ArticleRSC advances2024
Migration of total petroleum hydrocarbon and heavy metal contaminants in the soil-groundwater interface of a petrochemical site using machine learning: impacts of convection and diffusion.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Soil Pollution and Its Interrelation with Interfacial Chemistry.Molecules (Basel, Switzerland) · 2025Review
- Efficient wastewater treatment and biomass co-production using energy microalgae to fix C, N, and P.RSC advances · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
Convection and diffusion are key pathways for the migration of total petroleum hydrocarbons (TPH) and heavy metals (HMs) from soil to groundwater. However, the extent of their influence on pollutant migration, as well as the nonlinear relationships between these processes and pollutants, remains unclear. This study investigates the spatial distribution of TPH and HMs at a petrochemical site with complex hydrogeological conditions in southwestern China. In addition, machine learning (ML) was used to assess the effects of convection and diffusion on pollutant migration at the soil-groundwater interface. The analysis identifies and reveals TPH, Co, and Ni as the primary pollutants, with soil concentrations reaching 47.427, 7.024, and 4.766 times their respective screening values. Among various ML models, Random Forest (RF) was identified as the most effective, based on
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Registered trials
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