Representing soil pollution by heavy metals using continuous limitation scores

Abstract

The paper suggests a methodology to represent overall soil pollution in a sampled area using continuous limitation scores. The interpolated heavy metal concentrations are first transformed to limitation scores using the exponential transfer function determined by using two threshold values: permissible concentration (0 limitation points) and seriously polluted soil (4 limitation points). The limitation scores can then be summed to produce the map of cumulative limitation scores and visualize the most critically polluted areas. The methodology was illustrated using the 784 soil samples analyzed for Cd, Cr, Cu, Ni, Pb and Zn in the central region of Croatia. The samples were taken at $1\times 1$ and $2\times 2\mathrm{km}$ grids and at fixed depths of 20 cm. Heavy metal concentrations in soil were determined by ICP-OES after microwave assisted aqua regia digestion. The sampled concentrations were interpolated using block regression-kriging with geology and land cover maps, terrain parameters and industrialization parameters as auxiliary predictors. The results showed that the best auxiliary predictors are geological map, ground water depth, NDVI and slope map and distance to urban areas. The spatial prediction was satisfactory for Cd, Ni, Pb and Zn, and somewhat less satisfactory for Cu and Cr. The final map of cumulative limitation scores showed that 33.5% of the total area is suitable for organic agriculture and 7.2% of the total area is seriously polluted by one or more heavy metals. This procedure can be used to assess suitability of soils for agricultural production and as a basis for possible legal commitments to maintain the soil quality.

Introduction

The problem of soil pollution by heavy metals has been receiving an increasing attention in the last few decades. In Europe, decision makers and spatial planners more and more require information on soil quality for different purposes: to locate areas suitable for organic (ecologically clean) farming and agro-tourism; to select sites suitable for conversion of agricultural to non-agricultural land, particularly for urbanization; setting up protection zones for groundwater pumped for drinking water; to estimate costs of remediation of contaminated areas and similar. Heavy metals occur naturally in rocks and soils, but increasingly higher quantities of them are being released into the environment by anthropogenic activities. Every decision on the application of any measures in the environment relating to soil quality and management, whether statutory regulations or practical actions, must be based on reliable and comparable data on the status of this part of environment in the given area. Various aspects must be considered by the society to provide a sustainable environment, including a soil clean of heavy metal pollution. The first among them is to identify environments (or areas) in which anthropogenic loading of heavy metals puts ecosystems and their inhabitants at health risk. Maps indicating areas with pollution risks can provide decision makers or local authorities with critical information for delineating areas suitable for the planned land use or soil clean up (Van der Gaast et al., 1998, Broos et al., 1999). Maximum permissible concentrations of heavy metals in soil are now regulated by law in many countries.

Before any solution for the problem of soil heavy metal pollution can be suggested, a distinction needs to be made between natural anomalies and those resulting from human activities. Namely, it often happens that also natural concentrations and distribution of potentially toxic metals could present health problems, like in the case of chromium, cobalt, and particularly nickel in ultramafic soils (Proctor and Baker, 1994). Rock type and geological–geochemical processes can change markedly in a relatively small area, resulting in great spatial variability in the soil content of elements. Soils in the vicinity of urban areas and industry are exposed to input of potentially toxic elements, and the situation of agricultural soils gets additionally complicated due to continuous application of agrochemicals.

In practice, soil pollution by heavy metals is commonly assessed by interpolating concentrations of heavy metals sampled at point locations, so that each heavy metal is represented in a separate map (Webster and Oliver, 2001, Juang et al., 2003). The first problem of working with maps of separate heavy metal concentrations (in further text HMCs) is that the limiting values for polluted soils are commonly set as crisp boundaries. For example, a soil is polluted by zinc and not suitable for organic agriculture if the measured values are larger than $150\mathrm{mg}{\mathrm{kg}}^{-1}$ (Official Gazette, 2001). This means that a soil with zinc concentration of $149\mathrm{mg}{\mathrm{kg}}^{-1}$ and a soil with a concentration of $151\mathrm{mg}{\mathrm{kg}}^{-1}$ will be classified differently although the difference may be due to the measurement or interpolation error. Similarly, if the concentration of zinc at a location is $151\mathrm{mg}{\mathrm{kg}}^{-1}$ and at neighboring location $300\mathrm{mg}{\mathrm{kg}}^{-1}$, both locations will be classified as not suitable although the latter shows two times higher concentration. The second problem with HMCs is that different elements come in different ranges of values. This makes it fairly difficult to get the picture about the overall soil quality. For example the threshold value for zinc is $150\mathrm{mg}{\mathrm{kg}}^{-1}$ and for cadmium $0.8\mathrm{mg}{\mathrm{kg}}^{-1}$. If we measure, at a point, values Zn=130 (suitable) and Cd=1.1 (not suitable), this makes this location unsuitable but how serious is the problem? Now imagine a case with tens of HMCs—how can we sum these values to get the compound picture about the quality of soil?

To solve a problem of presenting overall polluted areas, Romić and Romić (2003) applied factor analysis prior to interpolation and then interpolated only the first factor indicating anthropogenic loads of heavy metals. Van der Gaast et al. (1998) used maps of background values of soil contaminants focusing on the 90-percentiles. Hanesch et al. (2001) tested fuzzy classification algorithms to distinguish different sources of pollution. Amini et al. (2004) classified HMCs using unsupervised fuzzy $k$-means to partition the values optimally. The final outputs are maps of memberships to each cluster, which commonly reflect the combination of most correlated heavy metals. In all these examples the procedures are statistically valid, but the meaning of such factors and continuous memberships is hard to interpret. In practice, decision makers usually only wish to see the areas that are polluted without any training in (geo)statistics.

In this paper, we propose an approach to interpolate sampled HMCs using numerous environmental predictors and then represent the overall pollution by using the continuous limitation scores (LS). We advocate the use of cumulative LS because they can be summed and used to represent areas of overall high pollution. Such visualizations can supplement maps of separate HMCs so that the end-users can more easily delineate areas of high overall pollution and focus their actions where their are more needed.