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

Heavy metal contamination and its impact on the food chain: exposure, bioaccumulation, and risk assessment DOI Creative Commons

B Raksha Shetty,

Jagadeesha B Pai, S A Salmataj

et al.

CyTA - Journal of Food, Journal Year: 2025, Volume and Issue: 23(1)

Published: Jan. 2, 2025

Non-essential heavy metals (HMs) are one of the most toxic substances released into environment, affecting food chain and posing a threat to security. The research data was collated after carefully observing some studies conducted on commonly consumed products highlighting metal exposure pathways crops techniques adapted quantification HMs in chain. tools developed estimate ecological health risks induced via ingestion HM-contaminated both children adults India discussed. It is observed that Cd, Cr, Cu, Pb, Zn studied products. Bioaccumulation indices Indian revealed varying intake. Children suffer more from consuming contaminated with than adults. This review summarizes distribution HMs, their pollution, correlation between each HM concentration.

Language: Английский

Citations

2
Melatonin protects against cadmium-induced endoplasmic reticulum stress and ferroptosis through activating Nrf2/HO-1 signaling pathway in mice lung DOI

Ziyang Huang,

Rui‐Jia Xu,

Zhongjun Wan

et al.

Food and Chemical Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 115324 - 115324

Published: Feb. 1, 2025

Language: Английский

Citations

1
Silicon and selenium alleviate cadmium toxicity in Artemisia selengensis Turcz by regulating the plant-rhizosphere DOI
Zhen Wang, Yin Wang,

Jiliang Lü

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 252, P. 119064 - 119064

Published: May 6, 2024

Language: Английский

Citations

8
Heavy Metal Contamination in Urban Soils: Health Impacts on Humans and Plants: A Review DOI Creative Commons
Abdul Wahid Monib, Parwiz Niazi,

Azizaqa Azizi

et al.

European Journal of Theoretical and Applied Sciences, Journal Year: 2024, Volume and Issue: 2(1), P. 546 - 565

Published: Jan. 1, 2024

This research looks at how the growth of cities and industries affects levels heavy metals in soil, which can impact people's health. We find out where pollution comes from, such as factories, car fumes, improper waste disposal, by reviewing existing studies. use different methods to test soil for study exposure these urban areas The evidence shows a connection between high city health problems like breathing issues, brain disorders, overall toxicity body. also explore get into human body, highlighting importance understanding they are available ways people exposed. To deal with polluted soils, we look manage suggest sustainable reduce metal pollution. Our discoveries add what know about environmental health, emphasizing need actions protect residents. Ultimately, this aims give important information insights policymakers, planners, public officials managing lessening risks linked contamination soils.

Language: Английский

Citations

7
Hotspot mapping and risk prediction of fluoride in natural waters across the Tibetan Plateau DOI
Yi Yang, Ru Zhang,

Yangzong Deji

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 465, P. 133510 - 133510

Published: Jan. 13, 2024

Language: Английский

Citations

6
Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms DOI Creative Commons

Mengge Zhou,

Yonghua Li

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(14), P. 2681 - 2681

Published: July 22, 2024

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 (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, Sentinel-2) was used compare predictive performance empirical bayesian kriging regression, random forest, CatBoost models. The model exhibited highest with both training testing datasets, an average MAE 1.86, RMSE 3.11, R2 0.59 datasets. Among explanatory factors, Na most important for predicting EC1:5, followed by normalized difference vegetation index organic carbon. Soil 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 saline–alkali soils > ds/m. Our results provide valuable insights policymakers improve land quality plan regional agricultural development.

Language: Английский

Citations

4
Revealing the Synergistic Spatial Effects in Soil Heavy Metal Pollution with Explainable Machine Learning Models DOI

Yibo Yan,

Yong Yang

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 482, P. 136578 - 136578

Published: Nov. 19, 2024

Language: Английский

Citations

4
Impacts of quaternary ammonium compounds on the ecological risks of cadmium, enzyme activities, and bacterial community in soils DOI Creative Commons

Jie Li,

Haiyan Chen,

Fengrui Zi

et al.

Environmental Technology & Innovation, Journal Year: 2025, Volume and Issue: unknown, P. 104047 - 104047

Published: Jan. 1, 2025

Language: Английский

Citations

0
Utilization of Antagonistic Interactions Between Micronutrients and Cadmium (Cd) to Alleviate Cd Toxicity and Accumulation in Crops DOI Creative Commons
Muhammad Shahzad, Ayesha Bibi, Ameer Khan

et al.

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 707 - 707

Published: Feb. 26, 2025

The presence of cadmium (Cd) in agricultural soils poses a serious risk to crop growth and food safety. Cadmium uptake transport plants occur through the various transporters nutrient ions that have similar physical chemical properties Cd, indicating genetic manipulation these agronomic improvement Cd-antagonistic nutrients could be good approach for reducing Cd accumulation crops. In this review, we discuss interactions between some micronutrients, including zinc (Zn) manganese (Mn), focusing on their influence expression genes encoding Cd-related transporters, ZIP7, NRAMP3, NRAMP4. Genetic improvements enhancing specificity efficiency optimizing micronutrient nutrition can inhibit by transporters. This comprehensive review provides deep insight into fighting against contamination sustainable production.

Language: Английский

Citations

0
GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks DOI Creative Commons
Olusegun Folorunso, Oluwafolake Ojo, Mutiu Abolanle Busari

et al.

Frontiers in Sustainable Food Systems, Journal Year: 2025, Volume and Issue: 9

Published: March 6, 2025

Introduction Most farmers in Nigeria lack knowledge of their farmland’s nutrient content, often relying on intuition for crop cultivation. Even when aware, they struggle to interpret soil information, leading improper fertilizer application, which can degrade and ground water quality. Traditional analysis requires field sample collection laboratory analysis; a tedious time-consuming process. Digital Soil Mapping (DSM) leverages Machine Learning (ML) create detailed maps, helping mitigate depletion. Despite its growing use, existing DSM-based ML methods face challenges prediction accuracy data representation. Aim This study presents GeaGrow, an innovative mobile app that enhances agricultural productivity by predicting properties providing tailored recommendations yam, maize, cassava, upland rice, lowland rice southwest using Artificial Neural Networks (ANN). Materials The presented method involved the samples from six states were analysed compile primary dataset mapped coordinates. A secondary was compiled iSDAsoil’s API augmentation validation. two sets pre-processed normalized Python, ANN employed predict such as NPK, Organic Carbon, Textural Composition pH levels through regressive while building composite model Texture Classification based predicted composition. model’s performance yielded Mean Absolute Error (MAE) 1.9750 NPK Carbon prediction, 3.5461 0.1029 prediction. For classification texture, results showed high value 99.9585%. Results highlight effectiveness combining texture with retention, optimize application. GeaGrow provides accessible, location-based insights personalized recommendations, marking significant advancement technology. also smallholder scalable, ease adoption use developed Conclusion research demonstrates potential transform management improve yields, contributing sustainable farming practices Nigeria.

Language: Английский

Citations

0