Spatial Variability of Metal Elements in Soils of a Waste Disposal Site in Khulna: A Geostatistical Study H. Nath and I. M. Rafizul 1 Introduction Today, thousands of processes are constantly contaminating soils on a daily basis. Accumulation of heavy metals and metalloids due to pollution from rapidly devel- oping industrial areas, mine littering, disposal of high metal wastes, excess of lead in gasoline and paints, application of compost to grounds, animal compost, sewage sludge, pesticides, wastewater irrigation, leftovers from coal combustion, petrochem- icals spilling and atmospheric deposition, etc., can be some of the major examples [1]. Heavy metals comprise some ill-defined groups of inorganic chemical hazards in the contaminated sites. The most hazardous and toxic heavy metals found in these groups are lead (Pb), chromium (Cr), arsenic (As), zinc (Zn), cadmium (Cd), copper (Cu), mercury (Hg), nickel (Ni), etc. In the soil, the concentration of these metals holds for a very long time and puts on a substantial threat to human health and the ecological system. Soil samples are taken from various points of the site to determine the concentration of heavy metals in a contaminated site, and several geostatistical approaches can provide precise predictions at the unsampled locations. Spatial prediction, usually referred to as spatial interpolation, is a widely used analytical technique for estimating an unknown spatial value using known values observed at a range of sample locations [2]. The techniques of interpolation are based on the principles of spatial autocorrelation, which assumes that the points closer to each other are more similar than the farther ones [3]. There are several space inter- polation methods, each according to different estimation criteria that are considered to produce a good prediction. This research focuses on four of the most commonly used methods for spatial interpolation: inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and empirical Bayesian kriging (EBK). Such methods approximate values at unsampled locations with certain allocated weights H. Nath (B ) · I. M. Rafizul Department of Civil Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Arthur et al. (eds.), Advances in Civil Engineering, Lecture Notes in Civil Engineering 184, https://doi.org/10.1007/978-981-16-5547-0_3 25