Using Spatial Regression to Model Potentially Toxic Metal (PTM) Mobility Based on Physicochemical Soil Properties

Abstract

Mining processes generate waste rock, tailings, and slag that can increase potentially toxic metal (PTM) concentrations in soils. Un-reclaimed, abandoned mine sites are particularly prone to leaching these contaminants, which may accumulate and pose significant environmental and public health concerns. e characterization and spatial delineation of PTMs in soils is vital for risk assessment and soil reclamation. Bumpus Cove, a once active mining district of eastern Tennessee, is home to at least 47 abandoned, un-reclaimed mines, all permanently closed by the 1950s. is study evaluated soil physicochemical properties, determined the spatial extent of PTMs (Zn, Mn, Cu, Pb, and Cd), and examined the influence of soil properties on PTM distribution in Bumpus Cove, TN. Soil samples (n � 52) were collected from a 0.67 km 2 study area containing 6 known abandoned Pb, Zn, and Mn mines at the headwaters of Bumpus Cove Creek. Samples were analyzed for Zn, Mn, Cu, Pb, and Cd by microwave-assisted acid digestion and flame atomic absorption spectrometry (FAAS) (12-1,354 mg/kg Zn, 6-2,574 mg/kg Mn, 1-65 mg/kg Cu, 33-2,271 mg/kg Pb, and 7-40 mg/kg Cd). Of the measured PTMs, only Pb exceeds permissible limits in soils. In addition to the PTM analyses, soil physical (texture, moisture content, and bulk density) and chemical (pH, cation exchange capacity (CEC), and total organic carbon (TOC)) properties were evaluated. Spatially weighted multivariate regression models developed for all PTMs using soil physicochemical properties produced improved results over ordinary least squares (OLS) regression models. Models for Zn (R 2 � 0.71) and Pb (R 2 � 0.69) retained covariates e pH , moisture content, and CEC (Zn), and pH and CEC (Pb). is study will help define PTM concentration and transport and provide a reference for state and local entities responsible for contaminant monitoring in Bumpus Cove, TN.

Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA

“…In particular, clay content, soil pH, and CEC were known to be highly correlated with Co, Cu, Cr, Ni, Pb, and Zn concentrations in Spain's Northern Plateau [21], while physico-chemical soil properties contributed to the spatial distribution of heavy metals in and surrounding an industrial plant in China [23]. In prior research by authors of the current study, spatially weighted multivariate regression models created for Zn, Pb, Cu, Mn, and Cd using soil physico-chemical properties yielded better results over ordinary least squares regression models in an abandoned mine site in Bumpus Cove, TN [22].…”

“…Magno et al (2019) [22] describe statistical analyses and development of spatial regression models for the metals (Mn, Zn, Pb, Cu, and Cd) using physico-chemical characteristics of the soil samples. This research builds upon that analysis using the findings to select variables for cokriging interpolation models and to select an appropriate neighborhood size.…”

“…For each metal, normality was assessed by examining histograms, and where appropriate, a log transform was applied. Two kriging interpolation models were produced in ArcGIS Pro 2.7.0 [46] and compared: (1) ordinary kriging using the optimize utility; (2) ordinary cokriging using the covariates retained in the spatial regression models in the prior research [22]. The number of neighbors for each cokriging model was calculated by [22] describe statistical analyses and development of spatial regression models for the metals (Mn, Zn, Pb, Cu, and Cd) using physico-chemical characteristics of the soil samples.…”

“…Two kriging interpolation models were produced in ArcGIS Pro 2.7.0 [46] and compared: (1) ordinary kriging using the optimize utility; (2) ordinary cokriging using the covariates retained in the spatial regression models in the prior research [22]. The number of neighbors for each cokriging model was calculated by [22] describe statistical analyses and development of spatial regression models for the metals (Mn, Zn, Pb, Cu, and Cd) using physico-chemical characteristics of the soil samples. This research builds upon that analysis using the findings to select variables for cokriging interpolation models and to select an appropriate neighborhood size.…”

“…Bioavailability, movement, and spatial distribution of PTMs in soils is also affected by total concentration [13,14], pH [15], organic matter [16], texture [17,18], cation exchange capacity (CEC) [19], and redox reactions [5,20]. In this regard, researchers evaluated various soil physico-chemical properties in relation to PTM spatial accumulation, determined their influence on and correlation with the PTMs, and estimated the concentration of PTMs based on the soil properties [21][22][23]. In particular, clay content, soil pH, and CEC were known to be highly correlated with Co, Cu, Cr, Ni, Pb, and Zn concentrations in Spain's Northern Plateau [21], while physico-chemical soil properties contributed to the spatial distribution of heavy metals in and surrounding an industrial plant in China [23].…”