Assessing sediment organic pollution via machine learning models and resource performance. (October 2022)

• Record Type: Journal Article
• Authors: Huang, Na; Gao, Kai; Yang, Weiming; Pang, Han; Yang, Gang; Wu, Jun; Zhang, Shirong; Chen, Chao; Long, Lulu
• Is Part Of: Bioresource technology. Volume 361(2022)
• Publication Date: 2022-10
• Subjects: Machine learning -- Organic pollution indicators -- Sediment -- Resource utilization -- Persulfate activation; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127710
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Organically polluted sediments were an efficient catalyst precursor for SR-AOPs. The link between catalytic performance and pollution indicators was established. Machine learning models could predicted organic pollution indicators of sediment. New insights into sediment assessment and resource utilization were presented. Abstract: Due to the potential ecological risks of organic pollution in sediments, aquatic ecosystems are currently facing substantial environmental threats. Assessing and controlling sediment pollution has become a huge challenge. Therefore, this study proposes a novel strategy for predicting organic pollution indicators for sediment, as well as an effective resource-utilization method. Contaminated sediments were converted into catalysts for sulfate radical advanced oxidation technologies by a one-step calcination method. The results revealed that the catalyst excelled in activating peroxymonosulfate to degrade tetracycline via a non-radical pathway. Most importantly, a predictive model of organic pollution indicators was established by machine learning. This study provides a novel approach for resource utilization and a strategy for assessing organic pollution in sediments.