A Comprehensive Study of Spatial Distribution, Pollution Risk Assessment, and Source Apportionment of Topsoil Heavy Metals and Arsenic DOI Creative Commons
Honghua Chen, Xinxin Sun,

Longhui Sun

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2151 - 2151

Published: Dec. 10, 2024

Accurately identifying pollution risks and sources is crucial for regional land resource management. This study takes a certain coastal county in eastern China as the object to explore spatial distribution, risk, source apportionment of heavy metals topsoil. A total 633 samples were collected from topsoil with depth ranging 0 20 cm, which came different topographical use types (e.g., farmland, industrial areas, mining areas), concentrations HMs As measured by using atomic fluorescence spectrometry inductively coupled plasma mass spectrometry. Firstly, distribution soil (Cd, Cr, Hg, Ni, Pb) arsenic (As) was predicted incorporating environmental variables strongly affecting formation into geostatistical methods machine learning approaches. Then, various indicators employed conduct evaluations, potential ecological risk assessments implemented based on generated map. Finally, conducted random forest (RF), absolute principal component score–multiple linear regression (APCS-MLR), correlation analysis, As. Findings this research reveal that RF approach yielded best prediction performance (0.59 ≤ R2 0.73). The Nemerow geoaccumulation indices suggest levels exist area. average As, Ni are 7.233 mg/kg, 0.051 27.43 mg/kg respectively, being 1.14 times, 1.27 1.15 times higher than background levels, respectively. central–northern region presented slight Hg Cd identified primary factors. Natural, agricultural, transportation, activities main sources. These findings will assist design targeted policies reduce urban offer useful guidelines similar regions.

Language: Английский

Spatial-machine learning framework for rapid identification of soil cadmium risk in high geochemical background areas DOI
Cheng Li, Zhongfang Yang, Dong‐Xing Guan

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: unknown, P. 138091 - 138091

Published: April 1, 2025

Language: Английский

Citations

0
Application of machine learning methods for predicting selenium accumulation in the soil‒rice system of a typical karst area DOI
Molan Tang, Bolun Fan,

LU Guang-hui

et al.

Journal of Soils and Sediments, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Language: Английский

Citations

0
Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration DOI Open Access
Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(5), P. 996 - 996

Published: May 14, 2024

As urbanization accelerates, the contamination of urban soil and consequent health implications stemming from expansion are increasingly salient. In recent years, a plethora cities regions nationwide have embarked on rigorous geological surveys with focus environmental quality, yielding invaluable foundational data. This research aims to develop scientifically robust rational land-use planning strategies while assessing levels heavy metal pollution associated risks. The agglomeration encompassing Zhengzhou, Luoyang, Kaifeng (referred as Zheng–Bian–Luo Urban Agglomeration) in Henan Province was designated study area. Leveraging Nemerow comprehensive index method alongside Hakanson potential ecological risk assessment method, this delved into ramifications nine metals, namely Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, Co. Research indicates that hierarchy individual risks ranges most least significant follows: Cd > Pb Cr Ni Cu Zn Mn concentrations both Zhengzhou surpassed established background levels. Furthermore, mean single-factor values for metals exceeded 1, signifying state minor pollution. P is between 1 < Pcomp ≤ 2, which considered mild other seven elements all less than 0.7, reaching clean (alert) level. Predominantly, primary factor superficial comparatively minimal. quality within area remains secure, although certain localized areas pose A current essential establish theoretical foundation provide technical support protection, mitigation, sustainable utilization.

Language: Английский

Citations

3
Distribution, sources, and contamination evaluation of heavy metals in surface sediments of the Qizhou Island sea area in Hainan, China DOI

Jianxiu Fan,

Lin Zhang, Anqi Wang

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 208, P. 116933 - 116933

Published: Sept. 10, 2024

Language: Английский

Citations

3
Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models DOI

Mengge Zhou,

Yi Yang, Yan Guo

et al.

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 217, P. 118072 - 118072

Published: May 5, 2025

Language: Английский

Citations

0
Uptake and translocation of cadmium and trace metals in common rice varieties at different growth stages DOI
Palanisamy Vasudhevan, Shengyan Pu, G. Sridevi

et al.

