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The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ's spatiotemporal distribution pattern using the Google Earth Engine platform. Then, the Geodetector model was employed to identify the driving mechanisms responsible for EEQ variation under multi-factor coupling. The results show the following: (1) Over the past two decades, the EEQ has consistently achieved moderate to good levels and has exhibited an overall trend of improvement. (2) At the spatial scale, the distribution pattern of the RSEILA in Anhui Province was characterized by high values in the south and low values in the north, which was closely associated with the natural geographic conditions and land use patterns. (3) Multi-factor coupling exerted a significant spatiotemporal scale effect on the drivers of EEQ levels. At the temporal scale, EEQ levels were predominantly influenced by policy measures, while spatially topography and human activities were identified as the primary drivers of the EEQ changes. The findings of this research provide a theoretical foundation for enhancement and administration of the EEQ in Anhui Province and analogous regions.
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http://dx.doi.org/10.1038/s41598-025-13944-x | DOI Listing |
Front Plant Sci
August 2025
College of Agriculture, Shihezi University, Shihezi, China.
Introduction: Existing facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. These limitations significantly constrain the effectiveness of precise environmental regulation in agricultural facilities.
Methods: To address these challenges, this paper proposes a novel facility environment prediction model (LSTM-AT-DP) integrating Long Short-Term Memory networks with attention mechanisms and advanced data preprocessing.
Ying Yong Sheng Tai Xue Bao
July 2025
State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Agricultural University of Hebei, Baoding 071001, Hebei, China.
Red and blue light are the primary spectra absorbed by photosynthetic pigments in plants. Through the signal pathways mediated by phytochromes (PHY) and cryptochromes (CRY)/phototropins (PHOT), they coope-ratively regulate photosynthetic carbon assimilation, and plant growth and development. We reviewed the regulatory mechanisms of red and blue light on photosynthetic characteristics and plant growth and development.
View Article and Find Full Text PDFInt J Occup Saf Ergon
August 2025
School of Safety Science and Engineering, Henan Polytechnic University, China.
In response to the coal mining industry's high-risk nature and limitations of traditional accident analysis, this study constructs a multi-factor coupling analysis framework using 481 accident reports. Parsing unstructured text reveals 'core-periphery' structural characteristics in accident causation systems. Key contributions of the study are as follows: methodologically, it employs text mining to automate factor extraction and integrates social network analysis (SNA) to quantify node centrality and transmission intensity; theoretically, 18 core causations (e.
View Article and Find Full Text PDFGlob Chang Biol
August 2025
Qingdao Institute of Marine Geology, China Geological Survey, Qingdao, China.
Coastal blue carbon ecosystems (BCEs) face accelerating degradation from synergistic climate-human pressures, threatening their carbon sink function. This review synthesizes nonlinear interactions governing BCE carbon cycles by developing a novel DPSIR (Drivers-Pressures-State-Impacts-Responses) conceptual model. Our framework integrates biogeochemical processes (e.
View Article and Find Full Text PDFSci Rep
August 2025
School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ's spatiotemporal distribution pattern using the Google Earth Engine platform.
View Article and Find Full Text PDF