Research on key technologies of atmospheric carbon emission monitoring and early warning based on big data and spectral measurement

Na Ren; Hongjiang Wang; Yan Xia; Nan Zhang; Wenqiang Zhang; Zhengda Li

doi:10.1117/12.2675430

25 May 2023 Research on key technologies of atmospheric carbon emission monitoring and early warning based on big data and spectral measurement

Na Ren, Hongjiang Wang, Yan Xia, Nan Zhang, Wenqiang Zhang, Zhengda Li

Author Affiliations +

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263607 (2023) https://doi.org/10.1117/12.2675430
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

The distribution of carbon emissions varies greatly among regions and industries in China. In order to master the current situation and trend of carbon emissions in different regions and industries, and formulate and adjust relevant policies in a timely manner, it is necessary to conduct real-time, continuous, accurate and digital monitoring of carbon emissions in major regions and industries. Therefore, "carbon peaking" and "carbon neutralization" put forward higher requirements for the environmental monitoring industry and also brought greater opportunities. In this paper, the spectral information data in typical atmospheric samples are collected in real time, processed and stored structurally through the atmospheric spectral detection method based on mid far infrared spectrum and terahertz spectrum, the spectral feature extraction algorithm based on deep neural network, and the atmospheric carbon emission data analysis and visual monitoring based on big data technology; Run the spectral classification algorithm with low cost and high efficiency on the big data platform, obtain the detailed data of gas sample composition in real time, and display the analysis results visually. Through simulation experiments, the results show that the proposed method can better achieve the monitoring and early warning of atmospheric carbon emissions based on big data and spectral measurement.

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Na Ren, Hongjiang Wang, Yan Xia, Nan Zhang, Wenqiang Zhang, and Zhengda Li "Research on key technologies of atmospheric carbon emission monitoring and early warning based on big data and spectral measurement", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263607 (25 May 2023); https://doi.org/10.1117/12.2675430

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KEYWORDS

Carbon

Environmental monitoring

Data storage

Atmospheric monitoring

Data modeling

Atmospheric modeling

Atmospheric sensing

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