Environmental Geochemistry and Health, Journal Year: 2024, Volume and Issue: 46(9)

Published: Aug. 14, 2024

Language: Английский

Citations

2
Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils DOI
Ram Proshad, S Asha,

Rong Kun Jason Tan

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 481, P. 136536 - 136536

Published: Nov. 19, 2024

Language: Английский

Citations

2
Comprehensive Perspective on Contamination Identification, Source Apportionment, and Ecological Risk Assessment of Heavy Metals in Paddy Soils of a Tropical Island DOI Creative Commons
Yan Guo, Yi Yang, Yonghua Li

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(8), P. 1777 - 1777

Published: Aug. 13, 2024

The closed-loop material and energy cycles of islands increase the susceptibility their internal ecosystem components to heavy metal accumulation transfer. However, limited research on island scale hinders our understanding environmental geochemistry in this unique environment. This study focused assessing a tropical island’s ecological risk by investigating contamination potential sources. results revealed elevated cadmium nickel concentrations 0.44–1.31% soil samples, particularly coastal plains developed areas. Using absolute principal component score-multiple linear regression (APCS-MLR) model assisted GIS mapping, we identified three sources: geological factors, agricultural activities, traffic emissions. Network analysis indicated direct exposure risks vegetation microorganisms contaminated (0.4611 0.7687, respectively), with posing highest risk, followed Zn, Cd, Pb, Cu, Cr transferring across trophic levels. These findings provide crucial insights for mitigating associated metals controlling priority pollutants sources environments.

Language: Английский

Citations

1
A Comprehensive Study of Heavy Metals in Centralized Drinking Water Sources of the Yangtze River Basin: Levels, Sources, and Probabilistic Health Risk DOI Open Access

Kunfeng Zhang,

Sheng Chang,

Xiang Tu

et al.

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3495 - 3495

Published: Dec. 4, 2024

The water quality of centralized drinking sources (CDWSs) in the Yangtze River Basin (YRB) has received widespread public attention. Regrettably, due to lack large-scale and high-frequency monitoring data, trends, sources, risks heavy metals (HMs) CDWSs YRB are still unclear. In addition, correlation between HMs parameters natural not been established, which greatly affects efficiency management. Herein, we collected data for eight twelve physical–chemical from 114 71 prefecture-level cities region. An unprecedented spatial distribution map region was drawn, response nutrient levels studied. Overall, level HM pollution low, but threat chloride, nitrogen, phosphorus exists. detection rates ranged 60.00% (Ti) 99.82% (Fe), mean concentrations were ranked as follows: Fe (36.576 ± 36.784 μg/L) > Mn (7.362 7.347 Ti (3.832 6.344 Co (2.283 3.423 Se (0.247 0.116 Cd (0.089 0.286 Be (0.054 0.067 Tl (0.015 0.012 μg/L). large geographic area, total exhibited a fluctuating decay trend over time 2018 2022. Geographically, industrial agricultural production geological coupling factors led significant heterogeneity following order: midstream downstream upstream. Importantly, this study proved that Cl−, SO42−, may drive absorption transfer water. Fortunately, exposure does cause adverse health effects humans.

Language: Английский

Citations

1
Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms DOI

Mengge Zhou,

Yonghua Li, Xiaoyong Liao

et al.

Published: Jan. 1, 2024

Soil salinization is a major soil degradation process threatening ecosystems and posing great challenge to sustainable agriculture food security worldwide. This study aimed evaluate the potential of state-of-the-art machine learning algorithms in salinity (EC1: 5) mapping. Further, we predicted distribution patterns under different future scenarios Yellow River Delta. A geodatabase comprising 201 samples 19 conditioning factors was used compare predictive performance ordinary kriging, inverse distance weighting regression, random forest, CatBoost models. The model exhibited highest with both training (MAE=0.383, RMSE = 0.601) testing datasets (MAE=0.403, 0.670). Among explanatory factors, Na2O most important for predicting EC1:5, followed by normalized difference vegetation index organic carbon. EC1:5 predictions suggested that Delta region faces severe salinization, particularly coastal zones. three increases carbon content (1, 2, 3 g/kg), 2 g/kg scenario resulted best improvement effect on saline-alkali soils > ds/m. Our results provide valuable insights policymakers improve land quality plan regional agricultural development.

Language: Английский

Citations

0