Environmental Earth Sciences最新文献

Tie-lines are used in aeromagnetic surveys to detect measurement errors and compensate for insufficient measurement data in the north-south direction. However, increasing the number of tie-lines to improve the accuracy of interpretation can be very challenging. The aim of this study was to overcome this issue by combining aeromagnetic and related multi-parametric data. Therefore, we generated an aeromagnetic map using simple kriging with local varying means (SK-lvm) based on topographic data correlated with the distribution patterns of the aeromagnetic data. The generated aeromagnetic map achieved reduced uncertainties and increased resolution compared with the conventional aeromagnetic map as a result of the interpolation of the magnetic data, which leveraged the dense distribution of the topographic data points and enabled a clearer analysis of geological structures. Generally, topographic and aeromagnetic data were positively correlated with deviating patterns displayed in some regions; however, all the regions shown were classified into Types 1–4 based on the correlation regression equation. Types 1 and 4 showed positive correlations, while Types 2 and 3 exhibited negative correlations with the following characteristics: Type 2 regions are characterized by high altitude and low magnetic anomalies, while Type 3 regions are characterized by low altitude and high magnetic anomalies. The analysis revealed that Type 2 regions are distributed around high-altitude mountainous areas covered with igneous and sedimentary rocks with low magnetic anomalies, whereas Type 3 regions abound with igneous rocks with high magnetic anomalies and mining sites in low-altitude alluvium or coastal areas. Thus, the proposed aeromagnetic map improved resolution and enhanced the reliability of the interpretations (compared with those of a conventional aeromagnetic map) by combining topographic datasets using SK-lvm to process and interprets the comprehensive aeromagnetic survey data. Moreover, the correlation analysis results for the two datasets are expected to serve as useful reference data for determining the characteristics type of rocks distributed in the study area.

Here, we explore the dynamic interplay of climate, tectonic forces and human impacts, that have shaped the current hydrology of the 27 main endorheic continental playa-lakes in southern Spain, most of them protected as Natural Reserves. This research offers a thorough examination of the hydro-geomorphic, geochemical, and tectonic characteristics that influenced the development of these wetlands. We introduce and apply several hydro-morphological indices that effectively characterize the hydrological functioning of these wetlands. Importantly, the examination of the geological setting and analysis of recent tectonics has been found to be effective in reconstructing the evolution of some of the major playa-lakes, including Fuente de Piedra playa-lake and El Gosque playa-lake. Lastly, the analysis of satellite information using the online tool water occurrence change intensity (WOCI) of Surface Water Explorer in the studied playa-lakes was found to be inconsistent in the long-term analysis. Enhanced attention is required to refine the WOCI tool, thereby ensuring a more precise depiction of wetland dynamics in the Mediterranean. Fine-tuning will better represent complex behaviors, thus improving its utility for monitoring and managing wetlands in the region.

To address the issue of divergent views among existing studies concerning the impact of environmental regulation (ER) on innovation, this paper utilizes a spatial panel Durbin model, leveraging provincial-level panel data from 2011 to 2021, to conduct an empirical investigation into the low-carbon innovation effect (LCIE) stemming from heterogeneous ER within the context of spatial spillovers. The empirical findings indicate that the varied ERs substantially stimulate local low-carbon technology innovation (LCTI), and both administrative and market-driven ERs exhibit pronounced positive spatial spillover effects on LCTI in adjacent regions. Moreover, the advancement of green finance significantly enhances the LCIE prompted by heterogeneous ER. From a temporal perspective, there exist diverse nonlinear correlation patterns between heterogeneous ER and LCTI, leading to distinct outcomes in the composition of LCTI. Furthermore, the role of green finance in augmenting the LCIE is emphasized through its facilitation of capital flow towards environmentally sustainable projects. The development of financial instruments such as green bonds, carbon credits, and environmental risk insurance not only supports local enterprises in adopting LCTI but also reinforces the positive externalities associated with ER across borders. The study also identifies that the nonlinear dynamics between heterogeneous ER and LCTI are influenced by factors such as regional economic development levels, industrial structures, and technological readiness.

The extraction of minerals on an extensive scale, though a catalyst for economic advancement, precipitates notable ecological concerns. In recent years, due to decarbonization initiatives and the closure of numerous open-pit mines, increasing attention and research focus have been directed toward evaluating the effectiveness of ecological restoration in mining areas. This study leverages Landsat series imagery and employs the pseudo-invariant feature (PIF) method for radiometric normalization of remote sensing images, all within the framework of ecological engineering. In light of the significant consideration given to soil erosion and air pollution factors in the acceptance standards for ecological engineering, the Mine Remote Sensing Ecological Index (MRSEI) is developed based on the Pressure-State-Response (PSR) framework. This index is employed to perform spatiotemporal analysis and dynamic monitoring of the ecological quality in the restoration area of Wangping coal mine. The results illustrate that: Compared to the Remote Sensing Ecological Index (RSEI), the first principal component of the MRSEI consolidates the information of various sub-indicators more effectively. This allows for a more objective representation of the ecological quality. From 1990 to 2021, the average value of the MRSEI in the Wangping coal mining area shows an overall upward trend, increasing from 0.429 in 1990 to 0.731 in 2021, representing an improvement of 70.40%. The validation of the MRSEI indicates that this index accurately reflects the objective patterns of local ecological quality changes. Moreover, it is strongly correlated with various individual ecological indicators. The application and promotion of the MRSEI offer valuable insights for policymakers in developing plans for mine ecological restoration projects and strategies for regional coordinated development.

