Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards.

The evaluation of the vulnerability of coupled socio-ecological systems is critical for addressing and preventing the adverse impacts of various environmental hazards and devising strategies for climate change adaptation. The initial step in vulnerability assessment involves exposure assessment, which entails quantifying and mapping the risks posed by multiple environmental hazards, thereby offering valuable insights for the implementation of vulnerability assessment methodologies. Consequently, this study sought to model the exposure of coupled social-ecological systems in mountainous regions to various environmental hazards. By a set of socio-economic, climatic, geospatial, hydrological, and demographic data, as well as satellite imagery, and examining 11 hazards, including droughts, pests, dust storms, winds, extreme temperatures, evapotranspiration, landslides, floods, wildfires, and social vulnerability, this research employed machine learning (ML) techniques and the fuzzy analytical hierarchy process (FAHP). Expert opinions were utilized to guide hazard weighting and calculate the exposure index (EI). Through the precise spatial mapping of EI variations across the socio-ecological systems in mountainous areas, this investigation provides insights into vulnerability to multiple environmental hazards, thereby laying the groundwork for future endeavors in supporting national-level vulnerability assessments aimed at fostering sustainable environments. The findings reveal that social vulnerability and pests receive the highest weighting, while floods and landslides are ranked lower. All hazards demonstrate significant correlations with the EI, with droughts exhibiting the strongest correlation (r > 0.81). Spatial analysis indicates a north-south gradient in forest exposure, with southern regions showing higher exposure hotspots (EI 29.08) compared to northern areas (EI 10.60). Validation based on Area Under Curve (AUC) and Consistency Rate (CR) in FAHP demonstrates robustness, with AUC values exceeding 0.78 and CR values below 0.1. Considering the anticipated intensification of hazards, management strategies should prioritize reducing social vulnerability, restore degraded areas using drought-resistant species, combat pests, and mitigate desertification. By integrating multidisciplinary data and expert opinions, this research contributes to informed decision-making regarding sustainable forest management and climate resilience in mountain ecosystems.

A Preliminary Assessment of Global CO2: Spatial Patterns, Temporal Trends, and Policy Implications.

This study offers a comprehensive analysis of the distribution, evolution, and driving factors of CO2 emissions from 1990 to 2016 at multiple spatial scales. Utilizing 26 indicators encompassing various facets of CO2 emissions, it is employed principal component analysis (PCA) and empirical orthogonal functions (EOFs) to identify the dominant characteristics of global CO2 emissions. This model retained three core components, accounting for 93% of the global CO2 variation, reflecting emission trajectories and associated economic metrics, such as Gross domestic product (GDP). The analysis differentiated the effects of these components based on countries' economic standings. Using a novel aggregated index, significant national contributors to global CO2 emissions are pinpointed. Notably, the leading contributors are found among developed nations (e.g., the United States, Canada, Japan), Gulf states (e.g., Saudi Arabia, Qatar), and emerging economies (e.g., China, Brazil, Mexico). Furthermore, these results highlight that shifts in global CO2 emissions over the past 30 years are predominantly influenced by factors like industrial emissions and GDP. Results also demonstrate a distinct relationship between a country's CO2 emissions and its physical and socioeconomic factors. Specifically, the nation's coastline length, population density in coastal regions, and the diversity of its climatic conditions significantly influence its carbon footprint.

Investigating the succession process of native desert plants over hydrocarbon-contaminated soils using remote sensing techniques.

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.

Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar.

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.

Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities.

The aim of the study is, therefore, to analyze the formation of the UHIs in eight different cities in arid and semi-arid regions. The analysis is based on land cover (LC) classification (urban, green, and bare areas). The study found that bare areas had the highest mean LST values compared to the urban and green areas. The results show that the difference in temperatures between the bare areas and the urban areas ranges between 1 and 2 °C, between the bare areas and green areas ranges between 1 and 7 °C, and between the urban areas and green areas ranges between 1 and 5 °C. Furthermore, the LST values varied for each of the LULC categories, and hence some areas in the three categories had lower or higher LST values than in other categories. Hence, one category may not always have the highest LST value compared to other categories. The outcomes of this study may, therefore, have critical implications for urban planners who seek to mitigate UHI effects in arid and semi-arid urban areas.