Study on the influence of surrounding urban SO2, NO2, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms. (20th July 2020)

• Record Type: Journal Article
• Authors: Zhang, Xin‐xin; Gu, Lei‐lei; Chen, Hong; Jia, Guo‐zhu
• Is Part Of: Concurrency and computation. Volume 32:Number 24(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-20
• Subjects: Beijing, China -- boosting algorithm -- MF‐DCCA -- oxides -- PM2.5; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5921
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The formation of haze depends on the complex evolution behavior of SO2, NO2, and CO. We explore the influence of surrounding urban oxides on haze formation in Beijing. From the perspective of time evolution, multifractal detrended cross‐correlation analysis (MF‐DCCA) method quantitatively shows that all oxides reveal persistent cross‐correlations ( h xy : 0.640 sim; 0.995) in the short term and long term. The correlation characteristics of the same oxide in different regions are compared. In the short term, Zhangjiakou (SO2 /NO2 ) and Tianjin (CO) have the strongest multifractal features, while Zhangjiakou (SO2 /NO2 /CO) is more prominent in the long term. Their sources are mainly Fat‐tailed. Ignoring the characteristics of timing, the fitting degree ( R 2 ) of LightGBM model to PM2.5 concentration regression is 0.862 after the addition of neighboring oxides, an increase of 2.0%. CatBoost's R 2 improves by 6.4%. The feature importance score indicates that Langfang's SO2 and Chengde's CO contribute the most to the formation of PM2.5. This study has certain reference value for the formulation of oxide emission strategy in the surrounding area of Beijing.