自然资源智能体

To overcome the shortcomings of traditional correlation and static spatial statistics in disaster causal analysis, a geographic causal inference mechanism is constructed, combining causal discovery, individualized effect estimation, and spatially constrained clustering methods to identify regional differences in disaster risk. The computational process involves a structural causal model and watershed topology weights. The DML algorithm collaborates with the causal forest algorithm to extract CATEs (category-experienced exponents), and the consistency of GNN causal regularization with panel models is tested. Counterfactual simulation techniques are used to establish the ΔRisk–ΔCost Pareto frontier, establishing guiding principles for policy zoning and resource allocation. Regional empirical testing based on EM-DAT (2015–2024) data reveals that this technique significantly reduces CATE errors in regions with high exposure and strong spillover effects, enhancing the stability of causal consistency indicators and policy rankings. This study proposes specific technical implementation paths for regional management, budget improvement, and online risk control in the context of extreme climate change.

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We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices.

Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things, for which situational awareness is normally the crucial first step. While traditional static surveillance data on cities have been available for decades, this review reports a type of relatively new yet highly important urban data source, i.e., the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city. We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System (GPS). This technique enjoys advantages such as a large penetration rate (∼50% urban population covered), uniform spatiotemporal coverage, and high localization precision. We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced. Then we introduce two suites of empowering technologies that help fulfill the requirements of (1) cybersecurity insurance for smart cities and (2) spatiotemporal modeling and visualization for situational awareness, both via big mobile data. The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications. 智慧城市态势感知近年来成为学术圈、 产业界及政府部门关注的热门话题. 其整合尖端信息技术的潜力可望解决现代城市面临的诸多挑战. 在最近一期五年规划中, 中国政府强调利用前沿信息技术(如大数据、物联网)赋能智慧城市管理, 其中态势感知通常是关键的第一步. 近年来, 面向城市态势的静态监测数据已广泛存在. 与之不同的是, 本文报告了一类相对新颖且极为重要的新兴城市数据源, 即在移动设备上收集的大规模移动数据, 可代表现代城市中公共车辆和个人用户的移动情况与分布. 具体而言, 我们重点关注一种代表性数据源, 整合了数十万移动软件应用程序中获取的百亿条GPS定位数据, 服务于智慧城市态势感知. 这种数据源具有较高的用户渗透率(覆盖约50%的城市人口)、 均匀的时空覆盖程度和高定位精度等优势. 本文首先详述了智慧城市态势感知的需求与挑战, 之后重点介绍了两类面向态势感知的移动大数据分析技术: (1)智慧城市的安全保障方法; (2)智慧城市移动大数据的时空建模与可视化分析方法. 本文主要贡献在于全面阐述智慧城市态势感知的技术框架, 并通过实际应用案例展示其技术可行性.

为了调整人与自然的关系，近年来，新的城市发展理念不断出现，这为现代化城市的发展指出了新的方向，也有力提升了我国城市化进程的科学水平。海绵城市理论旨在提升城市抵御自然灾害的弹性应对能力，强化雨水吸水、储水、净化二次利用等功能，实现对自然资源的最大效用化利用，助力于解决城市内涝、合理规划设计管网等。本文以此为题，分析海绵城市理论提出理念及其与风景园林规划设计的连带性、优势所在，并分析两者融合共生的应用路径。

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The remediation of heavy metal contaminated soil has always been an important issue of concern for environmental researchers both domestically and internationally. In recent years, the application of nanomaterials in this field has gradually received widespread attention. This article mainly reviews the application of nanomaterials in the remediation of heavy metal contaminated soil and the effect of nanocomposite technology on the remediation of heavy metal contaminated soil. It proposes the application and diffusion ability of nanomaterials and technology in soil heavy metal pollution remediation, providing theoretical support for promoting the preparation and application of nanomaterials and technology, and providing theoretical basis for soil heavy metal pollution remediation.

The Chinese nation’s sustainable development is based on the lifeblood of grain production, cultivated land. Unfortunately, 40% of this land has been degraded and its quantity and quality have drastically declined, posing a grave threat to national food security. Analysing the sources and features of degraded cultivated land ecosystems has a critical part in advancing cultivated land quality, ecological preservation, and conservation; it is also beneficial to surmount impediments such as desertification, salinity, barrenness, etc., and is of great significance for the restoration of degraded cultivated land ecosystem according to local conditions.

There are many polymetallic mining areas in nature. The types of heavy metals are associated and comprehensive, and the pollution caused by them has the characteristics of multiple elements. Wastes such as tailings from polymetallic mining areas, ores and acid mine drainage are the main reasons for the pollution of polymetallic mining areas. At present, the pollution of cadmium (Cd), arsenic (As) and lead (Pb) in the soil around the nonferrous metal mining areas is relatively serious. The mining area soil-plant system is the environmental carrier and final receptor of heavy metal pollutants such as Pb and Cd, the diversity of environmental factors and driving factors increases the difficulty of ecological restoration and restoration. The current research shows that although phytoremediation has many advantages and has screened out plant species and physiological characteristics with cumulative effects, there are still some limitations in the management and protection measures after remediation, which need to be further studied.

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Based on the theory of financial empowerment, this study systematically explores the mechanism, practical dilemmas and optimization paths of financial empowerment on the innovation and sustainable development of urban consumption spaces, using panel data from 35 large and medium-sized cities across China from 2018 to 2023 and micro-survey data from 200 consumption space operation enterprises. The research shows that the total effect of financial empowerment on the innovation of urban consumption spaces is significant: for every 100 million yuan increase in the scale of green credit, the number of green renovation projects in consumption spaces increases by 0.82; for every 10% increase in the coverage rate of digital finance, the growth rate of online-offline integration projects in consumption spaces reaches 6.7%; and for every 10% increase in the penetration rate of inclusive finance, the survival rate of innovation projects in small and medium-sized consumption spaces rises by 12.3%.At present, the development of urban consumption spaces faces three core dilemmas: first, imbalanced financing structure—indirect financing accounts for more than 75%, and the single financing model relying on bank credit cannot meet the long-term construction and operation needs of consumption spaces, with insufficient application of direct financing tools such as equity financing and asset securitization. Second, high costs of green transformation—the average cost of green renovation of consumption spaces (e.g., upgrading energy-saving equipment, building low-carbon scenarios) is 25% higher than that of traditional renovation, making it difficult for small and medium-sized operation enterprises to afford transformation investment due to financial pressure. Third, superficial application of digital finance—the digitalization rate of small and medium-sized consumption spaces is only 32%, and most of them remain at the basic payment level, failing to achieve in-depth integration of "digital finance + consumption scenarios" (e.g., intelligent passenger flow analysis, personalized service push).To address these issues, this study proposes a three-dimensional optimization path of "diversified financing + green tools + digital empowerment": on the financing side, construct a diversified system of "bank credit + REITs + industrial funds" and encourage consumption space projects to issue public infrastructure REITs to achieve a virtuous cycle of funds; on the green transformation side, innovate tools such as green credit interest subsidies and carbon asset pledge loans to reduce the financing cost of green renovation; on the digital integration side, promote joint development of "scenario-based digital financial products" by financial institutions and consumption space operators, such as credit loans based on consumption data and installment services adapted to online-offline integration. This research can provide theoretical support and practical paradigms for consumption upgrading and sustainable economic growth under the background of new-type urbanization.

