A Machine-Learning Framework for the Spatial Distribution Modeling of Potentially Toxic Elements in Urban and Peri-Urban Soils

被引:0
|
作者
Suleymanov, A. R. [1 ,2 ,3 ]
Kulagin, A. A. [4 ]
Suleymanov, R. R. [3 ]
机构
[1] Ufa State Petr Technol Univ, Decarbonisat Technol Ctr, Lab Artificial Intelligence Environm Res, Ufa 450064, Russia
[2] Ufa State Petr Technol Univ, Dept Environm Protect & Prudent Exploitat Nat Reso, Ufa 450064, Russia
[3] Russian Acad Sci, Ufa Inst Biol, Ufa Fed Res Ctr, Lab Soil Sci, Ufa 450054, Russia
[4] Nizhnevartovsk State Univ, Fac Ecol & Engn, Dept Ecol, Nizhnevartovsk 628600, Russia
来源
WATER AIR AND SOIL POLLUTION | 2025年 / 236卷 / 03期
关键词
Toxic elements; Heavy metals; Random forest; Urban soils; Machine learning; HEAVY-METAL CONCENTRATIONS; CONTAMINATION LEVEL; ROADSIDE SOILS; SURFACE SOILS; LAND-USE; POLLUTION; VARIABILITY; PREDICTION; REGION; PLAIN;
D O I
10.1007/s11270-025-07791-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Despite significant advances in knowledge about the spatial distribution of potentially toxic elements (PTEs), the accurate determination them in urban areas remains a challenge due to multiple sources. We investigated concentrations and spatial patterns for six PTEs, namely Pb, Cu, Ni, Zn, Cd and As in the top layer of urban and peri-urban soils. The study was conducted on the territory of the Ufa city (Russia), with a population of more than 1 million people and an area of more than 700 km2. For these purposes, a total of 250 soil samples were collected at 0-20 cm depth. Random Forest algorithm, in combination with environmental and anthropogenic variables, was applied for the spatial prediction of PTEs. The covariates were represented by distance from river, topographic attributes, remote sensing data, geology and soil properties, distance from highways and railroads, their density, distance from combined heat and power, and refineries. Results showed that Pb, Cu, Ni, Zn, Cd and As contents ranged from 1 to 98.1 mg/kg, 1.5 to 360 mg/kg, 1.7 to 110 mg/kg, 1 to 336 mg/kg, 0.2 to 1 mg/kg, and 0.1 to 7.4 mg/kg, respectively. The average values of elements did not exceed the maximum and approximate permissible concentrations (MPC, APC). Terrain and anthropogenic-related covariates were estimated as the most important predictors. The generated maps revealed geographic trends and hotspot concentrations of Pb, Cu, Ni and Zn. Our findings and generated maps can provide useful information for future digital soil mapping of PTEs in urban areas.
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页数:13
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