Optimal allocation of technical reclamation and ecological restoration for a cost-effective solution in Pingshuo Opencast Coal Mine area of China.

Limiting adverse consequences of mining activities requires ecosystem restoration efforts, whose arrangement around mining areas is poorly designed. It is unclear, however, where best to locate ecological projects to enhance ecosystem services cost-effectively. To answer this question, we conducted an optimized ecological restoration project planning by the Resource Investment Optimization System (RIOS) model to identify the restoration priority areas in the Pingshuo Opencast Coal Mine region in Shanxi Province.

Impact of mandatory environmental information disclosure on the capital cost: Evidence from listed companies in China.

Mandatory environmental information disclosure is characterized by formal environmental regulation and that the content of the disclosure promotes public participation and oversight in the environmental field. This paper studies the impact of mandatory environmental information disclosure (EID) enforcement in China on corporate capital cost using a difference-in-difference model. The results indicate that mandatory EID increases debt capital cost, but has no significant impact on equity capital cost, which differs from mainstream research that suggest EID can reduce capital cost.

Grazing on the Tibetan plateau currently exceeds the safe boundary of its ecosystem services.

Under the influence of climate warming, overgrazing may exacerbate ecosystem degradation. To determine the safe grazing boundaries and potential degradation areas in different regions. The study used the Generalize Linear Model (GLM) to assess the combined effects of drought and grazing on the ecosystem services of the Tibetan Plateau (TP), and identified safe grazing boundaries and overgrazing areas.

Machine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas.

An accurate understanding of the spatial distribution of soil heavy metals (HMs) is crucial for the effective prevention of soil pollution and remediation strategies. Traditional machine learning models often overlook the spatially stratified heterogeneity inherent to environmental data, which can impair predictive accuracy. Therefore, we combined the Geodetector model (GDM) with machine learning models.

Enhancing active fire detection in Sentinel 2 imagery using GLCM texture features in random forest models.

The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions.

[Construction of Key Technology System for Mine Ecological Restoration Under System Perspective].

Mine ecological restoration has experienced a long-term development process in China, in which various technologies have been constantly developing and integrating. Based on the related theoretical research and field program, the technical system of mine ecological restoration was constructed, and the characteristics of key technologies were specifically grasped. In this research, the environment, including natural elements and the spatial environment, is the object of mine ecological restoration, which requires further long-term investigation and monitoring.

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ensemble learning (EL) was first used to analysis the 23 factors in 8 categories (heavy metal pollution characteristics, soil properties, geographical characteristics, meteorological factors, socio-economic factors, environmental factors, rice type, and nutrient element) in typical regions of China based on the results of 193 research papers from 2000 to 2024 in Web of Science database.

Synergistic effects of carbon and heat under disturbance of human activities: Evidence from a resource-based city of China.

For resource-based cities, the rapid development of industrialization and urbanization has led to significant carbon emissions (CEs), accelerated the rise of urban land surface temperatures (LSTs) and hindered sustainable urban development. This study constructed a model to measure the carbon-heat relationship to clarify the complex relationship between LSTs and CEs in resource-based cities. The results show that:1) High-temperature areas are primarily concentrated around the urban center and large industrial zones, with average LSTs reaching a peak of 35.

Exploring the natural-socioeconomic driving and response mechanisms of ecosystem services interactions to optimize ecosystem management: A case study in the Yarlung Zangbo River Basin.

Revealing the complex interactions between ecosystem services (ESs) and their underlying mechanisms is a prerequisite for formulating sustainable ecological management strategies. However, few studies have conducted a comprehensive analysis of the driving and response mechanisms of ESs interactions. Therefore, this study established an integrated framework to first quantify the interactions between ESs, then identify their dominant natural-socioeconomic drivers, explore their spatial non-stationary responses, and ultimately propose corresponding strategies to optimize ecosystem management.

Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.

Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent years, machine learning (ML) has been gradually employed for adsorbent materials, but there is no comprehensive research on the Schwertmannite adsorbent.

