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Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) that appeared in 2019 gave rise to a major global health crisis that is still topping global health, socioeconomic, and intervention program agendas. Although the outbreak of COVID-19 has had substantial and devastating impacts on developed countries, the countries of the Global South share a higher proportion of the epidemic's effects as shown particularly in morbidity and mortality rates in low-income countries. Modeling the effects of underlying factors and disease mortality is essential to plan effective control strategies for disease transmission and risks. The relationship between COVID-19 mortality rates and sociodemographic and health determinants can highlight various epidemic fatality risks. In this research, geographic information systems (GIS) and a multilayer perceptron (MLP) artificial neural network (ANN) were adopted to model and examine variations in COVID-19 mortality rates in the Global South. The model's performance was tested using statistical measures of mean square error (MSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2). The findings indicated that the most important variables in explaining spatial mortality rate variations were the size of the elderly (sixty-five and older) population, accessibility to handwashing facilities, and hospital beds per 1,000 population. Mapping the explanatory variables and estimated mortality rates and determining the importance of each variable in explaining the spatial variation of COVID-19 death rates across countries of the Global South can shed light on how public health care and demographic structures can offer policymakers invaluable guidelines to planning effective intervention strategies. [ABSTRACT FROM AUTHOR]

This study considered two types of congestion pricing, high-occupancy toll (HOT) lanes and cordon pricing, and the factors influencing their public acceptability if they are to be implemented in Abu Dhabi city in the United Arab Emirates (UAE). A revealed preference (RP) and stated preference (SP) surveys were conducted in the city to collect data. The public acceptability was analyzed based on respondents' willingness to pay (WTP) a toll or cordon fee to circumvent congestion. Furthermore, the distribution of the generated revenue was analyzed as an additional indicator of public acceptability. The results indicated that trip conditions (travel speed, travel distance, and trip urgency); respondents' age, income, employment status, and car ownership; and toll fees are significant factors in determining public acceptability of HOT lanes, while respondents' average monthly income, age, employment status, car ownership, and saving in travel time are significant factors of determining public acceptability of cordon pricing. [ABSTRACT FROM AUTHOR]

The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies for emission reduction and validating those strategies. Given the complexity of quantifying total lifecycle emissions in residential buildings, this study delves into assessing the CF focusing on water and electricity consumption in two types of residential buildings: mainly residential villas and residential flats. This assessment was carried out in Doha City, Qatar, using data on water and electricity consumption from 2017 to 2020. The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. The annual electricity CF emissions from these buildings are approximately 7 MtCO 2 equivalent, with residential villas contributing about 83% of this total. Concurrently, the annual average water CF in residential buildings is around 0.06 MtCO 2 equivalent, predominantly attributed to villas. The findings highlight the substantial impact of residential structures, particularly villas, on the city's overall CF emissions. Furthermore, the findings underscore the significant impact of residential structures, especially villas, on Doha's CF emissions, revealing a marked seasonal increase, particularly during the summer months and a notable spike in 2020. The spatial analysis of CF emissions reveals consistent spatial clustering patterns across different seasons in 2020. Elevated CF emissions from villas are concentrated in Doha's central, northern, and northeastern regions, while cold spots are predominantly in the eastern and southern areas. Understanding CF in residential settings is crucial for developing strategies to reduce emissions, enhance energy efficiency, and address climate change. This research helps inform targeted interventions for more sustainable residential energy and water use, aligning with broader environmental and climate goals. [ABSTRACT FROM AUTHOR]

Governments and policymakers have initiated a wide range of responses to mitigate the propagation of the disease. These responses have consisted of different policies and the devising of protocols and standards. As these responses proliferate, there is a pressing need to evaluate the stringency, effectiveness, and results of imposing them on the COVID-19 infection rates. The aim of this study is to measure the effects of policy control and restrictions on COVID-19 prevalence in the Global South. Six policy responses to COVID-19 were used to investigate and explain the stringency and effectiveness of the imposed policies in mitigating the disease's transmission. The evaluation of these policies was based on the population's responses as of 4 June 2020 compared to an assessment period (the median value from the five-week period between 3 January and 6 February 2020). Using geographic information systems (GIS) techniques and the analytical hierarchical process (AHP) method, an overall government response and stringency index was constructed as an effective proxy to assess populations' responses to such policies in the Global South countries. The findings show variations in people's responses toward the lockdown policies imposed by the countries in the Global South. [ABSTRACT FROM AUTHOR]

