Urban quality of life (UQoL) study is very important for many applications such as services distribution, urban planning, and socioeconomic analysis. The objective of this study is to create an urban quality of life index map for Al Ain city in the United Arab Emirates (UAE). The research aligns with the United Nations Sustainable Development Goals number ten (reduce inequalities) and eleven (sustainable cities and communities). In this study, remote sensing images and GIS vector datasets were used to extract biophysical and infrastructure facility indicators. The biophysical indicators are normalized difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), enhanced normalized difference impervious surfaces index (ENDISI), normalized difference built-up index (NDBI), land surface temperature (LST), slope, and land use land cover (LULC). In addition, infrastructure facility indicators such as distances to main roads, parks, schools, and hospitals were obtained. Additional infrastructure facility variables namely built-up to green area and build-up to bare soil area ratio were extracted from the LULC map. Machine learning was used to classify satellite images and generate LULC map. Random Forest (RF) was found as the best machine learning classifier for this study. The overall classification and Kappa hat accuracy was 95.3 and 0.92, respectively. Both biophysical and infrastructure facility indicators were integrated using principal component analysis (PCA). The PCA analysis identified four components that explain 75% of the variance among the indicators. The four factors were interpreted as the effect of LULC, infrastructure facility, ecological, and slope. Finally, the components were assigned weights based on the percentage of variance they explained and developed the UQoL map. Overall, the result showed that greenness has a greater effect on the spatial pattern of UQoL in Al Ain city. The study could be of a value to policy makers in urban planning and socioeconomic departments.

The catastrophic implication of harmful algal bloom (HAB) events in the Arabian Gulf is a strong indication that the study of the spatiotemporal distribution of chlorophyll-a and its relationship with other variables is critical. This study analyzes the relationship between chlorophyll-a (Chl-a) and sea surface temperature (SST) and their trends in the Arabian Gulf and the Gulf of Oman along the United Arab Emirates coast. Additionally, the relationship between bathymetry and Chl-a and SST was examined. The MODIS Aqua product with a resolution of 1 × 1 km² was employed for both chlorophyll-a and SST covering a timeframe from 2003 to 2019. The highest concentration of chlorophyll-a was seen in the Strait of Hormuz with an average of 2.8 mg m⁻³, which is 1.1 mg m⁻³ higher than the average for the entire study area. Three-quarters of the study area showed a significant correlation between the Chl-a and SST. The shallow (deep) areas showed a strong positive (negative) correlation between the Chl-a and SST. The results indicate the presence of trends for both variables across most of the study area. SST significantly increased in more than two-thirds of the study area in the summer with no significant trends detected in the winter.

This study examines the relationship between food waste and gender, income, and where students live. The research aligns with the United Nations Sustainable Development Goals 2, 11, and 12; moreover, the massive global food shortage in 2022 due to the conflict between Russia and Ukraine makes saving food a priority. A questionnaire was used to collect data from 201 students at the United Arab Emirates University (UAEU). A large proportion of the respondents were female (71.6%). The most common reasons cited by the respondents for food waste were over purchasing (31% of respondents), attitude (26.5%), and poor management (24%), while the most common reasons for having extra-cooked food were expecting guests (46%) and wanting to eat the food that had been prepared (35%). The majority of the respondents (57%) agreed that young people waste more food than older people. The methodology used in this study could be adopted by other researchers around the globe, and the output may help in developing policies and designing educational material for food waste intervention programs. Beneficiaries may therefore include food producers/consumers, environmental departments, and charitable organizations. The research contributes to the knowledge about food waste, perception, and intervention programs.

In 2022, the increased utilization of electric vehicles (EVs) curtailed global carbon dioxide emissions by 13 gigatons. While EV ownership has been on the rise in the United Arab Emirates (UAE), accounting for approximately 1.3% of passenger car vehicles in 2022, it has not yet attained a level of economic feasibility compared to petroleum vehicles. The authors examined consumers’ opinions in the UAE toward adopting EVs for light-duty transport from the economic and geographic perspectives. The main research question was the following: to what extent do economic and geographic factors affect consumers’ opinions toward adopting EVs in the UAE? The objectives were to determine if a relationship exists between economic factors, such as saving money on petroleum, the cost of car maintenance, the cost of purchase, and income level, and geographic factors, including 14 cities across the seven Emirates that affect consumers’ opinions toward adopting EVs. We designed a survey that was distributed to a sample of 5459 respondents to examine this relationship. Descriptive and inferential statistics as well as PyData analytical techniques complemented by the application of data visualization tools such as Seaborn, Plotly, and Matplotlib were employed to examine the dataset. The findings demonstrated that respondents across all income levels have a positive outlook regarding the appeal of EVs for saving money on petroleum, while their interest in saving money on car maintenance and the initial cost of purchase became more pronounced in the higher income levels. Moreover, there were variations in preferences in highly populated Emirates (e.g., Dubai and Abu Dhabi) compared to Emirates with lower populations (e.g., Ras Al Khaimah and Umm al Quwain), confirming that much more emphasis is required to promote EVs in rural areas. When comparing multiple income groups to determine the impact of different factors, the ANOVA confirmed the prevailing patterns evident in the associations.