Aligning with the United Nations' Sustainable Development Goals, the focus on creating safe, sustainable cities and enhancing the wellbeing of individuals across all age groups has become a central aspect of urban planning and environmental management. The environments we live in significantly influence our thoughts, emotions, and interactions with the world around us. Our study aims to unveil the social inequity of citizens' wellbeing, reflected by their perception on neighborhood environment (e.g., feeling of depression), across different social/vulnerable groups (i.e., White, Black, Asian, Hispanic, low-income, low-educated, and unemployed) via crowdsourced street view imageries and computer vision. Specifically, we quantified the actual built environment in the 5D dimensions (i.e., density, diversity, design, distance, and destination) based on multiple sources; measured six types of neighborhood visual environment (i.e., perception of beautiful, safe, wealthy, liveable, boring and depressing) and the overall neighborhood soundness index by using computer vision technique and street view imageries collected from Mapillary; and unveiled the actual built environmental features that are associated with people's visual perception towards the surrounding environment via multi-model machine learning methods. Our pilot study in Los Angeles County finds that neighborhoods with higher concentrations of Black, Hispanic, low-income, low-educated, and unemployed populations are perceived as less beautiful, liveable, safe, and wealthy. The most important actual built environment features positively influencing human perception include the density of canopy, followed by the density of multiple units, the distance to CBD, and car commuting to destinations, regardless of social groups. Our key findings provide place-based evidence for the design and upgrading of the community environment that further affects people's daily activity and living style. Our framework and methods can be applied to cross-disciplinary studies, aiding urban planning and healthy city initiatives with place-based evidence.

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM, PM, and PM), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.

We propose a generative design workflow that integrates a stochastic multi-agent simulation with the intent of helping building designers reduce the risk posed by COVID-19 and future pathogens. Our custom simulation randomly generates activities and movements of individual occupants, tracking the amount of virus transmitted through air and surfaces from contagious to susceptible agents. The stochastic nature of the simulation requires that many repetitions be performed to achieve statistically reliable results. Accordingly, a series of initial experiments identified parameter values that balanced the trade-off between computational cost and accuracy. Applying generative design to a case study based on an existing office space reduced the predicted transmission by around 10% to 20% compared with a baseline set of layouts. Additionally, a qualitative examination of the generated layouts revealed design patterns that may reduce transmission. Stochastic multi-agent simulation is a computationally expensive yet plausible way to generate safer building designs.

The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding-alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.

COVID-19 has laid a context for holistic research and practical approaches towards health issues in buildings. This study focuses on one particular residential building type, which is a combination of a modern apartment building with private double-oriented terraces, and a traditional courtyard building. This principle improves several aspects of healthy buildings and contributes to address indoor-outdoor interactions, daylighting, and the use of natural ventilation. The purpose of this study is to determine the factors underlying a particular type of semi-outdoor space within building forms and to explain their microclimatic behavior in buildings. One solid model and twelve porous apartment buildings with different numbers of porous sides, and terrace widths are evaluated using computational fluid dynamics. The k-ε turbulence model is adapted to simulate airflow in and around a four-story building. CFD simulations were validated against the wind-tunnel measurements. Investigations indicated that increasing the number of porous sides reduces the internal mean and maximum ages of air by -15.75 and -36.84%, which means improved ventilation performance. However, it leaves a negative trace on ventilation of the semi-outdoor spaces. Meanwhile, increasing the width of the terraces enhances the ventilation performance by reducing the mean age of air in units, courtyards, and terraces by -20%, -20%, and -9%, respectively.

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA D model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA B). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA D performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.

The COVID-19 pandemic has disrupted people's daily routines, including travel behaviors, social interactions, and work-related activities. However, the potential impacts of COVID-19 on the use of campus locations in higher education such as libraries, food courts, sports facilities, and other destinations are still unknown. Focusing on three largest universities in Texas (Texas A&M university, the University of Texas at Austin, and Texas Tech University), this study compares changes in campus destination visitations between pre and post COVID-19 outbreak (2019 Fall and 2021 Fall semesters, respectively) using the mobility data from SafeGraph. It also examines the potential moderation effects of walkable distance (i.e. 1 km) and greenery (i.e. NDVI value). The results presented the significant effects of COVID-19 on decreasing visitations to various campus places. The visitation decreased more significantly for people living within 1 km (defined as a walkable distance) of campus and for the food, eating, and drinking places and the sports, recreation, and sightseeing places. This finding suggests that those living near campus (mostly students) decreased their reliance on campus destinations, especially for eating/drinking and recreation purposes. The level of greeneries at/around campus destinations did not moderate campus visitations after COVID-19. Policy implications on campus health and urban planning were discussed.

Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago's lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.

