Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km² by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions.

Coastal environments need continuous environmental risk assessment, especially with increasing coastal development and human activities. The present work evaluates the distribution, contamination, and environmental risk of potentially toxic elements (PTEs) in coastal sediments between Al-Jubail and Al-Khafji cities along the Arabian Gulf, Saudi Arabia, and documents the influence of background references applied in pollution indices. Thirty-two sediment samples were collected for analysis of Ni, Cu, Cr, As, Zn, Pb and Hg using ICP-AES. The ranges of PTEs (mg/kg) were in the following order: Cr (3.00–20.0), Ni (2.00–32.0), Zn (2.00–14.0), As (2.00–4.00), Pb (1.50–5.00), Cu (1.00–5.00), and Hg (0.50–1.00). The coastal sediments show severe enrichment with As and Hg, and no to minor enrichment and a low contamination with Cr, Cu, Cr, Zn, and Pb. Based on sediment quality guidelines, concentrations of Cu, Pb, Zn, As, and Cr do not represent a concern for benthic communities, while Ni and Hg show a risk for benthic communities in four and 17 sampled areas, respectively. Multivariate analysis indicated a geogenic source for Zn, Cr, Cu, Ni, and Pb, mixed natural and anthropogenic sources for As, and an anthropogenic source for Hg, mostly from oil pollution, sewage, and industrial effluents spreading near Al-Jubail industrial city.

Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps persist in integrating Landsat imagery (7 and 8), meteorological data, and Geographic Information System (GIS) tools to evaluate the thermal effects of specific LULC types, including cooling and warming transitions, and their influence on air temperature under variable precipitation patterns. This study investigates LST variations in Islamabad, Pakistan, from 2000 to 2020 using quantile classification at three intervals (2000, 2010, 2020). The thermal contributions of each LULC type across the LST-based temperature classes were analyzed using the Land Contribution Index (LCI). Finally, Warming and Cooling Transition (WCT) maps were generated by intersecting LST classes with 2000 as the baseline. Results indicated a rise in LST from 32.39 °C in 2000 to 45.63 °C in 2020. The negative LCI values revealed that vegetation and water bodies in lower temperature zones (Ltc_1 to Ltc_3) contributed to cooling effects, while positive LCI values in built-up and bare land areas in higher temperature zones (Ltc_5–Ltc_7) exhibited warming effects. The WCT map showed a general warming trend (cold-to-hot type) from 2000 to 2020, particularly in newly urbanized areas due to a 49.63% population increase, while cooling effects (hot-to-cold type) emerged in the newly developed agricultural lands with a 46.46% rise in vegetation. The mean annual air temperature gap with LST narrowed from 11.55 °C in 2000 to 2.28 °C in 2020, reflecting increased precipitation due to increasing yearly rainfall from 982.88 mm in 2000 to 1365.47 mm in 2020. This change also coincided with an expansion of water bodies from 2.82 km² in 2000 to 6.35 km² in 2020, impacting the local climate and hydrology. These findings highlight the importance of green spaces and water management to mitigate urban heat and improve ecological health.

The Birimian of West African Craton (WAC) is known for its gold potential. Among Birimian structures, N-S and NE-SW trends have been defined as controlling gold mineralizations in Kedougou-Kenieba Inlier (KKI), whereas some of these structures are not gold-bearing. To determine structures related to gold mineralization, airborne electromagnetic data collected during the “Système Minier” of Mali have been processed and interpreted. Identified lineaments have been followed in the ground to establish their link with gold showings in the Kenieba area. Geophysical interpretations show trends similarity for different orders of conductivity. They allowed for characterizing resistance and conductive structures with prevalent N-S and NE-SW directionalities. Conductive structures are qualified as good conductors and are strongly related to known Artisanal Mining Sites (AMS). They coincide with tourmaline sandstones and quartz-albite veins, which are both often artisanally recognized as indicators of gold mineralization in Western Mali. Field observations show that resistance structures correspond to felsic rocks. These structures can bear gold only when silicified and they have spatial relations with Artisanal Mining Sites (AMS) within the Kenieba area. This study shows the efficiency of electromagnetic methods to characterize Birimian structures in relation to the gold mineralization in Kedougou-Kenieba Inlier (KKI).

The contamination of seawater with anthropogenic factors is a global challenge because of its negative impacts on marine environments and coastal societies. Therefore, assessing water contamination is crucial. The present work uses pollution indices and multivariate statistical analyses to document high-level heavy metal concentrations and identify potential polluters by analyzing 35 seawater samples collected from Al-Uqair coastline, Arabian Gulf, Saudi Arabia. The total content of heavy metals (HMs) (μg/L) ranged from 7109 to 7398 for Sr, 7.00–14.50 for Cr, 3.30–9.90 for Zn, 3.00–8.80 for Fe, 4.40–7.60 for As, 1.20–6.90 for Ni, 2.30–4.70 for V, 1.10–3.80 for Cu, 2.50–3.10 for Se, 0.31–1.43 for Al, 0.18–1.10 for Hg, 0.04–0.08 for Cd, 0.09–0.43 for Pb, and 0.02–0.10 for Sb. The recorded average concentrations of HMs were below the maximal admissible concentration of the World Health Organization (WHO). The heavy-metal pollution index (HPI) indicates that 14.29% of the water samples were below medium pollution, and 85.71% were below high pollution. All seawater samples were categorized under low contamination and good water on the basis of the degree of contamination (Cd) and water pollution index (PIj), respectively. Multivariate analysis indicates mixed anthropogenic and natural sources for the investigated metals, with increasing control of the anthropogenic factors for Cr, Zn, As, V, Ni, Se, Cu, Hg, Pb, Sb, and Cd; and the control of natural factors for Sr, Fe, and Al.