Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations

COVID-19 has notably impacted the world economy and human activities. However, the strict urban lockdown policies implemented in various countries appear to have positively affected pollution and the thermal environment. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and aerosol optical depth (AOD) data were selected, combined with Sentinel-5P images and meteorological elements, to analyze the changes and associations among air pollution, LST, and urban heat islands (UHIs) in three urban agglomerations in mainland China during the COVID-19 lockdown. The results showed that during the COVID-19 lockdown period (February 2020), the levels of the AOD and atmospheric pollutants (fine particles (PM2.5), NO2, and CO) significantly decreased. Among them, PM2.5 and NO2 decreased the most in all urban agglomerations, by >14 %. Notably, the continued improvement in air pollution attributed to China's strict control policies could lead to overestimation of the enhanced air quality during the lockdown. The surface temperature in all three urban agglomerations increased by >1 °C during the lockdown, which was mainly due to climate factors, but we also showed that the lockdown constrained positive LST anomalies. The decrease in the nighttime urban heat island intensity (UHIInight) in the three urban agglomerations was greater than that in the daytime quantity by >25 %. The reduction in surface UHIs at night was mainly due to the reduced human activities and air pollutant emissions. Although strict restrictions on human activities positively affected air pollution and UHIs, these changes were quickly reverted when lockdown policies were relaxed. Moreover, small-scale lockdowns contributed little to environmental improvement. Our results have implications for assessing the environmental benefits of city-scale lockdowns.

Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.

Co-occurrence of urban heat and the COVID-19: Impacts, drivers, methods, and implications for the post-pandemic era

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.

Impact of COVID-19 restrictions on air quality and surface urban heat island effect within the main urban area of Urumqi, China

The outbreak of coronavirus in 2019 (COVID-19) posed a serious global threat. However, the reduction in man-made pollutants during COVID-19 restrictions did improve the ecological environment of cities. Using multi-source remote sensing data, this study explored the spatiotemporal variations in air pollutant concentrations during the epidemic prevention and control period in Urumqi and quantitatively analyzed the impact of different air pollutants on the surface urban heat island intensity (SUHII) within the study area. Urumqi, located in the hinterland of the Eurasian continent, northwest of China, in the central and northern part of Xinjiang was selected as the study area. The results showed that during COVID-19 restrictions, concentrations of air pollutants decreased in the main urban area of Urumqi, and air quality improved. The most evident decrease in NO2 concentration, by 77 ± 1.05% and 15 ± 0.98%, occurred in the middle of the first (January 25 to March 20, 2020) and second (July 21 to September 1, 2020) COVID-19 restriction periods, respectively, compared with the corresponding period in 2019. Air pollutant concentrations and the SUHIIs were significantly and positively correlated, and NO2 exhibited the strongest correlation with the SUHIIs. We revealed that variations in the air quality characteristics and thermal environment were observed in the study area during the COVID-19 restrictions, and their quantitative relationship provides a theoretical basis and reference value for improving the air and ecological environment quality within the study area.

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE

Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019-2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from "very good" (MAPE of 8.64% at the Liwa station with the closed loop) to "acceptable" (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, ^{Surface Urban Heat Island} (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, ^{UV Aerosol Index} (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m²) and CO (1.64 mol/m²) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m²). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m^2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Introduction

The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world.

Methods

Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH4 (Methane), CO_1 (CO), H2O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO2 (Nitrogen oxides), O3 (ozone), SO2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate.

Result

The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH4, O3, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch4 is statistically significant for all countries except Azerbaijan.

Conclusions

The comparison with and without adjusted p values declares the decreases in some air pollution in these countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-022-01528-x.

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO₂ in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

A review of the environmental implications of the COVID-19 pandemic in the United Arab Emirates

This paper reviews the environmental implications associated with the COVID-19 pandemic at the individual and community levels in the UAE. The positive effects emanating from the pandemic include improved air quality and reduced contamination of public spaces with pollutants. On the other hand, far-reaching negative effects include poor disposal of medical plastic waste and facemasks and the rise in unhygienic health practices amongst residents of UAE. The long-term ecological implications of the pandemic are still not well understood. The findings shed the light on the importance of addressing the consequences of the COVID-19 pandemic through preventative policies and strategies for better environmental health and readiness for future crises. Future research could assess the long-term environmental conse-quences of the pandemic on the UAE.