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This paper studies the dynamic feedback and information construction technology of tunnel in karst area based on BIM (Building information model). Firstly, this paper summarizes the application status of BIM technology in the field of tunnel construction, and analyzes the particularity and challenges of tunnel construction in karst area in detail. Then, the paper deeply discusses the analysis and feedback mechanism of tunnel dynamic construction information based on IFC standard, establishes tunnel construction information model (TCIM), and realizes the rapid extraction and accurate analysis of construction information. In addition, the research also carries on the research of tunnel dynamic construction multi-source data information, including data acquisition, pre-processing, fusion and integration, and the application analysis of these data in tunnel construction. Finally, based on the above research results, a highly efficient and accurate tunnel dynamic construction information analysis and feedback system is built, which has functions such as real-time data analysis, early warning and feedback, user interface and interaction, and provides comprehensive information support for tunnel construction.

With increasingly complex environments in karst tunnel construction, traditional methods based on plan views and empirical guidelines are insufficient to meet demands for high precision and multi-field collaboration. This paper explores BIM technology’s application during tunnel design, using unified data standards and parameterized component libraries to achieve efficient integration and updating of geological information and structural elements. In data preprocessing, wavelet denoising and principal component analysis optimize geological data quality, complemented by inverse distance weighted interpolation to fill gaps and construct a 3D geological model accurately reproducing complex underground conditions. Additionally, optimizing information exchange mechanisms enables cross-platform data sharing, supporting collaboration among diverse professional teams. A case study of the Metro Project from the Sci-Tech Center to JiBei Station on Line 7 of Jinan’s Urban Rail Transit illustrates BIM’s practical application effects in real projects. This approach enhances design accuracy and provides references for future intelligent tunnel construction.

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Les eaux souterraines sont présentes à faible profondeur sous de nombreuses villes côtières de basse altitude. Malgré l’importance de la protection des zones côtières urbanisées contre les inondations et l’élévation du niveau de la mer induite par le changement climatique, les effets des fluctuations des eaux souterraines à faible profondeur sont rarement étudiés. L’objectif de cette étude était de déterminer les caractéristiques des aquifères de faible profondeur, y compris les tendances spatiales et temporelles en profondeur des eaux souterraines et leur relation avec les facteurs de stress naturels et anthropogéniques. L’étude utilise des mesures de la profondeur des eaux souterraines provenant d’un réseau de surveillance particulièrement étendu et densément espacé à Ōtautahi/Christchurch, en Nouvelle-Zélande. Des approches d’analyse basées sur les données ont été appliquées, notamment l’interpolation spatiale, l’autocorrélation, le regroupement, la corrélation croisée et l’analyse des tendances. Ces approches ne sont pas couramment utilisées pour l’évaluation des eaux souterraines, bien qu’elles puissent fournir des informations sur les systèmes à l’échelle de la ville. L’approche globale a révélé des groupes et des tendances perceptibles dans l’ensemble des données. Les réponses aux contraintes telles que les précipitations et le débit des cours d’eau ont été classées avec succès à l’aide d’une analyse de regroupement. L’analyse des séries chronologiques a indiqué que dans les zones où les eaux souterraines sont peu profondes, les niveaux varient peu, ce qui a également été constaté à l’aide de l’analyse de regroupement. Toutefois, l’attribution de certains groupes à des attributs hydrogéologiques ou à des facteurs de stress spécifiques a posé des problèmes. La principale caractéristique de la classification des hydrogrammes s’est avérée être la proximité des rivières influencées par la marée et leur corrélation avec les signaux de cette dernière. Ces résultats soulignent l’intérêt d’utiliser de vastes ensembles de données pour caractériser la variabilité spatiale et temporelle des eaux souterraines peu profondes dans les zones côtières urbaines et pour aider à la planification des infrastructures de surveillance face aux futurs risques liés au changement climatique. Groundwater is present at shallow depth under many coastal low-lying cities. Despite the importance of protecting coastal urbanised areas from flooding and climate-change-induced sea-level rise, the effects of shallow groundwater fluctuations are rarely investigated. The aim of this study was to determine characteristics of shallow groundwater, including spatial and temporal trends in depths to groundwater and their relationship to natural and anthropogenic stressors. The study uses depth to groundwater measurements from a uniquely extensive and densely spaced monitoring network in Ōtautahi/Christchurch, New Zealand. Data-driven analysis approaches were applied, including spatial interpolation, autocorrelation, clustering, cross-correlation and trend analysis. These approaches are not commonly applied for groundwater assessments despite the potential for them to provide insights and information for city-wide systems. The comprehensive approach revealed discernible clusters and trends within the dataset. Responses to stresses such as rainfall events and stream flow were successfully classified using clustering analysis. The time series analysis indicated that in areas of shallow groundwater, low variation in levels occurred and this was also found using clustering. However, attributing some clusters to specific hydrogeological attributes or stressors posed challenges. The primary feature in hydrograph classification proved to be the proximity to tidal rivers and their correlation with tidal signals. These results highlight the value of using large datasets to characterise spatial and temporal variability of shallow groundwater in urban coastal settings and to assist with monitoring infrastructure planning in the face of future climate-change hazards. El agua subterránea está presente a escasa profundidad bajo muchas ciudades costeras de baja altitud. A pesar de la importancia de proteger las zonas urbanizadas costeras de las inundaciones y de la subida del nivel del mar inducida por el cambio climático, rara vez se investigan los efectos de las fluctuaciones de las aguas subterráneas poco profundas. El objetivo de este estudio era determinar las características de las aguas subterráneas poco profundas, incluidas las tendencias espaciales y temporales de las profundidades a las aguas subterráneas y su relación con los factores de estrés naturales y antropogénicos. El estudio utiliza mediciones de la profundidad de las aguas subterráneas de una red de monitoreo excepcionalmente extensa y densamente espaciada en Ōtautahi/Christchurch, Nueva Zelanda. Se aplicaron enfoques de análisis basados en datos, como la interpolación espacial, la autocorrelación, la agrupación, la correlación cruzada y el análisis tendencial. Estos métodos no suelen aplicarse a la evaluación de las aguas subterráneas, a pesar de su potencial para proporcionar información sobre sistemas urbanos. El enfoque global reveló agrupaciones y tendencias discernibles en el conjunto de datos. Las respuestas a las presiones, como las precipitaciones y el caudal de los arroyos, se clasificaron con precisión mediante el análisis de agrupaciones. El análisis de las series temporales indicó que en las zonas de aguas subterráneas poco profundas se producía una escasa variación de los niveles, lo que también se comprobó mediante la agrupación. Sin embargo, la atribución de algunas agrupaciones a atributos hidrogeológicos específicos o a factores de estrés planteó dificultades. La característica principal en la clasificación de los hidrogramas resultó ser la proximidad a los canales de marea y su correlación con las señales de marea. Estos resultados ponen de relieve el valor de la utilización de conjuntos de datos de gran tamaño para caracterizar la variabilidad espacial y temporal de las aguas subterráneas poco profundas en entornos costeros urbanos y para contribuir a la planificación de infraestructuras de monitoreo de cara a futuros riesgos derivados del cambio climático. 在许多沿海低洼城市，地下水水位埋深浅。尽管保护沿海城市免受洪水和气候变化引发的海平面上升的重要性不言而喻，但浅层地下水波动的影响很少被研究。本研究旨在确定浅层地下水特征，包括地下水埋深的时空变化趋势，以及它们与自然和人为影响因素的关系。该研究利用新西兰Ōtautahi/Christchurch的独特广泛且分布密集的监测网络的地下水埋深测量数据。采用了数据驱动的分析方法，包括空间插值、自相关、聚类、交叉相关和趋势分析。尽管这些方法在地下水评估中并不常见，但它们有潜力为城市范围的系统提供认识和信息。综合的方法揭示了数据集中可辨识的聚类和趋势。对降雨事件和河流流量等压力的响应成功地通过聚类分析进行了分类。时间序列分析表明，浅层地下水区的水位变化较小，这也是通过聚类分析发现的。然而，将某些聚类归因于特定的水文地质属性或影响因素存在困难。水文曲线分类中的主要特征被证明是与邻近潮汐河流以及它们与潮汐信号的相关性。这些结果突显了利用大型数据集来表征城市沿海环境中浅层地下水的空间和时间变异性的潜力，并有助于在未来气候变化危害面前进行监测基础设施规划。 Água subterrânea está presente em profundidades rasas abaixo muitas cidades costeiras de baixa altitude. Apesar da importância da proteção de áreas costeiras urbanizadas contra inundações e o aumento do nível do mar induzido pelas mudanças climáticas, os efeitos da flutuação das águas subterrâneas rasas são raramente investigados. O objetivo deste estudo foi determinar as características das águas subterrâneas rasas, incluindo padrões espaciais e temporais em profundidades de águas subterrâneas e suas relações com estressores naturais e antropogênicos. O estudo utiliza medições da profundidade das águas subterrâneas de uma rede de monitoramento exclusivamente extensa e densamente espaçada em Ōtautahi/Christchurch, Nova Zelândia. Análises orientadas pelos dados foram aplicadas, incluindo interpolação espacial, autocorrelação, agrupamento, correlação cruzada e análise de tendência. Essas abordagens não são comumente aplicadas para avaliação de águas subterrâneas apesar do potencial delas em prover entendimentos e informações para sistemas amplos em cidades. A abordagem abrangente revelou agrupamentos discerníveis e tendencias dentro do banco de dados. Respostas a estresses como eventos de chuva e fluxo de rios foram classificados com sucessos utilizando análise de agrupamentos. A análise das séries temporais indica que em áreas de águas subterrâneas rasas, pouca variação nos níveis ocorreu e isto também foi encontrado utilizando os agrupamentos. Entretanto, atribuir algum agrupamento à atributos hidrogeológicos específicos ou estressores ainda se apresenta como um desafio. A primeira característica na classificação hidrográfica provou ser a proximidade a rios de maré e sua correlação com sinais de maré. Esses resultados destacam o valor de usar grandes bancos de dados para caracterizar a variabilidade espacial e temporal de águas subterrâneas rasas em configurações urbanas costeiras e em auxiliar com o planejamento do monitoramento de infraestruturas em frente a perigos futuros das mudanças climáticas.