A review of hydroxyapatite synthesis for heavy metal adsorption assisted by machine learning.

Heavy metals (HMs) represent a persistent and significant threat to aquatic ecosystems. Hydroxyapatite (HAp) has emerged as a utilized material in the remediation of environmental HMs, owing to its exceptionally high porosity, expansive surface area, and the presence of three-dimensional ordered channels. An in-depth study of the synthesis strategy of HAp and its adsorption properties can help reduce the cost of remediating HMs in aquatic environments and alleviate the water shortage.

Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China.

This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky-Golay smoothing, followed by four spectral transformations-continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance-which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping.

Research on performance and potential of distributed heating system for peak shaving with multi-energy resource.

Climate change and its negative effects are driving the global shift from fossil fuels to renewable energy sources. To tackle the dependency on traditional energy sources in harsh winter regions and improve heating quality during periods of thermal demand fluctuations, this paper proposes a new distributed heating peak shaving system (DHPS). The system combines municipal heat and clean energy within the secondary network while reducing the return water temperature in the primary network.

Changes in gross ecosystem product and its response to land use changes in forest resource dominant regions: A case study of Zixi County, Jiangxi Province, China.

Forests are one of the most important terrestrial ecosystems and play a crucial role in the construction of ecological civilization. Understanding the changes in gross ecosystem product (GEP) and its response to land use change in areas with unique advantages in forest resources is of great significance for promoting sustainable development. We examined the GEP of Zixi County of Jiangxi Province.

Effective removal of Pb (II) from wastewater by zinc-iron bimetallic oxide-modified walnut shell biochar: A combined experimental and DFT calculation approach.

The modified walnut shell biochar (WBC) was prepared through zinc-iron bimetallic oxide modification (ZF@WBC) at 600 °C under oxygen-limited conditions in this study. Through adsorption experiments, characterization analyses, and density functional theory (DFT) calculations, the adsorption properties of ZF@WBC to Pb (II) were investigated and the mechanism underlying such adsorption was elucidated. Characterization results showed that the surface area (375.

Unveiling electron transfer and radical transformation pathways in coupled electrocatalysis and persulfate oxidation reactions for complex pollutant removal.

The degradation of multiple organic pollutants in wastewater via advanced oxidation processes might involve different radicals, of which the types and concentrations vary upon interacting with different pollutants. In this study, electrochemical activation of peroxymonosulfate (E/PMS) using advanced activated carbon cloth (ACC) as electrode was applied for simultaneous degradation of mixed pollutants, e.g.

Quantification and sensitivity assessment of Chinese provincial ecological compensation in the perspective of carbon deficit redistribution.

The ecological compensation mechanism is a tool for managing regional development and promoting the green economy. This paper proposes a revised model for carbon emissions and absorption based on IPCC, then analyses the spatial and temporal variations of carbon emissions, absorption, and deficit in 31 provinces of China from 2001 to 2019. A model was developed to redistribute carbon deficits and compensate for ecological imbalances, with the aim of eliminating background differences between regions.

Enhanced phytoremediation of cadmium-contaminated soil using chelating agents and plant growth regulators: effect and mechanism.

The heavy metal cadmium (Cd) is a major threat to food safety and human health. Phytoremediation is the most widely used remediation technology, and how to improve the remediation efficiency of phytoremediation has become a key issue. In this study, we constructed an intensive phytoremediation technology for remediation of Cd-contaminated soil with biodegradable chelating agent and plant growth regulator combined with maize and investigated the mechanism of this technology.

Evaluation of ecosystem quality and stability based on key indicators and ideal reference frame: A case study of the Qinghai-Tibet Plateau.

China has explicitly prioritized the enhancement of ecosystem quality and stability(EQS) as a governmental objective. However, our understanding of systematic and comprehensive assessment methods for EQS remains limited. The development and investigation of corresponding evaluation frameworks and their underlying mechanisms remain insufficiently explored.