Abstract
Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression ( and normalized root mean squared error (re-scaled to [0,1]) = ) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.
Highlights
Contamination due to heavy metal pollution has emerged as one of the leading environmental concerns in the last few decades, causing a variety of adverse effects on both human and overall ecosystem health
Published literature further indicates that arsenic is toxic to human neural development: consuming arsenic has been linked with oxidative stress, which interferes with the biological ability to detoxify accumulating oxygen reactive species and potentially contributes to aging, neurogenerative diseases, and cancer [7]
Observe how similar the 47 mg/kg and 49 mg/kg spectra are to each other, while both are clearly different from their higher concentration counterparts (Figure 5)
Summary
Contamination due to heavy metal pollution has emerged as one of the leading environmental concerns in the last few decades, causing a variety of adverse effects on both human and overall ecosystem health. Human activities such as poor waste management, fossil fuel exploitation, and excessive mining of metal ores release arsenic (As) into soil and groundwater [1], from where it can spread throughout the environment. Past medical research and data show that inorganic arsenic exposure can cause many different types of cancer such as lung cancer [5], leading to its classification as a Group 1 human carcinogen [6]. Traditional approaches to arsenic contamination detection involve two methods: lab-based techniques and colorimetric field tests
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