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Addressing the difficulty in balancing detection accuracy and model lightweighting in vehicle detection tasks under foggy conditions, this paper proposes an improved foggy vehicle detection algorithm based on RT-DETR-r18. This algorithm overcomes the computational bottleneck of the model. First, an improved backbone network (C2F_DYNCB) module is designed, combining a dynamic convolution weight (DKW) mechanism and parameter sharing (DyInConv) to construct a novel dynamic convolution unit (DAIN Mixer). This module enables adaptive depth convolution and feature fusion, significantly reducing the number of parameters and computations while still maintaining detailed processing of input feature maps. Second, a PEMD module is proposed, introducing the linear attention mechanism Pola Attention and EDFFN, which significantly improves the discriminative power of the attention map and model performance while maintaining linear complexity. Then, a lightweight adaptation block, the MOEN module, is designed to further integrate local information and stabilize feature distribution. Finally, WIoU-v3 is used as the regression loss, adaptively adjusting the weights of positive and negative samples during training to increase attention to subtle bounding boxes. Experimental results show that on the REFYG foggy dataset, the improved algorithm's mAP50 is 3.05 percentage points higher than the original algorithm, and the model's computational complexity and number of parameters are reduced by 39.4% and 32.8%, respectively, achieving significant lightweighting. 针对雾天场景下车辆检测任务中，检测精度与轻量化难以平衡的问题，提出了一种基于RT-DETR-r18的改进雾天车辆检测算法，该算法克服了模型的计算瓶颈，首先，设计了一种改进的主干网络（C2F_DYNCB）模块，结合动态卷积权重（DKW）机制和参数共享（DyInConv），构建了一种新型的动态卷积单元（DAIN Mixer）。该模块能够根据输入特征自适应调节深度卷积与特征融合方式，在显著减少参数量和计算量的同时，仍保持对多尺度空间特征的有效建模能力。其次，提出PEMD模块，通过引入Pola Attention线性注意力机制与EDFFN结构，在保持线性复杂度的同时大幅提高注意力图的判别性和模型性能；然后设计一个轻量化适配块MOEN模块，用于进一步整合局部信息并稳定特征分布。最后，采用WIoU-v3作为回归损失，通过在训练过程中自适应调整正负样本权重，增加对细微框的关注。实验结果表明，在REFYG雾天数据集上，改进算法的mAP50比原算法提高了3.05个百分点，模型的计算量与参数量分别降低了39.4%和32.8%，实现了显著的轻量化。

Ground-based microwave radiometers (MWRs) operating in the K- and V-bands (20–60 GHz) can help us obtain temperature and humidity profiles in the troposphere. Aside from some soundings from local meteorological observatories, the tropospheric atmosphere over the Tibetan Plateau (TP) has never been continuously observed. As part of the Chinese Second Tibetan Plateau Scientific Expedition and Research Program (STEP), the Tibetan Plateau Atmospheric Profile (TP-PROFILE) project aims to construct a comprehensive MWR troposphere observation network to study the synoptic processes and environmental changes on the TP. This initiative has collected three years of data from the MWR network. This paper introduces the data information, the data quality, and data downloading. Some applications of the data obtained from these MWRs were also demonstrated. Our comparisons of MWR against the nearest radiosonde observation demonstrate that the TP-PROFILE MWR system is adequate for monitoring the thermal and moisture variability of the troposphere over the TP. The continuous temperature and moisture profiles derived from the MWR data provide a unique perspective on the evolution of the thermodynamic structure associated with the heating of the TP. The TP-PROFILE project reveals that the low-temporal resolution instruments are prone to large uncertainties in their vapor estimation in the mountain valleys on the TP. 整个青藏高原上空的对流层大气廓线从未被连续观测过, 第二次青藏高原科学考察与研究计划利用中国自主生产的多通道微波辐射计构建了覆盖西风、 季风断面的全天候大气廓线实时观测系统---TP-PROFILE. TP-PROFILE旨在建立一个能够覆盖整个高原对流层大气的多通道微波辐射计观测网络, 以研究青藏高原上空的天气过程和环境变化. 本文利用TP-PROFILE收集的近三年数据, 结合8个探空站的观测数据, 对TP-PROFILE反演的高原对流层大气温度和湿度的精度做了全面评估. 系统介绍了TP-PROFILE观测站点的布设、 观测的变量、 数据质量和精度, 并展示了研究人员利用TP-PROFILE获得的一些观测结果. 主要结论如下: 与空间距离最近的无线电探空仪观测相比较, TP-PROFILE系统能够监测青藏高原上空对流层大气的水热结构变化, 从TP-PROFILE数据中获得的连续温度和湿度分布为分析高原加热引起的“热岛”效应提供了独特的观测视角. TP-PROFILE利用22.2、 30.0和51.2GHz通道亮温数据在降水前快速增加的信息能够对高原降水的发生进行提前预报. TP-PROFILE的温度偏差比湿度偏差要小. TP-PROFILE在昌都、 那曲观测到的高原“热岛”效应最显著. 利用TP-PROFILE每2分钟的资料表明, 低时间分辨率的观测仪器在青藏高原山地的水汽估计中会有相对高的偏差.

