Sustainable Cities and Society最新文献

The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (-0.11 °C to -2.6 °C), USA (-0.70 °C) and Canada (-0.27 °C) in March-May of the pandemic year 2020 compared to the mean of 2015-2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. -0.46 °C to -0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1-2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation.

Under the current COVID-19 epidemic conditions, stratum ventilation can provide treated fresh air directly into the human breathing zone, improving the air quality for inhalation. However, in the design of air supply parameters for stratum ventilation, the traditional trial-and-error and experimental methods are inefficient and time consuming, and they cannot be used to identify the optimal air supply parameters from a large number of parameters. Therefore, in this paper, the inverse design method based on proper orthogonal decomposition (POD) was applied to the design of ventilation parameters for a room with stratum ventilation. Predicted mean vote (PMV), predicted percentage dissatisfied (PPD) and droplet nuclei concentration in the human breathing zone were selected as design objectives to optimize air supply parameters. The transmission of COVID-19 was controlled by reducing the concentration of droplet nuclei in the respiratory area. The results show that, compared with the trial-and-error method, the inverse design method based on POD is more than 90% faster. POD method can greatly expand the sample size. Considering the dispersion of exhaled droplet nuclei in the room, the appropriate stratum ventilation parameters can reduce the concentration of fine droplet nuclei by more than 20% compared with the traditional design parameters.

During the COVID-19 pandemic, an increasing amount of evidence has suggested that the virus can be transmitted through the air inside buildings. The ventilation system used to create the indoor environment would facilitate the transmission of the airborne infectious diseases. However, the existing ventilation systems in most buildings cannot supply sufficient clean outdoor air for diluting the virus concentration. To reduce the airborne infection risk and minimize energy consumption, especially in existing buildings with well-mixed ventilation systems, this investigation used an ultraviolet-C (UV-C) air disinfection device (Rheem's third generation products, RM3) with 99.9% disinfection efficiency to clean air carrying the COVID-19 virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) which could help promote environmental sustainability and create healthy cities. This investigation assessed the impact of the RM3 UV-C units on the infection risk, the number of RM3 UV-C units required, and the strategy for decreasing the infection risk, with the use of computational-fluid-dynamics (CFD) numerical simulations. An actual office building with a combination of individual offices and workstations was selected as an example for the research. According to the numerical results, the best strategy would be to use a combination of 100% outside air and UV-C in heating, ventilation and air-conditioning (HVAC) ducts with air disinfected by the RM3 UV-C units. The infection risk in the office building could thus be reduced to a negligible level. These findings could provide theoretical basis and engineering application basis for COVID-19 epidemic prevention and control.

During COVID-19, the building and service characteristics of residential buildings turned out to be more critical due to lockdowns. The present research assesses the importance of new sustainability indicators for residential buildings in three categories (e.g., Health and Safety, Environmental Resources Consumption, and Comfort) that provide resilience for pandemic periods. The opinions of stakeholders on the identified indicators were collected and then analyzed. 'Health and Safety' category is found to be the most critical among the others. The prevention of virus propagation, mental health, and building air quality are three crucial indicators playing essential roles in the health and safety category. In more detail, innovative smart technologies, including touchless technologies, are identified as a priority in preventing virus propagation. Outdoor spaces and safe indoor places for socialization are weighted as essential in supporting the well-being and mental health of the resident. Finally, air filtration and segregation of medical waste indicators are considered critical in preventing the spread of viruses. There was a consensus among the local and international experts since they did not significantly report differing opinions for the majority of the indicators. However, there was a shift in experts' opinions towards pandemic-oriented indicators compared to conventional sustainability indicators.

This paper focuses on the risk assessment methods of novel coronavirus pneumonia virus (COVID-19) pneumonia virus spreading in closed marine environment. Firstly, the possibility of the spread of new coronavirus in closed environments at sea and the consequences of the harm caused by the epidemic are classified into five levels, and the corresponding risk assessment framework is constructed, and the risk assessment model of the spread of COVID-19 pneumonia virus in closed environment at sea is established taking the Japanese "Diamond Princess" as an example, the model is applied and its output is analysed. Finally, the proportion of the cumulative confirmed cases on the Diamond Princess is calculated, and the possibility of viral infection of Diamond Princess passengers in this COVID-19 pneumonia epidemic in four different risk transmission stages are assessed, and the corresponding risk assessment is undertaken. Through the calculation of risk assessment value, the five stages of the epidemic are established to assess the risk of the "Diamond Princess" outbreak. The research method in the present work helps to provide a risk assessment and analysis idea for the risk of spread of the COVID-19 epidemic in a closed environment at sea.

Expansion of urban areas and alteration of natural land cover exacerbate the local climate change. To find out the effect of land cover changes on the local climate, in this study, the Local Climate Zone (LCZ) concept was utilized to detect urban morphology in Tehran Metropolis. LCZ and Land Surface Temperature (LST) can be identified and classified based on available remote sensing products. Firstly, LCZ maps of Tehran metropolis were extracted using Landsat imagery, and secondly, relationships between LCZ and LST were explored for three years (1990, 2004, and 2018). We found that Tehran urban structure has 13 LCZs based on imagery from Landsat 5 and 14 LCZs based on images for Landsat 7 and 8. Overall accuracy and kappa coefficient were estimated at 62% and 0.60, respectively. Results show that built-up classes including compact high-rise, compact mid-rise, and heavy industrial areas tended to increase the surface temperature, while except for bare land, all other land cover types tended to decrease the surface temperature. The findings also suggest that complementary optical and thermal remote sensing data, such as the combination of OLI with TIRS imageries, were sufficient for supervised LCZ and LST classification in a semi-arid region of Tehran metropolis.