Search

The COVID-19 pandemic has greatly affected the mobility of individuals everywhere. This has been especially true in China, where many restrictions, including lockdowns, have been widely applied. This paper discusses the impact of the pandemic on walkability, an important factor in promoting urban neighborhoods, in the main urban area of Xi'an, China, one of China's four great ancient capitals. Based on the street view data obtained before and after the pandemic, the paper quantitatively compares changes in specific components of selected streetscapes through a deep learning (DL) street view analysis. The aim is to identify the impact of the pandemic on walkability and determine the elements that influence increased walkability in Xi'an's historical area, using a walkability evaluation model based on a regression analysis involving three factors (streetscape components, walkability check scores, and street connectivity of space syntax for every image). Although Xi'an's urban structure did not change significantly, the pandemic has clearly impacted street vitality, especially in terms of reducing pedestrian flow and commercial value. Based on study results, the street environment has great room for improvement, especially in the city's historical blocks, by reconsidering safety measures to pedestrians and the important role of atmospheric aspects on the streets. [ABSTRACT FROM AUTHOR]

Background: The purpose of this paper is to study how the Delta variant spread in a China city, and to what extent the non-pharmaceutical prevention measures of local government be effective by reviewing the contact network of COVID-19 cases in Xi'an, China. Methods: We organize the case reports of the Shaanxi Health Commission into a database by text coding and convert them into a network matrix. Then we construct a dynamic contact network for the corresponding analysis and calculate network indicators. we analyze the cases' dynamic contact network structure and intervals between diagnosis time and isolation time by using data visualization, network analysis method, and Ordinary Least Square (OLS) regression. Results: The contact network for this outbreak in Xi'an is very sparse, with a density of less than 0.0001. The contact network is a scale-free network. The average degree centrality is 0.741 and the average PageRank score is 0.0005. The network generated from a single source of infection contains 1371 components. We construct three variables of intervals and analyze the trend of intervals during the outbreak. The mean interval (interval 1) between case diagnosis time and isolation time is − 3.9 days. The mean of the interval (interval 2) between the infector's diagnosis time and the infectee's diagnosis time is 4.2 days. The mean of the interval (interval 3) between infector isolation time and infectee isolation time is 2.9 days. Among the three intervals, only interval 1 has a significant positive correlation with degree centrality. Conclusions: By integrating COVID-19 case reports of a Chinese city, we construct a contact network to analyze the dispersion of the outbreak. The network is a scale-free network with multiple hidden pathways that are not detected. The intervals of patients in this outbreak decreased compared to the beginning of the outbreak in 2020. City lockdown has a significant effect on the intervals that can affect patients' network centrality. Our study highlights the value of case report text. By linking different reports, we can quickly analyze the spread of the epidemic in an urban area. [ABSTRACT FROM AUTHOR]

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O 3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone. [Display omitted] • Emergency response to COVID-19 (ER) had spill-over effect on air quality. • Empirical analysis was made with RDD to evaluate the impact of ER on air quality. • AQI and PM 2.5 , PM 10 , SO 2 and NO 2 concentrations decreased by approximately 10%. • Traffic restrictions and shutdown of industries during ER were important factors. • Socioeconomic resumption and ER termination did not affect air quality. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

Governments often disclose pandemic-related information to reduce public mobility behavior during the initial outbreak stage of a pandemic like COVID-19. Previous studies have examined the effects of whether to disclose relevant information on public behavior but neglected the effects of information content. Based on the disclosed personal information of COVID-19 confirmed cases from 316 cities in China, we find that disclosing demographic information of confirmed cases does not affect public mobility, whereas disclosing their infection traceability information increases public mobility and trajectory range information decreases public mobility. We also find a U-shaped effect of information diversity on public mobility behavior. [ABSTRACT FROM AUTHOR]

Resilience has a critical role to promote human well-being in turbulent environments. However, extensive interventions and stimulus measures not only achieve resilience goals with numerous unacceptable consequences, but also highlight the narrow definition of resilience. The study proposes the concept of resilience effectiveness to constrain the unsustainability of resilience in concept and practice. The resilience effectiveness of Chinese cities in the early stages of the pandemic is first implemented for evaluation based on input-output relationship, and then patterns and determinants are investigated using the GIS methods and the geographically optimal zones-based heterogeneity (GOZH) model. The study finds that the average resilience effectiveness is not high, indicating that China's strong resilience is the loss of safety, greenness, inclusiveness, and efficiency in the early stage of the pandemic. Resilience effectiveness has significant spatial heterogeneity and is dominated by innovation ability. The optimal geographic zones of Chinese cities for resilience effectiveness are identified, revealing that it is a complex and multifactorial process. This paper broadens resilience research and provides implications for city planning in the post-pandemic era. [ABSTRACT FROM AUTHOR]