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Soil erosion and wildfires are frequent natural disasters that threaten the environment. Identifying and zoning susceptible areas are crucial for the implementation of preventive measures. The Šar Mountains are a national park with rich biodiversity and various climate zones. Therefore, in addition to protecting the local population from natural disasters, special attention must be given to preserving plant and animal species and their habitats. The first step in this study involved collecting and organizing the data. The second step applied geographic information systems (GIS) and remote sensing (RS) to evaluate the intensity of erosion using the erosion potential model (EPM) and the wildfire susceptibility index (WSI). The EPM involved the analysis of four thematic maps, and a new index for wildfires was developed, incorporating nine natural and anthropogenic factors. This study introduces a novel approach by integrating the newly developed WSI with the EPM, offering a comprehensive framework for assessing dual natural hazards in a single region using advanced geospatial tools. The third step involved obtaining synthetic maps and comparing the final results with satellite images and field research. For the Šar Mountains (Serbia), high and very high susceptibility to wildfires was identified in 21.3% of the total area. Regarding soil erosion intensity, about 8.2% of the area is affected by intensive erosion, while excessive erosion is present in 2.2% of the study area. The synthetic hazard maps provide valuable insights into the dynamics of the erosive process and areas susceptible to wildfires. The final results can be useful for decision-makers, spatial planners, and emergency management services in implementing anti-erosion measures and improving forest management in the study area.

Recently, domain adaptation has emerged as a powerful technique for on-site partial discharge (PD) condition assessment in gas-insulated switchgear (GIS). However, most existing methods face three major challenges: 1) relying on a single source domain for model development poses difficulties in effectively utilizing source domain samples with distribution differences; 2) limited condition assessment for unknown fault samples on-site, which faces distributional differences between multiple source domains; and 3) handling only a single task, which makes it challenging to generalize to multiple tasks simultaneously. To address these concerns, we propose a class alignment multisource domain adaptation network (CLMSDAN) for GIS PD condition assessment with unknown faults. First, a diversity feature extractor is developed to extract diverse features while addressing the negative transfer issue caused by knowledge differences by mining both interdomain and intradomain features, thus enabling the transfer of rich knowledge at multiple levels. Second, a novel multisource domain adaptation approach is employed from multiple perspectives to align distribution and distinguish between shared and unknown classes. Finally, a multiclassifier complementary strategy is introduced to recognize unknown faults, which automatically filters out source domain irrelevant class samples while distinguishing the contributions of different source domains to the target task. Experimental results show that CLMSDAN achieves 94.86% accuracy in diagnosis and 93.38% in severity assessment, outperforming baseline methods by over 10% in both tasks. This highlights its superior generalization and robustness across varying conditions and noise levels.

Groundwater, an invaluable resource crucial for irrigation and drinking purposes, significantly impacts human health and societal advancement. This study aims to evaluate the groundwater quality in the Mnasra region of the Gharb Plain, employing a comprehensive analysis of thirty samples collected from various locations, based on thirty-three physicochemical parameters. Utilizing tools like the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), as well as Multivariate Statistical Approaches (MSA), and the Geographic Information System (GIS), this research identifies the sources of groundwater pollution. The results revealed Ca2+ dominance among cations and Cl− as the primary anion. The Piper and Gibbs diagrams illustrated the prevalent Ca2+-Cl− water type and the significance of water–rock interactions, respectively. The PIG values indicated that 86.66% of samples exhibited “Insignificant pollution”. NPI showed notable nitrate pollution (1.48 to 7.06), with 83.33% of samples rated “Good” for drinking based on the WQI. The IWQI revealed that 80% of samples were classified as “Excellent” and 16.66% as “Good”. Spatial analysis identified the eastern and southern sections as highly contaminated due to agricultural activities. These findings provide valuable insights for decision-makers to manage groundwater resources and promote sustainable water management in the Gharb region.

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This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial and Evaluation Laboratory (DEMATEL) methods for assessing flood risk in the Kosynthos River basin in northeastern Greece. The study aims to address challenges arising from data limitations and provide decision-makers with effective flood risk management strategies. The integration of DEMATEL is crucial, providing a robust framework that considers interdependencies among factors, particularly in regions where conventional numerical modeling faces difficulties. DEMATEL is preferred over other methods due to its proficiency in handling qualitative data and its ability to account for interactions among the studied factors. The proposed method is based on two developed causality diagrams. The first diagram is crucial for assessing flood hazard in the absence of data. The second causality diagram offers a multidimensional analysis, considering interactions among the criteria. Notably, the causality diagram referring to flood vulnerability can adapt to local (or national) conditions, considering the ill-defined nature of vulnerability. Given that the proposed methodology identifies highly hazardous and vulnerable areas, the study not only provides essential insights but also supports decision-makers in formulating effective approaches to mitigate flood impacts on communities and infrastructure. Validation includes sensitivity analysis and comparison with historical flood data. Effective weights derived from sensitivity analysis enhance the precision of the Flood Hazard Index (FHI) and Flood Vulnerability Index (FVI). • The proposed Causality Diagram addresses multidimensionality regarding the vulnerability. • The proposed Causality Diagram addresses data gaps regarding the hazard. • The causality diagrams are exploited by using the DEMATEL method. • GIS leverages quantitative map data to assess high-risk areas comprehensively. • Sensitivity refines Flood Hazard Index and Flood Vulnerability Index precision.

In port management, the integration of geographic information systems (GIS) is essential for geospatial analysis in a complex environment shaped by digitalisation and energy transition. Although the adoption of GIS and spatial data infrastructures (SDI) are growing, their use remains with challenges in interoperability and collaborative data management. This study conducts a systematic review to identify the main publications from the past 10 years on the use of GIS and SDI in the maritime sector, using the Scopus and Web of Science databases. The results revealed an annual growth of 8.59% in scientific publications over the past decade, with a focus on environmental monitoring, machine learning, and digitalisation. The findings also suggest the limited use of SDI in the maritime sector, reinforcing the need for future research on interoperability and spatial data integration. Nevertheless, the main trends include the integration of GIS with machine learning, advanced spatial applications, and artificial intelligence, showing an increasing focus on sustainability, environmental monitoring, and innovative management systems.

The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ‘very low’ suitability of site covering 4.866% of the study area, ‘low’ suitability of site 13.190%, ‘moderate’ suitability of site 31.640%, ‘good’ suitability of site 32.347%, and ‘very good’ suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions.

This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

ABSTRACT The major objective of this study is to apply integrated data-driven methods to estimate cyclist risk and discomfort in Berlin based on the OpenSenseMap dataset. The proposed approach makes use of crowd-sourced sensor data collected during cycling (speed, bike vibration, distance to other objects), spatial statistics and multi-criteria decision analysis to provide a continuous estimation of cycling discomfort and risk in the traffic network. We employed cycling traffic volume and route discomfort estimation techniques to determine the spatiotemporal patterns of cycling traffic volume and discomfort levels. Accordingly, a GIS-based multiple criteria analysis approach was applied to map areas with high cycling traffic volume based on the condition of the cycling lanes, environmental, road traffic, land use and sociodemographic characteristics. The results show that the central area of Berlin has a high cycling traffic volume as well as a high level of discomfort. In this context, we found a significant spatial correlation between the cycling traffic volume and discomfort with the land use characteristics such as commercial or residential areas, motor vehicle traffic volume and sociodemographic characteristics in Berlin. Furthermore, results revealed a correlation between intensive traffic volume and commercial zones, schools and university areas. As we identified high-risk cycling directions and their autocorrelation with relevant indicators, the obtained results from this study will support decision-makers and authorities in recognizing the high-risk cycling areas and optimizing the risk areas, which accordingly increase the cycling safety and wellbeing of citizens.

