Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multi-temporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, Lahore, Peshawar, and Quetta in Pakistan using the Smile Random Forest (SRF) algorithm within the Google Earth Engine (GEE) platform. Classification accuracies ranged from 86–90%, with Cohen’s Kappa coefficients between 0.86 and 0.92, demonstrating substantial to almost perfect agreement. The results reveal significant increases in urban areas: Karachi expanded from 12.4% in 1990 to 41.3% in 2020, Lahore from 15.2% to 39.8%, and Islamabad from 9.1% to 28.6%, primarily at the expense of vegetation and barren land. Elevation also influenced LULC dynamics, with higher-altitude cities like Quetta exhibiting slower but more resource-constrained urban development. A change matrix quantified class transitions, showing that urban land predominantly expanded into agricultural and vegetative land areas, raising concerns about long-term food security. Future projections using the MOLUSCE–ANN model indicate continued urban expansion by 2030, particularly in Karachi and Lahore, where built-up areas are projected to exceed 45% of total land cover. Compared with previous studies that employed Classification and Regression Trees (CART), Support Vector Machine (SVM), and CA–Markov models in single-city or short-term contexts, this study provides a multi-decadal, multi-city analysis with predictive capacity and robust validation, offering novel insights into Pakistan’s urbanization trajectory. By linking LULC change to the implications for natural resources and food security, the study contributes actionable evidence to support actions against disaster risk reduction, sustainable development and SDGs-aligned with urban policies.

Understanding pollution levels, ecological health risks, and sources of potentially toxic metals (PTMs) in the soil from university campuses is critical for assessing environmental safety. Soil samples were collected from 12 locations across urban parks and green areas at Sohag University in Egypt. The samples were processed and analysed for heavy metals, including iron (Fe), manganese (Mn), cobalt (Co), nickel (Ni), chromium (Cr), lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd). Pollution levels were evaluated using indices such as the pollution index (PI), pollution load index (PLI), geo-accumulation index (I_geo), and enrichment factors (EFs). Among the pollution indices, the EFs showed the highest sensitivity in detecting anthropogenic contributions, particularly for Cd, Pb, and Cr. Spatial distribution maps and multivariate statistical analyses, including correlation matrix (CM), principal component analysis (PCA), and cluster analysis (CA), were applied to identify the relationships between PTMs and soil properties, and source apportionment was performed using positive matrix factorisation (PMF). The results indicated that Mn, Ni, and Co were primarily geogenic, whereas Pb, Zn, Cr, and Cd showed higher concentrations, suggesting moderate-to-significant anthropogenic pollution. Pb and Cd pose considerable ecological risks, whereas other metals such as Cr and Cu exhibit moderate ecological threats. The non-carcinogenic and carcinogenic risks to the students were within safe limits, as defined by United States Environmental Protection Agency (USEPA) threshold values. Source apportionment using PMF identified five main sources of PTMs: industrial and anthropogenic activities (30.0%), traffic emissions (25.0%), natural soil processes (20.0%), agricultural practices (15.0%), and mixed industrial traffic sources (10.0%). These findings emphasise the importance of controlling anthropogenic activities to ensure a safer campus environment.

The Palaeocene Lockhart Formation, a carbonate-rich succession abundant in Larger Benthic Foraminifera, represents a significant potential hydrocarbon reservoir extending throughout the Kohat, Potwar and Hazara basins of Pakistan. This study examines two stratigraphic sections of the Lockhart Formation in the Hazara Basin—Bagran and Karhaki—providing crucial insights into its biostratigraphy and microfacies analysis. The formation comprises medium- to fine-grained limestone with shale intercalations, exhibiting argillaceous to compacted textures. Biostratigraphic analysis revealed a diverse assemblage of Larger Benthic Foraminifera, with 23 species identified across 9 genera, including Miscellanea miscella, Lockhartia haimei, Lockhartia conditi and Ranikothalia sindensis. These fossils indicate deposition within Shallow Benthic Zone (SBZ) 4 during the Late Thanetian, suggesting a dynamic palaeoenvironment. Seven distinct microfacies types were identified: bioclastic mudstone, mixed bioclastic wackestone, miliolidal bioclastic wackestone, foraminiferal wackestone–packstone, foraminiferal wackestone, foraminiferal packstone and bioclastic foraminiferal packstone. These microfacies indicate varied depositional settings, from shallow subtidal and lagoonal to shallow restricted and open marine environments, spanning inner ramp to distal mid-ramp conditions. This research advances our understanding of Late Thanetian depositional environments within the Lockhart Limestone, with implications for regional sedimentology, palaeogeographic reconstruction and reservoir characterisation.

The mechanical characteristics of the excavation‐damaged zone (EDZ) are essential in tunnel engineering for scientific design, safe construction, stability evaluation, and support optimization. Due to the lack of quantitative research on the mechanical characteristics of the EDZ and their impact on engineering support design, this paper proposes a quantitative investigation of the EDZ and a method for tunnel support optimization based on field monitoring studies. Then, the Gaoligong Mountains tunnel was analyzed using this method to quantify the mechanical characteristics of the EDZ during the tunnel boring machine (TBM) excavation and its impact on engineering support design. Firstly, the quantitative investigation of EDZ and engineering support optimization method was proposed based on the zonal crack density statistics in the EDZ, the evaluation of the zonal equivalent mechanical parameters using the Hoek–Brown criterion, stability analysis of surrounding rock considering EDZ zonal deterioration, and the engineering support design method. Secondly, the evaluation of the EDZ depth, rock mass wave velocity, and crack propagation in the surrounding rock mass during TBM excavation was analyzed based on field monitoring tests (the ultrasonic test, acoustic CT test, and digital borehole camera test) result of the Gaoligong Mountain tunnel. Thirdly, using the above method, the zonal equivalent mechanical parameters of the rock mass in the EDZ were calculated, and stability analysis of the surrounding rock considering EDZ zonal deterioration was carried out. Finally, the anchor design parameters (the length, pitch, and row spacing of the anchor) of the surrounding rock for the Gaoligong Mountain tunnel were analyzed and optimized. A second stability analysis of the surrounding rock was also carried out to validate the new approach using the conventional rock mass mechanical parameter equivalent. The depth of the plastic zone (7.4 m) in the proposed method was more in agreement with the field monitoring data (6∼8 m) as compared to the traditional method (9.2 m). Hence, this method could provide a stronger foundation for assessing and optimizing the tunnel design scheme and supporting measures.