Natural Hazards最新文献

{"title":"Investigation of North Sea high-frequency sea-level extremes using long-term sea-level measurements.","authors":"Mia Pupić Vurilj, Gozde Guney Dogan, Marijana Balić, José A A Antolínez, Jadranka Šepić","doi":"10.1007/s11069-026-08343-y","DOIUrl":"10.1007/s11069-026-08343-y","url":null,"abstract":"<p><p>Extreme sea-level events occur across a range of temporal and spatial scales, including high-frequency oscillations (periods T < 2 h). However, most research uses hourly or daily data and neglects higher-frequency processes such as seiches and meteotsunamis. This study investigates the characteristics of high-frequency (HF) sea-level extremes along the North Sea coast. Long-term (1993-2025) measurements were analysed from 29 tide gauges. After quality control, the astronomical tide was removed to isolate residuals, which were decomposed into low-frequency (T > 2 h) and high-frequency (T < 2 h) components. Next, extremes were extracted from the HF signal alone (HF extremes) and from the residual (residual extremes). Clustering techniques (K-medoids with Dynamic-Time-Warping and Euclidean distances) were applied to HF extremes to classify event types and identify regional patterns. HF extremes were grouped into six event types, and selected stations yielded five spatial clusters. Each event type was characterised by period, intensity, and its relationship to residual extremes. HF extremes were generally low compared with tidal ranges, and compound events (i.e., events in which an HF extreme coincided with a residual extreme) were infrequent (12.2% of HF extremes on average). Nevertheless, during compound events, alignment of HF and residual peaks substantially amplified sea levels. Spatial patterns highlighted the most intense events along the Dutch coast (IJmuiden Buitenhaven, Scheveningen, Brouwershavensche Gat, and Terschelling). These results were influenced by differences in local environmental conditions and sampling. Therefore, this study advances understanding of HF sea-level variability and compound events along the North Sea coast, supporting improved coastal hazard assessments.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11069-026-08343-y.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 18","pages":"616"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148830458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A coastal exposure index for Ireland: relative hazard exposure and the protective role of coastal habitats.","authors":"Kevin Walsh, Larissa Macedo Cruz de Oliveira, Jennifer Keenahan, Aaron Lim","doi":"10.1007/s11069-026-08361-w","DOIUrl":"10.1007/s11069-026-08361-w","url":null,"abstract":"<p><p>The concurrent rise of global sea levels and coastal population growth is intensifying exposure to climate-driven hazards. In Ireland, where most major urban centres are on, or connected to the coast, the nature and severity of hazard impacts vary across diverse coastal landscapes. A spatially explicit assessment of coastal exposure is therefore critical for effective risk management and climate adaptation planning. This study presents a national-scale Coastal Exposure Index for Ireland, developed using the Coastal Vulnerability Model within the InVEST^® software suite. The index integrates physical and climatic variables, including 30 years of wind and wave records, sea-level rise rates, and storm surge levels. Coastal habitats, including dunes, machair, saltmarsh and seagrasses, were directly represented to assess their potentially protective role. Exposure levels of coastal populations and cultural heritage sites were also evaluated. A critical appraisal of the InVEST^® approach was undertaken, including a sensitivity analysis to assess how model modifications affect outcomes. Approximately 8575 km of coastline was assessed at an alongshore spacing of 250 m, with exposure reported on a relative scale. Results reveal that 38% of the coast benefits from potential exposure reduction provided by coastal habitats. The west coast exhibits the highest relative exposure values and contains the largest extent of \"Very High Exposure\" coastline. The model equally identifies exposure distribution along the east coast, with several eastern local authorities having some of the highest proportions of \"Very High Exposure\" coastline relative to their overall length. Coastal populations within each exposure category were estimated at multiple distances from the coast, revealing almost 10,000 residents in both Dublin City and Galway City live within 500 m of a \"Very High Exposure\" coastline. Cultural heritage sites were similarly mapped across categories and distances, with several exposure hotspots identified along the west and northwest coasts. By assessing the potentially protective role of coastal habitats, this study supports the integration of nature-based solutions in into coastal zone management and provides a national-scale screening assessment of relative exposure to guide evidence-based adaptation strategies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s11069-026-08361-w.