Abstract
Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM2.5 concentration should be better understood. This research examined seasonal variations of PM2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM2.5 concentration. For the Jing-Jin-Ji region, the higher PM2.5 concentration, the stronger influences meteorological factors exert on PM2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.
Highlights
Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging
PM2.5 concentration is analyzed for each season respectively
General characteristics of PM2.5 concentration for different cities are demonstrated as Table 1 and Fig. 2
Summary
Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging. To map spatial variations of PM2.5 concentration across large areas, some researchers[25,26] employed different remote sensing sources and spatial data analysis methods Among these studies, a large body of research has been conducted to examine the correlations between meteorological factors and airborne pollutants. The analysis of the sensitivity of airborne pollutants to individual meteorological parameters remains difficult[29], as different meteorological parameters are inherently linked and may affect airborne pollutants through both direct and indirect mechanisms In this case, Pearce et al.[29] suggested that multiple models and methods should be comprehensively considered to quantify the role of meteorological factors in affecting local air pollution
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