Environmental Science and Ecotechnology最新文献_第4页

Accelerating bioelectrodechlorination via data-driven inverse design 通过数据驱动的逆设计加速生物电脱氯

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-09-27 DOI: 10.1016/j.ese.2025.100625

Zhiling Li , Tianyi Huang , Fan Chen , Junqiu Jiang , Aijie Wang

Microbial electrorespiration harnesses bacteria to drive reductive dechlorination, offering a sustainable method to remediate environments contaminated with persistent chlorinated organic pollutants (COPs). However, aquifers' complex hydrogeological and hydrochemical conditions, combined with uneven COP distribution, create substantial spatial and temporal variability in biochemical reactions, environmental factors, and microbial communities. Traditional trial-and-error experiments are labor-intensive and slow, impeding the quick identification of conditions that accelerate dechlorination rates. Here we show that a machine learning framework, integrating experimental design with cathodic biofilm data, uncovers key interrelationships among environmental variables, dechlorination kinetics, electrochemical properties, and functional microbes, enabling rapid optimization of bioelectrodechlorination. Trained on literature-derived datasets using models such as extreme gradient boosting, random forest, and multilayer perceptron, this framework identifies temperature and cathode potential as primary drivers in experimental design while highlighting key biofilm genera, including Clostridium, Desulfovibrio, Dehalococcoides, Pseudomonas, Dehalobacter, Arcobacter, Lactococcus, and Geobacter. It supports inverse design to determine optimal parameters—such as cathode potential, temperature, and additives—for dechlorinating representative COPs, including tetrachloroethene, trichloroethene, and 1,2-dichloroethane, achieving reaction rate predictions with errors below 6 %. This approach surpasses conventional methods by increasing efficiency, cutting costs, and accelerating bioremediation without extensive laboratory testing. By incorporating microbial community insights into predictive models, our data-driven strategy advances the scalable application of microbial electrorespiration for COP-contaminated water remediation and paves the way for broader bioelectrochemical uses in environmental engineering.

微生物电呼吸利用细菌驱动还原性脱氯，为修复被持久性氯化有机污染物（cop）污染的环境提供了一种可持续的方法。然而，含水层复杂的水文地质和水化学条件，加上COP分布不均匀，造成了生物化学反应、环境因子和微生物群落的巨大时空变异。传统的试错实验是劳动密集型和缓慢的，阻碍了快速确定加速脱氯速率的条件。在这里，我们展示了一个机器学习框架，将实验设计与阴极生物膜数据相结合，揭示了环境变量、脱氯动力学、电化学性质和功能微生物之间的关键相互关系，从而实现了生物电脱氯的快速优化。使用极端梯度增强、随机森林和多层感知器等模型对文献数据集进行训练，该框架将温度和阴极电位确定为实验设计的主要驱动因素，同时突出了关键的生物膜属，包括Clostridium、Desulfovibrio、Dehalococcoides、Pseudomonas、Dehalobacter、Arcobacter、Lactococcus和Geobacter。它支持逆向设计，以确定最佳参数，如阴极电位、温度和添加剂，用于脱氯代表性cop，包括四氯乙烯、三氯乙烯和1,2-二氯乙烷，实现反应速率预测误差低于6%。该方法超越了传统方法，提高了效率，降低了成本，加速了生物修复，而无需大量的实验室测试。通过将微生物群落洞察纳入预测模型，我们的数据驱动策略推进了微生物电呼吸在二氧化碳污染水修复中的可扩展应用，并为环境工程中更广泛的生物电化学应用铺平了道路。

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引用次数: 0

Temperature-dependent microbial dynamics in touchless sensor faucets during short-term stagnation 在短期停滞期间，非接触式传感器水龙头中的温度依赖微生物动力学

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-09-24 DOI: 10.1016/j.ese.2025.100624

Anran Ren , Zihan Dai , Xiaoming Li , Walter van der Meer , Joan B. Rose , Gang Liu