The significant predicament of sustainable urbanism in contemporary cities of the Gulf region is being addressed by developing policies designed to make cities safe, inclusive, resilient and sustainable. By examining the accessible planning documents, and based on the analysis of the ongoing world-class developments and megaprojects within the Gulf cities, particularly in Doha and Dubai, we argue that there is an inconsistency between the master-planning phase, usually conducted by western consultants, and the economic, political, socio-cultural, and environmental dynamics of the Gulf. Our analysis revealed insensitivity to the local, economic and socio-cultural patterns of the Arab Gulf countries and a governmental lack of capacity of national planners that may erode the opportunities to implement sustainable urbanism. It is suggested that, although globalization and modernization may have brought some benefits to the Gulf cities such as improvement in living standards and changes in society and lifestyles, yet an innovative master planning that merges land use and strategic planning based on building national capacities, and a holistic understanding of the social, cultural and oil-dominated economies and community engagement, is essential to deliver sustainable urbanism in the region. Recommendations for moving towards more capable, participatory and sustainable planning system are suggested in the paper. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 marks a transformative era for businesses and industries, characterized by advanced technologies like automation, Internet of Things (IoT), artificial intelligence (AI), smart factories, and cyber-physical systems. This revolution promises significant advantages, including enhanced productivity, sustainable progress, and heightened resilience. However, the integration of Industry 4.0 is challenged by the need for a skilled workforce with expertise in areas such as information technology and data analytics. Higher education institutions (HEIs) play a vital role in equipping future professionals with these skills, necessitating curriculum updates and infrastructure enhancements. Simultaneously, the importance of education for sustainable development (ESD) has been underscored by global initiatives like the Sustainable Development Goals (SDGs). ESD instills a sense of responsibility for economic, ecological, and equitable well-being. As digital technologies blur the lines between industries, education faces the challenge of adapting to evolving demands. The integration of AI tools in education has emerged as a catalyst for reshaping learning experiences, fostering innovation, and preparing individuals for the digital age. AI chatbots such as ChatGPT have garnered widespread attention and possess the potential to revolutionize various aspects of education. However, their integration raises ethical concerns, necessitates curriculum redesign, requires strategies for continuous learning, and demands alignment with industry standards. While the potential of AI integration in education is promising, there is a notable gap in the existing literature when it comes to exploring the ethical implications, the influence of AI on ESD, the impact on the structure of Blooms Taxonomy, collaboration between academia and industry, strategies for continuous learning, and the effective integration of AI tools for personalized learning. This paper aims to critically examine the integration of AI tools, with a specific emphasis on ChatGPT, in education within the context of ESD. It delves into the transformative potential, ethical considerations, imperatives for continuous learning, and the role of industry partnerships. By providing insights and strategies, this paper contributes to the ongoing discussion about the evolving nature of education in a technologically driven world, equipping academic institutions to navigate the complexities and opportunities associated with AI integration in education more effectively. [ABSTRACT FROM AUTHOR]

This article analyses water, food, and energy security in the Gulf Cooperation Council (GCC) countries using the water-energy-food (WEF) nexus approach. The innovative focus is on identifying past and future development-based drivers of water-energy-food integration in the region. The study presents a critical review of WEF nexus in the Gulf region and identifies links to sustainable development in this area. It concludes that integrating water, energy, and food resources within the nexus is crucial for GCC nations to accomplish resource security and sustainable development. [ABSTRACT FROM AUTHOR]

This article analyses contemporary urbanisation patterns in Muscat, Dubai, and Doha cities, focusing on urban land cover change and the roles of governance, globalisation, oil revenues, internal migration, social factors, and urban planning forces in developing these cities. Remotely sensed and demographic data for the past 30 years were used to identify concurrent changes in urbanisation patterns, in order to gain a comprehensive understanding of the dynamics of urbanisation. The results show that the three cities have all experienced unprecedented urban transformation, with high urbanisation and population growth, but with differences in the patterns of development. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the impact of implementing HOT lanes and/or cordon pricing as two types of congestion pricing policies on traveller's willingness to pay and travel behaviour in the city of Abu Dhabi, United Arab Emirates (UAE) in the MENA Region. In addition, the paper investigates the potential equity implication of implementing a cordon pricing policy. Trip conditions, travellers' socioeconomic characteristics, and mode shift are used as benchmarks to evaluate the impacts of implementing these policies. Different numerical indicators are selected to evaluate each strategy, such as willingness to pay, trip urgency, trip speed and distance, travellers' income, age, gender, nationality, and vehicle ownership. Data are collected through stated preference and revealed preference survey questionnaires. The results show that travellers from different socioeconomic groups are willing to pay to use HOT lanes to escape congestion and that willingness to pay increases as the trip conditions worsen. However, the overall effect of cordon pricing would be regressive. High-income travellers benefit the most, and they are willing to pay the charges to save travel time. Low-income travellers have to pay the toll or use public transportation more often. [ABSTRACT FROM AUTHOR]

