Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods

Applied Sciences

Hyo Choi¹  Soo-Min Choi² 

[1] Atmospheric and Oceanic Disaster Research Institute, Gangneung 25563, Korea;Department of Computer Engineering, Konkuk University, Chungju 27478, Korea;

关键词: PM₁₀;    PM_2.5;    PM₁;    yellow dust;    COMS-AI satellite images;    correlation coefficient;   

DOI  :  10.3390/app112411958

来源: DOAJ

【 摘 要 】

Multiple statistical prediction modeling of PM₁₀, PM_2.5 and PM₁ at Gangneung city, Korea, was performed in association with local meteorological parameters (air temperature, wind speed and relative humidity) and PM₁₀ and PM_2.5 concentrations of an upwind site in Beijing, China, in the transport route of Chinese yellow dusts which originated from the Gobi Desert and passed through Beijing to the city from 18 March to 27 March 2015. Before and after the dust periods, the PM₁₀, PM_2.5 and PM₁ concentrations showed as being very high at 09:00 LST (the morning rush hour) by the increasing emitted pollutants from vehicles and flying dust from the road and their maxima occurred at 20:00 to 22:00 LST (the evening departure time) from the additional pollutants from resident heating boilers. During the dust period, these peak trends were not found due to the persistent accumulation of dust in the city from the Gobi Desert through Beijing, China, as shown in real-time COMS-AI satellite images. Multiple correlation coefficients among PM₁₀, PM_2.5 and PM₁ at Gangneung were in the range of 0.916 to 0.998. Multiple statistical models were devised to predict each PM concentration, and the significant levels through multi-regression analyses were p < 0.001, showing all the coefficients to be significant. The observed and calculated PM concentrations were compared, and new linear regression models were sequentially suggested to reproduce the original observed PM values with improved correlation coefficients, to some extent.

【 授权许可】

Unknown