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 18","pages":"627"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148897576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of land subsidence and future flood hazards for coastal cities of the Gulf of Mexico using time series InSAR.","authors":"Yuxin Yuan, Lei Wang","doi":"10.1007/s11069-026-08203-9","DOIUrl":"10.1007/s11069-026-08203-9","url":null,"abstract":"<p><p>Sea level rise poses a significant natural hazard to coastal cities, with profound socio-economic and environmental impacts. This study analyzes new measurements of ongoing subsidence in Houston, New Orleans, and Tampa using Sentinel-1A SAR data (2019-2024) and evaluates its impact on future flooding risks. While the overall surface in these cities remains relatively stable, this study has identified multiple previously unrecognized land deformation zones. InSAR time-series deformation rates in the line-of-sight (LOS) direction range from - 42 to 10 mm/year in Houston, - 48 to 19 mm/year in New Orleans, and - 144 to 24 mm/year in Tampa. InSAR-derived velocities are correlated with land cover, with higher subsidence rates observed in vegetated areas compared to developed regions. Geographically weighted regression (GWR) is applied to quantify the spatially varying relationships between subsidence and potential driving factors. The results indicate that both groundwater withdrawal and hydrocarbon-related activities contribute to the observed deformation patterns, although their influence varies across locations. To assess future flood risk, InSAR-derived deformation data are integrated with the SSP 5-8.5 sea level rise scenario. The results show that subsidence increases the inundation area over sea level rise alone by up to 86% in Houston, 5% in New Orleans, and 50% in Tampa. These findings provide valuable insights for disaster risk management and urban planning in coastal regions.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 10","pages":"446"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13176047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting compound sea level extremes: the 29-30 October 2018 Adriatic storm surge and meteotsunami extreme.","authors":"Krešimir Ruić, Jadranka Šepić, Maja Telišman Prtenjak, Kristian Horvath, Marko Kvakić","doi":"10.1007/s11069-026-08434-w","DOIUrl":"https://doi.org/10.1007/s11069-026-08434-w","url":null,"abstract":"<p><p>On 29-30 October 2018, an extreme sea level occurred in the Adriatic Sea, driven by a combination of long-period (storm surge) and short-period (meteotsunami) oscillations. Storm surges were strongest in the northern Adriatic, while shorter-period oscillations dominated in the middle Adriatic, where a meteotsunami was observed and recorded in Vela Luka Bay (Korčula Island). This type of extreme sea level event, arising from the combination of a storm surge and a meteotsunami, is termed a compound extreme and poses both a major threat and a forecasting challenge. Using this event as a case study, we evaluated two different approaches to forecasting compound sea level extremes. The first approach relies solely on numerical modelling: sea level response was modelled using the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), forced by outputs from the Weather Research and Forecasting atmospheric numerical model (WRF). This approach reproduced the storm surge but failed to reproduce the shorter-period oscillations, including the observed meteotsunami. The second, hybrid, approach combines the first approach for modelling the storm surge component with the shorter-period sea level oscillations obtained from SCHISM forced by the high-frequency component of measured air pressure time series. As the meteotsunami was strongest in Vela Luka, the second approach was tested only at this location. With the second approach, both the storm surge and the magnitude, time of onset and duration of the meteotsunami were successfully reproduced. The results suggest that the second approach could provide basis for an early, site-specific warning system for compound extremes.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 21","pages":"672"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising rainfall characteristics for determining landslide thresholds.","authors":"Himasha Abeysiriwardana, Thomas Kjeldsen, Cormac Reale","doi":"10.1007/s11069-025-07835-7","DOIUrl":"10.1007/s11069-025-07835-7","url":null,"abstract":"<p><p>This work contributes a new framework for establishing data-driven rainfall thresholds in high-risk, data-limited contexts. Rainfall thresholds are commonly used to characterise the precipitation needed to trigger landslides in a region. However, these empirical relationships are sensitive to the exact definition of a \"rainfall event\", especially how the minimum inter-event time (MIT) and triggering event (TE) are defined. Using Bayesian inference (BI) and nonlinear least-squares (NLS) techniques, this study evaluates how variations in MIT and TE definitions affect rainfall threshold estimation, considering both Event Rainfall-Duration [Formula: see text] and Intensity-Duration [Formula: see text] spaces. The dataset includes 15-min rainfall measurements from 52 gauges recorded from 2005 to 2023, as well as a regional landslide dataset compiled from British Geological Survey records covering the South Wales coalfields. Findings reveal that <i>BI</i>-derived thresholds are more stable than <i>NLS</i>-based thresholds, showing smaller parameter changes and fewer unrealistic curves, particularly in I-D space, where <i>NLS</i> often produces near-flat thresholds. Overall, both <i>BI</i> and <i>NLS</i> approaches demonstrate their strongest performance at MIT = 48 h, emphasising the role of extended antecedent rainfall in triggering spoil tip failures. This study demonstrates how the integration of robust Bayesian methods facilitates the downscaling of global thresholds to data-scarce regions and how careful event delineation practices can improve landslide prediction.