[논문]Correlation Analysis of Atmospheric Pollutants and Meteorological Factors Based on Environmental Big Data

Chao, Chen; Min, Byung-Won

doi:10.5392/ijoc.2022.18.1.017

Correlation Analysis of Atmospheric Pollutants and Meteorological Factors Based on Environmental Big Data 원문보기

International journal of contents, v.18 no.1, 2022년, pp.17 - 26

Chao, Chen (College of Computer Science and Engineering, Sichuan University of Science and Engineering) , Min, Byung-Won (Division of Information and Communication Convergence Engineering Mokwon University)

Abstract ▼ AI-Helper

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.

주제어

표/그림 (9)

그림 Figure 1. System architecture and design process
표 Table 1. Air Quality Index classification standards
표 Table 2. Sample data structure after processing
표 Table 3. Correlation coefficients among pollutants in Sichuan Province in March 2018
그림 Figure 2. Visualization of pollutant correlation (chord diagram)
그림 Figure 3. Visualization of the correlation between pollutants and meteorological factors (radar chart)
표 Table 4. Correlation coefficients between pollutants and meteorological factors in Sichuan Province in March 2018
그림 Figure 4. National air quality visual analysis system
그림 Figure 5. Correlation comparison chart of TOP3 cities

AI 본문요약
AI-Helper

제안 방법

In order to further explore the evolution of the Air Quality Index (AQI) and various pollutants and meteorological factors in various time and space series across the country, to find the pollution types and causes of the most polluted cities in each period, and to analyze the effects of various air pollution indicators. Correlation, through a large number of complex calculations, the national air quality visual analysis system has been realized.
The system adopts front-end and back-end agile development, obtains 34 administrative divisions across the country through the inverse address coding of AutoNavi Map API, adds data dimensions such as AQI index, wind speed, wind direction, and AQI level. The front-end uses Vue, Echarts, and D3 to achieve visualization, and the back-end uses Node builds an Express server, and the database uses MongoDB for data storage.

데이터처리

In the same way, in the experiment, the meteorological factors (TEMP, RH, PSFC, WINDY) of air pollution in Sichuan Province in March 2018 and the six pollutants in the same period were statistically analyzed by the Pearson correlation coefficient during the experiment, and the calculation results As shown in Table 4.
When the absolute value is larger, the correlation is stronger. This experiment will use the Pearson correlation coefficient to measure the linear correlation between the concentrations of different air pollutants.

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