A Study on the Nonlinear Lag Effects of Meteorological Factors on PM2.5 Concentrations in Nanjing, China: Based on GAM and DLNM Models

Abstract

Fine particulate matter(PM2.5), as the predominant atmospheric pollutant in China, poses a serious threat to public health and socioeconomic development. Meteorological factors play a critical role in shaping its occurrence and evolution. This study analyzes data from Nanjing City between 2015 and 2023, employing Trend Analysis, Generalized Additive Models (GAM), and Distributed Lag Nonlinear Models (DLNM) to investigate the spatiotemporal distribution of PM2.5 and the impact of meteorological variables. The results show that PM2.5 concentrations in the city declined overall from 2015 to 2023, at an average rate of -3.36 μg·(m³·yr)⁻¹, exhibiting distinct phased characteristics; PM2.5 concentrations were higher in winter than in summer, lower in the central urban area, and higher in industrially developed regions. The GAM model indicates that 2m temperature(T2M) and boundary layer height(BLH) are the primary driving factors, and the interaction between 2m temperature-10m wind speed(T2M-FG10) and BLH-FG10 exerts a strong regulatory effect on pollutant dispersion; When total precipitation(PRE) lies within the range of 0.008–0.013 m and FG10 within the range of 1.0–1.8 m/s, their interaction has a positive effect on PM2.5 concentrations. The results of DLNM show that the lag and cumulative effects of meteorological factors on PM2.5 are significant. The lag effect of 0-3 days is the most prominent, and the precipitation removal effect is the strongest in 0-7 days. When T2M increased, the lag effect changed from negative to positive, while BLH increased, the negative lag effect increased. This study reveals the spatiotemporal dynamics of meteorological factors on PM2.5 and provides a scientific basis for the precise management of air pollutants in Nanjing.