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 5","pages":"185"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147271542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid methods in flood inundation modeling: a systematic review.","authors":"Manimeldura Devki Devanga Perera, Athanasios Angeloudis, Adil Siripatana, Lindsay Beevers","doi":"10.1007/s11069-026-08078-w","DOIUrl":"10.1007/s11069-026-08078-w","url":null,"abstract":"<p><p>Flooding is a destructive natural hazard that is exacerbating due to climate change, causing significant socio-economic losses. Flood inundation models are critical tools in the mitigation and management of such hazards. Conventional standalone process-based models can be limited in real-time applications due to their computational intensity and the trade-off between accuracy, resolution and speed. Data-driven (machine learning) models offer an alternative route as surrogate models, promising computational efficiency, however, are noted to have limitations of high data-dependency and a black-box nature. Hybridization, implemented in machine learning models to enhance strengths and limit tradeoffs has been shown to increase physics awareness, real-time applicability, and adaptability. This paper conducts a systematic review of state-of-the-art research on hybrid flood models, and presents a definition of hybridization. A classification of hybridization techniques into enhancing inputs, structure and processing is proposed. Sequentially, common metrics used to evaluate flood models are classified into accuracy and speed-based metrics, and the usefulness of hybridization is discussed. An extensive benchmarking framework is proposed for the comparison of models before application, based on quantitative classification of the model and standardized datasets and tests. Finally, a way forward is proposed in the context of physics informed machine learning for flood inundation modeling.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11069-026-08078-w.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 8","pages":"357"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13046610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High impact low probability events: research landscape and future opportunities.","authors":"Lauren McMillan, Gianluca Pescaroli, Mhari Gordon, Margherita Maraschini, Silvia Torresan, José Palma-Oliveira, Beatriz Rosa, Ana Sarroeira, Benjamin D Trump, Igor Linkov","doi":"10.1007/s11069-026-08107-8","DOIUrl":"10.1007/s11069-026-08107-8","url":null,"abstract":"<p><p>High-impact, low-probability (HILP) events, characterised by their extreme consequences and inherent unpredictability, pose a growing challenge to disaster risk reduction in an era of systemic and cascading crises. Traditional risk assessment frameworks, which rely on probability-based models, often fail to capture the full scope of these events, leaving critical vulnerabilities unaddressed. This semi-systematic state-of-the-art review synthesizes current academic discourse on HILPs to identify their defining characteristics and implications for disaster preparedness and response. We analyse a dataset of 109 papers, highlighting the limitations of conventional planning tools and emphasising the need for adaptive, forward-looking strategies that integrate scenario planning, stress testing, and resilience assessment. The review finds that while HILPs are increasingly recognised in national risk registers and post-disaster response frameworks, their integration into preparedness, training, and governance systems remains limited. A key gap lies in translating theoretical insights into operational strategies that can be deployed before crises occur. The paper advocates for a hazard-agnostic, interdisciplinary approach that bridges risk and resilience thinking, enabling systems to anticipate, absorb, and adapt to a broad spectrum of threats. By advancing a research agenda beyond the Sendai Framework, this review contributes to a more robust understanding of HILPs and supports the development of more resilient and responsive disaster management systems.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 9","pages":"388"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13086801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How trust shapes individual resilience to natural hazards: a systematic review.","authors":"Joshua P Nicholas, Amy Donovan, Clive Oppenheimer, Louie Bell, Maximillian Van Wyk de Vries","doi":"10.1007/s11069-025-07816-w","DOIUrl":"10.1007/s11069-025-07816-w","url":null,"abstract":"<p><p>Trust is recognised as a critical element of disaster risk reduction; it shapes how people perceive risks, mobilise resources, and respond to hazards. Here, trust is defined as an individual's confidence in the ability and/ or intention of a system to act in their best interest. Importantly, many studies do not distinguish between ability- and intention-trust or acknowledge that such a