Environmental Modelling & Software最新文献_第3页

A novel interpretable ozone forecasting approach based on deep learning with masked residual connections 一种基于深度学习的残差连接可解释臭氧预测方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.envsoft.2026.106878

P. Reina-Jiménez , M.J. Jiménez-Navarro , G. Asencio-Cortés , F. Martínez-Álvarez , M. Martínez-Ballesteros

{"title":"A novel interpretable ozone forecasting approach based on deep learning with masked residual connections","authors":"P. Reina-Jiménez , M.J. Jiménez-Navarro , G. Asencio-Cortés , F. Martínez-Álvarez , M. Martínez-Ballesteros","doi":"10.1016/j.envsoft.2026.106878","DOIUrl":"10.1016/j.envsoft.2026.106878","url":null,"abstract":"<div><div>Air pollution is a growing threat, especially in low- and middle-income countries, causing over 4 million premature deaths annually. Ground-level ozone is a major concern, demanding accurate and interpretable prediction systems for effective public health management. However, existing time-series forecasting methods struggle to capture both linear and nonlinear dependencies in atmospheric data. This study introduces ResSelNet, a novel Residual Selection Network that integrates masked residual connections and embedded feature selection within a unified deep learning architecture. The model dynamically determines the optimal processing depth for each feature, allowing linear relationships to bypass nonlinear transformations while capturing complex patterns when necessary. Applied to five monitoring stations across Andalusia (Spain), ResSelNet consistently outperformed state-of-the-art baselines, achieving 8%–12% lower RMSE and MAE than LSTM and Transformer models. Beyond accuracy, the framework improves interpretability and robustness, revealing the hierarchical relevance of meteorological and pollutant variables. ResSelNet therefore offers an effective and explainable solution for multi-horizon environmental time-series forecasting.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106878"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A catalogue of Do's and Don'ts in the modeling of environmental systems 环境系统建模中该做与不该做的目录

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.envsoft.2026.106893

Xifu Sun , Anthony Jakeman , Serena H. Hamilton , Volker Grimm , Randall J. Hunt , Sondoss El Sawah , Hsiao-Hsuan Wang , Barry Croke , Min Chen

{"title":"A catalogue of Do's and Don'ts in the modeling of environmental systems","authors":"Xifu Sun , Anthony Jakeman , Serena H. Hamilton , Volker Grimm , Randall J. Hunt , Sondoss El Sawah , Hsiao-Hsuan Wang , Barry Croke , Min Chen","doi":"10.1016/j.envsoft.2026.106893","DOIUrl":"10.1016/j.envsoft.2026.106893","url":null,"abstract":"<div><div>Modeling plays a vital role in understanding and managing complex environmental systems, but its credibility and quality depend heavily on a comprehensive set of defensible model activities and practices, especially when the system of interest is plagued with uncertainties and conflicting stakeholder perspectives. This paper proposes a catalogue of Do's and Don'ts to guide modelers in addressing the many pertinent considerations through the whole modeling cycle. This practical tool provides advice on approaching modeling effectively through adhering to good modeling practice. It emphasizes model choices that align with the model purpose and context, and the justification and documentation of modeling decisions and assumptions. Managing uncertainty is a core consideration. The identification, assessment and reporting of these uncertainties is important across the entire modeling process, which spans problem framing, technical design, implementation and application phases. Such good practices are critical for transparency and reliability of the modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106893"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

hydroMOPSO: A flexible and model-independent multi-objective optimisation R package for environmental and hydrological models hydroMOPSO：一个灵活的和模型无关的多目标优化环境和水文模型R包

