Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations.

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO2, O3, SO2, PM10, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO2 (up to 22%), SO2 (up to 17%), and AOD (up to 40%) with small changes in O3 (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO2 (49% - 57%) and PM10 (19% - 64%); however, the SO2 and O3 levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO2, PM10, and AOD. The data also demonstrated significant correlations between the mobility and NO2 and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model.

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

Spatio-temporal analysis of urban growth and its impact on floods in Ajman City, UAE.

The negative consequences of urbanisation have been recently recognised despite the social and economic benefits it provides to the community. Effects of urbanisation include increases in surface runoff, frequency and magnitude of floods and urban water harvesting capacity. Accordingly, this study utilised multi-spectral and multi-resolution satellite images combined with field data to conduct a quantitative assessment of the impact of urbanisation on urban flooding for the period of 1975-2015 in Ajman City, United Arab Emirates (UAE). Results showed that urban areas in the city have increased by approximately 12-fold over the period 1975-2015, whilst the population increased by approximately 16-fold. Owing to a substantial increase in urbanisation (as impervious areas expanded), minimum precipitation to generate runoff in built areas dropped from approximately 16.37 mm in 1975 to approximately 13.3 mm in 2015, which caused a substantial increase in the surface runoff. To visualise the flooding potential, urban flooding maps were generated using a well-established decision analysis technique called Analytical Hierarchy Process. The latter adopted three thematic factors, namely excess rain, elevation and slope. Flooding potential was then found to have increased substantially, specifically in the downtown area. Finally, this study is expected to contribute highly to flood protection and sustainable urban storm water management in Ajman City.