Journal of Environmental & Earth Sciences

Application of AFEP and RWP in Water Hazard Detection for Skip Mining Working Faces

2025-10-30T09:46:50+08:00

In deep coal mining, skip mining techniques are increasingly adopted, yet their discontinuous extraction sequences and unique coal pillar support mechanisms create complex overburden failure patterns. This complexity gives rise to severe multi-source water hazards, including persistent threats from bed-separation water, goaf water accumulation, and structural water ingress. The intricate hydro-geological conditions, characterized by variable resistivity and significant electromagnetic interference, often render single geophysical detection methods inadequate, leading to interpretive ambiguities and potential oversight of critical risks.To address these challenges, this study innovatively proposes and demonstrates an integrated detection methodology that synergistically combines the Audio Frequency Electric Penetration (AFEP) method and the Radio Wave Penetration (RWP) method. The core innovation of this research is the design of a coordinated observation system meticulously tailored to the spatial distribution of coal pillars. Beyond data acquisition, a systematic, graded classification framework was established for the comprehensive analysis and fusion of the dual-method results. Crucially, these classification outcomes directly inform the formulation of targeted and tiered governance recommendations, translating detection data into actionable mitigation strategies.Practical application at the 22213 face yielded highly positive results. The integrated approach successfully delineated the spatial distribution of water-bearing anomalies and their connecting channels with a clarity unattainable by either method alone. This not only significantly enhanced the accuracy and reliability of the hydrological threat assessment but also provided a robust scientific foundation for implementing effective water hazard prevention and control measures, thereby ensuring the safe and efficient extraction of the skip mining face.

The Optimization of High-Temperature Dust Capture System in the Blast Furnace Tapping Field

2025-12-05T10:42:02+08:00

During the tapping process of a blast furnace, a large amount of high-temperature dust is generated. Relying solely on dust removal systems to control the spread of dust within the workshop will generate huge energy consumption. Optimizing the high-temperature dust capture system is crucial for improving the working environment, reducing air pollution, and achieving energy savings and emission reductions. Considering the structural layout of workshops and the tapping characteristics of small and medium-sized blast furnaces in China, this study optimized the design of the particulate capture system by incorporating local dust hoods through numerical simulation, while also taking into account the local capture of particles at the hot metal ladle. The research found that to prevent dust escape, adding a small dust hood above the tapping hole and a side suction hood with a capacity of 100,000 m³/h near the tap hole allowed all particles to be directly captured, reducing both their residence time and travel distance. Additionally, the height of thermal stratification within the workshop decreased, and the area of high-temperature zones was reduced. After adding a side suction hood in the hot metal ladle area, the temperature under the hood improved significantly, with the air temperature around the ladle dropping to approximately 40 ℃. When the side suction hood’s airflow exceeded 100,000 m³/h, the capture efficiency reached 99.2%. However, when the observation hole of the top suction hood above the ladle was opened, the temperature inside the hood decreased by 10 °C, and approximately 11.9% of the particles escaped through the observation hole into the workshop.

Developing and Validating a Liaoning Drought Monitor with Multi-Source Remote Sensing Downscaling and Land-Surface Thermal Correction

2025-12-16T15:51:11+08:00

The low spatial resolution of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), a mainstream Global Precipitation Measurement (GPM) product, limits its use in refined drought analysis over complex monsoon underlying surfaces. To resolve this issue, this study proposes an integrated meteorological drought monitoring framework (SPTI), which enhances accuracy by coupling 0.05° downscaled IMERG precipitation data with Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data. First, a Normalized Difference Vegetation Index (NDVI)-topography guided multivariate regression kriging downscaling scheme was built using monthly precipitation data from 23 Liaoning stations (2010–2018), geographic-topographic factors, and NDVI covariates, downscaling original 0.1° IMERG data to 0.05° (achieving R² > 0.7 in ten months, except for June and July). Second, a high-performance multi-source drought model was established via regression of Standardized Precipitation Evapotranspiration Index (SPEI) against downscaled IMERG-Z Index (derived from IMERG precipitation using the drought Z-index method) and Temperature Condition Index (TCI). Finally, SPTI was validated with four typical Liaoning drought events during 2014, 2015, 2017, and 2018. Results show that: (1) 0.05° precipitation data captures fine spatial details, clearly depicting Liaoning’s southeast-to-northwest precipitation gradient and central plain rainfall zones; (2) SPTI outperforms standalone IMERG-Z—it accurately identifies severe/extreme droughts, mitigates IMERG-Z’s underestimation bias, and reasonably characterizes drought alleviation after heavy rains by integrating TCI, avoiding IMERG-Z’s "abrupt drought-to-waterlogging" misjudgment; (3) SPTI results align well with the Liaoning Meteorological Disaster Bulletin, confirming its suitability for refined monsoon drought monitoring.