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.envsoft.2025.106851

Rodrigo Marinao , Mauricio Zambrano-Bigiarini , Oscar M. Baez-Villanueva

{"title":"hydroMOPSO: A flexible and model-independent multi-objective optimisation R package for environmental and hydrological models","authors":"Rodrigo Marinao , Mauricio Zambrano-Bigiarini , Oscar M. Baez-Villanueva","doi":"10.1016/j.envsoft.2025.106851","DOIUrl":"10.1016/j.envsoft.2025.106851","url":null,"abstract":"<div><div>This article introduces <span>hydroMOPSO</span>, a multi-objective, model-independent R package for the calibration of hydrological and environmental models. It supports both R-based and R-external models through wrapper functions, providing flexibility for a wide range of optimisation problems. The package includes fine-tuning options to generate a Pareto-optimal front. The performance of <span>hydroMOPSO</span> was compared to the <span>caRamel</span> R package using benchmark functions and case studies involving two R-based hydrological models in an Andean catchment. <span>hydroMOPSO</span> outperformed <span>caRamel</span> on benchmarks, with faster convergence in the two hydrological models. An R-external case study demonstrated the flexibility and ease of use of <span>hydroMOPSO</span>, through its application to the calibration of the <span>SWAT+</span> model. The package also enables the generation of informative outputs for modellers, with particular emphasis on hydrographs and parameter sets from the Pareto-optimal front. <span>hydroMOPSO</span> constitutes a valuable tool for researchers and practitioners seeking to implement multi-objective optimisation in environmental and hydrological modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106851"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A Transformer-based neural network for global short-range dust forecasting 基于变压器的神经网络全球短期粉尘预报

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.envsoft.2026.106898

Shikang Du , Siyu Chen , Jiaqi He , Yu Fu , Lulu Lian

{"title":"A Transformer-based neural network for global short-range dust forecasting","authors":"Shikang Du , Siyu Chen , Jiaqi He , Yu Fu , Lulu Lian","doi":"10.1016/j.envsoft.2026.106898","DOIUrl":"10.1016/j.envsoft.2026.106898","url":null,"abstract":"<div><div>Dust forecasting holds significant scientific and societal value. Traditional numerical weather prediction (NWP) models predict dust by solving differential equations that simulate the physicochemical processes of dust aerosols. However, uncertainties in initial and boundary conditions, coupled with the complexity of modeling dust processes, result in significant challenges in both accuracy and computational cost. In this study, we introduce DustReal, a Transformer-based short-range dust forecasting model that leverages deep learning for enhanced accuracy and efficiency. DustReal takes MERRA-2 reanalysis data as input to generate hourly forecasts of surface dust concentration (DUSMASS) and dust optical depth at 550 nm (DOD) over the next 24 h, with a spatial resolution of 0.5° × 0.625°. Evaluation on an independent test set from 2022 to 2023 demonstrates robust forecasting accuracy, with DustReal outperforming three operational NWP dust forecast systems in Asia and excelling at capturing the fine-scale spatiotemporal evolution of dust events. Our results highlight that DustReal can deliver high-quality dust forecasts at a fraction of the computational cost required by traditional NWP models. As a lightweight, deep learning-based short-range model, DustReal offers a practical solution for sectors such as aviation and solar energy, facilitating the development of operational dust forecasting systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106898"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A surrogate-assisted calibration framework for computationally expensive urban fluvial flood models 一个计算代价昂贵的城市河流洪水模型的代理辅助校准框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.envsoft.2026.106902

Congji Han , Takahiro Koshiba , Keiko Wada , Kenji Kawaike

{"title":"A surrogate-assisted calibration framework for computationally expensive urban fluvial flood models","authors":"Congji Han , Takahiro Koshiba , Keiko Wada , Kenji Kawaike","doi":"10.1016/j.envsoft.2026.106902","DOIUrl":"10.1016/j.envsoft.2026.106902","url":null,"abstract":"<div><div>Parameter calibration for urban fluvial flood models is challenging due to model complexity and computational budget limitations. To address this issue, this study proposes a novel parameter calibration framework that integrates urban flood models with widely used building treatment methods and a classifier-assisted level-based learning swarm optimizer. The stability and independence of the calibrated parameters were evaluated through laboratory experiments, and the proposed framework successfully reduced the RMSE by 23.75% and 19.92% for the building-resistance and building-porosity methods, respectively. The applicability of different building treatment methods from a parameter calibration perspective was also discussed. The proposed framework was then applied to a real-world flood case, where the RMSE values of three cross-validation combinations using flood mark data were 0.4464, 0.3690, and 0.3992, respectively, demonstrating its generalization ability. Furthermore, under the same computational budget, the proposed framework achieved higher computational efficiency compared with traditional evolutionary algorithms.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106902"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Advancing participatory decision support system in water management: An agent-based model for socially-informed collective allocation 推进水管理中的参与式决策支持系统：基于主体的社会知情集体分配模型

