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18-06-2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019 Open Access

Neural Computing and Applications 5/2019

Study on the spatial–temporal change characteristics and influence factors of fog and haze pollution based on GAM

Neural Computing and Applications > Issue 5/2019
Zhuang Wu, Shuo Zhang


PM2.5 (particulate matter) is an important object for air quality monitoring, and the research on related influence factors and diffusion process of PM2.5 plays a key role in the fight against pollution of fog and haze. Based on the air quality monitoring data and related meteorological data of 16 districts of Beijing during November 2016 and December 2017, such methods as time-series analysis and nonparametric test are adopted to describe the variation trend of PM2.5 concentration in space and time and its disparities in different seasons, time periods and areas. Linear regression method is used in most of the previous research on influence factors and prediction of PM2.5 concentration, but actually, the relation between these factors is rather intricate and it is usually nonlinear. So, generalized additive model (GAM) is used in this paper to analyze the impact that different influence factors, especially their interaction, have on PM2.5 concentration and its diffusion process. The result shows that in the dimensionality of time, PM2.5 concentration has strong autocorrelation over time and it is most significant in the first to the third order (lag 0–lag 3). Throughout the year, PM2.5 concentration is relatively high in winter and low in summer. It is usually the lowest during 16:00–18:00 and the highest during 9:00–11:00 every day and far higher at night than in the daytime (MD = − 6.455, P = 0.003). And in terms of space, PM2.5 concentration shows distinct spatial gradient and it gradually decreases from south to north (MD = − 19.250, P = 0.004). It is found in the analysis of influence factors of PM2.5 concentration that the change of PM2.5 concentration is a complex nonlinear time series driven and affected by many factors; among these factors, the interaction between air pollutants and meteorological elements is the most prominent, while average wind speed (WS lag 1) plays a decisive role in the entire diffusion process, and it explains the whole diffusion of PM2.5 concentration to a large extent.
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