24 μgg and 22.77506.95 μgg dry weight 43 respectively. Geospatial mapping provided insight into the spatial behavior of different metals in plain and mountain 44 regions. In plains, Cr, Cd, Cu and Pb had higher concentration having higher coverage area, while metals like Cd 45 and Hg had highest localized distribution indicating point sources. Observations indicated that apart from local 46 sources, meteorological conditions specially wind direction also plays important role in spatial behavior of the 47 metals, which has been verified by the bioaccumulation pattern of metals in lichen samples from mountainous 48 region. Among which three mountainous states of North Western India, Uttarakhand has higher concentration of 49 metals which may be attributed to the wind direction together with local anthropogenic sources. 50


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Heavy metals pose serious threat to the ecosystem due to its bioaccumulation and biomagnification 53 properties. Both anthropogenic and geogenic (lithogenic) sources result in environmental contamination at 54 local/regional/global scale. The diversity and distribution of various metals is known to be mainly influenced by widespread data for precise regional representation of the variables from multiple sources as well as locales. The 61 statistical interpolation methods illustrate spatial prediction at unknown points and can be used for diverse mapping 62 applications for assessment of 'spatial risk' in health science, geochemistry, pollution modeling or climatic 63 phenomenon at regional as well as global scale (Lam 1983;Watson 1992).

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Spatial prediction of the above phenomenon's can be approximated by a function depending on the spatial 65 heterogeneity and location of irregularly distributed sample points in geographical space (Mitas and Mitasova 1999).

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Kriging, one of the widely accepted interpolation techniques, has the advantage of considering the spatial structure

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The use of lichens in estimating the quality of the air, from different parts of the world are available and 72 well studied more than four decades and published >2000 account on the lichen and environmental studies (Garty  Liu et al. 2016). However, the lichen biomonitoring data has been well utilized to predict 74 the air quality of the area of interest, yet very few studies have been conducted in the country to investigate the 75 regional pattern of metal pollution and its dispersal (Bajpai et al. 2010.

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In the present study effort has been made to utilize spatial interpolation technique to compare the metal 77 accumulation pattern at regional scale in plain and mountainous regions of North western India using lichen 78 biomonitoring data to investigate the spatial behavior of metals studied and identify the pollution in disturbed areas 79 and for framing regulatory policies.

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The standard reference materials of metals/metalloids (E-Merck, Germany) were used for the calibration 91 and quality assurance for each analytical batch. Analytical data quality of metals/metalloids was ensured with 92 repeated analysis (n=3) of quality control samples, and the results were found within (±2.82) the certified values.

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Recovery of Fe, Zn, Mn, Cu, Co, Se, Cr, Pb and As from the samples were found to be more than 98%, as

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The 'semivariogram function' best fitted to the theoretical model is used to generate semivariogram 108 parameters i.e. nugget (C 0 ), sill (C 0 + C 1 ) and range (C 2 ) using weighted least square technique. The best fitted 109 semivariogram model allows predicting the variables ( ′) at unknown locales ( ) (Hohn 1999). 119 120 The derived value of semivariance [ (ℎ)] of the indicator codes and Lagrange multiplier ( ) is used to variable also range in class 0 to 1.
The suitability of exponential semivariogram models for prediction of indicator values are validated using

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The predicted probability maps of individual metals showed that spatial continuity of metals is more in 168 plains while mountainous sites showed local hot spots, only mercury and cadmium showed highest spatial continuity 169 in mountainous region (Fig 4 and Fig 5). Mercury is known to be emitted to the atmosphere from both natural and

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High spatial continuity of cadmium may be attributed to heavy tourist activity in mountain region due to like Co and Ni known to found in organic matter and clays its mobility increases in acidic condition

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The relative variation in rate of dispersion is known to be a controlling factor for the spatial continuity of in Uttarakhand, as due to westerly winds the state is directly exposed to pollution load of northern India and 193 dominant vehicular activity due to pilgrimage.

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The present observations reveals the application of lichen biomonitoring data together with GIS tools to 195 record the variation in the metal concentration and its probable correlation with other abiotic and biotic factors 196 provides valuable information regarding its influence in controlling dispersal of pollutants at regional scale. 197

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The study shows that metal profile is influenced by both anthropogenic and natural sources. The

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topography also plays an important role in the spatial distribution of metal in different region therefore, an integrated 200 approach is required to understand the environmental consequence of metal and associated health effects. Though

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the study has been conducted taking few sites representative from various states but it presents the applicability of 202 lichen biomonitoring data for regional mapping. Interpolation requires point data to be distributed normally in space.

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The study could be more reliable in future if proceed with a large number of samples data. However, the 204 topographical constrain of mountainous region of northern India allow only limited data to collect. The study

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demonstrates that geographic information system may be predicting the probable zones of high risk. The study could 206 help in planning sustainable environmental development of the concern region on a long term basis. also collected samples and assist in metals estimation and AR perform kriging analysis of data.

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Authors CPS and DKU provide fruitful discussions and comments for improving the manuscript.