TITLE:
Joint Dependence Structure and Spatiotemporal Heterogeneity between NO2 and PM2.5 in Beijing: A Mixture Copula-Based Analysis
AUTHORS:
Xiating Chen
KEYWORDS:
Dependence Modeling, Mixture Copula, Air Pollution, Clustering, Spatiotemporal Characteristics
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.14 No.3,
March
18,
2026
ABSTRACT: Under the context of urban compound air pollution, the joint dependence structure and spatiotemporal characteristics between NO2 and PM2.5 have not yet been systematically quantified. To reveal their co-variation mechanisms, this study utilizes daily average data from 31 air quality monitoring stations covering urban and suburban areas of Beijing during 2023-2024. A staged analytical framework integrating overall modeling, spatial clustering, and seasonal stratification is constructed. First, a mixture copula model is employed to characterize the overall joint distribution of NO2 and PM2.5. Subsequently, statistical fingerprint clustering combined with seasonal analysis is applied to identify spatial and temporal heterogeneity in the dependence structure. The results indicate a moderately strong positive dependence between NO2 and PM2.5 across Beijing, with an asymmetric structure featuring upper-tail-dominant dependence structure. This suggests that the two pollutants are more likely to increase synchronously under high-pollution conditions, implying a joint amplification effect during extreme pollution episodes. Spatial clustering analysis reveals significant gradient differences in concentration levels among monitoring stations, particularly for NO2, which exhibits significant spatial stratification. However, the form of the dependence structure remains relatively stable, indicating that spatial location primarily influences pollution intensity rather than coupling mechanisms. Seasonal variation substantially restructures the joint distribution. During the heating season, the two pollutants exhibit strong positive dependence and lower tail dependence, whereas in the non-heating season, the dependence is characterized by upper-tail-dominant feature. These findings suggest that seasonal factors exert a stronger regulatory effect on the pollutant dependence structure than spatial differences. This study reveals the spatiotemporal characteristics of the synergistic evolution of NO2 and PM2.5 in Beijing, providing support for joint risk assessment of extreme pollution events and seasonally differentiated coordinated control strategies.