Microbial contamination in building plumbing systems poses significant risks to public health at the point of use. Stagnation and warm temperatures are well-known drivers of microbial regrowth, but the effects of common short-term stagnation in touchless sensor faucets—widely used for hygiene and comfort—remain poorly understood. Here we show that microbial water quality in touchless sensor faucets changes during short-term stagnation (0.25–10 h) at varying temperatures (10, 30, and 40 °C). We identify two pivotal time points—2 and 4 h—where microbial diversity decreases and Legionella pneumophila concentrations increase significantly, driven by accelerated chlorine decay and biofilm contributions. Heating to 30 °C maximizes microbial biomass (measured as ATP) but minimizes L. pneumophila proliferation, whereas 40 °C reduces biomass while promoting L. pneumophila growth. These findings reveal a temperature-dependent microbial water quality guarantee period of 2–4 h, beyond which flushing is necessary to mitigate health risks. Optimizing faucet temperatures between 30 and 40 °C could balance microbial safety, user comfort, and energy efficiency, offering practical guidance for managing water quality in modern plumbing systems.

建筑管道系统中的微生物污染在使用点对公众健康构成重大风险。停滞和温暖的温度是众所周知的微生物再生的驱动因素，但在广泛用于卫生和舒适的非接触式传感器水龙头中，常见的短期停滞的影响仍然知之甚少。在这里，我们展示了在不同温度（10、30和40°C）下，非接触式传感器水龙头中的微生物水质在短期停滞（0.25-10小时）期间的变化。我们确定了两个关键时间点- 2和4h -微生物多样性下降，嗜肺军团菌浓度显著增加，由加速氯衰变和生物膜贡献驱动。加热至30°C可使微生物生物量（以ATP衡量）最大化，但使嗜肺乳杆菌增殖最小化，而加热至40°C可减少生物量，同时促进嗜肺乳杆菌生长。这些研究结果表明，温度相关的微生物水质保证期为2-4小时，超过这个时间就需要冲洗以减轻健康风险。优化30至40°C之间的水龙头温度可以平衡微生物安全，用户舒适度和能源效率，为现代管道系统中的水质管理提供实用指导。

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引用次数: 0

Tropical intertidal microbiome response to the 2024 Marine Honour oil spill 热带潮间带微生物组对2024年海洋荣誉石油泄漏的反应

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-09-19 DOI: 10.1016/j.ese.2025.100623

Christaline George , Hashani M. Dharan , Lynn Drescher , Jenelle Lee , Yan Qi , Yijin Wang , Ying Chang , Serena Lay Ming Teo , Benjamin J. Wainwright , Charmaine Yung , Federico M. Lauro , Terry C. Hazen , Stephen B. Pointing

Marine fuel oil (MFO) spills in tropical coastal environments are under-characterized despite increasing risk from maritime activities. Microbial and geochemical responses to the June 2024 Marine Honour MFO spill on Singapore's intertidal sediments were analyzed in real time over 185 days. Using metagenomics and hydrocarbon profiling, microbial community shifts and hydrocarbon degradation were quantified across visibly oiled (high-impact) and clean (low-impact) sites. Microbiomes at all sites adapted rapidly to the spill through increased diversity and abundance of genes encoding alkane and aromatic compound degradation, detoxification, and biosurfactant production. The dominant hydrocarbon-degrading bacteria differed markedly from those reported in other crude oil spills and in regions with different climates. Oil deposition intensity strongly influenced microbial succession and hydrocarbon-degrading gene profiles, and this reflected early toxicity constraints in heavily oiled areas. The persistence of hydrocarbon degradation genes beyond hydrocarbon detection in sediments suggested long-term functional priming may occur. The study provides novel genome-resolved insight into the microbial response to MFO pollution, advances understanding of marine environmental biodegradation, and provides urgently needed baseline data for oil spill response strategies in Southeast Asia and beyond.