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1016/j.envsoft.2026.106903

Déborah Santos de Sousa , Célia Ghedini Ralha , Conceição de Maria Albuquerque Alves

{"title":"Advancing participatory decision support system in water management: An agent-based model for socially-informed collective allocation","authors":"Déborah Santos de Sousa , Célia Ghedini Ralha , Conceição de Maria Albuquerque Alves","doi":"10.1016/j.envsoft.2026.106903","DOIUrl":"10.1016/j.envsoft.2026.106903","url":null,"abstract":"<div><div>Modeling human–water interactions becomes increasingly vital for sustainable water governance as climate variability and population growth intensify water conflicts. This paper presents an agent-based model that integrates hydrological and social dynamics to support decision-making in river basin water allocation. Designed using the Tropos methodology and implemented on the GAMA platform, the model captures stakeholder behaviors, preferences, and interactions in various collective allocation scenarios. Using real irrigation data from the Brazilian Formoso River Basin (FRB) , the model simulates strategic cooperation among water users and regulators . Under a cooperative collective allocation scenario, the cumulative withdrawal in the FRB was found highly sensitive to water availability, whereas non-cooperative collective behaviour led to higher cumulative consumption (<span><math><mo>></mo></math></span>3.0 × 10<sup>6</sup> m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>) and no reduction was observed near critical periods. The framework is adaptable for other river basins with different dynamic agent profiles and negotiation mechanisms.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106903"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A review of tools and resources to support Decision-Making Under Deep Uncertainty 深度不确定性下支持决策的工具和资源综述

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.envsoft.2026.106900

Julius Schlumberger , David Gold , Valeria Di Fant , Gundula Winter , Mehmet Ümit Taner , Jan Kwakkel

{"title":"A review of tools and resources to support Decision-Making Under Deep Uncertainty","authors":"Julius Schlumberger , David Gold , Valeria Di Fant , Gundula Winter , Mehmet Ümit Taner , Jan Kwakkel","doi":"10.1016/j.envsoft.2026.106900","DOIUrl":"10.1016/j.envsoft.2026.106900","url":null,"abstract":"<div><div>Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance regarding theory and methods of DMDU application, with some bias toward water-related applications. Technical, method-specific resources on how to implement existing frameworks remain limited. Our results identify tools supporting all core DMDU components, though they highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106900"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A hybrid intelligence approach to predicting and explaining land use and land cover change using Bayesian classifiers 使用贝叶斯分类器预测和解释土地利用和土地覆盖变化的混合智能方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.envsoft.2026.106904

Christopher R. Stephens , Constantino González-Salazar , José García-Hernández

{"title":"A hybrid intelligence approach to predicting and explaining land use and land cover change using Bayesian classifiers","authors":"Christopher R. Stephens , Constantino González-Salazar , José García-Hernández","doi":"10.1016/j.envsoft.2026.106904","DOIUrl":"10.1016/j.envsoft.2026.106904","url":null,"abstract":"<div><div>The future health of land systems depends on our ability to quantify and understand the causes of land cover change. This requires the integration of data of different types and spatio-temporal resolutions and the creation of accurate, explainable prediction models. To achieve this, a framework is presented where all variables are converted to binomial form at the same spatio-temporal resolution, co-occurrences between classes of interest, <span><math><mi>C</mi></math></span>, and a set of predictive features, <span><math><mi>X</mi></math></span>, are computed and converted into a Bayesian classifier <span><math><mrow><mi>P</mi><mrow><mo>(</mo><mi>C</mi><mo>|</mo><mi>X</mi><mo>)</mo></mrow></mrow></math></span>, where each feature <span><math><msub><mrow><mi>X</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow></msub></math></span> is quantified by its effect size, coverage, statistical significance and potential causality. As a use case, a prediction model for deforestation at the municipal level in Mexico in 2020 is created using a set of 98 Climatic, Social, Economic and Governmental factors as putative causes, showing that besides the impact of alternative land uses, the models infer an important role for the <em>Sembrando Vida</em> government program.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106904"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Seamless hourly PM2.5 mapping across China with a graph spatiotemporal deep neural network 利用图时空深度神经网络无缝绘制中国每小时PM2.5地图

