Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 9; p. 1698
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Fathizad, Hassan, Ali Hakimzadeh Ardakani, Mohammad, Sodaiezadeh, Hamid, Kerry, Ruth, Heung, Brandon, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.04.2021
• Subjects: algorithms; Arid regions; Arid zones; Catchment scale; Chemical contaminants; Contaminants; Contamination; Digital Elevation Models; digital soil mapping; Geology; georeferencing; Groundwater; Heavy metals; Hypercubes; Industrial development; Iran; Land use; Latin hypercube sampling; Lead; machine learning; Manganese; Metals; Nickel; Pollutants; Pollution control; Pollution sources; random forest; Remote sensing; Salinity; Sampling methods; Sediments; soil absorbable heavy metals; Soil contamination; Soil pollution; Soils; Spatial distribution; Temporal distribution; Trends; watersheds; Iran
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13091698

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.