尽管海上活动的风险越来越大，但热带沿海环境中的海洋燃料油（MFO）泄漏尚未得到充分描述。在185天的时间里，实时分析了新加坡潮间带沉积物对2024年6月海洋荣誉MFO泄漏的微生物和地球化学反应。利用宏基因组学和碳氢化合物分析，微生物群落的变化和碳氢化合物降解在明显油污（高影响）和清洁（低影响）的地点进行了量化。通过增加编码烷烃和芳香族化合物降解、解毒和生物表面活性剂生产的基因的多样性和丰度，所有地点的微生物组迅速适应了泄漏。优势烃类降解细菌与其他原油泄漏和不同气候地区的报告明显不同。石油沉积强度强烈影响微生物演替和烃降解基因谱，这反映了重油区早期毒性限制。沉积物中烃类降解基因的持久性超过了烃类检测，这表明可能会发生长期的功能启动。该研究为微生物对MFO污染的反应提供了新的基因组解析见解，促进了对海洋环境生物降解的理解，并为东南亚及其他地区的溢油响应策略提供了迫切需要的基线数据。

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引用次数: 0

Real-time sludge moisture monitoring via jet imaging and deep learning 通过喷射成像和深度学习实时监测污泥水分

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-09-01 DOI: 10.1016/j.ese.2025.100614

Tiefu Xu , Bo Zhang , Yue Sun , Man Wang , Yuejia Chen , Penghe Zhu , Binqiao Ren , Yanhong Jie , Guotao Wang

Waste activated sludge from wastewater treatment plants poses a major environmental challenge, with its high moisture content complicating disposal and resource recovery processes across global industries. Efficient sludge management requires precise moisture monitoring to optimize treatment methods, reduce costs, and enhance outcomes such as anaerobic digestion and composting. Traditional approaches for moisture measurement are time-intensive and batch-based, while emerging techniques, such as infrared or nuclear magnetic resonance methods, suffer from inaccuracies, high costs, or limitations in real-time applications. Here we show that sludge jet characteristics, reflecting its non-Newtonian fluid properties, can be captured via high-speed imaging and analyzed with deep learning to accurately predict moisture content within 20 s. By developing a laboratory-scale system of instantaneous capturing of activated sludge jet expansion images (iCASJEI), we acquired over 11,000 jet images across 79–94 % moisture ranges and trained convolutional neural networks, with VGG-16 outperforming AlexNet and LeNet under optimized conditions (0.2 MPa pressure, 4 mm aperture), achieving 93.5 % validation accuracy at 2 % precision and 87.6 % at 1 % precision. These findings show that incorporating iCASJEI to extract non-Newtonian fluid characteristics from sludge jets with deep learning algorithms can substantially reduce testing time for sludge moisture content. This approach could also be applicable to other sectors where non-Newtonian fluid characteristics enable real-time moisture detection in viscous liquids.

来自污水处理厂的废弃活性污泥对环境构成了重大挑战，其高水分含量使全球工业的处理和资源回收过程复杂化。有效的污泥管理需要精确的水分监测，以优化处理方法，降低成本，并提高厌氧消化和堆肥等结果。传统的水分测量方法耗时且基于批量，而新兴技术，如红外或核磁共振方法，在实时应用中存在不准确、高成本或限制。本研究表明，反映其非牛顿流体特性的污泥射流特性可以通过高速成像捕获，并通过深度学习进行分析，从而在20秒内准确预测水分含量。通过开发一个实验室规模的即时捕获活性污泥射流膨胀图像（iCASJEI）系统，我们在79 - 94%的湿度范围内获得了超过11,000张射流图像，并训练了卷积神经网络，vgg16在优化条件（0.2 MPa压力，4 mm孔径）下优于AlexNet和LeNet，在2%精度下达到93.5%的验证精度，在1%精度下达到87.6%。这些研究结果表明，将iCASJEI与深度学习算法结合，从污泥射流中提取非牛顿流体特性，可以大大减少污泥含水率的测试时间。这种方法也可以应用于其他领域，在这些领域中，非牛顿流体特性可以实时检测粘性液体中的水分。

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引用次数: 0

Two decades of ecological wisdom and scientific progress in China 二十年中国生态智慧和科学进步

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-08-12 DOI: 10.1016/j.ese.2025.100613

Jinnan Wang

引用次数: 0

Adjoint analysis of PM2.5 and O3 episodes in priority control zones in China 中国重点控制区PM2.5和O3的伴随分析

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-08-09 DOI: 10.1016/j.ese.2025.100612

