Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

• Main Authors: Asma Shaheen, Javed Iqbal
• Format: Article
• Language: English
• Published: MDPI AG 2018-03-01
• Series: Sustainability
• Subjects: spatial interpolation; heavy metals; variogram model; soil contamination; Random Forest
• Online Access: http://www.mdpi.com/2071-1050/10/3/799

In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr), cadmium (Cd), and lead (Pb), at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF) function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples) at three depths (0–15 cm, 15–30 cm, and 60–90 cm). The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC), organic matter (OM), pH, phosphorus (P), potassium (K), and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC) had a significant (p ≤ 0.05) negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P) in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of Cr, Cd, and Pb revealed spatial variability regarding subsoil horizons. Significant contamination was discovered near the Deg drain and the Bed Nallah irrigated area that indicated a high Cr topsoil contamination, and in a homogenous pattern in Cd and Pb (p < 0.05). Consequently, different soil management strategies can be adopted in an industrial irrigated area to reduce the contamination load of heavy metals in soil.