Environmental Science and Ecotechnology最新文献_第4页

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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引用次数: 0

Fine-tuning large language models for interdisciplinary environmental challenges 微调大型语言模型以应对跨学科的环境挑战

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

Environmental Science and Ecotechnology

Pub Date : 2025-07-28 DOI: 10.1016/j.ese.2025.100608

Yuanxin Zhang , Sijie Lin , Yaxin Xiong , Nan Li , Lijin Zhong , Longzhen Ding , Qing Hu

Large language models (LLMs) are revolutionizing specialized fields by enabling advanced reasoning and data synthesis. Environmental science, however, poses unique hurdles due to its interdisciplinary scope, specialized jargon, and heterogeneous data from climate dynamics to ecosystem management. Despite progress in subdomains like hydrology and climate modeling, no integrated framework exists to generate high-quality, domain-specific training data or evaluate LLM performance across the discipline. Here we introduce a unified pipeline to address this gap. It comprises EnvInstruct, a multi-agent system for prompt generation; ChatEnv, a balanced 100-million-token instruction dataset spanning five core themes (climate change, ecosystems, water resources, soil management, and renewable energy); and EnvBench, a 4998-item benchmark assessing analysis, reasoning, calculation, and description tasks. Applying this pipeline, we fine-tune an 8-billion-parameter model, EnvGPT, which achieves 92.06 ± 1.85 % accuracy on the independent EnviroExam benchmark—surpassing the parameter-matched LLaMA-3.1–8B baseline by ∼8 percentage points and rivaling the closed-source GPT-4o-mini and the 9-fold larger Qwen2.5–72B. On EnvBench, EnvGPT earns top LLM-assigned scores for relevance (4.87 ± 0.11), factuality (4.70 ± 0.15), completeness (4.38 ± 0.19), and style (4.85 ± 0.10), outperforming baselines in every category. This study reveals how targeted supervised fine-tuning on curated domain data can propel compact LLMs to state-of-the-art levels, bridging gaps in environmental applications. By openly releasing EnvGPT, ChatEnv, and EnvBench, our work establishes a reproducible foundation for accelerating LLM adoption in environmental research, policy, and practice, with potential extensions to multimodal and real-time tools.

大型语言模型（llm）通过实现高级推理和数据合成，正在彻底改变专业领域。然而，由于环境科学的跨学科范围、专业术语和从气候动力学到生态系统管理的异构数据，环境科学面临着独特的障碍。尽管在水文和气候建模等子领域取得了进展，但目前还没有一个集成的框架来生成高质量的、特定领域的培训数据或评估法学硕士在整个学科中的表现。这里我们引入一个统一的管道来解决这个差距。它包括envdirective，一个用于提示生成的多智能体系统；ChatEnv，一个平衡的1亿个令牌指令数据集，涵盖五个核心主题（气候变化、生态系统、水资源、土壤管理和可再生能源）；EnvBench是一个4998项的基准测试，用于评估分析、推理、计算和描述任务。应用该管道，我们对80亿个参数模型EnvGPT进行了精细调整，该模型在独立的EnviroExam基准上达到92.06±1.85%的准确率，比参数匹配的LLaMA-3.1-8B基准高出约8个百分点，与闭源gpt - 40 -mini和9倍大的Qwen2.5-72B相媲美。在EnvBench上，EnvGPT在相关性（4.87±0.11），真实性（4.70±0.15），完整性（4.38±0.19）和风格（4.85±0.10）方面获得了llm分配的最高分数，在每个类别中都优于基线。这项研究揭示了如何有针对性地对策划领域数据进行监督微调，以推动紧凑的法学硕士达到最先进的水平，弥合环境应用中的差距。通过公开发布EnvGPT、ChatEnv和EnvBench，我们的工作为加速法学硕士在环境研究、政策和实践中的应用奠定了可复制的基础，并有可能扩展到多模式和实时工具。

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引用次数: 0

Quantifying greenhouse gas emissions from wastewater treatment plants: A critical review 量化温室气体排放从污水处理厂：一个关键的审查