Ruixin Zhang , Zhihong Chen , Xueyan Wu , Qiming Liu , Zelin Mai , Zhiyu Zheng , Yilin Chen , Shu Tao , Yongtao Hu , Shunliu Zhao , Amir Hakami , Armistead G. Russell , Huizhong Shen

Understanding and mitigating PM_2.5 and ozone (O₃) pollution remains challenging due to the nonlinear atmospheric chemistry and spatially heterogeneous nature of pollutant emissions. Traditional forward modeling approaches suffer from high computational cost and limited diagnostic resolution to precisely attribute emissions sources at fine spatial, temporal, and chemical scales. Adjoint modeling has emerged as an efficient alternative, enabling high-resolution, multi-pollutant source attribution in a single integrated framework; however, its application to simultaneous PM_2.5–O₃ pollution episodes is limited, particularly in densely populated regions experiencing complex co-pollutant interactions. Here we apply a newly developed multiphase adjoint of the Community Multiscale Air Quality (CMAQ) model to quantify the emission sensitivities of PM_2.5 and O₃ concentrations during pollution episodes in major urban agglomerations. Our results indicate that local emissions predominantly drive PM_2.5 concentrations, contributing up to 79 μg m⁻³. In contrast, O₃ episodes are largely initiated by regional transport (3.8–7.3 ppbv), surpassing local emission contributions during episode onset. The sensitivity analyses reveal distinct spatial emission signatures and pollutant-specific influences from critical precursors, including volatile organic compounds (VOCs; up to 15.9 ppbv O₃, 11.4 μg m⁻³ PM_2.5), nitrogen oxides (NO_x; 16.6 ppbv O₃, 13.8 μg m⁻³ PM_2.5), and ammonia (NH₃; up to 8.7 μg m⁻³ PM_2.5). This study demonstrates the diagnostic strength and predictive capabilities of adjoint modeling in unraveling complex source–receptor relationships. By offering detailed, pollutant-specific emission sensitivity information, our approach provides a robust foundation for precision-driven emission control strategies and improved cross-regional policy coordination, substantially advancing air quality management frameworks.

由于大气化学的非线性和污染物排放的空间异质性，理解和缓解PM2.5和臭氧污染仍然具有挑战性。传统的正演模拟方法存在计算成本高和诊断分辨率有限的问题，无法在精细的空间、时间和化学尺度上精确地确定排放源的属性。伴随建模已成为一种有效的替代方法，可在单一集成框架中实现高分辨率、多污染源归属；然而，它在同时发生的PM2.5-O3污染事件中的应用是有限的，特别是在经历复杂的共污染物相互作用的人口稠密地区。本文采用一种新开发的社区多尺度空气质量（CMAQ）模型的多相伴随模型，量化了主要城市群污染期间PM2.5和O3浓度的排放敏感性。我们的研究结果表明，当地排放主要驱动PM2.5浓度，贡献高达79 μg m - 3。相比之下，O3发作主要是由区域运输引起的（3.8-7.3 ppbv），超过了发作时当地排放的贡献。敏感性分析揭示了不同的空间排放特征和关键前体污染物的特定影响，包括挥发性有机化合物（VOCs）；高达15.9 ppbv O3, 11.4 μg m−3 PM2.5)，氮氧化物(NOx；16.6 ppbv O3, 13.8 μg m−3 PM2.5)和氨(NH3；高达8.7 μg m−3 PM2.5)。本研究证明了伴随模型在揭示复杂的源-受体关系方面的诊断强度和预测能力。通过提供详细的特定污染物排放敏感性信息，我们的方法为精确驱动的排放控制策略和改进的跨区域政策协调提供了坚实的基础，大大推进了空气质量管理框架。

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引用次数: 0

Steep sustainability challenges in transboundary basins worldwide 全球跨界流域面临严峻的可持续性挑战

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-08-07 DOI: 10.1016/j.ese.2025.100611

Yiqi Zhou , Yanfeng Di , Xianjin Huang , Shilin Fu , Xinxian Qi , Chao He , Georgia Destouni