Monitoring and Early Warning Index System for Mountain, Water, Forest, Field, Lake, Grassland, and Sand: A Case Study Approach in China

2025-10-20T13:23:26+08:00

Against the backdrop of China's escalating ecological challenges driven by urbanization, industrialization, and climate change, this study addresses the critical need for a unified, nationwide ecological monitoring and early warning system. Focused on China's seven core ecosystems—mountains, waters, forests, fields, lakes, grasslands, and deserts—it analyzes existing fragmented monitoring frameworks and proposes an integrated index system to enhance ecological risk prediction and management. By synthesizing secondary data, including satellite imagery, government reports, and case studies of the Loess Plateau, Poyang Lake, Dongting Lake, and Bayanbulak Grassland, the research evaluates current monitoring efficacy and identifies implementation gaps. It develops a conceptual framework encompassing three key pillars: selection of ecosystem-specific indicators, integration of multi-source data via GIS and remote sensing technologies, and predictive modeling using statistical analysis and machine learning to forecast risks like desertification, flooding, and water pollution. The proposed system demonstrates strong predictive capabilities across diverse ecosystems, accurately identifying high-risk areas and enabling timely interventions. However, challenges persist, including data quality inconsistencies, limitations in short-term extreme event forecasting, and the need to integrate socio-economic factors such as land-use changes and population dynamics. Policy implications emphasize the establishment of a national-level monitoring system, intersectoral collaboration, and enhanced local data collection infrastructure. By refining predictive models and fostering community engagement, the system can significantly strengthen China's ecological resilience, supporting sustainable development goals and ensuring the long-term health of its natural resources. This research provides a foundational framework for scalable, adaptive ecological management in a rapidly changing environment.

Study on the Delimitation of Environmental Influence Zone for Engineering Construction in Karst Area

2025-12-18T14:32:56+08:00

With the increasing scale of underground space construction, urban karst geological disasters in China are becoming more and more serious, having a serious impact on people’s daily lives and engineering construction. In this paper, a variety of monitoring methods are used to analyze the spatial and temporal variation characteristics of regional land subsidence during the construction of the Huanggekeng area in Longgang, Shenzhen, and the method of delimiting the environmental influence zone around the project construction is discussed. The results show that InSAR land subsidence monitoring has high measurement accuracy, and has the advantages of low cost and high efficiency in regional land subsidence monitoring. PS-InSAR technology can effectively delineate the environmental influence zone for engineering construction in the early stage of foundation pit excavation, and provide the basis for the delineation of the surrounding environment investigation range and the layout of the construction monitoring network in the key areas during the construction process. Through different stages of PS-InSAR monitoring data and construction progress, the boundary of the environmental influence zone can be dynamically updated, which is of great significance for the surrounding environment investigation, disaster prevention, and responsibility identification of the engineering.

Progress and Prospect of Flood Monitoring with Fengyun Meteorological Satellite

2025-12-09T17:17:02+08:00

With the intensification of global climate change, flood disasters have become increasingly frequent, and satellite remote sensing has become a core technical means for flood monitoring. The Fengyun meteorological satellites, independently developed by China, hold irreplaceable application value in the timely and efficient monitoring of flood disasters. As a systematic review, this study aims to address the lack of systematic regarding the evolutionary trajectory and application status of Fengyun satellites in flood monitoring. By integrating relevant domestic and international research, it systematically reviws the FengYun-1 to FengYun-4 satellite series in flood monitoring and their application practices on a global scale, and clarifies the complete evolutionary of water body identification technologies—from the early visual interpretation method and the threshold method that dominated in the 1980s–1990s, to the machine learning method emerged in the 1990s, and further to the mixed-pixel decomposition technology pursuing sub-pixel-level accuracy. This study identifies the applicable scenarios and limitations of various water body identification technologies, analyzes the key issues in current applications, summarizes the core advantages of Fengyun meteorological satellites and technical bottlenecks that need to be overcome, and provides an outlook on future development directions in flood monitoring. Finally, it offers systematic theoretical references and practical guidance for the technological upgrading and operational application of flood monitoring based on China's independent satellite remote sensing.