The main goal of this study is to investigate the feasibility of implementing cordon pricing as a mitigation policy to curb traffic congestion in Abu Dhabi Island. Two research objectives are identified to address the research goal. First, this study examines the impact of cordon pricing on commuters’ travel behaviour based on their socio-economic characteristics. Second, this study investigates commuters’ willingness to pay to escape congestion on the roads of the city of Abu Dhabi. A stated preference survey was used to gather data, and the analysis was based on 6054 responses. Multinomial logit modelling, chi-square test, and one-way ANOVA were used to assess the impact of cordon pricing on commuters. The results of this study show that the respondents have a slight intention to reduce their auto use by driving less or changing to other modes of transportation. Toll charges are found to be an important factor that would trigger some income groups to change their travel behaviour because of the additional burden and cost to their daily trips. [ABSTRACT FROM AUTHOR]

This study examines the feasibility of implementing high-occupancy toll (HOT) lanes in a Middle Eastern country to obtain an improved understanding of such a policy and how it may affect the behavior of travelers in this region. This study focuses on the relationship between travelers' willingness to pay (WTP) for HOT lanes and different factors, such as traveler characteristics and trip conditions. A combination of the stated preference and revealed preference surveys was used to collect data, and the analysis is based on approximately 6000 responses. Urgency, speed, and distance are three trip conditions that were utilized to determine drivers' WTP to use HOT lanes to avoid congestion. The influence of various traveler characteristics on drivers' WTP was also examined. The results indicated that WTP decreases as trip conditions improve. Furthermore, the analysis revealed that drivers' WTP to avoid congestion is affected by their income, trip frequency, nationality, and number of vehicles owned. [ABSTRACT FROM AUTHOR]

Very few tourism geographers have investigated the way meetings, incentives, conventions, and exhibitions (MICE) travelers search for information and their travel behavior based on cultural background and country of origin. In addition, little attention has been paid to examining the factors that affect MICE travelers' decisions to visit a certain destination. To address this knowledge gap, this paper aims to examine the impact of cultural factors on the information acquisition and travel behaviors of MICE tourists. It also examines the factors that affect travelers' decisions to visit a certain destination in cross-cultural settings. The primary data, related to the impact of cultural aspects of MICE travelers on their information searching and travel behaviors, were collected through a self-administered survey. The survey tool was composed of key elements—the country of normal residence, behaviors in information searching, the arrangement of the present trip, travel behaviors, significant effects on tourists' choices and satisfaction, and the socio-demographic characteristics of MICE travelers. The sample population was composed of Chinese-, Arabic-, and English-speaking MICE travelers at one of the top Middle Eastern MICE tourism destinations, Doha, Qatar. The relevant data were collected from MICE travelers at the Doha Exhibition and Convention Center, which is considered the main MICE sector setting in Doha. To test the proposed hypotheses, which concern categorical variables, a series of chi-square tests for independence or relatedness were conducted. These tests are appropriate for analyzing the relationship between two categorical variables. The study revealed notable differences between the three respondent groups. This paper proposes that destinations marketers should develop targeted marketing strategies based on the information and travel behavior of each cultural group. Particular marketing implications for Doha are discussed. [ABSTRACT FROM AUTHOR]

Origin–destination data are used to assess the vertical equity effects of a hypothetical road pricing zone in Canada's largest city. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. The analysis also highlights that professionals, those who live in one- and two-person households, and those who are aged 65 and older would be disproportionately affected; those who work in manufacturing would be less affected. The equity effects of road pricing arise out of the commuting patterns of different sub-populations. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine whether the Greater Toronto Area (GTA) is moving toward or away from sustainable transportation through exploring the spatial and temporal changes of urban form and travel behavior, related policies and responses to these changes during the period between 1986 and 2006. Transportation services and infrastructure in the GTA are regulated by different governing bodies and at different scales and levels. Support for sustainable transportation initiatives has varied across governments and over time. It is important at this point to review the main initiatives taken by different governing bodies over the last several decades to achieve sustainable transportation in the GTA. By analyzing the Transportation Tomorrow Survey data through the last two decades, it can be determined whether these initiatives have been successful in making transportation in the GTA more sustainable or, alternatively, whether more measures should be taken to achieve transportation sustainability. The major finding of this study is that, overall, the GTA is not moving in the direction of sustainable transportation, and gives credence to the idea that demand-management interventions are needed as a transport policy to make transportation systems more sustainable in this area. [ABSTRACT FROM AUTHOR]