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.envsoft.2026.106915

Mengfan Teng , Miaomiao Liang , Shuo Wang , Yu Ding

{"title":"Seamless hourly PM2.5 mapping across China with a graph spatiotemporal deep neural network","authors":"Mengfan Teng , Miaomiao Liang , Shuo Wang , Yu Ding","doi":"10.1016/j.envsoft.2026.106915","DOIUrl":"10.1016/j.envsoft.2026.106915","url":null,"abstract":"<div><div>Fine particulate matter (PM<sub>2.5</sub>) poses serious threats to public health and the environment. Current satellite-based PM<sub>2.5</sub> estimates often lack nighttime data, leading to significant temporal discontinuities. To overcome this, this study developed a novel graph-based spatiotemporal deep neural network (G-STDNN) that generates seamless hourly PM<sub>2.5</sub> concentrations across China. We first produced continuous daytime and nighttime aerosol optical depth (AOD) by filling missing Himawari-8 AHI data with MERRA-2 AOD. This improved AOD, combined with ERA5 meteorology, TROPOMI NO<sub>2</sub>, nighttime light, and geographical data, served as model input. The G-STDNN effectively captures complex spatiotemporal patterns of air pollution. For 2019–2020, the model demonstrated high accuracy in sample-based (R<sup>2</sup> = 0.942, RMSE = 10.81 μg/m<sup>3</sup>). Using the filled AOD significantly improved estimation performance (R<sup>2</sup>value increased from 0.74 to 0.85). Nighttime estimates remained robust (R<sup>2</sup> ≈ 0.84). This study provides a continuous, high-accuracy hourly PM<sub>2.5</sub> dataset essential for exposure assessment and air quality management in China.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106915"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Improved river transmission loss modelling for environmental flow releases during droughts 改进了干旱期间环境流量释放的河流传输损失模型

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.envsoft.2026.106880

Shaun S.H. Kim , Russell S. Crosbie , Warrick Dawes , Jai Vaze , Bill Wang , Cherry Mateo , Rebekah May , Sudeep Nair , Jahangir Alam

{"title":"Improved river transmission loss modelling for environmental flow releases during droughts","authors":"Shaun S.H. Kim , Russell S. Crosbie , Warrick Dawes , Jai Vaze , Bill Wang , Cherry Mateo , Rebekah May , Sudeep Nair , Jahangir Alam","doi":"10.1016/j.envsoft.2026.106880","DOIUrl":"10.1016/j.envsoft.2026.106880","url":null,"abstract":"<div><div>Basin-scale water resources models lack key physical factors such as antecedent conditions, that strongly influence transmission losses under dry conditions. This study presents the development and evaluation of two river transmission loss models for integration into river systems models: dynamic maximum alluvium as river storage (DMAARS) and DMAARS coupled with river dead storage (DMAARSDS). The models were applied to environmental flow events during the 2018/2019 drought in the northern Murray-Darling Basin and compared with a benchmark piecewise linear loss model. They provided significantly improved performance in 8 out of 12 fit metrics and more realistic estimates of environmental flow metrics. Scenario testing revealed that model choice significantly influences predictions, especially of baseline conditions and ecological benefits, e.g., peak water height, flow extent. Analyses also showed strong potential for use in long-term water resource planning. To enable adoption, the new models have been integrated into eWater Source as a community plugin.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106880"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0