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Spatiotemporal Distribution of the Impact of Urban Development on Air Pollution

Chen Bizhen, Ye Fengjiao, Xie Shanshan, Sun Dehong

Environment Protection Engineering

2025

Vol. 51, nr 1

21--35

This study applies China’s air pollution and urban development data from 2007 to 2021, and the temporally weighted regression (GTWR) model to analyze the spatiotemporal distribution characteristics of air pollution influencing factors. It was found that the temporal evolution of air pollution in different regions is highly consistent but its degree varies with the pollution severity. The impact of urban development on air pollution has significant spatiotemporal heterogeneity. Overall, urban green space area (UGSA), urban population density (UPD), and domestic waste removal volume (DWRV) have positive impacts, while urbanization rate (UR), per capita disposable income of urban residents (URI), and public vehicles per every 10 000 people (PTV) have negative impacts. The impact of USGA, UR, and URI is mainly visible in western provinces, the impact of UPD in northeast provinces, the impact of DWRV in eastern and central provinces, and the impact of PTV in eastern provinces.

A comparison of the statistical distributions of air pollution concentrations in Sinop, Turkey

Aydin Demet

Environment Protection Engineering

2024

Vol. 50, nr 4

47--68

The increasing population and industrialization are the reasons for environmental and air pollution around the world. Air pollution is a major threat, especially to human health, both biological and economic. Therefore, determining the properties of air pollutants is very important for researchers and practitioners working in this field. In this study, the statistical distributions of some air pollutants are determined using the Gumbel, Weibull, generalized Pareto, log-normal, gamma, Rayleigh, and inverse Weibull distributions. The data was obtained from stations Boyabat and Merkez stations in Sinop province in 2017. The Kolmogorov–Smirnov test was used to determine the underlying distributions of the air pollution data. Then we use the root mean square error and coefficient of determination criteria to determine which distribution better fits the air pollution data. Finally, numerical results have shown that the generalized Pareto distribution demonstrates the best overall modeling performance, followed by log-normal and inverse Weibull distributions.