Mr. Zifan Rong Profile

Zifan Rong

at Northwest A&F Univ

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Author

Publications (2)

SPIE Journal Paper | 6 August 2024

Spatiotemporal characteristics of PM_2.5 concentrations and responses to land-use change in Urumqi, China

Zifan Rong, Nurmemet Erkin, Junqian Ma, Mikhezhanisha Asimu, Yejiong Pan, Batur Bake, Maimaiti Simayi

JARS, Vol. 18, Issue 03, 038501, (August 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.038501

KEYWORDS: Data modeling, Atmospheric modeling, Vegetation, Statistical modeling, Machine learning, Pollution, Meteorology, Matrices, Aerosols, Performance modeling

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Proceedings Article | 21 February 2024 Paper

Analysis of annual average daily concentration of PM2.5 particles in Urumqi and prediction of average daily concentration in the next 5 years based on time series model

Zifan Rong, Nurmemet Erkin, Yangyi Chen, Batur Bake

Proceedings Volume 12988, 129880W (2024) https://doi.org/10.1117/12.3023970

KEYWORDS: Particles, Environmental monitoring, Data modeling, Atmospheric modeling, Time series analysis, Air quality, Statistical analysis, Atmospheric particles, Environmental management, Agriculture

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PM_2.5 particulate matter is one of the important pollutants in the atmosphere, which has a serious impact on human health and environment. Therefore, the study of the change of PM_2.5 particulate matter concentration has attracted more and more attention. This paper selected the monitoring data set of PM_2.5 particulate matter concentration from 2014 to 2022 at various detection points in Urumqi, aiming to deeply understand the change rule of PM_2.5 particulate matter concentration and provide a basis for future environmental management and prevention measures. In terms of data preprocessing, this paper fills in the vacant values, carries out statistical analysis on the daily average concentration of PM_2.5 particles at each data collection point, and calculates the mean value, variance, standard difference and other statistical indicators of PM_2.5 particle concentration in different regions. At the same time, the paper also calculates the daily average concentration of PM_2.5 particles at each data collection point, which provides a data basis for the subsequent time series analysis and prediction. In the aspect of time series analysis, the ARIMA model is used to predict the change of PM_2.5 particle average daily concentration in the next five years. Firstly, the ADF test and the data comparison graph before and after difference were used to judge whether the sequence was stationary. Then, the p and q values of the ARIMA model were determined by autocorrelation analysis. Finally, the order of backward prediction is obtained according to the model parameter table and the time series analysis diagram. The prediction of the ARIMA model can accurately analyze and predict the change of the average daily concentration of PM_2.5 particles in the next five years, and provide a scientific basis for environmental management and prevention measures. In conclusion, this paper makes a detailed analysis and prediction on the monitoring data set of PM_2.5 particulate matter concentration at various detection points in Urumqi from 2014 to 2022, which provides a basis for studying the change rule of PM_2.5 particulate matter concentration and a reference for future environmental management and preventive measures.

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