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This research aims to examine changes in the eastern part of Qatar's shoreline from 1982 to 2018 by means of satellite imagery. Five different time periods, namely 1982, 1992, 2002, 2013, and 2018, were analysed to determine shoreline movements and shoreline variations. Techniques such as maximum likelihood classification, the normalised difference vegetation index, and tasselled cap transformation were utilised to extract the shoreline data. Linear regression rate statistics were used to quantify the rate of shoreline variations. The results indicate that the majority of shoreline accretion is a result of human activities such as coastal construction, land reclamation, and building artificial islands, which are associated with the high economic activity over the past two decades. Significant changes were observed in Lusail City, The Pearl, and Hamad International Airport (HIA). Natural sediment accumulation was also observed in Al Wakra and on the southern side of HIA. In general, there were more land gains than losses throughout the study period, and the shoreline increased by twice its previous length. The field survey confirmed the presence of sandy and rocky beaches, as well as a shoreline with protective structures such as natural limestone rocks and concrete reinforcement. [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]

Abstract The Hamad International Airport (HIA) in Doha, Qatar is the only international airport operating in the country. The airport was officially opened in 2014 to function as the engine of the country's economic growth. Due to the anticipated high passenger demand of this airport, especially to account for the 2022 FIFA World Cup and in support of Qatar National Vision (2030), it is important to analyze, model, and evaluate the current and future airport ground access and traveler's modal choice. As such, the aim of this study is to analyze the current HIA ground access mode choice characteristics and users, examine the importance of introducing the Doha Metro to airport mode choice, and identify the preference of air travelers for this new mode. A revealed- and stated preference face-to face interview survey was conducted at the airport to collect data related to trip and user characteristics. Quantitative and qualitative methods were used to analyze the data. Binary logit mode was used to the current access mode choice, and a multinomial logit model was used to model the future access mode choice after introducing the Doha Metro. The models results showed that current and future access mode choice is significantly affected by the trip and socioeconomic characteristics of the HIA users. Highlights • The aim of this study is to examine a country airport ground access mode choice characteristics and users. • This study aims to examine the importance of introducing a Metro to airport mode choice and to better understand travellers' needs, reactions, and expectations to the new proposed Doha Metro along with what factors contribute in encouraging commuters to use this mode as their main mode of transportation to the airport. • Revealed- and stated preference face-to face interview surveys were conducted at the airport to collect data related to trip and user characteristics. • Models result showed that current and future access mode choice is significantly affected by different trip and socioeconomic characteristics of HIA users. [ABSTRACT FROM AUTHOR]

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022. • This study investigate the impact of COVID-19 pandemic on electricity consumption. • Three machine learning models were used in the analysis. • Electricity consumption patterns changes in different socioeconomic sectors. • The electricity consumption during the pandemic years affected the accuracy of model prediction. [ABSTRACT FROM AUTHOR]

The propagation of the pandemic times, especially during COVID-19, has caused millions of morbidity and mortality cases across the world, forcing people to change their lifestyles and governments to take different measures to slow down the spread of the disease. Consequently, electricity demand and consumption patterns across other socioeconomic sectors were affected by the pandemic. This study aims to investigate the impact of the COVID-19 pandemic on spatiotemporal electricity demand and consumption across six socioeconomic sectors in Doha City, Qatar. The electricity demand and consumption were assessed for three time periods in the pandemic year (pre-lockdown, lockdown, and post-lockdown) compared to electricity consumption prior to the pandemic year (2017–2019). The empirical analysis was performed on a geographically visualized map to determine the areas with high and low electricity consumption. The pattern has been compared before and during the pandemic with previous years. The results show that electricity consumption has increased in the residential and governmental sectors and declined in the industrial and commercial sectors during the lockdown period compared to the post-lockdown period, particularly in the summer season. Mapping the hot/cold spots areas and the temporal analysis of the changing patterns of electricity demand and consumption could provide useful insight to decision-makers for targeted interventions. • This study investigate the impact of the COVID-19 pandemic on spatiotemporal electricity demand. • The consumption pattern has been compared before and during the pandemic. • Mapping the hot/cold spots areas and performing temporal analysis. • Electricity consumption has increased in the residential and governmental sectors. • Electricity consumption declined in the industrial and commercial sectors during the lockdown period. [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]