Transportation is the main carrier of population movement, so it is significant to clarify how different transportation modes influence epidemic transmission. This paper verified the relationship between different levels of facilities and epidemic transmission by use of the K-means clustering method and the Mann–Whitney U test. Next, quantile regression and negative binomial regression were adopted to evaluate the relationship between transportation modes and transmission patterns. Finally, this paper proposed a control efficiency indicator to assess the differentiated strategies. The results indicated that the epidemic appeared 2–3 days earlier in cities with strong hubs, and the diagnoses were nearly fourfold than in other cities. In addition, air and road transportation were strongly associated with transmission speed, while railway and road transportation were more correlated with severity. A prevention strategy that considered transportation facility levels resulted in a reduction of the diagnoses of about 6%, for the same cost. The results of different strategies may provide valuable insights for cities to develop more efficient control measures and an orderly restoration of public transportation during the steady phase of the epidemic.

The global aviation industry has been experiencing catastrophic disruption since the beginning of 2020 due to the unprecedented impact of the COVID-19 pandemic on air traffic. Although the decline in regular commercial air travel has caused tremendous economic loss to aviation stakeholders, it has also led to the reduction in the amount of recorded air pollutants. Most of the aircraft emissions are released during the cruise phase of flight, however they have relatively small impact on humans due to the fact that those emissions are released directly into the upper troposphere and lower stratosphere. Therefore, the scope of this study is to investigate the ground-level aircraft emissions from landing and take-off (LTO) cycles, as they have a greater influence on the ambient environment of the airports in a specific region. In this paper, we study the variation of typical air pollutant concentrations (i.e., HC, CO, and NOx) from the LTO cycles during the outbreak of COVID-19 pandemic in both temporal and spatial scales. These ground-level emissions are estimated for the 22 airports in the Yangtze River Delta, China. The results indicate that the variation pattern of the three air pollutants were significantly influenced by the dramatic onset of the COVID-19 pandemic, as well as the pertinent policies to suppress the spread of the virus. The results also reveal non-uniform distribution of the emission quantified at different airports. It is noticeable that the emission quantity generally declined from the east coast to the central and western part of the research region. Furthermore, discrepancies in the target markets also create disparities in the variation pattern of the emissions at different airports under the context of COVID-19.

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NO x ) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.