Mr. Lei Yu Profile

Lei Yu

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Proceedings Article | 18 November 2024 Paper

Research on energy consumption carbon emissions at grid scale based on NPP-VIIRS nighttime light data

Huimin Wang, Lei Yu, Yanan Wang, Yu Li, Xingcheng Wang

Proceedings Volume 13398, 133980G (2024) https://doi.org/10.1117/12.3049716

KEYWORDS: Carbon, Statistical analysis, Remote sensing, Image processing

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This research applies the IPCC energy consumption method to calculate the energy consumption carbon emissions in various cities in Shaanxi Province from 2013 to 2021. Regression models between NPP-VIIRS nighttime light data and energy consumption carbon emissions are constructed, and grid scale spatiotemporal distribution maps of energy consumption carbon emissions in Shaanxi Province are generated. Based on the Logarithmic Mean Divisia Index (LMDI) method, the driving effects of energy structure, energy intensity, economic development and population size on carbon emissions are studied. The research results of the article are as follows: (1) From 2013 to 2021, the energy consumption carbon emissions in Shaanxi Province showed an increasing trend, and there were significant differences in carbon emissions among different cities. (2) The main urban areas of most cities gradually expanded in size and their carbon emissions increased from 2013 to 2021. The Xixian New Area has been significantly expanding since 2017. Carbon emissions in some areas of Yulin and Yan'an have been decreasing since 2018. (3) Economic development and population size are positive factors driving the growth of carbon emissions in Shaanxi Province. The contribution of economic development to carbon emissions is much greater than that of population size. Overall, energy structure and intensity have a suppressive effect on carbon emissions. The research results can provide reference and inform policy decisions related to low-carbon development in Shaanxi Province.

Proceedings Article | 3 June 2024 Paper

Monitoring technology of near-surface atmospheric carbon dioxide concentration in multiple regions based on unmanned aerial vehicle

Huimin Wang, Lei Yu, Yu Li, Yanan Wang, Kailei Xu, Xingcheng Wang

Proceedings Volume 13170, 131700J (2024) https://doi.org/10.1117/12.3032121

KEYWORDS: Atmospheric monitoring, Unmanned aerial vehicles, Vegetation, Carbon dioxide

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Due to the unclear distribution of near-surface atmospheric carbon dioxide (CO₂) in small areas, by choosing Yulin City and Xi'an City as the research areas and applying Unmanned Aerial Vehicle (UAV) to carry out atmospheric CO₂ concentration monitoring at multiple carbon monitoring points, this study explores the impact of ground object type, height and time on atmospheric CO₂ concentration. Regarding the temporal variation, the atmospheric CO₂ concentration of the vegetation-covered area in the afternoon is lower than that in the morning in summer, and only the evergreen vegetation-covered area in winter has similar characteristics as that in summer. The near-surface atmospheric CO₂ concentration in the vegetated area changes greatly in summer and winter. For the characteristics of vertical distribution of CO₂, the CO₂ concentration gradually decreases with increasing altitude. In summer, the CO₂ concentration in areas affected by human activities decreases rapidly from 0 to 50 meters and decreases slowly from 50 to 150 meters. The CO₂ concentration in vegetation-covered areas decreases slightly from 0 to 150 meters. In winter, the areas affected by human activities and the vegetation-covered areas both decrease rapidly from 0–20 meters and relatively slowly from 20 meters to 100 meters. The experimental results indicate that using UAVs can accurately monitor the atmospheric CO₂ concentration near the surface of typical land features in small areas. UAVs can form air-space-ground carbon monitoring networks with satellites and ground observation points, which is of great significance for quantitative analysis of carbon reduction paths to achieve peak carbon dioxide emission and carbon neutralization.

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