The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.
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http://dx.doi.org/10.1038/s41598-024-66053-6 | DOI Listing |
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June 2024
Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Łukasiewicza 5/7, 50-367 Wroclaw, Poland.
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November 2024
Advanced Institute of Technology and Innovation (IATI), 50751-310 Recife, Pernambuco, Brazil.
Background: This study aimed to produce, characterize, and apply a biosurfactant as a bioremediation tool for oil-contaminated coastal environments.
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JACS Au
December 2024
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware DE 19716, United States.
Zeolite coatings are studied as molecular sieves for membrane separation, membrane reactors, and chemical sensor applications. They are also studied as anticorrosive films for metals and alloys, antimicrobial and hydrophobic films for heating, ventilation, and air conditioning, and dielectrics for semiconductor applications. Zeolite coatings are synthesized by hydrothermal, ionothermal, and dry-gel conversion approaches, which require high process temperatures and lengthy times (ranging from hours to days).
View Article and Find Full Text PDFGeohealth
January 2025
Institute for Atmospheric and Climate Science, ETH Zurich Zurich Switzerland.
Heatwaves pose a range of severe impacts on human health, including an increase in premature mortality. The summers of 2018 and 2022 are two examples with record-breaking temperatures leading to thousands of heat-related excess deaths in Europe. Some of the extreme temperatures experienced during these summers were predictable several weeks in advance by subseasonal forecasts.
View Article and Find Full Text PDFHeliyon
December 2024
School of Mechanical Engineering, Institute of Technology, Wallaga University, P.O. Box 395, Nekemte, Ethiopia.
Turning AISI (American Iron and Steel Institute) D3 tool steel can be challenging due to a lack of optimal process parameters and proper coolant application to achieve high surface quality and temperature control. Machine learning helps in predicting the optimal parameters, whereas nanofluids enhance cooling efficiency while preserving both the tool and the workpiece. This work intends to utilize advanced machine learning approaches to optimize process parameters with the application of hybrid nanofluids (AlO/graphene) during the CNC turning of AISI D3.
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