Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS.

Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R2 ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.

Comparison of long-term energy efficiency and microbial community dynamics of different reactors in response to increased loadings of water hyacinth juice.

Water hyacinth is considered to be among the worst invasive weed species globally, causing detrimental environmental and social problems worldwide. It rapidly grows, and therefore has significant potential as a resource. Due to its high moisture content (approximately 95%), the by-product obtained by dehydrating water hyacinth yields a considerable amount of water hyacinth juice (WHJ). In this study, we performed a comparative assessment of long-term energy efficiency, maximum treatment capacity limits, and microbial community dynamics of modified internal circulation (MIC) and up-flow anaerobic sludge blanket (UASB) reactors in response to increasing loadings of WHJ. The MIC reactor exhibited a higher energy recovery rate and stronger performance compared with the UASB reactor. The optimal organic loading rates of the MIC and UASB reactors were 17.93 and 8.85 kg chemical oxygen demand (COD)/m3/d, with methane conversion rates of 0.21 and 0.15 m3 CH4/kg COD, respectively. Furthermore, the engineering costs and project floor space required by the MIC reactor are less than those in the case of the UASB reactor. The high-throughput sequencing analysis indicated that the dominant phyla (e.g. Firmicutes and Bacteroidetes) were more abundant using the MIC reactor than with the UASB reactor, which may indicate WHJ degradation efficiency. Both reactors had similar predominant methanogens, suggesting that acetoclastic methanogenesis was the predominant metabolic pathway of methane formation. The results of this study provide new insights into the sustainable management of water hyacinth as a resource by establishing a regional ecosystem with biogas engineering applications.