Fuzzy soil quality index using resistivity and induced polarization for contamination assessment in a lead and zinc drainage irrigation field study

Abstract

Soil pollution is a significant environmental threats that cannot be directly measured from the surface. Therefore, it is essential to have metrics for assessing soil's environmental quality efficiently. The Fuzzy Soil Quality Index (FSQI) is constructed by analyzing resistivity and chargeability using four Gaussian membership functions to achieve an FSQI ranging from 0 to 100, where 0 denotes the lowest quality, and 100 denotes the highest quality. SigmaSQI and PiSQI are also introduced and statistically analyzed to enhance the performance of FSQI. Cross-validations indicate good fits, and the results are justified by experimental data. By changing the resistivity due to contaminants in the soil, the presence or absence of contamination can be traced. To achieve this, two geophysical profiles with a surveying length of 50 m on a track were performed time-lapse, once after olive and grape shrubs were irrigated with lead and zinc drainage and then again after two weeks. The contamination spread vertically and laterally so that the lateral resistivity of less than 18 Ωm reached more than 371 Ωm after the spread of pollution. As time went by, the contaminated area is reduced due to the absorption of pollutants by plant roots and microorganisms in the soil. The research results demonstrate that plants have high efficiency in absorbing pollutants due to their broad-rooted system and can reduce the risk of leaching and the movement of heavy metal contaminants to groundwater resources. Finally, to validate the presented models, five soil samples in different depths related to the presence of pollutants in the interpreted models were collected, these samples were analyzed in critical sections, and the results presented by the introduced methods showed good compliance.