Transboundary hydrological basins span international borders and are essential to global water systems, human development, and environmental sustainability. Nearly 40 % of the world's population lives within these basins, which supply critical resources such as freshwater, food, energy, and biodiversity. Yet their sustainability remains poorly understood, as existing assessments often overlook the unique social, environmental, and political complexities of transboundary basins. This study addresses that gap by developing and applying a systematic framework to assess Sustainable Development Goals (SDGs) progress across 310 transboundary basins worldwide. Here we show that transboundary basins score significantly lower on average SDGs achievement (an SDG Index score of 42 on a scale of 0–100) compared to national averages (a score of 67), with considerable variation between regions. We identify four distinct types of transboundary basins in terms of SDGs achievement and associated challenges. We also show that progress on a specific set of goals can drive broader sustainability within each basin type. Notably, achieving clean water (SDG 6), sustainable economic growth (SDG 8), and healthy livelihoods (SDG 3) is linked to overall SDGs success in 38 % of transboundary basins worldwide. Our results highlight the importance of basin-level analysis for revealing sustainability patterns overlooked by national assessments. This framework can inform future basin research and support policy development in transboundary regions.

跨界水文盆地跨越国际边界，对全球水系统、人类发展和环境可持续性至关重要。世界上近40%的人口生活在这些盆地内，这些盆地提供淡水、粮食、能源和生物多样性等重要资源。然而，由于现有的评估往往忽视了跨界流域独特的社会、环境和政治复杂性，人们对其可持续性的了解仍然很少。本研究通过开发和应用一个系统框架来评估全球310个跨界流域的可持续发展目标（sdg）进展，解决了这一差距。研究表明，与全国平均水平（67分）相比，跨界流域在可持续发展目标平均成就方面的得分明显较低（可持续发展目标指数在0-100分范围内得分为42分），区域之间存在较大差异。根据可持续发展目标的实现和相关挑战，我们确定了四种不同类型的跨界盆地。我们还表明，在一组特定目标上取得进展可以在每种流域类型中推动更广泛的可持续性。值得注意的是，在全球38%的跨界流域实现清洁水（可持续发展目标6）、可持续经济增长（可持续发展目标8）和健康生计（可持续发展目标3）与可持续发展目标的总体成功相关。我们的研究结果强调了流域水平分析对于揭示被国家评估所忽视的可持续性模式的重要性。这一框架可以为未来的流域研究提供信息，并支持跨界区域的政策制定。

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引用次数: 0

Viruses are a key regulator of the microbial carbon cycle in the deep-sea biosphere 病毒是深海生物圈微生物碳循环的关键调节者

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-08-05 DOI: 10.1016/j.ese.2025.100609

Xinyi Zhang , Tianliang He , Jiyong Zhou , Xiaobo Zhang

The marine biosphere profoundly influences atmospheric chemistry and climate through its carbon cycle. Viruses, the most abundant and diverse entities in marine ecosystems, significantly shape global carbon dynamics by infecting microbes and altering their metabolism. Both DNA and RNA viruses drive these processes in surface oceans, yet their roles in the deep sea—a sunlight-independent ecosystem that stores vast carbon reserves—remain largely unexplored. Here we show that viruses regulate the microbial carbon cycle in the deep-sea biosphere, based on viromic analysis of 66 global sediment samples spanning 1900 to 24,000 years. We identified 324,772 DNA viruses and 61,066 RNA viruses, revealing high diversity and long-term persistence. These viruses co-participate in host carbon metabolism via synergistic genes that encode carbohydrate-active enzymes, with DNA viruses primarily aiding synthesis and RNA viruses supporting decomposition. Integrated virome and microbiome data indicate that viral genes form novel metabolic branches, compensating for host deficiencies and enhancing pathway efficiency in processes like fructose-mannose and pyruvate metabolism. Our findings position deep-sea viruses as key regulators of marine microbial carbon cycling, with implications for global biogeochemical models and climate resilience. This work offers the first holistic perspective on DNA and RNA viruses in deep-sea carbon dynamics, illuminating their ecological significance across geological timescales.