Effects of the COVID-19 pandemic on the environment, waste management, and energy sectors: a deeper look into the long-term impacts

The COVID-19 pandemic not only has caused a global health crisis but also has significant environmental consequences. Although many studies are confirming the short-term improvements in air quality in several countries across the world, the long-term negative consequences outweigh all the claimed positive impacts. As a result, this review highlights the positive and the long-term negative environmental effects of the COVID-19 pandemic by evaluating the scientific literature. Remarkable reduction in the levels of CO (3 - 65%), NO2 (17 - 83%), NOx (24 - 47%), PM2.5 (22 - 78%), PM10 (23 - 80%), and VOCs (25 - 57%) was observed during the lockdown across the world. However, according to this review, the pandemic put enormous strain on the present waste collection and treatment system, resulting in ineffective waste management practices, damaging the environment. The extensive usage of face masks increased the release of microplastics/nanoplastics (183 to 1247 particles piece-1) and organic pollutants in land and water bodies. Furthermore, the significant usages of anti-bacterial hand sanitizers, disinfectants, and pharmaceuticals have increased the accumulation of various toxic emerging contaminants (e.g., triclocarban, triclosan, bisphenol-A, hydroxychloroquine) in the treated sludge/biosolids and discharged wastewater effluent, posing great threats to the ecosystems. This review also suggests strategies to create long-term environmental advantages. Thermochemical conversions of solid wastes including medical wastes and for treated wastewater sludge/biosolids offer several advantages through recovering the resources and energy and stabilizing/destructing the toxins/contaminants and microplastics in the precursors.

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures.

About the Annual Course of Moscow Heat Island and the Impact on It of the Quarantine Measures to Prevent the COVID-19 Pandemic in 2020

Seasonal differences in the Moscow urban heat-island intensity (UHII) have been studied in detail based on data obtained in 2018–2020 by the meteorological network of stations located in Moscow and Moscow region. It is shown that the annual cycle of this phenomenon is slightly pronounced. In most cases, the UHI is manifested stronger in summer and weaker in winter; however, in some months, the situation may be reverse. The question of the statistical significance of seasonal differences remains open. The closest statistical relationship was revealed between the UHI and lower clouds during the night hours, so that its highest intensity is observed in the least cloudy seasons (usually in summer). The UHII distribution functions are close to the normal law in summer and spring, and, in winter and fall, they are characterized by a noticeable positive asymmetry, because their values decrease and the mode approaches the lower physical limit. The period of strict quarantine restrictions during the COVID-19 pandemic in the spring and early summer of 2020 led to a rapid and statistically significant decrease in the Moscow UHII, probably due to both natural factors (increased cloudiness) and human activities (rapidly decreased anthropogenic heat fluxes and a weakened urban industrial haze creating an additional counterradiation).

Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Greater Cairo, Egypt, which lies in the apex of the Nile Delta, is one of the most populated regions in the world. Air pollution is a profound environmental issue prevailing in the urban/rural landscapes of this crowded megacity. The objectives of the present study were to utilize remotely sensed data in order to address the seasonal variations of the nocturnal surface urban heat island intensity (SUHII) as extracted from the American Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the related seasonal distribution of selected air pollutants, including nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) as extracted from the European TROPOspheric Monitoring Instrument (TROPOMI) for the period from 2018 to 2021. It is observed that there is clear nocturnal urban heat island over Greater Cairo, particularly at the administrative districts dominated by urban land use with high density of population and at the industrial and power generation locations. The highest SUHII is observed during winter. On the other hand, the selected pollutants also represent an urban pollution island (UPI) capping the regions of high SUHII. At the seasonal level, the highest NO2 correlation with the SUHII occurs during spring (R2 = 0.59), while the CO correlates maximum during winter (R2 = 0.51). Nonetheless, the seasonal SO2 distribution is poorly related to the SUHII as this specific pollutant is significantly associated with the industrial land use. Climatic and topographic factors could intensify the distribution of air pollution in the study area. Results of this study demonstrate the significance of geospatial technology tools in the subtle analysis and addressing regional air pollution. The outputs are also of a paramount implication on the management of urban environment and the adaptation of urban air quality.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Examining the temporal and spatial distribution of potential urban heat island formations

Due to urbanization worldwide, gradual increase in construction and use of irregular urban topography affect urban climate negatively, triggering urban heat island (UHI) formations in cities and thereby causing them to become uninhabitable places for human comfort. This study, which covers the province of Diyarbakır in Turkey, aims to determine the spatial and temporal distribution of areas with potential urban heat island (UHI) by using remote sensing methods and satellite/terrain data available between 2001 and 2019. According to the Landsat 7 satellite, an area with a potential of 27.4 km² in 2001, 20.8 km² in 2006, 27.4 km² in 2008, 16.7 km² in 2010, and 12.2 km² in 2012 was determined. According to the Landsat 8 satellite, it was measured as 14.49 km² in 2017 and 15.67 km² in 2018. According to Landsat 8 satellite data, areas with UHI potential increased by 14.6% over a 3-year period. According to Landsat 7 data, there has been a continuous fluctuation over the years. One of the important results of this study is that between 2001 and 2019, the higher the rate of change according to the surface temperature, the larger the area with the potential of the heat island. At the same time, it has been determined that spatially potential UHIs have a great potential not in the city center, but in the surrounding areas close to the center and in the topographically hollow areas.

