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

Abstract

Abstract: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.

Spatial distribution of heavy metal concentrations surrounding a cement factory and its effect on Astragalus gossypinus and wheat in Kurdistan Province, Iran

“…Heavy metal accumulation in soil will vary depending on factors such as soil reaction, organic matter, and gypsum and lime contents in the soil [11]. In the current study, the signifi cantly higher contents of heavy metals in affected soil samples compared with the control was consistent with the fi ndings of Bi, et al [20] and Wu, et al [21].…”

“…Reports show that soil samples in the vicinity of cement factories contain higher levels of exchangeable calcium, sodium, hydrogen, and magnesium, as well as soil organic matter, calcium oxide, sulfur oxide, Pb, Zn, and Cd compared to areas remote from these factories [9]. Along with distance, variables including the direction, soil depth, soil characteristics and biologic factors can change the concentration of heavy metals in industrialized areas [5,10,11].…”

“…Although different materials such as tree rings, tree barks, lichens etc. have been used to detect deposition and assess distribution of heavy metals, soil and plant samples have been proved to be the most effi cient and realistic way to detect, determine and monitor heavy metals distribution within the soil profi le and their bioavailability at different depths of soil for plant growth [11].…”

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

“…To this end, a group of important variables is then selected, which is done in this study by using the Boruta algorithm. Boruta is a feature selection wrapper built around the random forest classification algorithm and helps to determine important variables [64,65]. A detailed description of this feature selection technique can be found in [65,66].…”

Distribution Characteristics and Pollution Assessment of Soil Heavy Metals under Different Land-Use Types in Xuzhou City, China

“…Soil is one of the basic environmental media, and it can be contaminated through the accumulation of heavy metals through the discharge of effluents from mine tailings, coal combustion, sewage sludge, the disposal of high-metal wastes, and land application of fertilizers. Moreover, metal-contaminated crops from contaminated soils pose a threat to human health as a consequence of inhalation or ingestion through the food chain [5,6].…”