[Display omitted] • There is an association between water and electricity consumption. • Lockdown period increased electricity and water consumption by 30% and 6% in 2020 compared to 2019. • The electricity consumption in the industrial and commercial sectors dropped due to the pandemic. • The water and electricity consumption in the residential sectors increased during the summer. • The spatial impact shows variation across the six sectors. This study investigates the water – electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water – electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran's I , Anselin Local Moran's I , and Getis-Ord G i ∗ statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people's mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions. [ABSTRACT FROM AUTHOR]

Hosting mega sports events is a key driver for sustainable development, particularly through fostering tourism marketing and planning. Although the 2022 FIFA World Cup has received considerable attention, as the first global event hosted in the Middle East and Arab world, studies that highlight and investigate the potential benefits of hosting such an event are rare. This study assessed the geographical accessibility to archaeological sites, monuments, and museums across Qatar, providing clear guidelines on how to represent the national cultural and historical heritage to global audiences during the 2022 mega event, to maximise socioeconomic revenue. Spatial data were assembled within GIS platforms to assess accessibility utilising geospatial techniques, such as the average nearest neighbour, near analysis, spatial autocorrelation (Moran 'I Index), and cost distance to estimate travel times. The analysis indicated that the archaeological sites are spatially clustered, predominantly concentrated in Doha and Al-Rayan municipalities, where historical and cultural landmarks are located. However, most castles, forts, and archaeological sites located outside the capital zone are less accessible, and at a long distance from hotels and residential areas. As the hosting stadiums are located along the northeast coast and within the most populous zone, the museums, towers, and cultural landscapes are easily accessible within a short travel time (less than 10 min). As a host community, planners and policymakers in Qatar may benefit from this research, as a spatial guideline to promote sustainable development of tourism, through facilitating accessibility to monuments and museums, as well as enriching the representation of the national tangible heritage to a global audience. • For host country, FIFA World Cup has crucial impacts on sustainable tourism. • Qatar World Cup 2022 offers a great opportunity to represent the national heritage. • Facilitating accessibility to monuments is a strategic pillar for the host country. • Geospatial techniques have been utilised to measure accessibility patterns. • Monuments within the capital zone are spatially clustered and easily accessed. [ABSTRACT FROM AUTHOR]

Hydrocarbon-contaminated soils are considered as one of the major environmental issues that harm human well-being, particularly in arid regions of the world. Phytoremediation is a possible mitigation measure for this issue and has been suggested as it is cost-effective compared with other remediation technologies for soil clean-up, such as soil thermal treatment and soil washing. However, there are still gaps in the literature regarding the behavior of annual and perennial desert plants and their ability to survive in hydrocarbon-contaminated soils in arid ecosystems. Therefore, this study aims to develop an integrated approach using remote sensing techniques to understand the behavior of annual and perennial desert plants over different types of oil-contaminated soils (oil tarcrete, wet-oil lake, bare soil, and vegetation cover) in the Kuwait Desert and to explore the impact of climate and physical soil properties on the regrowth of native desert plants. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and ferrous iron (Fe2+) index (FI) were used to determine the changes in oil contamination and vegetation cover from 1992 to 2002, and 2013–2020. Subsequently, statistical tests were performed to determine the influence of climatic and soil physical characteristics on changes in hydrocarbon contamination and desert plant behavior. The results showed that hydrocarbon contamination was high at the study sites in the first six years (1992–1997) after contamination, and then decreased in the following years. However, vegetation cover was low in the first six years but significantly increased after 1998, reaching >65%. It was also found that annual plants had the highest distribution rate compared to perennial plants, which mainly depended on the soil type. We concluded that certain annual and perennial plants could successfully grow over tarcrete-contaminated sites, making these sites more suitable for the restoration of native desert plants than hydrocarbon-contaminated sites. We also observed that the succession process of vegetation growth over hydrocarbon-contaminated soils could be associated with vegetation growth on a clean sediment layer covering the oil layer. Additionally, we observed that the remobilization of aeolian sediment over many contaminated sites in Kuwait resulted in the accumulation of organic matter, plant seeds, and dust particles that create layers of nutrient-rich soil for the initial growth of plants. [ABSTRACT FROM AUTHOR]