When the COVID-19 pandemic swept across the world, people tended to seek more individualized and viable transportation modes, such as a bicycle. In this study, we examined the factors influencing changes in public bike sharing (PBS) in Seoul, to assess this trend post-pandemic. We conducted an online survey of 1,590 Seoul PBS users between July 30 and August 7, 2020. Using a difference-in-differences analysis, we found that participants who were affected by the pandemic used PBS 44.6 h more than unaffected individuals throughout the year. In addition, we used a multinomial logistic regression analysis to identify the factors affecting changes in PBS usage. In this analysis, the discrete dependent variables of , and were considered, representing the changes in PBS usage after the COVID-19 outbreak. Results revealed that PBS usage increased among female participants during weekday trips such as commuting to work and when there were perceived health benefits of using PBS. Conversely, PBS usage tended to decrease when the weekday trip purpose was for leisure or working out. Our findings offer insight into PBS user behaviors within the context of the COVID-19 pandemic and present policy implications to revitalize PBS usage.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.

Compound hazards are derived from independent disasters that occur simultaneously. Since the outbreak of COVID-19, the coupling of low-probability high-impact climate events has introduced a novel form of conflicting stressors that inhibits the operation of traditional logistics developed for single-hazard emergencies. The competing goals of hindering virus contagion and expediting massive evacuation have posed unique challenges for community safety. Yet, how a community perceives associated risks has been debated. This research utilized a web-based survey to explore the relationship between residents' perceptions of conflicting risks and emergency choices made during a historic compound event, the flooding in 2020 in Michigan, US that coincided with the pandemic. After the event, postal mail was randomly sent to 5,000 households living in the flooded area, collecting 556 responses. We developed two choice models for predicting survivors' evacuation options and sheltering length. The impact of sociodemographic factors on perceptions of COVID-19 risks was also examined. The results revealed greater levels of concern among females, democrats, and the economically inactive population. The relationship between evacuation choice and concern about virus exposure was dependent upon the number of seniors in the household. Concern about a lack of mask enforcement particularly discouraged evacuees from extended sheltering.

During the post-COVID-19 era, it is important but challenging to synchronously mitigate the infection risk and optimize the energy savings in public buildings. While, ineffective control of ventilation and purification systems can result in increased energy consumption and cross-contamination. This paper is to develop intelligent operation, maintenance, and control systems by coupling intelligent ventilation and air purification systems (negative ion generators). Optimal deployment of sensors is determined by Fuzzy C-mean (FCM), based on which CO concentration fields are rapidly predicted by combing the artificial neural network (ANN) and self-adaptive low-dimensional linear model (LLM). Negative oxygen ion and particle concentrations are simulated with different numbers of negative ion generators. Optimal ventilation rates and number of negative ion generators are decided. A visualization platform is established to display the effects of ventilation control, epidemic prevention, and pollutant removal. The rapid prediction error of LLM-based ANN for CO concentration was below 10% compared with the simulation. Fast decision reduced CO concentration below 1000 ppm, infection risk below 1.5%, and energy consumption by 27.4%. The largest removal efficiency was 81% when number of negative ion generators was 10. This work can promote intelligent operation, maintenance, and control systems considering infection prevention and energy sustainability.

The COVID-19 pandemic challenged emergency management in cities worldwide. Many municipalities adopted restrictive, one-size-fits-all spatial regulations such as lockdowns without fully considering the inhabitants' daily activities and local economies. The existing epidemic regulations' unintended detrimental effects on socioeconomic sustainability necessitate a transition from the "lockdown" approach to more precise disease prevention. A spatially and temporally precise approach that balances epidemic prevention with the demands of daily activities and local economies is needed. Thus, the aim of this study was to propose a framework and key procedures for determining precise prevention regulations from the perspectives of the 15-minute city concept and spatiotemporal planning. Alternative regulations of lockdowns were determined by delineating 15-minute neighborhoods, identifying and reconfiguring facility supplies and activity demands in both normal and epidemic conditions, and performing cost-benefit analyses. Highly adaptable, spatially- and temporally-precise regulations can match the needs of different types of facilities. We demonstrated the process for determining precise prevention regulations in the case of the Jiulong 15-minute neighborhood in Beijing. Precise prevention regulations-which meet essential activity demands and are adaptable for different facility types, times, and neighborhoods-have implications for long-term urban planning and emergency management.

In December 2019, coronavirus disease (COVID-19) was detected in Wuhan, China. Due to the rapid spread of the disease, containment measures were adopted, which caused unprecedent shifts in individual mobility. Although some studies explored the impacts of the COVID-19 pandemic on travel patterns and resilience of transport systems based on different analysis techniques, there is a lack of studies addressing the impacts of the pandemic on the sustainability and resilience of urban mobility systems using in-depth and holistic methods, such as system dynamics. This research aims to characterize the dynamics present in urban mobility systems when exposed to pandemics and analyze the changes needed for systems to increase their resilience to pandemics using qualitative system dynamics modeling. The framework comprises the characterization of cause-and-effect relationships and the creation of systems' causal loop diagrams (CLD) in their basic state of functionality, when affected by pandemics, and still operating owing to its resilience. Our findings indicated that the CLD of a resilient system is driven by strategic preparedness and response plans, as well as research and development, which balance the spread of the pandemic and increase support on technological strengths and the activities performed from home.

