New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China

Abstract

• Gaussian surface model can well depict the spatial characteristics of UPI effect. • Intensity, footprint, and capacity reveal intensified but heterogeneous UPI effect. • Anthropogenic factors act as the key influencer on the variation of UPI effect. • Panel, cross-section, and time-series analysis show spatiotemporal driven difference. The phenomenon of the urban PM2.5 pollution island (UPI, the PM2.5 concentration difference along the urban–rural gradient) has been a growing concern due to its pernicious impacts on both residential and environmental health. To date, however, studies on the spatialization and indexation of the UPI effect and what is might be attributed to are still lacking. In this study, the Gaussian surface fitting model was innovatively used to depict the spatial morphology of the UPI effect for 13 cities in the Beijing–Tianjin–Hebei urban agglomeration region of China. Multiple indicators were introduced from the fitted Gaussian surface to portray the UPI intensity (magnitude), footprint (impact extent), and capacity (cumulative risk load) characteristics. Then, their potential relationships with several representative natural and anthropogenic factors were examined based on panel, cross-section, and time-series analysis. The main findings can be summarized as follows: 1) The intensity, footprint, and capacity indicators based on the fitted Gaussian model complementarily depict the spatial characteristics of the UPI effect. The spatiotemporal analysis showed that most of the case cities experienced remarkably intensified but heterogeneous UPI effects from 2000 to 2015. 2) The panel, cross-section, and time-series analysis enriched the attribution portrayal of the UPI effect. Anthropogenic factors assigned to urbanization generally had more impact on the UPI effect than natural factors. However, the specific contributors varied across cities and times. Based on the Gaussian surface fitting model, multidimensional indicators, and multi-perspective analysis, the results of this study offer new insight into our understanding of the UPI effect and its potential associations, which is useful to broaden both the research methodology and the perspectives in the current study and thus benefit future environmental regulations by providing a worthwhile scientific reference.