The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east-west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.
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http://dx.doi.org/10.1038/s41598-024-69573-3 | DOI Listing |
Environ Sci Pollut Res Int
January 2025
Faculty of Geography, Lomonosov Moscow State University, 119991, Moscow, Russia.
The content of 39 metals and metalloids (MMs) in submicron road dust (PM fraction) was studied in the traffic zone, residential courtyards with parking lots, and on pedestrian roads in parks in Moscow. The geochemical profiles of PM vary slightly between different types of roads and courtyards but differ significantly from those in parks. In Moscow, compared to other cities worldwide, submicron road dust contains less As, Sb, Mo, Cr, Cd, Sn, Tl, Ca, Rb, La, Y, U, but more Cu, Zn, Co, Fe, Mn, Ti, Zr, Al, V.
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January 2025
Civil and Environmental Engineering Department, Khalifa University, Abu Dhabi, UAE.
Estimating spatiotemporal maps of greenhouse gases (GHGs) is important for understanding climate change and developing mitigation strategies. However, current methods face challenges, including the coarse resolution of numerical models, and gaps in satellite data, making it essential to improve the spatiotemporal estimation of GHGs. This study aims to develop an advanced technique to produce high-fidelity (1 km) maps of CO and CH over the Arabian Peninsula, a highly vulnerable region to climate change.
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January 2025
Direction Milieux et impacts sur le vivant, Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.
Emissions due to tires retread/repair and incineration are a cause of concern owing to the presence of nanoparticles in the products. The assessment exposure to humans hereto related is a challenge in an environmental context. The first object of this work is to develop a method to characterize the emission sources using online (counting and sizing) and offline measurements.
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January 2025
Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; University of Chinese Academy of Sciences, Beijing 101408, China. Electronic address:
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM) were investigated in the Yellow Sea and Bohai Sea of China, by introducing the cutting-edge network inference tool of deep learning. The concentration of particulate organic carbon (POC) was determined to characterize the status of POM, and the fractions and fluorescent properties of DOM were identified through 3D excitation-emission-matrix spectra (3D-EEM) combined parallel factor analysis (PARAFAC).
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January 2025
Yunnan Key Laboratory of Internal Combustion Engine, Kunming University of Science and Technology, Kunming 650500, China.
Ammonia is a highly promising carbon-neutral fuel. The use of ammonia as a fuel for internal combustion engines can reduce fossil energy consumption and greenhouse gas emissions. However, the high ignition energy required for ammonia and the slow flame propagation rate result in low combustion efficiency when ammonia is used directly in internal combustion engines.
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