Study on the Concentration Distribution and Grey Prediction of Gas Pollutants in Beijing -- A Case Study of Nitrogen Oxides

Jianing Li, Hongmei Li, Shuang Wu

Abstract


With the acceleration of industrialization, air pollution has become a problem that can not be ignored in the process of urban development. Nitrogen oxides (NOX) are chemically active and hazardous, and play an important intermediary role in the formation of acid rain and other atmospheric complex pollution. for which a series of prevention and control measures have been implemented in Beijing, but due to the late development of NOX control technology in China, NOX pollution is still a focus issue, Therefore, this paper investigates the spatial and temporal distribution and concentration changes of NOX in Beijing. Firstly, based on the relevant concentration data of Beijing from 2014 to 2021, the limits for actual and policy concentrations of NOX, O3, CO and SO2 are compared, and then the spatial and temporal variation trends of NOX  concentrations are described, and a grey prediction model is established by python software to estimate and predict the NOX concentrations in Beijing from 2022 to 2024. The results show that  from 2014 to 2021, the air quality in Beijing will continue to improve and the NOX concentrations will continue to decrease, with a spatial distribution of "high in the south and low in the north and highest in the middle", and the differences between the north - south and suburban areas of NOX concentrations will be significantly reduced in 2021. The grey model forecast results show that the NOX concentration in Beijing will still decrease in the next three years, but the decrease is not significant. It is recommended to further improve the "winter disease and summer treatment" of air pollution in Beijing, continue to carry out in-depth regional joint prevention and control, and strengthen the collaborative pollutant reduction strategy.


Keywords


Air Pollution; Nitrogen Oxides; Air Quality Index; Grey Prediction Model

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References


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DOI: https://doi.org/10.18686/pes.v4i4.1532

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