Environmental Science and Ecotechnology最新文献

{"title":"Fine-tuning large language models for interdisciplinary environmental challenges","authors":"Yuanxin Zhang ,&nbsp;Sijie Lin ,&nbsp;Yaxin Xiong ,&nbsp;Nan Li ,&nbsp;Lijin Zhong ,&nbsp;Longzhen Ding ,&nbsp;Qing Hu","doi":"10.1016/j.ese.2025.100608","DOIUrl":"10.1016/j.ese.2025.100608","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100608"},"PeriodicalIF":14.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying greenhouse gas emissions from wastewater treatment plants: A critical review","authors":"Xinyue He ,&nbsp;Haiyan Li ,&nbsp;Juanjuan Chen ,&nbsp;Huan Wang ,&nbsp;Lu Lu","doi":"10.1016/j.ese.2025.100606","DOIUrl":"10.1016/j.ese.2025.100606","url":null,"abstract":"<div><div>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 <em>in situ</em> measurements and variability in quantification methods. Here we review advances in GHG measurement techniques, integrating literature data with our <em>in situ</em> 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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100606"},"PeriodicalIF":14.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models","authors":"Yu-Qi Wang ,&nbsp;Xiao-Qin Luo ,&nbsp;Han-Bo Zhou ,&nbsp;Jia-Ji Chen ,&nbsp;Wan-Xin Yin ,&nbsp;Yun-Peng Song ,&nbsp;Hao-Bo Wang ,&nbsp;Bai Yu ,&nbsp;Yu Tao ,&nbsp;Hong-Cheng Wang ,&nbsp;Ai-Jie Wang ,&nbsp;Nan-Qi Ren","doi":"10.1016/j.ese.2025.100604","DOIUrl":"10.1016/j.ese.2025.100604","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100604"},"PeriodicalIF":14.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global spillover of land-derived microbes to Ocean hosts: Sources, transmission pathways, and one health threats","authors":"Hai-Chao Song ,&nbsp;Hany Elsheikha ,&nbsp;Tao Yang ,&nbsp;Wei Cong","doi":"10.1016/j.ese.2025.100603","DOIUrl":"10.1016/j.ese.2025.100603","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100603"},"PeriodicalIF":14.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracing CO2 emissions across megacity landscapes: beyond citywide totals to structural heterogeneity and mitigation","authors":"Yiwen Zhu ,&nbsp;Yuhang Zhang ,&nbsp;Yi Zhang ,&nbsp;Bo Zheng","doi":"10.1016/j.ese.2025.100602","DOIUrl":"10.1016/j.ese.2025.100602","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100602"},"PeriodicalIF":14.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Restoring landscapes and communities: Insights from critical, urban, and plant ecology","authors":"Alexandria N. Igwe ,&nbsp;Karlisa A. Callwood ,&nbsp;Delia S. Shelton","doi":"10.1016/j.ese.2025.100601","DOIUrl":"10.1016/j.ese.2025.100601","url":null,"abstract":"<div><div>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 <em>Plantago</em>, 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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"27 ","pages":"Article 100601"},"PeriodicalIF":14.3,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microplastic pollution threatens mangrove carbon sequestration capacity","authors":"Xiaotong He ,&nbsp;Shiguang Xu ,&nbsp;Han Ren ,&nbsp;Xiaobing Yang ,&nbsp;Feizhou Su ,&nbsp;Shuo Gao ,&nbsp;Chenxi Xie ,&nbsp;Junhui Zhao ,&nbsp;Zhan Jin ,&nbsp;Xiangjin Shen ,&nbsp;Rongxiao Che ,&nbsp;Derong Xiao","doi":"10.1016/j.ese.2025.100593","DOIUrl":"10.1016/j.ese.2025.100593","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"26 ","pages":"Article 100593"},"PeriodicalIF":14.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence of things for sustainable smart city brain and digital twin systems: Pioneering Environmental synergies between real-time management and predictive planning","authors":"Simon Elias Bibri,&nbsp;Jeffrey Huang","doi":"10.1016/j.ese.2025.100591","DOIUrl":"10.1016/j.ese.2025.100591","url":null,"abstract":"&lt;div&gt;&lt;div&gt;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, &lt;em&gt;Urban Brain&lt;/em&gt; (UB) and &lt;em&gt;Urban Digital Twin&lt;/em&gt; (UDT) have emerged as prominent AIoT-powered city platforms. Defined by their complex functionalities and multi-layered architectures, these systems exemplify &lt;em&gt;Cyber-Physical Systems of Systems&lt;/em&gt; (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","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"26 ","pages":"Article 100591"},"PeriodicalIF":14.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-task deep neural network reveals inflowing river impacts for predictive lake management","authors":"Han Yan ,&nbsp;Haoyang Fu ,&nbsp;Zhuo Chen ,&nbsp;An-Ran Liao ,&nbsp;Mo-Yu Shen ,&nbsp;Yi Tao ,&nbsp;Yin-Hu Wu ,&nbsp;Hong-Ying Hu","doi":"10.1016/j.ese.2025.100592","DOIUrl":"10.1016/j.ese.2025.100592","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"26 ","pages":"Article 100592"},"PeriodicalIF":14.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How soon will landfilled plastics integrate into the geological carbon cycle?","authors":"Yicheng Yang ,&nbsp;Junjie Qiu ,&nbsp;Hua Zhang ,&nbsp;Pinjing He ,&nbsp;Fan Lü","doi":"10.1016/j.ese.2025.100590","DOIUrl":"10.1016/j.ese.2025.100590","url":null,"abstract":"<div><div>Approximately half of plastic waste ends up in landfills, where fragmentation leads to the leakage of microplastics, nanoplastics, and petrogenic carbon back into ecosystems. However, the timeframe for plastic re-entry into the geological carbon cycle remains unknown. Using landfill-derived field data, we developed a model predicting fragmentation of various polymers into macroplastics, microplastics, fine microplastics, and nanoplastics. We find total waste plastic concentrations range from 85 to 414 mg g⁻¹, with microplastic, fine microplastic, and nanoplastic generation rates of 2–69, 0.5–36.8, and 0.04–1.9 mg per g of plastic, respectively. Plastic distribution depends more on landfill depth than disposal age. Polyethylene terephthalate fragments faster than polypropylene or polyethylene. Our model predicts peak microplastic and fine microplastic fractions within 157–382 and 412–2118 years, respectively, with approximately half of the plastic-derived carbon available for geological cycling in 80–208 years. This research helps clarify the environmental fate of pervasive plastic pollution.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"26 ","pages":"Article 100590"},"PeriodicalIF":14.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}