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Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, −23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.