Regional metal pollution risk assessment based on a long short-term memory model: A case study of the South Altai Mountain mining area, China. (15th December 2022)

• Record Type: Journal Article
• Authors: Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; Yan, Jining; De Maeyer, Philippe; Van de Voorde, Tim
• Is Part Of: Journal of cleaner production. Volume 379:Part 2(2022)
• Publication Date: 2022-12-15
• Subjects: Copper mines -- Heavy metal pollution -- Risk assessment -- LSTM model -- Southern Altai mountains; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.134755
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The discharge of wastewater and waste rock in mining production activities is a significant hidden cause of soil heavy metal pollution. The accumulation of heavy metals in soil occurs through a variety of processes, and exposure to these metals can permanently damage the human body. Due to multiple factors, such as the formation causes, sources, and distribution trends of heavy metals in mineral resources, reasonably applying different machine learning methods to monitor and evaluate heavy metal pollution remains challenging. In this paper, we choose the copper mining area in the southern Altai Mountains of China as the study area, and 19 different types of spatial data are uniformly managed in a distributed database to improve monitoring efficiency. Furthermore, we propose a heavy metal pollution evaluation framework based on a stacked long short-term memory (LSTM) model, which considers spatial data correlations and extracts spatial clustering features. Information is screened through state updating in the framework, the short-term memory features of long sequences are extracted, and an effective prediction model is established. The results show that 26 of the 31 mining occurrences in the study area are in moderate- and high-pollution-risk grids, suggesting that the spatial distribution of copper mines is consistent with the predicted spatial distribution of pollution risk. Overall, these results show that using the optimized stacked LSTM model to integrate multisource geological features and mine the internal rules of feature information has a positive effect on improving the risk assessment of heavy metal pollution. Graphical abstract: Image 1 Highlights: Big data technology and multivariate high-resolution data were used for high-risk area identification. An efficient deep learning-based method for the spatiotemporal modeling of heavy metal pollution. A wide range of heavy metal pollution assessment results was obtained with a good performance. Mineral exploration affected the distribution of heavy metal pollutants in soil.