In recent years, particularly following the definition of the UN Sustainable Development Goals (SDGs) for 2030, Nature-Based Solutions (NBS) have gained considerable attention, capturing the interest of both the scientific community and policymakers committed to addressing urban environmental issues. However, the need for studies to guide decision-makers in identifying suitable locations for NBS implementation within urban stormwater management is evident. To address this gap, the present study employs a methodological approach grounded in multi-criteria analysis integrated with Geographic Information Systems (GIS) to identify areas with potential for NBS implementation. In this process, ten NBS were proposed and tested in the drainage area of a shallow tropical urban lake in Londrina, southern Brazil. Additionally, the study investigates areas hosting lower-income populations, a relevant aspect for public managers given the diverse economic subsidies required to implement NBS. Furthermore, the study incorporates a preliminary analysis that evaluates the potential ecosystem benefits to determine the most suitable NBS for a specific site. The result shows that all the ten analyzed NBS were deemed suitable for the study area. Rain barrels had the highest percentage coverage in the study area (37.1%), followed by tree pits (27.9%), and rain gardens (25.4%). Despite having the highest distribution in the basin area, rain barrels exhibited only moderate ecosystem benefits, prompting the prioritization of other NBS with more significant ecological advantages in the final integrated map. In summary, the methodology proposed showed to be a robust approach to selecting optimal solutions in densely populated urban areas.

Floating photovoltaics (FPVs) are appearing as a promising and an alternative renewable energy opinion in which PV panels are mounted on floating platforms in order to produce electricity from renewable energy on water such as seas, dams, rivers, oceans, canals, fish farms, and reservoirs. So far, such studies related to the body knowledge on financial, technical, and environmental aspects of installation of FPV have not been performed in Turkey while expanding steadily in other countries. In this study, suitable site selection for installation of FPV power plants on three lakes in Turkey was studied by performing geographic information system (GIS) and the fuzzy analytic hierarchy process (FAHP) as multi-criteria decision-making (MCDM) method. This detailed study revealed that the criterion of global horizontal irradiance (GHI) was determined as the most crucial criterion for the installation of FPV on Beysehir Lake, Lake of Tuz, and Van Lake. Additionally, it was clearly seen that the Beysehir Lake had the highest value approximately 52% among other lakes for installation, that is why Beysehir Lake is selected as the best option for installation of an FPV system with this multi-criteria approach.

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.

The digital transformation of port infrastructure is a key element in the evolution towards Smart Ports and Industry 4.0. This paper presents an optimized port asset management system based on Digital Twin technology and BIM/GIS integration, aiming to enhance efficiency, sustainability, and decision-making in port operations. The proposed system leverages real-time data acquisition, predictive maintenance, and resource optimization, addressing critical challenges in port asset lifecycle management. By integrating Digital Twin models with Internet of Things (IoT) sensors, cloud computing, and machine learning algorithms, this approach enables data-driven decision-making, which improves operational performance and minimizes costs. The Frankenstein Strategy is introduced as an innovative methodology for port digitalization, allowing incremental integration of digital twins into existing infrastructures. The results demonstrate that this system provides enhanced asset monitoring, optimized maintenance planning, and increased operational resilience, contributing to the automation and optimization of production processes in Industry 4.0. This research highlights the potential of Digital Twin technology to revolutionize port asset management, establishing a framework for smart, data-driven, and sustainable port operations.

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The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitor and plan based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential and it has been defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP). The validity of the results was tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, these results are useful to maintain the groundwater. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others on the ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.

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Groundwater quality assessment is crucial for sustainable water resource management in Maharashtra, India, where groundwater helps for main water sources for irrigation, domestic, and industrial sectors. Despite numerous studies on regional groundwater quality, there remains a lack of integrated research combining hydrogeochemical analyses with advanced spatial and statistical techniques. This study addresses this gap by developing a comprehensive groundwater quality assessment framework that uniquely integrates hydrogeochemical analyses, geographic information system (GIS) techniques, water quality index (WQI), and multivariate statistical approaches in the Morna River Basin. A total of 82 water samples were analyzed for physicochemical parameters in the pre-monsoon (PRMS) and post-monsoon (POMS) seasons. The WQI analysis revealed that 46.15% of samples exhibited excellent water quality, while 48.72% showed good quality during both seasons, though a notable quality decrease was observed during the POMS. Correlation analysis identified significant positive associations (p < 0.05) between key parameters, including Mg-TH, EC-pH, and Ca2+-TH. Principal component analysis identified six components explaining 75.534% of total variance in PRMS, with the first component contributing 17.437%. In POMS, five components explained 70.963% of variance, with the first component contributing 20.653%. Factor analysis revealed that mineral dissolution, agricultural activities, and anthropogenic inputs were the primary factors influencing the water chemistry. The spatial distribution maps generated through GIS analysis identified hotspots of contamination. This integrated approach provided a robust framework for understanding the complex interactions between natural and anthropogenic factors impact on the groundwater quality. The results suggest regural monitoring of water quality and an identified hotspots and implementation of rules and regulations on the agricultural practices and waste disposal. This research contributes to support of groundwater management strategies and provides a methodological framework appropriate to similar hydrogeological settings in other area or worldwide.

Petabytes of raster maps exist that show numerous classes across a series of many time points. Scientists need new methods to summarize the temporal changes so scientists can understand broad patterns and have guidance for how to look deeper into particular places, trajectories, classes, and time intervals. Our manuscript addresses this need by presenting an innovative method that summarizes the temporal changes in terms of a map of four trajectories and bar charts of five components: quantity, allocation exchange, allocation shift, alternation exchange, and alternation shift. An application illustrates the method using 16 land cover classes at 7 time points from 1990 to 2020 in western Bahia, Brazil. Results reveal a substantial component of alternation shift, which exists when pixels transition from savanna to temporary crops to soybean. Readers may apply the method using the app available for free at https://github.com/antoniovfonseca/summarize‐change‐components.

This study presents a comprehensive analysis of natural hazard susceptibility in the Makedonska Kamenica municipality of North Macedonia, encompassing erosion assessment, landslides, flash floods, and forest fire vulnerability. Employing advanced GIS and remote sensing (RS) methodologies, hazard models were meticulously developed and integrated to discern areas facing concurrent vulnerabilities. Findings unveil substantial vulnerabilities prevalent across the area, notably along steep terrain gradients, river valleys, and deforested landscapes. Erosion assessment reveals elevated rates, with a mean erosion coefficient (Z) of 0.61 and an annual erosion production of 182,712.9 m3, equivalent to a specific erosion rate of 961.6 m3/km2/year. Landslide susceptibility analysis identifies 31.8% of the municipality exhibiting a very high probability of landslides, while flash flood susceptibility models depict 3.3% of the area prone to very high flash flood potential. Forest fire susceptibility mapping emphasizes slightly less than one-third of the municipality’s forested area is highly or very highly susceptible to fires. Integration of these hazard models elucidates multi-hazard zones, revealing that 11.0% of the municipality’s territory faces concurrent vulnerabilities from excessive erosion, landslides, flash floods, and forest fires. These zones are predominantly located in upstream areas, valleys of river tributaries, and the estuary region. The identification of multi-hazard zones underscores the critical need for targeted preventive measures and robust land management strategies to mitigate potential disasters and safeguard both human infrastructure and natural ecosystems. Recommendations include the implementation of enhanced monitoring systems, validation methodologies, and community engagement initiatives to bolster hazard preparedness and response capabilities effectively.