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

Environmental Science and Ecotechnology

Pub Date : 2025-07-25 DOI: 10.1016/j.ese.2025.100606

Xinyue He , Haiyan Li , Juanjuan Chen , Huan Wang , Lu Lu

Greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs) are increasingly recognized as significant contributors to anthropogenic climate change, primarily through the release of methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Current research on GHG quantification in WWTPs predominantly relies on estimated emission factors. However, this introduces substantial uncertainties in emission estimates due to limited in situ measurements and variability in quantification methods. Here we review advances in GHG measurement techniques, integrating literature data with our in situ studies. We show that unit-based methods, such as flux chambers and optical gas imaging, pinpoint emission hotspots in individual processes, while plant-integrated approaches—like tracer gas dispersion, mobile laboratories and aerial surveys—deliver comprehensive plant-scale estimates. These techniques reveal wide variability in emissions, with CH₄ rates spanning 0.04–427 kg h⁻¹ and N₂O up to 22.1 kg h⁻¹, but most studies are short-term, gas-specific and neglect fossil CO₂, which can inflate IPCC inventories by up to 22.8 % upon inclusion. Technology- and plant-specific emission factors, calibrated via on-site data, markedly enhance accuracy by accounting for local factors like treatment processes and influent composition. We call for national emission inventories via long-term, multi-gas measurements, guiding targeted mitigation strategies and transforming WWTPs toward carbon-neutral, climate-smart infrastructures.

废水处理厂（WWTPs）的温室气体（GHG）排放越来越被认为是人为气候变化的重要贡献者，主要是通过释放甲烷（CH4）、氧化亚氮（N2O）和二氧化碳（CO2）。目前关于污水处理厂温室气体量化的研究主要依赖于估算的排放因子。然而，由于有限的现场测量和量化方法的可变性，这给排放估算带来了很大的不确定性。在这里，我们回顾了温室气体测量技术的进展，将文献数据与我们的原位研究相结合。我们表明，基于单元的方法，如通量室和光学气体成像，可以精确定位单个过程中的排放热点，而工厂集成方法，如示踪气体分散、移动实验室和航空测量，可以提供全面的工厂规模估计。这些技术揭示了排放的广泛变异性，CH4速率范围为0.04-427 kg h - 1， N2O速率可达22.1 kg h - 1，但大多数研究是短期的、特定于气体的，并且忽略了化石二氧化碳，一旦纳入，化石二氧化碳可使IPCC清单膨胀22.8%。通过现场数据校准的技术和工厂特定排放因子，通过考虑处理过程和进水成分等当地因素，显著提高了准确性。我们呼吁通过长期、多种气体测量建立国家排放清单，指导有针对性的缓解战略，并将污水处理厂转变为碳中和、气候智能型的基础设施。

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引用次数: 0

Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models 利用场景差异在水厂迁移机器学习模型中进行跨任务泛化

IF 14 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-23 DOI: 10.1016/j.ese.2025.100604

Yu-Qi Wang , Xiao-Qin Luo , Han-Bo Zhou , Jia-Ji Chen , Wan-Xin Yin , Yun-Peng Song , Hao-Bo Wang , Bai Yu , Yu Tao , Hong-Cheng Wang , Ai-Jie Wang , Nan-Qi Ren

Machine learning (ML) models are increasingly deployed in urban water systems to optimize operations, enhance efficiency, and curb resource consumption amid growing sustainability demands. Yet, their transferability across plants is hampered by scenario differences—variations in environmental factors, protocols, and data distributions—that erode performance and necessitate energy-intensive retraining. While existing strategies focus on minimizing these differences via domain adaptation or fine-tuning, none exploit them as inherent prior knowledge for improved generalization. Here we show an environmental information adaptive transfer network (EIATN) framework that can leverage scenario differences to enable effective generalization across distinct prediction tasks within the same water plant. By evaluating EIATN across four scenario categories and 16 diverse ML architectures—yielding 64 models in total—we demonstrate its feasibility, with bidirectional long short-term memory emerging as the top performer, achieving a mean absolute percentage error of just 3.8 % while requiring only 32.8 % of the typical data volume. In a case study of Shenzhen's urban water system, it reduced carbon emissions by 40.8 % compared to fine-tuning and 66.8 % relative to direct modeling from scratch. EIATN unlocks the reuse of vast existing ML models in water systems, yielding substantial energy savings and fostering equitable, low-carbon intelligent management.

