Slime Mold Algorithm: a novel approach for estimating total petroleum hydrocarbons in soil

Area of Science:

• Environmental Science
• Geochemistry
• Computational Modeling

Background:

• Soil contamination by total petroleum hydrocarbons (TPHs) poses environmental risks.
• Accurate estimation of TPHs is crucial for effective environmental management.
• Previous methods may lack precision in predicting complex hydrocarbon pollution patterns.

Purpose of the Study:

• To model and predict total petroleum hydrocarbon (TPH) levels in soil using the Slime Mold Algorithm (SMA).
• To assess the accuracy of the SMA in estimating aliphatic and polycyclic aromatic hydrocarbons (PAHs).
• To provide a framework for environmental pollution management through advanced predictive modeling.

Main Methods:

• Collected 30 soil samples from Ahvaz Operation Unit 1 (2012-2021) using systematic grid sampling.
• Measured TPHs, including aliphatics and PAHs, using gas chromatography (GC).
• Implemented the Slime Mold Algorithm (SMA) incorporating climate data for enhanced prediction accuracy.

Main Results:

• The SMA model demonstrated high accuracy, with R² values of 0.72 for aromatics and 0.65 for aliphatics.
• Highest PAH contamination reported for Chrysene (Chr) and Benzo[a]anthracene (B.a.a).
• Statistically significant spatial variations in TPH concentrations were observed across sampling stations (p < 0.05).

Conclusions:

• The Slime Mold Algorithm (SMA) is a highly effective tool for modeling and predicting soil contamination.
• SMA offers a practical and accurate framework for environmental pollution management.
• Further validation across diverse climatic regions is recommended for broader applicability of the SMA model.