海洋生物圈通过其碳循环深刻地影响着大气化学和气候。病毒是海洋生态系统中最丰富和最多样化的实体，通过感染微生物并改变其代谢，显著地影响着全球碳动态。DNA和RNA病毒都在海洋表层推动着这些过程，但它们在深海中的作用——一个不依赖阳光的生态系统，储存着大量的碳储备——很大程度上仍未被探索。本文通过对1900年至24000年的66个全球沉积物样本进行病毒组学分析，证明了病毒调节深海生物圈中的微生物碳循环。我们鉴定出324,772种DNA病毒和61,066种RNA病毒，显示出高度的多样性和长期持久性。这些病毒通过编码碳水化合物活性酶的协同基因共同参与宿主的碳代谢，DNA病毒主要帮助合成，RNA病毒支持分解。病毒组和微生物组的综合数据表明，病毒基因形成了新的代谢分支，弥补了宿主的缺陷，并提高了果糖-甘露糖和丙酮酸代谢等过程的途径效率。我们的发现将深海病毒定位为海洋微生物碳循环的关键调节器，这对全球生物地球化学模型和气候适应能力具有重要意义。这项工作提供了深海碳动力学中DNA和RNA病毒的第一个整体视角，阐明了它们在地质时间尺度上的生态意义。

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引用次数: 0

Heavy metals trigger distinct molecular transformations in microplastic-versus natural-derived dissolved organic matter 重金属在微塑料中引发不同的分子转化，而不是天然衍生的溶解有机物

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-30 DOI: 10.1016/j.ese.2025.100610

Xianbao Zhong , Kaiying Zhao , Mengyuan Wu , Yaohui Zhang , Chiyue Ma , Hexiang Liu , Bokun Chang , Xiaohui Lian , Yujing Li , Zixuan Huang , Lang Zhu , Ming Zhang , Chi Zhang , Yajun Yang , Jialong Lv

Dissolved organic matter (DOM) is a key determinant of heavy metal fate in aquatic environments, influencing their mobility, toxicity, and bioavailability. Derived from natural sources such as soil and vegetation decomposition, natural DOM (N-DOM) typically features humic-like substances with abundant oxygen-containing functional groups that stabilize heavy metals through complexation. However, microplastic-derived DOM (MP-DOM), increasingly prevalent due to plastic degradation, may interact differently with heavy metals, potentially exacerbating environmental risks amid rising plastic pollution. Yet, how heavy metals drive molecular transformations in MP-DOM versus N-DOM remains unclear, hindering accurate pollution assessments. Here, we compare interactions between N-DOM and MP-DOM with cadmium, chromium (Cr), copper, and lead from both fluorescence and molecular perspectives. Our results show that N-DOM, dominated by humic-like substances (46.0–57.3 %), lignin-like (55.0–64.9 %), and tannin-like (10.1–17.6 %) compounds, forms more stable heavy metal complexes via carboxyl, phenolic hydroxyl, and ether groups than MP-DOM. By contrast, MP-DOM—enriched in protein/phenolic-like substances (13.8–24.0 %), condensed aromatic (12.1–28.5 %), and protein/aliphatic-like (8.6–12.4 %) compounds—yields less stable complexes and is highly susceptible to Cr-induced oxidation. Mass-difference network analysis and density functional theory calculations further reveal that both DOM types undergo heavy-metal-triggered decarboxylation and dealkylation, but N-DOM retains complex structures, whereas MP-DOM degrades into smaller, hazardous molecules such as phenol and benzene. This study underscores the potential for heavy metals to exacerbate the ecological risks associated with the transformation of MP-DOM, providing crucial insights to inform global risk assessment and management strategies in contaminated waters where plastic and metal pollution co-occur.

