This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Screening various plant species to act as hyperaccumulators and associated health risks could serve as a sustainable solution for the bioremediation heavy metals (HMs). For the first time, the present study explored the phytoremediation potential of native plants, soil enrichment, and human health risks associated with the contamination of HMs in soil and plant samples collected from a municipal solid-waste open dump site. Soil and plant samples (n = 18 + 18) from the dumpsite and (n = 18) from the control site were analyzed for selected HMs, i.e., Chromium (Cr), Lead (Pb), Nickel (Ni), Iron (Fe), and Zinc (Zn). The phytoremediation potential of plants was assessed using the bioaccumulation factor (BAF), bioaccumulation coefficient (BAC), and translocation factor (TF), while soil pollution levels were evaluated using the contamination factor (CF), geoaccumulation index (Igeo), enrichment factor (EF), potential ecological risk index (PERI), and human health risk indices. The results revealed that based on TF and BAC values, Alhagi maurorum Medic., Astragalus creticus Lam., Cichorium intybus L., Berberis lycium Royle, and Datura stramonium L. were hyperaccumulators for Cr while Parthenium hysterophorus L. was a promising species for both Ni and Cr. Similarly, CF values for Fe, Ni, Pb, and Cr were >6, thereby showing very high contamination, while Igeo values for Fe, Ni, Pb, and Cr were (class 6, >5), showing that the soil was extremely polluted. Furthermore, EF values for Fe, Ni, Pb, Cr, and Zn were 2 < EF ≤ 5, depicting moderate enrichment, while PERI values were in the range of 91.31–195.84, employing moderate ecological risks (95 < PERI < 190) from the dumpsite’s soil. Moreover, for non-carcinogenic exposure, none of the analyzed metals exceeded the threshold limit HRI values > 1 in both adults and children. Likewise, in the case of carcinogenic effects, the CRI values were lower than the tolerable limits (1 × 10⁻⁶–1 × 10⁻⁴) in both adults and children. Moreover, almost all studied plants could be utilized for the phytoextraction of mentioned HMs. In future, the present study can help in the implementation of public policies to ensure sustainability and developmental activities in contaminated sites. Based on these results, it is concluded that there is a dire need of monitoring solid waste dumpsites due to various types of potential risks associated with the contamination of HMs. Moreover, to minimize the potential health problems arising from the dumpsite, it is substantive that special attention should be paid to work on sustainable and eco-friendly remedial measures.

Urban expansion and changes in land use/land cover (LULC) have intensified in recent decades due to human activity, influencing ecological and developmental landscapes. This study investigated historical and projected LULC changes and urban growth patterns in the districts of Multan and Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, and predictive modelling from 1990 to 2030. The analysis of satellite images was grouped into four time periods (1990–2000, 2000–2010, 2010–2020, and 2020–2030). The Google Earth Engine cloud-based platform facilitated the classification of Landsat 5 ETM (1990, 2000, and 2010) and Landsat 8 OLI (2020) images using the Random Forest model. A simulation model integrating Cellular Automata and an Artificial Neural Network Multilayer Perceptron in the MOLUSCE plugin of QGIS was employed to forecast urban growth to 2030. The resulting maps showed consistently high accuracy levels exceeding 92% for both districts across all time periods. The analysis revealed that Multan’s built-up area increased from 240.56 km² (6.58%) in 1990 to 440.30 km² (12.04%) in 2020, while Sargodha experienced more dramatic growth from 730.91 km² (12.69%) to 1,029.07 km² (17.83%). Vegetation cover remained dominant but showed significant variations, particularly in peri-urban areas. By 2030, Multan’s urban area is projected to stabilize at 433.22 km², primarily expanding in the southeastern direction. Sargodha is expected to reach 1,404.97 km², showing more balanced multi-directional growth toward the northeast and north. The study presents an effective analytical method integrating cloud processing, GIS, and change simulation modeling to evaluate urban growth spatiotemporal patterns and LULC changes. This approach successfully identified the main LULC transformations and trends in the study areas while highlighting potential urbanization zones where opportunities exist for developing planned and managed urban settlements.

The escalating trend of global urbanization, particularly pronounced in the burgeoning urban areas of Pakistan, necessitates a meticulous examination of Land Use/Land Cover (LULC) changes and their extensive environmental repercussions. Understanding these transformations is crucial for informed decision-making in evolving ecological dynamics. This research utilizes Geographic Information Systems (GIS), Remote Sensing (RS), and statistical analyses to investigate LULC thoroughly changes in Okara District, Pakistan, from 1991 to 2023. Despite its considerable socio-economic significance, Okara District has remained relatively understudied, making this research a crucial contribution to understanding its evolving landscape. Key indicators include the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI), correlated with Land Surface Temperature (LST). Findings include a 22.6% decline in vegetation cover (415.1 km²) and a 64.1% increase in urban areas (110.7 km²) from 1991 to 2023. Correlations reveal a consistent negative relationship between NDVI and LST (R², 0.55–0.69) and a positive correlation between LST and NDBI (R² 0.62–0.69), indicating the persistent Urban Heat Island (UHI) effect. The study underscores the urgent need for sustainable urban planning to balance developmental needs and ecological preservation. Informed decision-making can mitigate the UHI effect, emphasizing the broader implications for global urbanization challenges. This research contributes to understanding LULC dynamics and fosters discussions on Sustainable Development Goals (SDGs), climate action, and the development of resilient cities and communities.

Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.