A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest.

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models.

A joint method to assess pollution status and source-specific human health risks of potential toxic elements in soils.

Potential toxic elements (PTEs) in soils follow various exposure pathways (e.g., ingestion, dermal contact, and inhalation) when migrating to the human body, and can threaten human health.

[Quantitative Evaluation of Human Health Risk of Heavy Metals in Soils Based on Positive Matrix Factorization Model and Geo-statistics].

The aim of this study was to quantitatively assess the human health risks derived from different exposure paths of heavy metals in the soil. Zhangqiu county was selected as the study area, and 425 soil samples were collected to measure the As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn contents. A descriptive statistical method was used to assess the heavy metal pollution status of the soils, and the quantitative sources for human health were then determined based on positive matrix factorization (PMF) and geo-statistical techniques.