The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies. [ABSTRACT FROM AUTHOR]

Mobility restriction measures were the main tools to control the spread of COVID-19, but the extent to which the mobility has decreased remained unsure. We investigated the change in local population mobility and its correlation with COVID-19 infections, using 1185 billion aggregated mobile phone data records in nine main cities in China from 10 January to 24 February 2020. The mobility fell by as much as 79.57% compared to the normal days in 2020 and by 58.13% compared to the same lunar period in 2019. The daily incidence of COVID-19 was significantly correlated with local daily mobility (R2 = 0.77, P < 0.001). The instantaneous reproduction number R(t) declined by 3% when mobility was reduced by 10% in the GLM analysis (P < 0.05). Our study indicated that the decreased mobility level, driven by a mixture effect of holiday and public health interventions, could substantially reduce the transmission of COVID-19 to a low level. Our study could provide evidence of mobility restriction to control local transmission for other places facing COVID-19 outbreaks or potential next waves. [ABSTRACT FROM AUTHOR]

The rapid global spread of the coronavirus disease 2019 (COVID-19) is now a reality. China has taken urban traffic control measures to prevent and control the epidemic, but this has prevented the flow of people between cities. This study investigates the mechanism of the impact of urban traffic control measures on the intercity population flow in China using the one-way causal measurement method. The results show that the impact of urban traffic control measures on the intercity flow of the population changes with time. Based on this, this study makes scientific suggestions for the government on how to reasonably undertake traffic control measures. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 promoted strict restrictions to human activities in China, which led to a dramatic decrease in most air pollutant concentrations (e.g., PM 2.5 , PM 10 , NO x , SO 2 and CO). However, an obvious increase in ozone (O 3) concentrations was found during the lockdown period in most urban areas of China. In this study, we conducted field measurements targeting ozone and its key precursors by utilizing a novel proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) in Changzhou, which is representative of the Yangtze River Delta (YRD) city cluster of China. We further applied the integrated methodology including machine learning, an observation-based model (OBM) and sensitivity analysis to obtain insights into the reasons causing the obvious increase in ozone. Major findings include the following: (1) by deweathered calculation, we found changes in precursor emissions contributed 1.46 ppbv to the increase in the observed O 3 during the full-lockdown period in 2020, while meteorology constrained 3.0 ppbv of O 3 in the full-lockdown period of 2019. (2) By using an OBM, we found that although a significant reduction in O 3 precursors was observed during the full-lockdown period, the photochemical formation of O 3 was stronger than that during the pre-lockdown period. (3) The NOx/VOC ratio dropped dramatically from 1.84 during the pre-lockdown to 0.79 in the full-lockdown period, which switched O 3 formation from a VOC-limited regime to the boundary of a NO x - and VOC-limited regime. Additionally, box model results suggested that the decrease in the NOx/VOC ratio during the full-lockdown period could increase the mean O 3 by 2.4 ppbv. Results of this study give insights into the relationship between O 3 and its precursors in urban area and demonstrate reasons for the obvious increase in O 3 in most urban areas of China during the COVID-19 lockdown period. This study also underlines the necessity of controlling anthropogenic oxygenated volatile organic compounds (OVOCs), alkenes and aromatics in the sustained campaign of reducing O 3 pollution in China. [ABSTRACT FROM AUTHOR]

Background: The outbreak of Coronavirus Disease is causing considerable acute risk to public health and might also have an unanticipated impact on the mental health of children and adolescents in the long run. This study collected data during the national lockdown period in China and aims to understand whether there is a clinically significant difference in anxiety, depression, and parental rearing style when comparing adolescents from Wuhan and other cities in China. This study also intends to examine whether gender, grade in school, single child status, online learning participation, parents' involvement in COVID-19 related work, and parents being quarantined or infected due to the disease would lead to clinically significant differences in anxiety and depression. Beyond that, this study explored the pathways among the different variables in order to better understand how these factors play a part in impacting adolescents' mental health condition.Results: Results showed that there was a statistically significant difference in anxiety symptoms between participants who were from Wuhan compared to other urban areas, but not in depressive symptoms. In addition, participants' grade level, gender, relative being infected, and study online have direct positive predictive value for depressive and anxiety symptoms, whereas location and sibling status have indirect predictive value. Having relatives who participated in COVID-19 related work only had positive direct predictive value toward depression, but not anxiety.Conclusions: This study discovered several risk factors for adolescents' depression and anxiety during the pandemic. It also called for a greater awareness of Wuhan parents' mental wellbeing and recommended a systematic approach for mental health prevention and intervention. [ABSTRACT FROM AUTHOR]