The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining

Abstract

PM2.5 pollution influences the population health and people's daily life. Because meteorological factors are main factor affecting the formation of PM2.5, the interaction between PM2.5 and meteorological factors needs to be better understood, both for air quality management and for PM2.5 projection. Here, we use a nonlinear state space method called the convergent cross mapping method to identify the complex coupling patterns between PM2.5 and meteorological factors in a plateau city: Xining. The results prove that PM2.5-meteorological coupling patterns change with seasons and PM2.5meteorological coupling patterns are fixed in spring, autumn and winter. In spring, there is a negative unidirectional effect from precipitation to PM2.5 and a negative bidirectional effect between relative humidity and PM2.5. In autumn, there are some negative bidirectional effects between PM2.5 and relative humidity, precipitation, and air pressure, while solar radiation has a positive bidirectional effect on PM2.5. In winter, there are negative bidirectional couplings between PM2.5 and wind speed and temperature and a positive bidirectional coupling between relative humidity and PM2.5. Furthermore, relative humidity is a consistent driving factor affecting PM2.5. Air quality managers may alleviate PM2.5 by increasing relative humidity. Thus, the results provide a meteorological means for improving air quality in plateau cities.

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020

“…The analysis of the role that meteorological factors play in the PM 2.5 concentration levels is of paramount importance [7,[75][76][77][78], because changes in these factors can considerably affect the concentration levels of this air pollutant. Therefore, the aim of the next paragraphs of the paper is to make a comparison between different meteorological variables that could influence the PM 2.5 concentration levels in the region under study.…”

Quantitative Estimation of Urban PM2.5 Pollution Baseline and Meteorological Resource Endowment Using Machine Learning in Chinese Yangtze River Economic Belt

“…Weather normalization (WN) is a comprehensive algorithm based on machine learning [7][8][9]. It effectively explains the relationship between PM2.5 and meteorological conditions and removes the meteorological component [10][11][12]. Meteorological resource endowment (MRE) was used to quantify the specific concentrations of PM2.5 influenced by meteorological factors.…”