The Danjiangkou Reservoir (DJKR), the source of the world’s longest inter-basin water diversion, the Middle Route of the South-to-North Water Diversion Project (MRSNWDPC), has attracted global attention for its water quality. This study collected water samples monthly from 31 monitoring sites between January 2017 and December 2022, assessing 18 water quality parameters. Multivariate statistical methods and five water quality index (WQI) models were employed to comprehensively evaluate water quality, optimizing critical parameter identification and enhancing the predictive reliability of water quality changes. Compared to traditional WQI approaches, our method offers improved accuracy in assessing water quality variations and risks. The findings reveal that the tributary river systems were classified by pollution degree, while reservoirs were categorized based on adjacent geographical locations. WQI values in the Danjiangkou Reservoir basin ranged from 82.70 to 91.24, indicating that the water quality was consistently “good” to “excellent”. Key water quality indicators, i.e., ammonia nitrogen (NH₃-N), permanganate index (COD_Mn), dissolved oxygen (DO), chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN), and heavy metals, i.e., selenium (Se), copper (Cu), mercury (Hg), and cadmium (Cd), were used to construct WQI_min models for different clusters. These models demonstrated explanatory solid power for evaluating water quality (R² > 0.9). Four weighting methods—equal weight, rank sum, rank reciprocal, and rank order centroid—were applied to determine the WQI_min and WQI models. The results identified equal weight as the optimal WQI_min model construction method. The carcinogenic risks of chromium (Cr) and arsenic (As) ranged from 4.03 × 10^–5 to 4.66 × 10^–5, within the acceptable level of 1 × 10^–4 to 1 × 10^–6. The hazard quotients for all heavy metals were below "1", and non-carcinogenic risks were higher for children than for adults. Overall, the heavy metals in DJKR presented no significant human health risks. After the MRSNWDPC became fully operational, the DJKR maintained excellent water quality, with the reservoir's water deemed safe for drinking and other uses. This study provides a scientific basis for managing water quality in DJKR and other large-scale hydroengineering projects.

Over the past decade, the Delhi (National Capital Region, NCR) has witnessed significant growth and has emerged as a vital center for commerce and education. This rapid urbanization owes itself to its strategic location, connecting important cities like Gurugram, Faridabad, Noida, Sonipat, and Rohtak. However, its proximity to active geological features such as the Main Himalayan Thrust (MHT), Main Boundary Thrust (MBT), and Main Central Thrust (MCT), the city has been susceptible to devastating earthquakes, making it imperative to conduct a comprehensive probabilistic seismic hazard assessment for the Delhi NCR area. To perform this assessment, a homogenized earthquake database from 1720 to 2023 within a 300 km radius of the epicenter of Delhi was utilized. This data enabled the calculation of peak ground acceleration (PGA) and Spectral Acceleration (Sa) at different time periods, representing 50%, 20%, 10%, 5%, and 2% probabilities of exceedance in 50 years at the bedrock level. A logic tree approach, incorporating Ground Motion Prediction Equations (GMPEs) with appropriate weighted factors, was applied to ensure accuracy. The findings of the updated seismic hazard assessment reveal that Delhi and its neighboring cities are highly vulnerable to seismic hazards, with expected PGA values of 0.10 g, 0.18 g, 0.26 g, 0.33 g, and 0.48 g for the respective probabilities of exceedance. These results are comparable with the Indian code IS:1893 Part I and Malhotra’s (2005) standards that validate their reliability. Furthermore, the seismic hazard results have been used to create a deaggregation plot, which helps to quantify the contributions of seismic sources in terms of magnitude and epicentral distance. This comprehensive understanding of seismic hazards in Delhi and its adjoining regions will aid in implementing appropriate measures to enhance preparedness and mitigate potential risks.

