Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

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 (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) 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.

Removal of arsenic from polluted water through adsorption onto selected soil

An arsenic contamination survey was conducted in Lake Datunhai which is contaminated by arsenic in Southwest China. The arsenic-contaminated=soil was selected and the arsenic adsorption on the soil solid in the polluted water was studied through field investigation and laboratory experiments. The results showed that the red soil in survey area contains a relatively large amount of arsenic in a range of 36.41–153.52 mg/kg, which is higher than the National Soil Environmental Quality Standard Grade III. The arsenic pollution in the surveyed lake was relatively serious, as the amount of arsenic contained in the water of lake Datunhai was between 0.23 and 0.663 mg/L. The amount of arsenic in the sediment was between 273 and 755 mg/kg. The results of laboratory adsorption experiments with different soils showed that when the ratio of soil and water was 3.5/1000, the amount of arsenic in the water sample taken from the lake reduces from 0.3 mg/L to 0.03 mg/L, which would satisfy the Water Quality Standard Grade III (below 0.05 mg/L). The selected red soil was relatively effective for arsenic absorption. Based on theoretical calculations, the thickness of sediment will increase by about 0.95 cm by applying fixed arsenic solid on arsenic polluted water treatment. HIGHLIGHT A arsenic contamination survey was conducted in Lake Datunhai which is contaminated by arsenic in Southwest China. The arsenic-contaminated soil was selected and the arsenic adsorption on the soil solid in the polluted water was studied through field investigation and laboratory experiments.