Climate change significantly impacts natural hydrological systems worldwide, affecting water availability and sedimentation dynamics. The upper Indus Basin is one of the most crucial basins in South Asia, which is undergoing severe climatic variations, resulting in extreme flooding. This study examines the impact of climate change on the hydrological cycle, water availability, and sedimentation dynamics in the Shyok River basin in the Karakoram region. The study focuses on investigating the increasing outflows on a seasonal basis, as found in the previous studies, by utilising daily river discharge data between 2003 and 2014 at Yugo hydrographic station operated by WAPDA with geographic information systems (GIS), SWAT+ (Soil and Water Assessment Tool), remote sensing, and statistical techniques. To analyse climatic variables, using only available ground weather stations inside the basin, we have utilised the ERA 5 reanalysis dataset to evaluate seasonal precipitation and temperature trends. The significance of ERA5‐derived climatic variables has been assessed using the Mann‐Kendall test, Sen's slope analysis and the Coefficient of Determination R2 from 2000 to 2020 on a monthly basis. Analysis of seasonal discharge data using SWAT+ reveals a decline in water flow from July to October and a general upward trend during the last 20 years, such as a significant decrease in streamflow in the simulated trend, which was 293 m3/s in 2005 and dropped to 158 m3/s in 2017. In contrast, the maximum actual vs. simulated discharge decreased by 10 m3/s in 2010 and 2019, respectively. The temperature has increased by approximately 1.5°C. Seasonal precipitation analysis reveals an increasing trend in winter, while other seasons have shown fluctuating trends. Where the precipitation trends were found to be non‐significant, with p > 0.05. An analysis of sediment load and discharge of the model output suggests active erosion in the channel at a rate of approximately 40 megatons/ha. The study highlights the impacts of climate change on water availability and sedimentation dynamics in the Shyok River basin in the Karakoram region. It attempts to contribute to the existing literature on the evaluation of climate‐induced changes in river channel morphology. The study also highlights the necessity of continuous climatic and hydrographic data at the basin scale.

Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.

This study examines the application of Geographic Information Systems (GIS) in tourism planning and sustainable destination management, using Gelnica, Slovakia, as a case study. The research highlights a key challenge—the absence of systematic visitor data collection—which hinders tourism market analysis, demand assessment, and strategic decision-making. The study integrates alternative data sources, including the Google Places API, to address this gap to analyse Points of Interest (POIs) based on user-generated reviews, ratings, and spatial attributes. The methodological framework combines data acquisition, spatial analysis, and GIS-based visualisation, employing thematic and heat maps to assess tourism resources and visitor behaviour. The findings reveal critical spatial patterns and tourism dynamics, identifying high-demand zones and underutilised locations. Results underscore the potential of GIS to optimise tourism infrastructure, enhance visitor management, and inform evidence-based decision-making. This study advocates for systematically integrating GIS technologies with visitor monitoring and digital tools to improve destination competitiveness and sustainability. The proposed GIS-driven approach offers a scalable and transferable model for data-informed tourism planning in similar historic and environmentally sensitive regions.

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This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts’ opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS. Findings indicate that the Inverse Distance Weighted (IDW) method is the most precise for interpolation, which was subsequently applied in the analysis. Results demonstrate that the GIS-based DBWM-Technique for Order Preference by Similarity to Ideal Solution (GIS-based DBWM-TOPSIS) model is the most stable, identifying slope as the primary criterion for SPPSS. Based on this model, 3% of the area is classified as very low suitability, 9% as low, 24% as moderate, 33% as high, and 31% as very high suitability. The study highlights the substantial impact of selecting appropriate spatial analysis techniques and uses normalization to standardize input criteria with varied units and ranges, enhancing comparability within the MCDM process. Eslamabad-e Gharb, Kangavar, and Gilan-e Gharb counties emerged as the most suitable locations for solar power plant (SPP) development.

No abstract available

This article aims to present an integration model of GIS with open data sourced from application programming interface (API) as a solution for the location set covering problem (LSCP) with an urban land dynamics model. The development of GIS which is increasingly advanced makes traditional GIS transition in the open data era to become more modern. One of the benefits is to help urban planners in determining the allocation of health facilities such as hospitals. This research takes the case of hospital service coverage during emergencies, especially during the COVID‐19 extraordinary event in Metropolitan Semarang, Indonesia. In addition to utilizing API‐base Location, the model process also uses a Cellular Automata‐based land use prediction model. Thus, the facility location plan not only considers service coverage but also land use growth which is a reflection of population growth. To analyze the problem of inequity of hospital services, this research combined the location‐based APIs‐based service area model with the urban growth model to evaluate the existing condition and predict the future of hospital service demand. It also uses the emergency standard with a maximum service distance of 1500 m and a maximum travel time of 7 min. The model confirmed that there are still critical spots not served by hospitals in Semarang City. According to the concept of health and place, it is essential to recommend adding two hospitals in unserved areas so that services are more evenly distributed in the future, especially in emergencies.

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.

Land Suitability Assessment (LSA) aids in identifying optimal crop cultivation sites; thereby, it is the key factor for proper planning to maximize production yield. It needs a combination of Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) to improve LSA for the production of barley, beans, maize, soybean, sugar beet, and wheat in Egypt’s New Delta. Topography (slope), and characteristics of the soil (depth, pH, texture, carbonate content, and salinity) were the six factors employed. Land was assessed on a five-level suitability scale—highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2). Pairwise comparisons and consistency ratios in AHP were used to determine weights for the criteria. Weighted overlay analysis produced land suitability maps. Key findings indicate slope as the primary factor for barley and wheat and soil properties as more significant for beans, soybean, and sugar beet. Barley, beans, maize, soybean, and wheat were assessed as highly suitable (S1), moderately suitable (S2), and marginally suitable (S3), but sugar beet was assessed as moderately suitable (S2). The results are valid for crop rotation for increased production and soil fertility by the appropriate use of AHP and GIS in sustainable land use management.

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas.

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions.

The Middle East region, with its arid and semi-arid climate, is one of the regions most affected by climate change and water scarcity. To address the severe issue of water scarcity in the western region of Iraq, this study identifies optimal potential rainwater harvesting (RWH) locations. Geographic Information System (GIS) and multi-criteria decision-making (MCDM) techniques were employed to generate themed layers for RWH. The nine primary criteria considered were rainfall, elevation, slope, stream order, soil texture, land use, groundwater depth, distance from the lake, and runoff depth. A weighted overlay assessment was used to identify probable RWH locations. The analytical hierarchical process was used to weight criteria depending on the study region, hydrological and socioeconomic parameters, and literature. The consistency ratio (CR = 3.16%) was calculated to validate the optimum weights of the comparison components, from which it was found that the weights assigned to each criterion were appropriate for comparative purposes. The results indicated that the optimum location (very high suitability) for RWH is mostly in middle regions of the study area, covering 286 km2 (13%), while for the other categories, high suitability is at 23% (498 km2), medium suitability at 29% (636 km2), low suitability at 21% (462 km2), and very low suitability at 14% (305 km2). Sensitivity analysis was used to identify the relative importance of the parameters and determine how each of the nine criteria influences the optimal RWH sites. These findings can assist decision makers and planners in devising strategies to mitigate the effects of climate change and increase any reclaimed area for agriculture.