This paper presents an approach for accessibility categorization in areas where there is no extensive data available to run the conventional analysis. The presented method has the advantages of the lower data requirements and the utility of the results. Three benchmarks were selected to evaluate transit accessibility. The first one (transit coverage) investigates the spread of the service, and it is used to assess the percentage of people in a district that can access the service within a comfortable access distance. The second (transit supply) is an effectiveness measure of the magnitude of available infrastructures such as stops, or stations over the population or land area of the underlying region. The third (route diversity) represents the variety of routes to and from different areas via transit services. Various indicators were used to assess these benchmarks. Within the context of multi-criteria decision-making, TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) allows the direct comparison of measures of different units, and it is used to evaluate the relative scores of the various districts' benchmark. A K-clustering method was used to numerically categorize the areas into five groups, reflecting on the criteria levels. A case study of Abu Dhabi city is used to demonstrate the details of the procedures. The detailed analyses were used to depict districts and corridors with particular deficiencies and as such suggesting remedy strategies. The method can help transit authorities identifying the most suitable types of facilities, infrastructure, or routes that a district may need to improve its accessibility. [ABSTRACT FROM AUTHOR]

Human-oriented factors present unavoidable challenges and uncertainties in building energy strategic planning. The uncertainties escalate when the target society is not fully known to the decision-maker and can create performance gaps between the expected and actual outcomes of sustainability targets. This article aims to investigate the role of socioeconomic and behavioral dimensions in residential energy consumption patterns among regions that host high proportions of migrant communities with diverse cultural and ethnic traits. This study evaluates the patterns in human-building interactions and energy behaviors among local and migrant communities based on empirical evidence and survey analysis. The survey data are investigated via machine learning approaches to identify the interdependencies between and feature importance of critical factors that influence human-building interactions and to determine elements that help to discern the energy behavior of locals and migrants. A simulation analysis is conducted to analyze residential energy consumption under different human indoor thermal comfort preferences in multiple case scenarios to demonstrate how improvements in human-building interaction can create saving opportunities. The findings capture the main socioeconomic and behavioral contributors in residential energy consumption and demonstrate the impact of human factor at a high level in regions with imbalanced demographics and societies in transition. • Demographics and behavioral factors diversify residential energy use patterns. • Expatriate communities follow gain goals while locals possess normative goals. • Energy subsidy is not a barrier against perceived responsibility in energy use. • Indoor temperature preference is the major behavioral energy use element. • Establishing habitual behaviors achieve noticeable energy/emission reduction. [ABSTRACT FROM AUTHOR]

Green water is primarily associated with the appearance of annual plants and plays a significant role in biomass production in both arid and semi-arid ecosystems. Herein, we aim to estimate the optimal threshold for determining the presence or absence of annual plants and use them as an ecological indicator to assess potential green water areas in Kuwait as a case study. We integrate remote sensing techniques and MaxEnt modeling. The AUC for the annual plant distribution with all examined factors is 0.847, and the standard deviation is 0.050. The results demonstrated that potential locations with high levels of green water cover <20% of the country. The annual plant distribution was significantly correlated with several types of perennial plants, maximum temperature, precipitation, and sandy soils. It was also found that annual plants are controlled by the spring and winter temperature decline and the timing of precipitation occurrence, especially the pattern and amount of rainfall received in November. Sandy loam and loam soils were found to be ideal for annual plants, although land depressions and soil types are crucial factors in determining annual plant distribution. Additionally, annual plants enhanced the growth of several perennial communities. To reiterate, our study's model helped to comprehend the significance of annual plants as an ecological indicator in sustaining soil moisture over a prolonged period, as well as factors controlling the distribution of annual plants. The developed model and indicators could support decision-makers in determining appropriate locations with adequate levels of green water for revegetation planning in arid landscapes. • The distribution of annual plants, suitable soils, and green water are significantly interconnected. • Machine learning techniques are useful for selecting suitable locations for revegetation. • Revegetation of native plants could help grazing pastures to enhance food security. [ABSTRACT FROM AUTHOR]

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too. [ABSTRACT FROM AUTHOR]