在不断增长的可持续性需求中，机器学习（ML）模型越来越多地应用于城市供水系统，以优化运营、提高效率并抑制资源消耗。然而，它们在工厂之间的可转移性受到情景差异的阻碍——环境因素、协议和数据分布的变化——这些差异会削弱性能，并需要进行能源密集型的再培训。虽然现有的策略侧重于通过领域适应或微调来最小化这些差异，但没有人利用它们作为固有的先验知识来改进泛化。在这里，我们展示了一个环境信息自适应传递网络（EIATN）框架，它可以利用场景差异来实现同一水厂内不同预测任务的有效泛化。通过在四个场景类别和16种不同的ML架构中评估eatn（总共产生64个模型），我们证明了它的可行性，双向长短期记忆成为表现最好的方法，平均绝对百分比误差仅为3.8%，而只需要32.8%的典型数据量。在深圳城市供水系统的一个案例研究中，与微调相比，它减少了40.8%的碳排放量，与直接从零开始建模相比，它减少了66.8%的碳排放量。EIATN解锁了水系统中大量现有ML模型的再利用，产生了大量的节能效果，并促进了公平、低碳的智能管理。

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引用次数: 0

Global spillover of land-derived microbes to Ocean hosts: Sources, transmission pathways, and one health threats 陆源微生物对海洋宿主的全球溢出：来源、传播途径和一种健康威胁

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

Environmental Science and Ecotechnology

Pub Date : 2025-07-23 DOI: 10.1016/j.ese.2025.100603

Hai-Chao Song , Hany Elsheikha , Tao Yang , Wei Cong

Terrestrial pathogens are increasingly being detected in marine organisms, raising concerns about ecosystem sustainability, biodiversity loss, and threats to human health. Over the past two decades, reports of microbial contaminants crossing from land to sea have increased, suggesting shifts in pathogen ecology driven by environmental changes and human activities. Pathogens originating on land can spread, adapt, and persist in marine environments, infecting a wide range of hosts and potentially re-entering terrestrial environments. Despite growing recognition of this issue, a comprehensive understanding of the distribution, diversity, and transmission pathways of these pathogens in marine ecosystems remains limited. In this Review, we provide a global analysis of terrestrial pathogen contamination in marine animal populations. Drawing from pathogen detection data across 66 countries, we used phylogenetic methods to infer land-to-sea transmission routes. We identified 179 terrestrial pathogen species, including 38 bacterial, 39 viral, 80 parasitic, and 22 fungal species, in 20 marine host species. Terrestrial pathogens are not only widespread but also highly diverse in marine ecosystems, highlighting the frequency and ecological significance of cross-system microbial exchange. By revealing the scale and complexity of land-to-sea pathogen flow, we show that climate change, pollution, and other anthropogenic pressures may intensify pathogen spillover events, with potential feedback effects on terrestrial systems. This highlights the urgent need for integrated surveillance and policy frameworks acknowledging the interconnectedness of terrestrial and marine health. Our work advocates a One Health approach to microbial ecology, stressing the need to safeguard marine and human populations from emerging cross-system threats.

越来越多地在海洋生物中发现陆生病原体，引起人们对生态系统可持续性、生物多样性丧失以及对人类健康的威胁的关注。在过去的二十年中，关于微生物污染物从陆地进入海洋的报道有所增加，这表明环境变化和人类活动驱动了病原体生态的变化。源自陆地的病原体可以在海洋环境中传播、适应和持续存在，感染广泛的宿主，并有可能重新进入陆地环境。尽管人们越来越认识到这一问题，但对这些病原体在海洋生态系统中的分布、多样性和传播途径的全面了解仍然有限。在这篇综述中，我们提供了海洋动物种群中陆源病原体污染的全球分析。根据66个国家的病原体检测数据，我们使用系统发育方法推断陆地到海洋的传播途径。在20种海洋宿主中鉴定出179种陆生病原体，其中细菌38种，病毒39种，寄生虫80种，真菌22种。陆生病原体在海洋生态系统中不仅分布广泛，而且种类繁多，突出了跨系统微生物交换的频率和生态意义。通过揭示陆海病原体流动的规模和复杂性，我们发现气候变化、污染和其他人为压力可能加剧病原体溢出事件，并对陆地系统产生潜在的反馈效应。这突出表明迫切需要建立承认陆地和海洋健康相互联系的综合监测和政策框架。我们的工作提倡对微生物生态采取“同一个健康”方法，强调需要保护海洋和人类免受新出现的跨系统威胁。

