Assessment of plastic pollution in coastal areas of Karachi: Case study of West Warf, Kemari Jetty, and Manora.

This study focused on marine pollution in coastal areas of Karachi, particularly West Warf, Kemari Jetty, and Manora. The research examined the sources and quantities of waste, from boat manufacturing, export units, and local commercial activities. Stakeholder interviews were conducted to understand waste management practices and identify the key contributors to ocean litter. The results indicated that restaurants, export units, boat construction, and tourist and commuter activities were the primary sources of marine pollution. Plastic was found to be the most prevalent litter category, with LDPE (e.g., single-use bags) and polystyrene (e.g., material in floating docks) being the most common types. Additionally, multi-layer packaging, such as chip wrappers, was frequently observed in the surveyed areas. Overall, this research highlights the urgent need for improved waste management and compliance measures in coastal regions to mitigate marine pollution.

AATSN: Anatomy Aware Tumor Segmentation Network for PET-CT volumes and images using a lightweight fusion-attention mechanism.

Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) provides metabolic information, while Computed Tomography (CT) provides the anatomical context of the tumors. Combined PET-CT segmentation helps in computer-assisted tumor diagnosis, staging, and treatment planning. Current state-of-the-art models mainly rely on early or late fusion techniques. These methods, however, rarely learn PET-CT complementary features and cannot efficiently co-relate anatomical and metabolic features. These drawbacks can be removed by intermediate fusion; however, it produces inaccurate segmentations in the case of heterogeneous textures in the modalities. Furthermore, it requires massive computation. In this work, we propose AATSN (Anatomy Aware Tumor Segmentation Network), which extracts anatomical CT features, and then intermediately fuses with PET features through a fusion-attention mechanism. Our anatomy-aware fusion-attention mechanism fuses the selective useful CT and PET features instead of fusing the full features set. Thus this not only improves the network performance but also requires lesser resources. Furthermore, our model is scalable to 2D images and 3D volumes. The proposed model is rigorously trained, tested, evaluated, and compared to the state-of-the-art through several ablation studies on the largest available datasets. We have achieved a 0.8104 dice score and 2.11 median HD95 score in a 3D setup, while 0.6756 dice score in a 2D setup. We demonstrate that AATSN achieves a significant performance gain while being lightweight at the same time compared to the state-of-the-art methods. The implications of AATSN include improved tumor delineation for diagnosis, analysis, and radiotherapy treatment.

Mapping potential groundwater accumulation zones for Karachi city using GIS and AHP techniques.

Karachi is the largest industrial metropolitan of Pakistan facing an acute water shortage which is leading to an overdraft of groundwater resources in the city. Groundwater is an important freshwater resource for the city as millions of people depend for sustenance. However, over-exploitation of groundwater has led to decreased groundwater levels within the city leading to environmental issues of depleting aquifers, deteriorating groundwater quality, land subsidence, and harm to groundwater-dependent ecosystems. The objective of the study was to assess the potential groundwater accumulation zones by integrating hydrogeological aspects of the city through nine thematic layers using the Geographic Information System (GIS) based multi-criteria decision analysis (MCDA) technique. The potential groundwater accumulation map reveals that 20% of the area has a low potential, 70% has moderate potential, and around 10% of the area in the city is composed of a high potential accumulation zone. The upstream regions of the city have the highest recharge potential because of sandy soil and barren land use, which promote high infiltration rates. The urbanized downstream areas have the lowest recharge potential due to impervious fabric. The findings reveal that the MCDA technique can be used with confidence in data-scarce regions for groundwater resource assessment and management. The recharge potential map can help better manage groundwater resources in the city by helping explore groundwater extraction opportunities and could hint at areas suitable for artificial recharge wells/ponds.