Monitoring the soil organic carbon (SOC) dynamics through temporal environmental controlling covariates could indicate the soil and environment quality status. In this study, we address the main challenge of SOC changes at the landscape scale in dry and semi-arid regions, particularly in West Azarbaijan, Kermanshah, and Hamadan provinces of northwest Iran. Environmental covariates such as remote sensing (RS) data (land use history and vegetation indexes derived from the time series multispectral remote sensing images of Landsat 7), climate variables, soil properties (clay, sand and silt) and digital elevation model attributes employed to develop the prediction model of SOC level. Additionally, the random forest algorithms were applied to estimate SOC change and comprehensively investigated the importance of covariates in modeling to produce SOC maps for 2007 and 2023. The dataset of soil samples represented diverse conditions including arid and semi-arid environments, various soil types, topographies, and land cover classes. Furthermore, we developed a new and accurate historical land use map based on Landsat bands. The modeling performance reached an overall accuracy of 79.0% for detection of SOC status. Results showed that significant SOC loss in 2.42–11.18% of province areas and a gain in 1.92–7.49% of lands. Vegetation is the most important covariate governing the losses in the short term. The outcomes unveiled significant SOC losses, particularly in dryland farming areas and grasslands, underscoring the need for improved farming systems and pasture management practices. These findings offer vital insights for sustainable agriculture policies, natural resource management, and soil fertility preservation, highlighting the potential of remote sensing data for large-scale SOC monitoring.

Groundwater nitrate pollution is a serious environmental problem worldwide, which is demonstrated influenced by land use/land cover (LULC) patterns. The current study was carried out to investigate the effects of LULC patterns to the nitrate pollution of shallow groundwater in the north piedmont plain of the Qinling Mountain (NPQM) using an ensemble machine learning method named random forest. Groundwater nitrate data and existing LULC patterns datasets were utilized to conduct the study. LULC patterns were quantified using a curved streamline shaped contributing area stratagem, and subsequently used to create training and test datasets together with the groundwater nitrate data for construction of random forest model. The results of this study indicated arable and urban land were the main LULC types in the NPQM, and urbanization induced the occupation of arable land by urban land from 2015 to 2019. Shallow groundwater in the NPQM was polluted by nitrate in both 2015 and 2019, with area of groundwater nitrate concentration exceeding the standard limitation recommended by the WHO (50 mg/L as NO₃^—) reduced from 2762.2 km² in 2015 to 2184.3 km² in 2019, showing an alleviating trend. Arable and urban land were the main LULC types contributing to groundwater nitrate pollution. Nitrate accumulated in the soil from manure and chemical fertilizer was the main source for groundwater nitrate pollution in arable land, while manure and sewage were the main source in urban land. The study provides scientific insights for sustainable groundwater protection in the NPQM.

The cofferdam is a structure that prevents water from entering the dam construction area and keeps the excavation area dry during the construction of the dam body. This study investigated the seepage conditions of the upstream cofferdam, which is designed as a clay core earth fill. It was planned impermeability under the upstream cofferdam with a slurry trench. The primary input parameter of the seepage analysis is the permeability coefficient. Permeability tests were conducted under field conditions on alluvial and volcanic rock units at the base of the cofferdam. Laboratory permeability tests were conducted for the permeable and impermeable materials used in the cofferdam construction. The plastic concrete, intended to reduce seepage in the soil beneath the dam body, was designed, and its physical, mechanical, and permeability properties were determined. Following the completion of the upstream cofferdam, seepage that could potentially affect the dam body excavation area was investigated using Rocscience Groundwater and Plaxis LE 2D software. During the excavation of the dam body, the flow rate of water seepage into the excavation pit was measured and compared with the results of the computer software analysis. The results of the field measurements were found to be compatible with those from the numerical analysis programs. It has also been demonstrated that the slurry trench method is an effective way to reduce ground seepage. As a result, it has been observed that the upstream cofferdam and the plastic concrete underneath provide impermeability, allowing the construction of the dam body to be carried out safely. Seepage was brought under control, and necessary measures were taken.

Landslides pose a significant threat to human settlements, infrastructure, and the environment, necessitating proactive measures for disaster risk reduction (DRR). This study explores the integration of Remote Sensing (RS), Geographic Information Systems (GIS) and Statistical Modelling (SM) techniques to create a comprehensive landslide susceptibility mapping model. The objective is to enhance our understanding of the spatial distribution and factors influencing landslide susceptibility, ultimately aiding in effective land-use planning and disaster management. Because of the extensive impacts of topography, hydrology, geology, geomorphology, and climatic conditions, the susceptibility to landslide risks in mountainous places, exhibits obvious regionalism. As a result, we proposed three statistical models (i.e., Frequency Ratio (FR), Information Value (InV), and Shannon Entropy (SE)) to evaluate susceptibility at the national level. Validation of the susceptibility model is performed using 30% of the historical landslide events using Receiver Operating Characteristic (ROC) analysis and area under the curve (AUC). The results demonstrate the reliability and effectiveness of the integrated RS-GIS-SM approach in predicting landslide susceptibility. The three models demonstrate strong agreement with negligible differences in AUC of 0.910, 0.909, and 0.908 for FR, SE, and InV, respectively. The study's findings provide valuable insights into land-use planners, environmental agencies, and decision-makers to prioritize high-risk areas for mitigation strategies. Additionally, the developed model serves as a basis for future research and refinement, contributing to ongoing efforts to enhance landslide susceptibility mapping accuracy and applicability in diverse geographic regions. The integration of RS-GIS-SM technologies offers a powerful toolset for understanding and managing landslide risk, ultimately promoting safer and more resilient communities.