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引用次数: 0

Tracing CO2 emissions across megacity landscapes: beyond citywide totals to structural heterogeneity and mitigation 追踪超大城市景观中的二氧化碳排放：超越城市总体结构异质性和缓解

IF 14 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-12 DOI: 10.1016/j.ese.2025.100602

Yiwen Zhu , Yuhang Zhang , Yi Zhang , Bo Zheng

Cities are central to global climate change mitigation efforts due to their substantial carbon emissions. Effective, evidence-based climate policy requires a detailed understanding of urban carbon metabolism, allowing for targeted mitigation pathways and the accurate evaluation of sustainability. However, a persistent lack of clarity on how carbon flows are distributed spatially and sectorally within cities has hindered tailored climate action, particularly in rapidly developing megacities. Here we map the shifting landscape of carbon emissions in Chinese megacities and show that accountability for these emissions has undergone a profound spatial and sectoral transformation. We found that the primary burden of emission responsibility has moved from production-focused sectors, such as industry and energy generation, to consumption-based end-users, including residential and commercial buildings. This transition is driven by a structural shift in accounting boundaries from direct fossil fuel combustion (Scope 1) to indirect emissions from electricity consumption (Scope 2), fundamentally redistributing carbon liability across urban districts. Our landscape-level framework reveals the hidden carbon dependencies of end-use sectors and provides a model for equitable and effective accounting, enabling the design of region-specific strategies to address the complexities of urban carbon emissions.

城市因其大量的碳排放而在全球减缓气候变化努力中处于中心地位。有效的、以证据为基础的气候政策需要详细了解城市碳代谢，从而确定有针对性的缓解途径并准确评估可持续性。然而，碳流如何在城市内的空间和部门分布一直不明确，这阻碍了有针对性的气候行动，特别是在快速发展的特大城市。在这里，我们绘制了中国特大城市碳排放的变化图景，并表明这些排放的问责制经历了深刻的空间和部门转型。我们发现，排放责任的主要负担已经从以生产为重点的部门，如工业和能源生产，转移到以消费为基础的最终用户，包括住宅和商业建筑。这一转变是由会计边界的结构性转变推动的，从直接化石燃料燃烧（范围1）到电力消耗的间接排放（范围2），从根本上重新分配了城市地区的碳责任。我们的景观级框架揭示了最终用途部门的隐性碳依赖，并提供了一个公平有效的核算模型，从而能够设计针对特定区域的战略，以解决城市碳排放的复杂性。

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引用次数: 0

Restoring landscapes and communities: Insights from critical, urban, and plant ecology 恢复景观和社区：来自关键、城市和植物生态学的见解

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

Environmental Science and Ecotechnology

Pub Date : 2025-07-12 DOI: 10.1016/j.ese.2025.100601

Alexandria N. Igwe , Karlisa A. Callwood , Delia S. Shelton

Humans shape the world through policies, practices, and behavior that create environmental heterogeneity. Political and critical ecology offer frameworks for understanding how societies have historically and currently used power, policies, and practices to shape environmental landscapes and conditions, ultimately influencing the ecology and evolution of biodiversity. We suggest that integrating political and critical ecology can enhance our understanding of anthropogenic influences, such as luxury effects and legacy effects, including redlining—a form of structural racism implemented in the United States. Here, we review the consequences of legacy and luxury effects on urban ecosystems, with a focus on their impact on the fauna and flora. We propose that legacy and luxury effects can have independent and interdependent influences on ecological diversity, abundance, biological invasions, and pollution exposure. Although these effects can persist, environmental remediation may provide a pathway to restorative justice. We also discuss Plantago, herbaceous plants with the potential to mitigate the impacts of cadmium, a notorious environmental contaminant whose disposition parallels redlining patterns. Phytoremediation can contribute to biofuels, biofoundries, and the green economy, offering solutions to restore affected communities. By applying political and critical ecology lenses, we can identify socio-ecological mechanisms that affect humans and the environment. These insights can inform the development of green infrastructure to help remediate adverse effects. Ideally, these approaches provide pathways to address historical injustices, enhance equity, and restore ecological landscapes.