This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city’s districts by employing advanced clustering algorithms, including Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that <xref ref-type="disp-formula" rid="deqn1">(1)</xref> the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots; <xref ref-type="disp-formula" rid="deqn2">(2)</xref> significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city’s fabric; <xref ref-type="disp-formula" rid="deqn3">(3)</xref> proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas; <xref ref-type="disp-formula" rid="deqn4">(4)</xref> through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses.

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The hydrological characteristics of the watershed in the southern Aseer and northern Jazan regions of Saudi Arabia (SA) were identified by integrated remote sensing (RS) and geographic information system (GIS) techniques using Shuttle Radar Topography Mission (SRTM) and Landsat data. For this purpose, the Wadi Ishran, Wadi Baysh, Wadi Itwad, Wadi Tabab, and Wadi Bayd drainage basins were extracted. Wadi Ishran is the largest, and Wadi Tabab is the smallest. Stream order and bifurcation ratio show that the Itwad and Bayd basins are permeable and of high aquifer potentiality. The multisupervised classification found seven rock units that were spread out in different ways across the basins. The areas with the highest vegetation were in the southeast, the centre, and the northwest. The bands’ ratios show more iron-rich sediments in the northeast and southwest. This paper’s outcomes serve as the basis for planning and managing groundwater resources. It finds potential groundwater zones, determines the risk of flooding, and chooses places where harvesting can be undertaken.

The primary driver of economic growth is energy, predominantly derived from fossil fuels, the demand for which has experienced a significant increase since the advent of the Industrial Revolution. The emissions of hazardous gases resulting from the utilization of these fuels have been well acknowledged, therefore exerting a notable impact on the environment. In the context of Ethiopia, it is observed that despite the presence of ample renewable resources, the accessibility to power continues to be constrained. In order to effectively tackle this issue, it is imperative to redirect attention towards the utilization of renewable sources, such as wind energy, as a means of enhancing the existing power grid infrastructure. The present study used geospatial tools to evaluate the appropriateness of the Wolayita region for the establishment of a wind power facility. The process of site selection is guided by multiple factors, and a multi-criteria approach is facilitated through the utilization of Geographic Information System (GIS). The evaluation of seven characteristics was conducted utilizing the Analytical Hierarchy Process (AHP) methodology, which involved pairwise comparisons and weighted scoring. The process of suitability mapping involves the classification of locations into four distinct categories, which range from the most suitable to the least suitable. The findings demonstrate that the area of 0.628% (28.00 km^2) is deemed the most suitable, while 54.61% (2433.96 km^2) is considered somewhat acceptable. Additionally, 0.85% (37.85 km^2) is identified as the least suitable, leaving a remaining 43.91% (1060.00 km^2) that is deemed unsuitable. The central, northwestern, and southern regions are identified as optimal geographic areas. The results of this study facilitate the process of investing in renewable energy, thereby assisting Ethiopian authorities and organizations in promoting sustainable development. This report serves as a crucial reference point for the wind energy industry.

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Abstract Species distribution and activity patterns vary across regions, and these patterns reveal key information about the biology of a given species. However, such data in Niubeiliang National Nature Reserve located in the Qinling Mountains, Shaanxi Province, China, are inadequate due to limited monitoring studies. From September 2017 to December 2019, 57 infrared cameras were installed to monitor species distribution relative to distance from National Highway 210, and spatial and temporal activity patterns in Niubeiliang National Nature Reserve (elevation range 1,100 to 2,802 m). Sixteen species of mammals were recorded in our survey. The 4 most frequently detected species were Reeves's Muntjac (Muntiacus reevesi), Wild Boar (Sus scrofa), Pere David's Rock Squirrel (Sciurotamias davidianus), and Golden Takin (Budorcas taxicolor bedfordi). Daily patterns of these species indicated that Reeves's muntjacs and golden takins were more active at dawn and dusk, whereas wild boars and Pere David's rock squirrels were more active during the day. The relative abundance index of these mammals varied seasonally. Wild boars and Pere David's rock squirrels showed no obvious preference in elevation distribution, Reeves's muntjacs preferred low-elevation habitats (1,300 to 1,600 m), and golden takins mainly inhabited high-elevation areas (1,900 to 2,100 m). Golden takins showed obvious avoidance of roads, with avoidance distance from the road of >300 m. In contrast, Reeves's muntjacs were remarkably abundant within 200 m of the road. For both wild boars and Pere David's rock squirrels, there was no significant difference in RAI among 5 highway ranges. These findings help describe the distribution and activity patterns of these species in Qinling, to monitor their population dynamics, and to develop tailored conservation strategies for the 4 species and sympatric wildlife. 摘 要 物种分布和活动规律在不同地区有所不同，这些规律能够揭示物种关键的生物学信息。然而，关于陕西秦岭牛背梁国家自然保护区物种分布和活动规律的研究还较少。2017年9月至2019年12月，在陕西牛背梁国家自然保护区，利用 57台红外相机监测物种空间分布、活动节律及210国道对物种的影响情况。结果显示，保护区内共监测到了16种哺乳动物，其中小麂(Muntiacus reevesi)、野猪(Sus scrofa)、岩松鼠(Sciurotamias davidianus)和羚牛(Budorcas taxicolor bedfordi)是相对多度指数(relative abundance index, RAI) 排前4的物种。对这4个物种进行时空活动规律分析表明，小麂和羚牛具明显的晨昏活动习性，而野猪和岩松鼠主要以白天活动为主，并且这4种哺乳动物随着季节变化表现出不同的活动规律。野猪和岩松鼠在海拔分布上没有明显的偏好，小麂在低海拔地区(1300 ∼ 1600 m)活动频繁，而羚牛主要在高海拔地区(1900 ∼ 2100 m)活动。道路影响情况研究显示，羚牛在距离道路300 m以外的范围活动显著增加，表现出明显的回避效应，相反小麂在距离道路200 m以内活动频繁。野猪和岩松鼠在5个公路范围内活动强度均无显著差异。本研究结果有助于了解这4个物种的种群动态变化并为制定有针对性的保护策略提供数据基础。

