ABSTRACT
Rapid urbanization in China has led to an increasing problem of atmospheric nitrogen dioxide (NO2) pollution, which negatively impacts urban ecology and public health. Nitrogen dioxide is an important atmospheric pollutant, and quantitative spatio-temporal analysis and influencing factor analysis of Chinese cities can help improve urban air pollution. In this study, the spatio-temporal analysis methods were used to explore the variations of NO2 pollution in Chinese cities from 2005 to 2020. The findings are as follows. In more than half of Chinese cities, NO2 levels remarkably decreased between 2005 and 2020. The effective NO2 reduction strategies contributed to the significant NO2 reduction during the 13th Five-Year Plan (2016–2020). Moreover, we found that the pandemic of COVID-19 alleviated NO2 pollution in China since it reduced the traffic, industrial, and living activities. The NO2 pollution in Chinese cities was found highly spatially clustered. The geographically and temporally weighted regression model was used to analyze the spatio-temporal heterogeneity of NO2 pollution influencing factors in Chinese cities, including natural meteorological and socio-economic factors. The results showed that the GDPPC, population densities, and ambient air pressure were positively correlated with NO2 pollution. In contrast, the ratio of the tertiary to the secondary industry, temperature, wind speed, and relative humidity negatively impacted the NO2 pollution level. The findings of this research contribute to the improvement of urban air quality, stimulating the achievements of the sustainable development goals of Chinese cities.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) outbreak caused people to pay significant attention to urban public safety issues. The city's public safety is an important part of the high-quality development and the construction of a liveable city. To understand whether and how factors at different levels affect the public security of particular group of people in a city. This study uses data from an extensive questionnaire survey by the Ministry of Housing and Urban-Rural Development of the People's Republic of China (MOHURD) in 11 cities. This study uses the descriptive statistical method and Hierarchical Linear Model (HLM) to study the perception of urban public safety (PUPS) and its influencing factors of floating population with higher education background (FPHEB) from the three levels of city-district-individual. The study finds that (1) when FPHEB is placed in a district and a city at the same time, the influence of the city on PUPS is greater than that of the district; (2) the urban's infrastructure security and economic development security positively affect the floating population; (3) the GDP and the number of stadiums and hospitals of the district are significantly positively correlated with the PUPS of the FPHEB, whereas the increase of population density and road density have negative effects; (4) FPHEB with distinct attributes will make their PUPS also different. This study is not only a reflection on the construction of urban public security after the COVID-19 outbreak but can also be used as a theoretical reference for the government in constructing urban public security. This study also enriches the research on the floating population and makes good scientific suggestions for the city's PUPS of the FPHEB. The research results can provide a better reference for the government's urban safety construction from the perspective of residents' perception.