Regional and global hotspots of arsenic contamination of topsoil identified by deep learning

Mengting Wu, Chongchong Qi, Sybil Derrible, Yosoon Choi, Andy Fourie, Yong Sik Ok

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Topsoil arsenic (As) contamination threatens the ecological environment and human health. However, traditional methods for As identification rely on on-site sampling and chemical analysis, which are cumbersome, time-consuming, and costly. Here we developed a method combining visible near infrared spectra and deep learning to predict topsoil As content. We showed that the optimum fully connected neural network model had high robustness and generalization (R-Square values of 0.688 and 0.692 on the validation and testing sets). Using the model, the relative As content at regional and global scales were estimated and the human populations that might potentially be affected were determined. We found that China, Brazil, and California are topsoil As-contamination hotspots. Other areas, e.g., Gabon, although also at great risk, are rarely documented, making them potential hotspots. Our results provided guidance for regions that require more detailed detection or timely soil remediation and can assist in alleviating global topsoil-As contamination.

Original languageEnglish
Article number10
JournalCommunications Earth and Environment
Issue number1
Early online date3 Jan 2024
Publication statusE-pub ahead of print - 3 Jan 2024


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