distinction exists. Here, we present a systematic review of 100 articles and book chapters published between 2000 and 2025 which reveals that trust can both strengthen and weaken resilience to hazard events, depending on geographical factors, cultural context, hazard type, and the parties in whom trust is placed (e.g., government, community, scientific institutions, or personal beliefs). From the 100 studies, we identified 209 relationships between trust and individuals' resilience to natural hazards. We find that in the majority of case studies, trust is associated with increased resilience (58%), compared with trust being associated with decreased resilience (33%), or no change to resilience (10%). Our findings highlight the need for clarity when defining or theorising trust, and recognise the dynamic and context-dependent nature of trust when seeking to improve resilience to support effective disaster risk reduction.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11069-025-07816-w.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 2","pages":"48"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12816113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146019084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel framework for expanding temperature intensity-duration-frequency curve utility.","authors":"Gregory E Tierney, Megan S Mallard, Tanya L Spero, Anna M Jalowska","doi":"10.1007/s11069-025-07811-1","DOIUrl":"10.1007/s11069-025-07811-1","url":null,"abstract":"<p><p>Extreme temperatures-both heat waves and cold snaps-pose a great hazard to human health, property, and infrastructure. Furthermore, projected changes in extreme temperature event frequency and intensity complicates adaptation and mitigation planning for local governments providing cooling/warming centers, entities depending on outdoor labor, and utilities responding to changing energy demand. Flexible tools evaluating the current and future risk of extreme temperature events could help to optimize hazard response. Here, we present an expanded framework for temperature intensity-duration-frequency (TIDF) curve analysis, including an objective fitting algorithm and uncertainty quantification, for the observational record. Prior to this study, the application of TIDF curves has been focused on heat waves. We expand that application to consider the utility of TIDF curves for extreme cold events as well as \"near-extreme\" events for a more robust quantification of the risk of extreme temperature events. Using a probabilistic approach to extremes, we also calculate confidence intervals, providing additional context for the severity and (where applicable) unprecedented nature of historically extreme events. Further analysis of confidence interval width offers insight to characterize differences in the uncertainty associated with hot and cold extremes. Finally, the flexibility of this new TIDF framework is demonstrated by analyzing two recent extreme temperature events-the 2021 Pacific Northwest heat wave and 2021 Texas cold snap-showcasing the broad utility and potential cross-sector application of a TIDF approach.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 1","pages":"5"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global parametric rain model for landfalling tropical cyclones: a case study for the U.S.","authors":"King Heng Lau, Sacha Czernichow, Nathan Sparks, Ralf Toumi","doi":"10.1007/s11069-026-08150-5","DOIUrl":"10.1007/s11069-026-08150-5","url":null,"abstract":"<p><p>Rainfall associated with tropical cyclones (TCs) is a crucial driver of TC hazards, yet estimating TC rain risk from observations is hindered by their relative infrequency. Parametric TC rain models coupled with stochastic TC risk models provide an efficient mean for quantifying such risk. This study introduces a new parametric rain model for landfalling TCs, integrated into the Imperial College Storm Model (IRIS), a statistical-thermodynamic global TC hazard model. Using a 10,000-year simulation, IRIS reproduces observed global return periods of landfall rain rate, storm-total rain volume, and lifetime rain production over land. Over the United States, the model captures observed rainfall climatology and event accumulations with skill comparable to or slightly exceeding existing parametric models. The capability of the model for climate projection is demonstrated through a United States case study using a storyline approach that isolates thermodynamic effects, specifically increases in potential intensity and total column water, under a +2 °C global warming scenario. The pre-landfall maximum azimuthal mean rain rate of United States hurricanes increases by 20.1%, while contraction of the rain field limits the storm-total rain volume increase to 3.9%. Across their lifetimes over land, hurricanes produce 14.6% more rainfall. Spatially, warming enhances rain rates and accumulations across the eastern and southern United States, with the largest absolute increases in the southeast but the strongest relative increases inland and in the northeast, indicating greater inland and poleward penetration of hurricane rainfall under warming.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11069-026-08150-5.</p>","PeriodicalId":18792,"journal":{"name":"Natural Hazards","volume":"122 10","pages":"415"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}