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

Cities, the main place of human settlements, are under various mega challenges such as climate change, population increase, economic growth, urbanization, and pandemic diseases, and such challenges are mostly interlinked. Urban heat, due to heatwaves and heat islands, is the combined effect of climate change and urbanization. The COVID-19 is found to be a critical intervention of urban heat. However, the interrelationship between COVID-19 and urban heat has not been fully understood, constraining urban planning and design actions for improving the resilience to the dual impacts of heat and the pandemic. To close this research gap, this paper conducted a review on the co-occurrence of urban heat and the COVID-19 pandemic for a better understanding of their synergies, conflicts or trade-offs. The research involves a systematic review of urban temperature anomalies, variations in air pollutant concentrations, unbalanced energy development, and thermal health risks during the pandemic lockdown. In addition, this paper further explored data sources and analytical methods adopted to screen and identify the interventions of COVID-19 to urban heat. Overall, this paper is of significance for understanding the impact of COVID-19 on urban heat and provides a reference for coping with urban heat and the pandemic simultaneously. The world is witnessing the co-existence of heat and the pandemic, even in the post-pandemic era. This study can enlighten city managers, planners, the public, and researchers to collaborate for constructing a robust and resilient urban system for dealing with more than one challenges.

The increasing expansion of urban environments with associated transformation of land-cover has led to the formation of urban heat islands (UHI) in many urbanized regions worldwide. COVID-19 related environmental impacts, through reduced urban activities, is worthy of investigation as it may demonstrate human capacity to manage UHI. We aim to establish the thermal impacts associated with COVID-19 induced urban 'lockdown' from 20 March to 20 April 2020 over Tehran. Areal changes in UHI are assessed through Classification and Regression Trees (CART), measured against background synoptic scale temperature changes over the years 1950-2020. Results indicate that monthly T T and T values during this time were considerably lower than long-term mean values for the reference period. Although the COVID-19 initiated shutdown led to an identifiable temperature anomaly, we demonstrate that this is not a product of upper atmospheric or synoptic conditions alone. We also show that the cooling effect over Tehran was not spatially uniform, which is likely due to the complexity of land uses such as industrial as opposed to residential. Our findings provide potentially valuable insights and implications for future management of urban heat islands during extreme heat waves that pose a serious threat to human health.

Recent events such as Covid-19 vaccine distribution issues and the blockage of the Ever Given ship in the Suez Canal raised concerns about how fragile the traditional supply chain is. Last-mile personalized fulfillment can have a catalyst role in the proliferation of the Industry 4.0. This growing trend will reduce standard production, bringing manufacturing closer to the client and, ultimately, boiling down the supply chain to the last mile. However, the literature is not clear about the breakdown of the supply chain to enhance cities' sustainability and reducing the number of transports and circulating vehicles. Stemming from an empirical study to simulate the existing gap in the market and the development of a case study through structured interviews with privileged interlocutors complemented by the document analysis, this paper highlights how the integration of local stakeholders can efficiently enhance a personalized service based on dynamic collaborations to set up the supply chain, by introducing the Last-Mile-as-a-Service (LMaaS) concept. This concept relies on a revenue-sharing framework based on an open marketplace composed by last-mile manufacturing, transport, and storage assets and stakeholders to disrupt the supply chain, enabling any company to provide personalized products in almost real-time to any location.

Using Sentinel-5P data, this study investigated the magnitude of change in the concentration of air pollutants (NO, HCHO, SO, O, CO, and aerosol index) in the air of ten cities and urban areas of the French region of Grand Est as a result of the first lockdown imposed between March 17, 2020 and May 11, 2020. The results showed that the air quality in the urban environments of Grand Est improved significantly compared to the same period in 2019 without lockdown. NO, O, aerosol index and CO were the pollutants that exhibited maximum reductions by an average of -33.98%, -5.94%, -26.82% and -0.66%, respectively (the observed maximum decreases were -54.7%, -7.7%, -13.1%, and -5.3%, respectively). The largest decrease occurred in the Public Establishments of Inter-municipal Cooperation (EPCI, in French: Établissement public de coopération intercommunale) areas of Eurométropole de Strasbourg, CA Colmar, and CA Mulhouse Alsace. The maximum decrease in air pollution first occurred in land cover classes close to cities, followed by built-up urban areas. In this study, a global depollution index known as the atmospheric clearance index (ACI) was developed, which involved several air pollution parameters, and quantitatively analyzed the decrease in contamination levels of the atmosphere in this region. In addition, the correlation between the novel ACI and other population and economic development indices was studied. The results indicated that there was a negative and statistically significant correlation between ACI and population density, gross domestic product, gross value added (GVA) at basic prices, number of employees, and active enterprises.