Background: Severe air pollution in China threatens human health, and its negative impact decreases the urban settlement intentions of migrants in destination cities. We establish a comprehensive framework based on the push-pull migration model to investigate this phenomenon. Methods: We employ a logistic model to analyze air pollution’s impact on the settlement intentions of the floating population based on the CMDS 2017 in China, combining the city-level socioeconomic variables with the individual-level variables. Results: Our results show that the annual average concentration of PM2.5 increases by 1 unit and that the probability of migrants’ settlement intentions will decrease by 8.7%. Using a heterogeneity analysis, we find that the following migrant groups are more sensitive to air pollution: males, people over 30 years old, less educated people, and migrants with nonagricultural hukou. With every 1 unit increase in PM2.5, each group’s settlement intentions decrease by 13.2, 16.7, 16.9, and 12.6%, respectively. Conclusions: Our results are consistent with existing studies. This study discovers that both external environment and internal factors influence migrants’ settlement intentions. Specifically, the differences in population sizes, economic development levels, public services, infrastructure conditions, and environmental regulations between cities play a significant role in migration decisions. We also confirm heterogeneous sensitivities to air pollution of different migrant subgroups in terms of individual characteristics, family factors, migration features, social and economic attributes.

COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.

As the COVID-19 pandemic recedes and travel resumes, it is important to understand how the influences on inter-city road travel varied across different stages of the pandemic. However, the underlying spatiotemporal heterogeneity in the relationship between the mobility shifts and its determinants at different pandemic stages is unclear. This research divides the pandemic timeline into four distinct stages based on the data from the Chinese Health Care Commission and Amap platform. By using a multiscale geographically and temporally weighted regression model (MGTWR), this paper analyzes how the pandemic factor, road infrastructure, population mobility motivations, and other external factors impact inter-city road travel at different pandemic stages. Our findings reveal a “falling-rising-stabilizing-falling” pattern in the overall volume of inter-city mobility over time. Despite the pandemic depressed the road travel volumes, it did not significantly alter the overall spatial patterns of inter-city mobility. However, Spatiotemporal heterogeneity is found in many influencing relationships. The impacts of COVID-19 cases and road infrastructure vary across stages and cities, while other factors are relatively temporally stable. These insights inform economic recovery and policy transitions in the post-pandemic era.