No abstract

No abstract

The still escalating COVID-19 pandemic also has a substantial impact on energy structure, requirements and related emissions. The consumption is unavoidable and receives a lower priority in the critical situation. However, as the pandemic continues, the impacts on energy and environment should be assessed and possibly reduced. This study aims to provide an overview of invested energy sources and environmental footprints in fighting the COVID-19. The required energy and resources consumption of Personal Protection Equipment (PPE) and testing kits have been discussed. The protecting efficiency returned on environmental footprint invested for masks has been further explored. The main observation pinpointed is that with a proper design standard, material selection and user guideline, reusable PPE could be an effective option with lower energy consumption/environmental footprint. Additional escalated energy consumption for aseptic and disinfection has been assessed. This includes the energy stemming from emergency and later managed supply chains. The outcomes emphasised that diversifying solutions to achieve the needed objective is a vital strategy to improve the susceptibility and provide higher flexibility in minimising the environmental footprints. However, more comprehensive research proof for the alternative solution (e.g. reusable option) towards low energy consumption without compromise on the effectiveness should be offered and advocated.

COVID-19 has been sweeping the world. The overall number of infected persons has been increased from 5 M in March 2020 to over 22 M in August 2020 and growing, which seems not to get its peak at the current stage. This has contributed to waste generation and different phases of challenges in waste management practices. The impacts including change in waste amount, composition, timing/frequency (temporal), distribution (spatial) and risk, which affects the handling and treatment practices. Recent impacts, challenges and developments on waste management in the response of COVID-19 have been assessed in this update. Singapore, the cities of Shanghai in China and Brno in the Czech Republic (a member state of the European Union), representing different pandemic development situation and also various cultural attitudes, are specifically analysed and discussed with current data. However, it should be noted that it is still fast developing. A varying trend in term of the waste amount is identified. Shanghai is showing a ~23% decline in household waste amount; however, Singapore is showing a ~3% increase, and Brno is showing a ~1% increase in household waste amount but ~40% decline in business and industrial waste. Manual sorting and recycling have been reported as restricted due to safety precaution. This is supported by the interview communication with ZEVO SAKO (the largest incineration plant in the Czech Republic). This study highlighted that the practices or measures at each place could serve as a guideline and reference. However, adaption is required according to the geographical and socioeconomic factors.

Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%–55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.

No abstract

No abstract

No abstract