It is known that 17β-estradiol (E2) can be transformed by reactions mediated by some oxidoreductases such as laccase in water. Whether or how such reactions can happen in soil is however unknown although they may significantly impact the environmental fate of E2 that is introduced to soil by land application of animal wastes. We herein studied the reaction of E2 in a model soil mediated by laccase, and found that the reaction behaviors differ significantly from those in water partly because of the dramatic difference in laccase stability. We also examined E2 transformation in soil using (14)C-labeling in combination with soil organic matter extraction and size exclusion chromatography, which indicated that applied (14)C radioactivity was preferably bound to humic acids. The study provides useful information for understanding the environmental fate of E2 and for developing a novel soil remediation strategy via enzyme-enhanced humification reactions.
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http://dx.doi.org/10.1016/j.envpol.2014.11.023 | DOI Listing |
Environ Sci Process Impacts
January 2025
Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Conventional practices for inorganic nitrogen fertilizer are highly inefficient leading to excess nitrogen in the environment. Excess environmental nitrogen induces ecological (, hypoxia, eutrophication) and public health (, nitrate contaminated drinking water) consequences, motivating adoption of management strategies to improve fertilizer use efficiency. Yet, how to limit the environmental impacts from inorganic nitrogen fertilizer while maintaining crop yields is a persistent challenge.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China.
Interactions between manganese dioxides (MnO) and dissolved organic matter (DOM) have long been the subject of scientific inquiry. However, the effect of MnO crystallinity on the DOM fate remains unclear. Herein, we comprehensively investigate the adsorption, protection, and mineralization of DOM by MnO with various crystallinities (order of crystallinity: γ-30 < γ-90 < γ-120).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, Milano, 20156, Italy.
This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.
View Article and Find Full Text PDFCarbohydr Polym
March 2025
Center of Food Colloids and Delivery for Functionality, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; Beijing Key Laboratory of Functional Food from Plant Resources, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; China Agricultural University-Sichuan Advanced Agricultural & Industrial Institute, Sichuan, Chendu, 610046. Electronic address:
Cereal arabinoxylans (AX) are complex non-digestible polysaccharides and their molecular structural features significantly influence their degradation and metabolic behaviors within the body. This study focuses on investigating the impact of wheat AX hydrolysates produced by different glycoside hydrolases on the gut microbiota during colonic fermentation. Endo-1,4-β-xylanase (XYN) and arabinofuranosidase (ARF) were used to hydrolyze the xylan backbone and remove the arabinose side chains, respectively.
View Article and Find Full Text PDFSci Total Environ
January 2025
Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China; Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangzhou 510045, China.
This study integrated data-driven interpretable machine learning (ML) with statistical methods, complemented by knowledge-driven discrimination diagrams, to identify the primary driving factors of heavy metal (HM) and polycyclic aromatic hydrocarbon (PAH) contamination in agricultural soils influenced by complex sources in a rapidly industrializing region of a megacity in southern China. First, the statistical characteristics of the concentrations of HMs and PAHs, and their correlations with the environmental covariates were explored. Three ML models and a statistical model comprising multiple environmental variable predictors were developed and assessed to predict the concentration of HMs in the agricultural soil.
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