Emerging Contaminants最新文献

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IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

Hydrochar supported Mg–Fe layered double hydroxide hybrid for efficient and reusable removal of tetracyclines from water 氢炭负载的Mg-Fe层状双氢氧化物混合物用于高效和可重复使用的水中四环素的去除

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01 DOI: 10.1016/j.emcon.2025.100623

Jeffrey Saúl Cedeño-Muñoz , Bryan Jesus Zumarraga-Valencia , Jaime E. Cevallos-Mendoza , Bryan Fernando Rivadeneira-Mendoza , Iris B. Pérez-Almeida , Krishna Kumar Yadav , María Dolores Saquete , Nuria Boluda-Botella , Joan Manuel Rodríguez-Díaz

A hybrid photocatalyst, Mg–Fe-LDH@HC (layered double hydroxide supported on corn-stalk hydrochar), was synthesized via coprecipitation and evaluated for the removal and photodegradation of tetracyclines in water. X-ray diffraction (XRD) confirmed the formation of a hydrotalcite-like phase with partial amorphous carbon contribution, while SEM–EDS mapping evidenced a uniform dispersion of Mg–Fe-LDH over the hydrochar surface. The hybrid exhibited an apparent optical bandgap of 1.81 eV, favoring visible-light absorption. Under ultraviolet irradiation and optimized operational conditions (H₂O₂ = 5.4 μM, pH = 6.0, 25 °C), Mg–Fe-LDH@HC achieved 99.09 % total tetracycline degradation after 120 min. Kinetic fitting followed a pseudo-second-order model (R² > 0.94), indicating a chemisorption-dominated mechanism coupled with reactive oxygen species (ROS) generation. Radical scavenging and EDTA inhibition tests demonstrated that photogenerated electrons and •OH radicals were the main oxidative agents, with additional contributions from a heterogeneous Fenton pathway with no evidence of a homogeneous Fenton mechanism. The catalyst retained 91.3 % of its initial efficiency after five reuse cycles, with partial recovery after mild regeneration. Acute toxicity assays using Artemia salina, Daphnia magna, and Raphidocelis subcapitata revealed a 65–80 % reduction in post-treatment toxicity compared to the untreated effluent. These results demonstrate that Mg–Fe-LDH@HC is an efficient, recyclable, and eco-compatible photocatalyst for the degradation of antibiotic contaminants in water systems.

采用共沉淀法合成了复合光催化剂Mg - Fe-LDH@HC（玉米秸秆炭负载层状双氢氧化物），并对其去除水中四环素的光降解效果进行了研究。x射线衍射（XRD）证实了类水滑石相的形成，其中有部分非晶态碳的贡献，而SEM-EDS映射证实了Mg-Fe-LDH在水合物表面的均匀分散。该杂化材料的光学带隙为1.81 eV，有利于可见光的吸收。在紫外光照射下，优化操作条件（H2O2 = 5.4 μM, pH = 6.0, 25℃）下，Mg - Fe-LDH@HC在120 min后总四环素的降解率达到99.09%。动力学拟合遵循伪二阶模型（R2 > 0.94），表明化学吸附为主的机制与活性氧（ROS）的产生相结合。自由基清除和EDTA抑制试验表明，光生电子和•OH自由基是主要的氧化剂，非均相芬顿途径也有额外的贡献，没有证据表明是均相芬顿机制。经过5次循环使用后，催化剂的效率仍保持在初始效率的91.3%，轻度再生后可部分恢复。急性毒性试验使用青蒿，大水蚤，和Raphidocelis subcapitata显示处理后毒性降低65 - 80%相比，未经处理的流出物。这些结果表明，Mg - Fe-LDH@HC是一种有效的、可回收的、生态相容的光催化剂，用于降解水系统中的抗生素污染物。

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Distribution, driving factors and prediction of antibiotics in Asia's largest River Basin: Comprehensive insights of Yangtze River Basin, China 亚洲最大流域抗生素分布、驱动因素及预测——以中国长江流域为例

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01 DOI: 10.1016/j.emcon.2026.100624

Jiamei Zhang , Yuyang Zhu , Yu Wang , Xing Chen , Guolian Li , Gege Cai , Fazhi Xie , Youqiang Sun

Antibiotics have been a global concern because of their widespread occurrence and adverse effects on aquatic environments, particularly in large-scale river basins. However, their environmental fate in the Yangtze River Basin remains underexplored. In the present study, we investigated 31 antibiotics in surface water throughout the Yangtze River Basin, the largest river system in China and Asia. The total antibiotic concentrations ranged from 142.05 to 345.57 ng/L, with fluoroquinolones (FQs) predominating and accounting for 75.68 % of the total concentration. Higher antibiotic concentrations were observed in the midstream and downstream regions, particularly in the three major economic zones of the Yangtze River Basin, likely driven by rapid economic development and urbanization. Agricultural and livestock farming intensity, along with nitrogen and phosphorus levels, has significant positive effects on antibiotic concentrations. Antibiotic concentrations showed negative correlations with microbial diversity and community composition, suggesting potential links to shifts in microbial community structure. Analysis using the Positive Matrix Factorization (PMF) model identified four major sources of antibiotic pollution: livestock waste (31.30 %), aquaculture runoff (24.89 %), hospital discharges (21.95 %), and sewage treatment effluents (21.86 %). We also employed four machine learning (ML) models to relate antibiotic concentrations to explanatory variables, enabling prediction of their distribution across the Yangtze River Basin. The Random Forest model demonstrated superior performance in predicting the concentrations of FQs and tetracyclines (TCs), with R² values of 0.75 and 0.74, respectively. The results of this study provide new insights into the transport behavior and prediction of antibiotic concentrations in large river basins.

抗生素因其广泛存在和对水生环境的不利影响而受到全球关注，特别是在大型河流流域。然而，它们在长江流域的环境命运仍未得到充分探讨。本研究对中国乃至亚洲最大水系长江流域地表水中的31种抗生素进行了研究。抗生素总浓度为142.05 ~ 345.57 ng/L，以氟喹诺酮类药物（FQs）为主，占总浓度的75.68%。中下游地区抗生素浓度较高，特别是长江流域三大经济区，这可能是由经济快速发展和城市化驱动的。农业和畜牧业强度以及氮和磷水平对抗生素浓度有显著的积极影响。抗生素浓度与微生物多样性和群落组成呈负相关，表明可能与微生物群落结构的变化有关。利用正矩阵分解（PMF）模型进行分析，确定了抗生素污染的四个主要来源：畜禽废弃物（31.30%）、水产养殖径流（24.89%）、医院排放（21.95%）和污水处理出水（21.86%）。我们还使用了四个机器学习（ML）模型将抗生素浓度与解释变量联系起来，从而能够预测它们在长江流域的分布。随机森林模型在预测FQs和四环素（TCs）浓度方面表现出较好的性能，R2分别为0.75和0.74。本研究结果为大型河流流域抗生素浓度的迁移行为和预测提供了新的见解。

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IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

IF 6.9 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2026-01-01

引用次数: 0

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