Research on spatial-temporal distribution and influencing factors of PM2.5 based on random forest

Xinnan Cai; Yuhan Jia

doi:10.1117/12.2634687

12 May 2022 Research on spatial-temporal distribution and influencing factors of PM2.5 based on random forest

Xinnan Cai, Yuhan Jia

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121731T (2022) https://doi.org/10.1117/12.2634687
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

It is of great significance to study the evolution characteristics of the spatial-temporal pattern of urban air quality and its influencing factors for deeply understanding the mutual feedback mechanism between urban environment and socioeconomic system and formulating efficient environmental control measures. Based on the real-time monitoring data of PM2.5 in Shaanxi Province in 2015, this paper discusses the temporal and spatial distribution law of Shaanxi Province, and uses the random forest model to reveal the nonlinear driving mechanism of meteorological factors, topography, vegetation, human activities, time and space factors on PM2.5, and sort out the key influencing factors.The results showed that the concentration of PM2.5 in Shaanxi Province not only showed a "U"-shaped monthly variation law, but also showed the characteristics of high in winter, followed by spring and autumn, and low in summer, with obvious seasonal differences. In space, it generally presents the distribution characteristics of northern Shaanxi < southern Shaanxi < Guanzhong. The model shows that aerosol optical depth (AOD) and boundary layer height (BLH) have relatively significant effects on PM2.5 concentration, while precipitation has the least effect on it.

Citation Download Citation

Xinnan Cai and Yuhan Jia "Research on spatial-temporal distribution and influencing factors of PM2.5 based on random forest", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121731T (12 May 2022); https://doi.org/10.1117/12.2634687

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KEYWORDS

Data modeling

Atmospheric modeling

Data centers

Environmental sensing

Pollution

Vegetation

Air contamination

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