Impacts of the COVID-19 confinement on air quality, the Land Surface Temperature and the urban heat island in eight cities of Andalusia (Spain)

The COVID-19 outbreak and ensuing global lockdown situation have generated a very negative impact on the world economy, but they have also lent us a unique opportunity to research and better grasp the impacts of human activity on environmental pollution and urban climates. Such studies will be of vital importance for decision-making on measures needed to mitigate the effects of climate change in urban areas, in order to turn them into resilient environments. This study looks at eight cities in the region of Andalusia (southern Spain) to comprehensively assess their environmental quality with parameters (Pm10, So2, No2, Co and O3) obtained from meteorological stations. The aim was to determine how these parameters affect the Land Surface Temperature (LST) and the Surface Urban Heat Island (SUHI), on the basis of Sentinel 3 satellite thermal images. Knowing to what extent improved air quality can reduce the LST and SUHI of cities will be essential in the context of future environmental studies on which to base sustainable decisions. The geographic situation of cities in the Mediterranean Sea basin, highly vulnerable to climate change, and the high pollution rates and high daily temperature variations of these urban areas make them particularly attractive for analyses of this sort. During the confinement period, average reductions of some environmental pollutants were achieved: So2 (-33.5%), Pm10 (-38.3%), No2 (-44.0%) and Co (-26.5%). However, the environmental variable O3 underwent an average growth of 5.9%. The LST showed an average reduction of -4.6 °C (-19.3%), while the SUHI decreased by 1.02 °C (-59.8%). These values exhibit high spatio-temporal variations between day and night, and between inland and coastal cities.

The case of Tehran's urban heat island, Iran: Impacts of urban 'lockdown' associated with the COVID-19 pandemic

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 Tmean, Tmax and Tmin 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.

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America

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.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in "mega" cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO2 in Amman, Beirut, and Jeddah decreased by -56.6%, -43.4%, and -32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO2 levels in megacities like Tehran (-0.9%) and Cairo (-3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey

The SARS-CoV-2 virus pandemic (COVID-19) has caused 2.25 million deaths worldwide by February 3, 2021 (JHU, 2021) and still causing severe health and economic disruptions with increasing rates. This study investigates the impact of lockdown measures on ambient air pollution and its association with human mobility in 81 cities of Turkey. We conducted a countrywide analysis using PM10 and SO2 measurement data by the Turkish Ministry of Environment and Urbanization and mobility data derived from cellular device movement by Google. We observed the most significant change in April 2020. PM10 and SO2 concentrations were lower in 67% and 59% of the cities, respectively in April 2020 compared to the previous five years (2015-2019). The correlation results show that Restaurant/Café, Transit, and Workplaces mobility is significantly correlated with PM10 and SO2 concentration levels in Turkey. This study is the first step of a long-term investigation to understand the air quality impacts on population susceptibility to COVID-19.

Impact of the COVID-19 Pandemic on the Energy Use at the University of Almeria (Spain)

The COVID-19 pandemic has caused chaos in many sectors and industries. In the energy sector, the demand has fallen drastically during the first quarter of 2020. The University of Almeria campus also declined the energy consumption in 2020, and through this study, we aimed to measure the impact of closing the campus on the energy use of its different facilities. We built our analysis based upon the dataset collected during the year 2020 and previous years; the patterns evolution through time allowed us to better understand the energy performance of each facility during this exceptional year. We rearranged the university buildings into categories, and all the categories reduced their electricity consumption share in comparison with the previous year of 2019. Furthermore, the portfolio of categories presented a wide range of ratios that varied from 56% to 98%, the library category was found to be the most influenced, and the research category was found to be the least influenced. This opened questions like why some facilities were influenced more than others? What can we do to reduce the energy use even more when the facilities are closed? The university buildings presented diverse structures that revealed differences in energy performance, which explained why the impact of such an event (COVID-19 pandemic) is not necessarily relevant to have equivalent variations. Nevertheless, some management deficiencies were detected, and some energy savings measures were proposed to achieve a minimum waste of energy.

Change of CO Concentration Due to the COVID-19 Lockdown in China Observed by Surface and Satellite Observations

The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach.