Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil

TL;DR

This paper introduces SoilScanner, a wireless RF-based system that detects lead contamination in soil by analyzing signal reflections affected by salts, achieving 72% accuracy in classifying soil samples.

Contribution

The study demonstrates the feasibility of using radio frequency signals and machine learning to detect lead in soil, offering a portable and cost-effective alternative to traditional methods.

Findings

SoilScanner classifies soil samples with 72% accuracy.

Different salts affect RF signals at distinct frequencies.

No high-Pb samples (>500 ppm) were misclassified.

Abstract

Widespread Pb (lead) contamination of urban soil significantly impacts food safety and public health and hinders city greening efforts. However, most existing technologies for measuring Pb are labor-intensive and costly. In this study, we propose SoilScanner, a radio frequency-based wireless system that can detect Pb in soils. This is based on our discovery that the propagation of different frequency band radio signals is affected differently by different salts such as NaCl and Pb(NO3)2 in the soil. In a controlled experiment, manually adding NaCl and Pb(NO3)2 in clean soil, we demonstrated that different salts reflected signals at different frequencies in distinct patterns. In addition, we confirmed the finding using uncontrolled field samples with a machine learning model. Our experiment results show that SoilScanner can classify soil samples into low-Pb and high-Pb categories (threshold at 200 ppm) with an accuracy of 72%, with no sample with > 500 ppm of Pb being misclassified. The results of this study show that it is feasible to build portable and affordable Pb detection and screening devices based on wireless technology.