Industrialization has though brought comfort to our daily lives, but it has placed a lot of pressure on the planet’s natural resources, subsequently, it has adversely affected the environment. As the need for cement in the construction sector has grown, it has climbed dramatically globally. Around the world, more than 10 billion cubic meters of concrete are produced each year; it is doubtful that this volume will decrease. A significant expected rise in CO_2 emissions is caused by increased cement demand. According to the UN Environment Program, buildings are responsible for up to 41% of global anthropogenic carbon emissions. The primary source of greenhouse gases utilized in the manufacturing of cement is clinker. Due to the unsustainable supply of fly ash, calcined clay appears to be a better Supplemental Cementitious Material (SCMs). Kaolin clay is widely available in Pakistan. The purpose of this investigation is to describe the mineral and thermal characteristics of Pakistani clays by examining their geographic distribution. Clay samples were gathered from 39 different places throughout Pakistan during a field investigation program. X-ray diffraction, X-ray Fluorescence, Reactivity, and thermogravimetric analyses were used to analyze the clay samples’ mineral content and thermal characteristics. This study demonstrates that Pakistan has a substantial amount of kaolin clay reserves close to existing groups of cement plants. Pakistani clays can be utilized as SCM in the production of limestone calcined clay cement (LC^3) due to the country’s vast kaolin clay reserves. This study further supports the viability of producing LC^3 in the nation by providing a thorough analysis of the cement business, known deposits of qualifying clay, and the country’s cement production process. 尽管工业化使我们的日常生活更加便捷, 但也给地球的自然资源带来了很大的负荷, 以及随之而来的环境负面影响。随着建筑业对水泥的需求不断增加, 全球水泥产量大幅攀升。当前每年全球生产的混凝土超过10亿立方米, 未来混凝土的产量可能还会继续增加, 因此可以预见的是水泥需求量的增加将导致CO_2排放量的上升。根据联合国环境规划署的数据, 建筑碳排放占全球人为碳排放量的41%。水泥生产中的温室气体主要来源于熟料生产。由于粉煤灰难以持续供应, 煅烧粘土应该是一种更好的辅助胶凝材料 (SCM) 。高岭土在巴基斯坦储量丰富, 这项调研的目的是通过研究巴基斯坦粘土的地理分布及它们的矿物组分和热物理特性。在实地调研中, 从巴基斯坦的39个不同地区收集了不同的粘土样本。通过采用XRD (X-ray diffraction) 、XRF (X-ray Fluorescence) 、反应活性和热重分析分析样本的矿物含量和热物理特性。这项研究表明, 巴基斯坦拥有大量的高岭土储量, 且分布临近现有水泥厂群。因此巴基斯坦拥有的丰富高岭土粘土可与石灰石一起用于LC^3水泥的生产。通过对水泥业务、已知合格粘土矿床和该国水泥生产过程的全面分析, 这项研究进一步支持了在巴基斯坦生产LC^3的可行性。

As the core unit of soil structure, soil aggregates play a key role in maintaining the stability of organic matter, regulating nutrient cycling and enhancing soil erosion resistance. Soils in the tropics are significantly different from temperate soils in terms of aggregate formation and stabilisation mechanisms due to their high degree of weathering, low organic matter inputs and unique mineral composition (kaolinite, montmorillonite and iron and aluminium oxides). Traditional hierarchical models emphasise the dominant role of organic matter in agglomerate formation, but the interaction of inorganic cementing materials (clay minerals and oxides) and cationic bridge bonding effects in tropical soils become important drivers. Studies have shown that biotic factors (microbial secretions), abiotic factors (Ca 2+ and Al 3+ ionic bridges) and human activities (land-use changes, farming practices) combine to influence agglomerate stability. However, the relationship between aggregate stability and organic matter in tropical soils is controversial and lacks universal theoretical models. In the future, we need to focus on the differences in soil-forming parent rocks, cation action mechanisms and multi-factor interaction effects, in order to reveal the dynamics of tropical soil aggregates, and provide a theoretical basis for carbon sequestration and sustainable soil management

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全球面临的最大挑战是如何确保获得充足且营养的食物。野生食用植物, 特别是代粮植物, 可在维持偏远地区的粮食安全和均衡饮食的营养方面发挥重要作用。本文采用民族植物学的方法, 对滇西北独龙族利用董棕的传统知识进行了调查, 采用食品科学的方法对董棕淀粉的化学成分、形态、功能和糊化特性进行了评价, 利用MaxEnt对董棕在亚洲的潜在地理分布进行了预测。董棕是独龙族重要的文化物种, 一种很好的潜在淀粉植物, 在中国南部、缅甸北部、印度西南部、越南东部等地均有大片适宜生境区。董棕作为一种潜在的淀粉植物, 不仅有助于保障当地的粮食安全, 还可以带来经济利益。因此有必要对董棕的育种和栽培, 以及淀粉的加工和开发利用进行研究, 以应对未来偏远地区长期的隐性饥饿问题。 The greatest global challenge is to ensure that all people have access to adequate and nutritious food. Wild edible plants, particularly those that provide substitutes for staple foods, can play a key role in enhancing food security and maintaining a balanced diet in rural communities. We used ethnobotanical methods to investigate traditional knowledge on Caryota obtusa , a substitute staple food plant of the Dulong people in Northwest Yunnan, China. The chemical composition, morphological properties, functional, and pasting properties of C. obtusa starch were evaluated. We used MaxEnt modeling to predict the potential geographical distribution of C. obtusa in Asia. Results revealed that C. obtusa is a vital starch species with cultural significance in the Dulong community. There are large areas suitable for C. obtusa in southern China, northern Myanmar, southwestern India, eastern Vietnam, and other places. As a potential starch crop, C. obtusa could substantially contribute to local food security and bring economic benefit. In the future, it is necessary to study the breeding and cultivation of C. obtusa , as well as the processing and development of starch, to solve long-term and hidden hunger in rural areas.

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To explore the nutrient characteristics of the rhizosphere of spontaneous recovery plants in coal gangue dumps, the study analyzed the physicochemical properties, organic carbon, and available nutrient content of the rhizosphere coal gangue of eight plant species, clarifying the impact of different plants on the nutrient characteristics of the rhizosphere coal gangue. The results indicated that the rhizosphere pH of Juncus effusus , Rumex acetosa , and Erigeron sumatrensis increased by 3.43, 3.86, and 3.18 units, respectively, while the EC values of Neyraudia reynaudiana and Rumex acetosa rhizosphere gangue were lower. The content of available phosphorus, available potassium, and organic carbon in the rhizosphere of Rumex acetosa were significantly higher than in other plants, while the content of alkali-hydrolyzable nitrogen in the rhizosphere of Polygonum hydropiper was the highest. Additionally, apart from Rumex acetosa , Polygonum hydropiper can also significantly increase the content of available phosphorus, available potassium, and organic carbon. In summary, both Rumex acetosa and Polygonum hydropiper had good effects in improving the nutrient content of coal gangue, and both were helpful in promoting the restoration of the ecosystem in mining areas. Therefore, in the vegetation restoration of the coal gangue mining areas in Guizhou, Rumex acetosa and Polygonum hydropiper should be given priority consideration.

Different from common knowledge on central–local information asymmetry, this study focuses on reverse information asymmetry, in which local governments may lack information on the intentions and resolve of the central government. Such information asymmetry can lead to a loss–loss scenario, wherein central‐government‐designed policies are eroded and local governments are punished. Arguably, with a multiwave inspection scheme, the central government can credibly signal its emphasis on certain policies and intentions to punish noncompliance, deterring yet‐to‐be‐inspected local governments. This argument is examined with China's Central Environmental Protection Inspection (CEPI) policy. Empirically, the CEPI policy can reduce pollution in the long run. Moreover, the credible signals generated by the previously completed inspection waves, which encourage local governments to rectify environmental regulations proactively, are essential. Furthermore, the strength of local compliance with central signaling largely depends on the hierarchical and geographical distances from the signaling source to the receiver.不同于对中央和地方政府间信息不对称的一般认识, 本研究关注中央和地方政府间的逆向信息不对称现象, 即地方政府可能缺乏对于中央政府意图和决心的了解。上述信息不对称的存在可能导致中央政府制定的政策无法得到有效落实, 同时地方政府由于未妥善履职而受到惩罚的双输局面。本研究指出, 通过分批次的督察机制, 中央政府可以就其对某些政策的重视程度和惩罚违规行为的决心发出可信的信号, 从而对尚未接受督察的地方政府形成有效威慑。本文以中国中央环境保护督察政策为例检验上述理论。实证结果表明, 长期来看中央环保督察政策可以有效控制污染。其核心机制在于, 已完成的督察批次所产生的可信信号, 可以激励其他地方政府主动改善环境规制, 形成对信号的有效响应。此外, 研究还发现地方政府对中央政府政策信号的响应强度取决于信号源的科层距离和地理距离。