PMID- 37714402
OWN - NLM
STAT- MEDLINE
DCOM- 20231026
LR  - 20231026
IS  - 1873-6424 (Electronic)
IS  - 0269-7491 (Linking)
VI  - 337
DP  - 2023 Nov 15
TI  - Integrative risk assessment method via combining geostatistical analysis, random 
      forest, and receptor models for potentially toxic elements in selenium-rich soil.
PG  - 122555
LID - S0269-7491(23)01557-9 [pii]
LID - 10.1016/j.envpol.2023.122555 [doi]
AB  - Revealing the spatial features and source of associated potentially toxic 
      elements (PTEs) is crucial for the safe use of selenium (Se)-rich soils. An 
      integrative risk assessment (GRRRA) approach based on geostatistical analysis 
      (GA), random forest (RF), and receptor models (RMs) was first established to 
      investigate the spatial distribution, sources, and potential ecological risks 
      (PER) of PTEs in 982 soils from Ziyang City, a typical natural Se-rich area in 
      China. RF combined with multiple RMs supported the source apportionment derived 
      from the RMs and provided accurate results for source identification. Then, 
      quantified source contributions were introduced into the risk assessment. 
      Eighty-three percent of the samples contain Cd at a high PER level in local 
      Se-rich soils. GA based on spatial interpolation and spatial autocorrelation 
      showed that soil PTEs have distinct spatial characteristics, and high values are 
      primarily distributed in this research areas. Absolute principal component 
      score/multiple line regression (APCS/MLR) is more suitable than positive matrix 
      factorization (PMF) for source apportionment in this study. RF combined with RMs 
      more accurately and scientifically extracted four sources of soil PTEs: parent 
      material (48.91%), mining (17.93%), agriculture (8.54%), and atmospheric 
      deposition (24.63%). Monte Carlo simulation (MCS) demonstrates a 47.73% 
      probability of a non-negligible risk (RI > 150) caused by parent material and 
      3.6% from industrial sources, respectively. Parent material (64.20%, RI = 229.56) 
      and mining (16.49%, RI = 58.96) sources contribute to the highest PER of PTEs. In 
      conclusion, the GRRRA method can comprehensively analyze the distribution and 
      sources of soil PTEs and effectively quantify the source contribution to PER, 
      thus providing the theoretical foundation for the secure utilization of Se-rich 
      soils and environmental management and decision making.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - Wu, Hao
AU  - Wu H
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Cheng, Nan
AU  - Cheng N
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Chen, Ping
AU  - Chen P
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Zhou, Fei
AU  - Zhou F
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Fan, Yao
AU  - Fan Y
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Qi, Mingxing
AU  - Qi M
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Shi, Jingyi
AU  - Shi J
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Zhang, Zhimin
AU  - Zhang Z
AD  - Shaanxi Hydrogeolog Engineering Geosciences and Environment Geosciences 
      Investigation Institution, China.
FAU - Ren, Rui
AU  - Ren R
AD  - Shaanxi Hydrogeolog Engineering Geosciences and Environment Geosciences 
      Investigation Institution, China.
FAU - Wang, Cheng
AU  - Wang C
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China.
FAU - Liang, Dongli
AU  - Liang D
AD  - College of Natural Resources and Environment, Northwest A&F University, Yangling, 
      Shaanxi, 712100, China; Key Laboratory of Plant Nutrition and the 
      Agri-environment in Northwest China, Ministry of Agriculture, Yangling, Shaanxi, 
      712100, China. Electronic address: dlliang@nwsuaf.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - England
TA  - Environ Pollut
JT  - Environmental pollution (Barking, Essex : 1987)
JID - 8804476
RN  - 0 (Soil)
RN  - H6241UJ22B (Selenium)
RN  - 0 (Metals, Heavy)
RN  - 0 (Soil Pollutants)
SB  - IM
MH  - Soil
MH  - *Selenium/toxicity/analysis
MH  - *Metals, Heavy/analysis
MH  - Environmental Monitoring/methods
MH  - Random Forest
MH  - *Soil Pollutants/toxicity/analysis
MH  - Risk Assessment/methods
MH  - China
OTO - NOTNLM
OT  - Monte Carlo simulation
OT  - Potentially toxic elements
OT  - Risk assessment
OT  - Selenium-rich soil
OT  - Source identification
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/16 05:41
MHDA- 2023/10/26 06:42
CRDT- 2023/09/15 19:15
PHST- 2023/07/20 00:00 [received]
PHST- 2023/08/30 00:00 [revised]
PHST- 2023/09/12 00:00 [accepted]
PHST- 2023/10/26 06:42 [medline]
PHST- 2023/09/16 05:41 [pubmed]
PHST- 2023/09/15 19:15 [entrez]
AID - S0269-7491(23)01557-9 [pii]
AID - 10.1016/j.envpol.2023.122555 [doi]
PST - ppublish
SO  - Environ Pollut. 2023 Nov 15;337:122555. doi: 10.1016/j.envpol.2023.122555. Epub 
      2023 Sep 13.