Appraise potassium chemistry and distribution patterns in tailing soil, India: Through quantity - Intensity relations and multi model statistical methods

Abstract

Tailings are waste materials left behind after mineral extraction. Giridih district of Jharkhand, India has the second largest ore of mica mines in the country. This study evaluated the forms of potassium (K⁺) and quantity-intensity relationships in soils contaminated by tailings around the abundant mica mines. A total of 63 rice rhizosphere soil samples (8-10 cm depth) were collected from agricultural fields near 21 mica mines in the Giridih district at different distances: 10 m (zone 1), 50 m (zone 2), and 100 m (zone 3). The samples were collected to quantify various forms of potassium in the soil and characterize non-exchangeable K (NEK) reserves and Q/I isotherms. The semi-logarithmic release of NEK with continuous extractions suggests a decrease in release over time. Significant values of threshold K⁺ levels were observed in zone 1 samples. As K⁺ concentrations increased, the activity ratio (AR_e^K) and its corresponding labile K⁺ (K_L) concentrations decreased. The AR_e^K, K_L, and fixed K⁺ (K_X) values were higher in zone 1 [AR_e^K: 3.2 (mol L^-1)^1/2 × 10^-4, K_L: 0.058 cmol kg^-1, and K_X: 0.038 cmol kg^-1), except for readily available K⁺ (K₀) for zone 2 (0.028 cmol kg^-1). The potential buffering capacity and K⁺ potential values were higher in zone 2 soils. In zone 1, Vanselow selectivity coefficients (K_V) and Krishnamoorthy-Davis-Overstreet selectivity coefficients (K_KDO) were higher, while Gapon constants were higher in zone 3. It was found that AR_e^K was significantly correlated with K₀, K_L, K⁺ saturation, -ΔG, K_V, and K_KDO. Different statistical methods such as positive matrix factorization, self-organizing maps, geostatistics, and Monte Carlo simulation approaches were employed to predict soil K⁺ enrichment, source apportionment, distribution patterns, availability for plants, and contribution to soil K⁺ maintenance. Thus, this study significantly contributes to understanding K⁺ dynamics in mica mine soils and operational K⁺ management.

Keywords: AR(e)(K); Multi statistical modelling; PBC(K); Q/I; Soil K(+) forms; Step - K and CR - K.