人类通过创造环境异质性的政策、实践和行为来塑造世界。政治生态学和批判生态学为理解社会在历史上和当前如何使用权力、政策和实践来塑造环境景观和条件，最终影响生态和生物多样性的进化提供了框架。我们认为，整合政治生态学和批判生态学可以增强我们对人为影响的理解，如奢侈效应和遗产效应，包括在美国实施的一种结构性种族主义形式。在这里，我们回顾了遗产和奢侈品对城市生态系统的影响，重点是它们对动植物的影响。我们认为遗产效应和奢侈效应对生态多样性、丰度、生物入侵和污染暴露具有独立和相互依存的影响。尽管这些影响可能持续存在，但环境补救可能为恢复性司法提供一条途径。我们还讨论了车前草，草本植物具有减轻镉影响的潜力，镉是一种臭名昭著的环境污染物，其处置方式与红线模式相似。植物修复可以促进生物燃料、生物铸造厂和绿色经济，为恢复受影响的社区提供解决方案。通过运用政治和批判生态学的视角，我们可以确定影响人类和环境的社会生态机制。这些见解可以为绿色基础设施的发展提供信息，以帮助纠正不利影响。理想情况下，这些方法提供了解决历史不公正、增强公平和恢复生态景观的途径。

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引用次数: 0

Microplastic pollution threatens mangrove carbon sequestration capacity 微塑料污染威胁着红树林的固碳能力

IF 14 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-01 DOI: 10.1016/j.ese.2025.100593

Xiaotong He , Shiguang Xu , Han Ren , Xiaobing Yang , Feizhou Su , Shuo Gao , Chenxi Xie , Junhui Zhao , Zhan Jin , Xiangjin Shen , Rongxiao Che , Derong Xiao

Microplastics are a pervasive environmental pollutant, altering microbial communities and disrupting global biogeochemical cycles. Mangrove forests, critical blue carbon habitats, are significant sinks for microplastic accumulation, yet they also cycle large amounts of methane, a potent greenhouse gas. The effect of plastic pollution on methane dynamics in these vital habitats remains, however, poorly understood. Here we show that microplastic pollution in mangrove soils is linked to an increased potential for methane production by favouring methanogenic archaea. Through a nationwide survey of Chinese mangroves, we found that microplastic concentrations were higher (6516 ± 1725 particles kg⁻¹) in surface soils (0–20 cm) and exhibited stronger association with methane-cycling microbes (four linkage pathways), compared to concentrations (2246 ± 497 particles kg⁻¹) and two linkage pathways in deeper soils (20–40 cm). Microplastics in topsoil were correlated with more complex microbial networks, consisting of 150 nodes and 237 links, relative to 113 nodes and 196 links in deeper soils. Furthermore, we directly linked elevated microplastic pollution in surface soils to secondary industry output, which positively correlated with the methanogens-to-methanotrophs gene ratio, establishing a clear anthropogenic driver for this shift. These findings reveal a critical, previously unrecognized mechanism by which industrial plastic pollution may compromise the net carbon sequestration capacity of mangrove ecosystems. Mitigating microplastic discharge is therefore not only a waste management issue but is also essential for preserving the climate-regulating function of these crucial habitats amid global conservation efforts.

微塑料是一种普遍存在的环境污染物，改变了微生物群落，破坏了全球生物地球化学循环。红树林是关键的蓝碳栖息地，是微塑料积累的重要汇，但它们也循环大量的甲烷，一种强有力的温室气体。然而，塑料污染对这些重要栖息地甲烷动态的影响仍然知之甚少。在这里，我们表明，红树林土壤中的微塑料污染有利于产甲烷古菌，从而增加了甲烷生产的潜力。通过对中国红树林的全国性调查，我们发现，与深层土壤（20-40 cm）的浓度（2246±497颗粒kg - 1）和两个连锁途径相比，表层土壤（0-20 cm）的微塑料浓度（6516±1725颗粒kg - 1）更高，与甲烷循环微生物（4个连锁途径）的关联更强。表层土壤中的微塑料与更复杂的微生物网络相关，包括150个节点和237个链接，而深层土壤中的微塑料网络由113个节点和196个链接组成。此外，我们将表层土壤微塑料污染的升高与第二产业产出直接联系起来，这与产甲烷菌与产甲烷菌的基因比例呈正相关，从而明确了这一转变的人为驱动因素。这些发现揭示了一个关键的、以前未被认识到的机制，通过这个机制，工业塑料污染可能会损害红树林生态系统的净固碳能力。因此，减少微塑料排放不仅是一个废物管理问题，而且对于在全球保护努力中保持这些重要栖息地的气候调节功能也至关重要。