溶解有机物（DOM）是水生环境中重金属命运的关键决定因素，影响其流动性、毒性和生物利用度。天然DOM （N-DOM）来源于土壤和植被分解等天然来源，通常具有腐殖质样物质，含有丰富的含氧官能团，通过络合作用稳定重金属。然而，由于塑料降解，微塑料衍生的DOM （MP-DOM）越来越普遍，可能与重金属发生不同的相互作用，可能加剧塑料污染日益严重的环境风险。然而，重金属如何驱动MP-DOM与N-DOM的分子转化仍不清楚，这阻碍了准确的污染评估。在这里，我们从荧光和分子角度比较了N-DOM和MP-DOM与镉、铬、铜和铅的相互作用。结果表明，N-DOM以腐植酸类物质（46.0 ~ 57.3%）、木质素类物质（55.0 ~ 64.9%）和单宁类物质（10.1 ~ 17.6%）为主，通过羧基、酚羟基和醚基形成比MP-DOM更稳定的重金属配合物。相比之下，mp - dom -富含蛋白质/酚类物质（13.8 - 24.0%），凝聚芳香（12.1 - 28.5%）和蛋白质/脂肪类（8.6 - 12.4%）化合物-产生不稳定的复合物，并且对cr诱导的氧化非常敏感。质量差网络分析和密度泛函数理论计算进一步表明，两种DOM类型都经历了重金属触发的脱羧和脱烷基反应，但N-DOM保留了复杂的结构，而MP-DOM降解为更小的有害分子，如苯酚和苯。该研究强调了重金属加剧与MP-DOM转化相关的生态风险的可能性，为塑料和金属污染共存的受污染水域的全球风险评估和管理策略提供了重要见解。

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引用次数: 0

Heterogeneity, nonlinearity, and multifactor interactions of polycyclic aromatic hydrocarbons in steelworks soils 钢铁厂土壤中多环芳烃的异质性、非线性和多因素相互作用

IF 14.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-29 DOI: 10.1016/j.ese.2025.100607

Yixuan Hou , Xiaoyong Liao , You Li , Hongying Cao

Industrial polycyclic aromatic hydrocarbons (PAHs) pollution threatens soil ecosystems worldwide, posing persistent risks due to their toxicity and intricate transport dynamics. In steelworks, a major PAH emitter, contaminant distribution arises from multifaceted interactions between production activities and geological features, complicating the elucidation of underlying mechanisms. Previous studies have largely overlooked the inherent heterogeneity in these influences, focusing instead on global relationships that may bias assessments of pollution drivers and PAH migration. Here we show heterogeneity, nonlinearity, and multifactor interactions in PAH contamination at a steelworks site using a multidimensional framework that integrates machine learning and spatial analysis. Applied to 3339 soil samples and nine influencing factors, the framework reveals distance to production facilities as the dominant driver, with a 60-m impact radius; production factors exert stronger effects on 2–3-ring PAHs than on 4–6-ring PAHs, particularly in deeper soil layers at depths of 9–20 m. Soil moisture and clay content synergistically control PAH mobility across strata, elevating the framework's explanatory power from 0.5 to 0.9 and enabling precise delineation of dynamics. This modular approach not only advances mechanistic insights into industrial PAH pollution but also provides scalable guidance for targeted prevention and remediation strategies across diverse contaminated sites.

工业多环芳烃（PAHs）污染威胁着全球土壤生态系统，由于其毒性和复杂的运输动力学，造成了持续的风险。在炼钢厂，多环芳烃的主要排放者，污染物分布源于生产活动和地质特征之间多方面的相互作用，使潜在机制的阐明变得复杂。以前的研究在很大程度上忽视了这些影响的内在异质性，而是关注全球关系，这可能会对污染驱动因素和多环芳烃迁移的评估产生偏差。在这里，我们使用集成了机器学习和空间分析的多维框架，展示了钢铁厂多环芳烃污染的异质性、非线性和多因素相互作用。应用于3339个土壤样本和9个影响因素，该框架显示与生产设施的距离是主要驱动因素，影响半径为60 m；生产因子对2 - 3环多环芳烃的影响强于对4 - 6环多环芳烃的影响，特别是在深度为9 ~ 20 m的较深土层。土壤水分和粘土含量协同控制多环芳烃在地层中的迁移，将框架的解释力从0.5提高到0.9，并能够精确描述动力学。这种模块化的方法不仅推进了对工业多环芳烃污染的机理见解，而且还为不同污染地点的有针对性的预防和修复策略提供了可扩展的指导。

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