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引用次数: 0

Artificial intelligence of things for sustainable smart city brain and digital twin systems: Pioneering Environmental synergies between real-time management and predictive planning 可持续智慧城市大脑和数字孪生系统的物联网人工智能：实时管理和预测性规划之间的开创性环境协同效应

IF 14 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-01 DOI: 10.1016/j.ese.2025.100591

Simon Elias Bibri, Jeffrey Huang

<div><div>Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the urgent need for innovative paradigms in urban development. In response, sustainable smart cities are increasingly leveraging advanced technological frameworks—most notably the convergence of Artificial Intelligence of Things (AIoT) and Cyber-Physical Systems (CPS)—as critical enablers for transforming their management and planning processes. Within this dynamic landscape, <em>Urban Brain</em> (UB) and <em>Urban Digital Twin</em> (UDT) have emerged as prominent AIoT-powered city platforms. Defined by their complex functionalities and multi-layered architectures, these systems exemplify <em>Cyber-Physical Systems of Systems</em> (CPSoS), offering a cohesive foundation for integrating real-time operational responsiveness with strategic predictive foresight. Despite notable technological progress, a critical gap persists in effectively integrating the distinct yet complementary capabilities of UB and UDT within a structured and scalable framework. To the best of our knowledge, research on the explicit fusion of UB's real-time analytics—enabled through stream processing—with UDT's predictive analytics—driven by simulation modeling—is scant, if not absent. Most existing studies continue to treat UB and UDT as siloed systems, failing to recognize the critical need to synchronize their respective operational and strategic functions. This fragmentation limits the ability of urban systems to respond both adaptively and proactively to the complex, interrelated challenges of environmental sustainability. To address this critical gap, this study introduces a novel foundational framework—Artificial Intelligence of Things for Sustainable Smart City Brain and Digital Twin Systems—designed to synergistically integrate UB and UDT as AIoT-enabled platforms within a unified CPSoS architecture. This framework addresses the critical disconnect between real-time operational management and strategic predictive planning, delivering an integrated pathway for advancing environmentally sustainable smart city development goals. Harnessing the complementary strengths of UB and UDT, it empowers cities to respond dynamically to immediate urban demands while ensuring consistent alignment with long-term sustainability goals. UB's real-time analytics enhance the efficiency of daily urban operations, whereas UDT's predictive modeling anticipates and simulates future scenarios. Together, they establish a synergistic feedback loop: UB's real-time insights continuously inform UDT's strategic simulations, while UDT's long-range forecasts iteratively refine UB's operational decision-making. The framework thus equips researchers, practitioners, and policymakers with a robust methodology for designing and implementing adaptive, efficient, and resilient urban ecosystems. It facilitates the development of intelligent urban environments that can advance environmental sustainabili

快速城市化、资源枯竭和生态退化加剧，迫切需要创新城市发展模式。作为回应，可持续的智慧城市正越来越多地利用先进的技术框架——最显著的是物联网人工智能（AIoT）和网络物理系统（CPS）的融合——作为改变其管理和规划流程的关键推动力。在这一动态景观中，城市大脑（UB）和城市数字孪生（UDT）已成为突出的物联网驱动的城市平台。这些系统具有复杂的功能和多层架构，是网络物理系统（cpso）的典范，为整合实时操作响应和战略预测远见提供了一个有凝聚力的基础。尽管取得了显著的技术进步，但在有效地将UB和UDT的独特而又互补的能力集成到一个结构化和可伸缩的框架中，仍然存在一个关键的差距。据我们所知，对UB的实时分析（通过流处理实现）和UDT的预测分析（由仿真建模驱动）的显式融合的研究很少，如果不是没有的话。大多数现有的研究继续将UB和UDT视为孤立的系统，未能认识到同步其各自的业务和战略功能的关键需要。这种碎片化限制了城市系统对复杂的、相互关联的环境可持续性挑战做出适应性和前瞻性反应的能力。为了解决这一关键差距，本研究引入了一个新的基础框架——可持续智慧城市大脑和数字孪生系统的物联网人工智能——旨在将UB和UDT作为aiiot支持的平台协同集成到统一的CPSoS架构中。该框架解决了实时运营管理与战略预测规划之间的严重脱节，为推进环境可持续的智慧城市发展目标提供了一条综合途径。利用UB和UDT的互补优势，它使城市能够动态响应当前的城市需求，同时确保与长期可持续发展目标保持一致。UB的实时分析提高了日常城市运营的效率，而UDT的预测建模预测和模拟了未来的情景。他们共同建立了一个协同反馈循环：UB的实时洞察不断为UDT的战略模拟提供信息，而UDT的长期预测迭代地完善了UB的运营决策。因此，该框架为研究人员、从业者和政策制定者提供了设计和实施适应性、高效和有弹性的城市生态系统的有力方法。它通过将坚实的理论基础与可行的战略相结合，促进智能城市环境的发展，从而促进环境的可持续性。

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引用次数: 0

A multi-task deep neural network reveals inflowing river impacts for predictive lake management 一个多任务深度神经网络揭示了流入河流对预测湖泊管理的影响

IF 14 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Science and Ecotechnology

Pub Date : 2025-07-01 DOI: 10.1016/j.ese.2025.100592

Han Yan , Haoyang Fu , Zhuo Chen , An-Ran Liao , Mo-Yu Shen , Yi Tao , Yin-Hu Wu , Hong-Ying Hu

Lake ecosystems, vital freshwater resources, are increasingly threatened by pollution from riverine inputs, making the management of these loads critical for preventing ecological degradation. Predicting the combined effects of multiple rivers on lake water quality is a significant challenge; traditional mechanistic models are computationally intensive and data-dependent, while conventional machine learning methods often fail to capture the system's multifaceted nature. This complexity creates a critical need for an integrated predictive tool for effective environmental management. Here we show a multi-task deep neural network (MTDNN) that can accurately and simultaneously predict four key water quality indicators—permanganate index, total phosphorus, total nitrogen, and algal density—at multiple locations within a complex lake system using data from its inflowing rivers. Our model, applied to Dianchi Lake in China, improves predictive precision by up to 56.3 % compared to established mechanistic and single-task deep learning models. Furthermore, the model pinpoints the specific contributions of each river and identifies water temperature and wastewater effluent as dominant, site-specific drivers of pollution. Scenario-based forecasting demonstrates that using reclaimed water for lake replenishment is a viable strategy that does not cause deterioration. This MTDNN framework offers a powerful and transferable tool for data-driven lake management, enabling targeted interventions and sustainable water resource protection.

湖泊生态系统作为重要的淡水资源，正日益受到来自河流输入的污染的威胁，因此管理这些负荷对于防止生态退化至关重要。预测多河流对湖泊水质的综合影响是一个重大挑战；传统的机械模型是计算密集型和数据依赖性的，而传统的机器学习方法往往无法捕捉系统的多面性。这种复杂性使得人们迫切需要一种集成的预测工具来进行有效的环境管理。在这里，我们展示了一个多任务深度神经网络（MTDNN），它可以准确地同时预测四个关键的水质指标——高锰酸盐指数、总磷、总氮和藻类密度——在一个复杂的湖泊系统的多个地点，使用来自其入流河流的数据。我们的模型应用于中国滇池，与已建立的机械和单任务深度学习模型相比，预测精度提高了56.3%。此外，该模型确定了每条河流的具体贡献，并确定水温和废水排放是污染的主要驱动因素。基于场景的预测表明，利用再生水进行湖泊补给是一种可行的策略，不会造成湖泊退化。MTDNN框架为数据驱动的湖泊管理提供了一个强大且可转移的工具，使有针对性的干预和可持续的水资源保护成为可能。

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引用次数: 0