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GEOSPATIAL INFORMATICS FOR COASTAL POLLUTION MONITORING IN ALBANIA: OPEN DATA AND PYTHON-BASED MAPPING
Abstract
Open data and informatics provide new opportunities for cost-effective environmental monitoring in data-scarce regions. This study applies a geospatial informatics approach to assess coastal microbial pollution patterns in Albania during 2023, using openly available datasets from the Albanian Institute of Statistics (INSTAT) and the European Environment Agency (EEA). Microbiological indicators of bathing water quality (intestinal enterococci, IE/100 ml) were combined with municipal solid waste statistics to explore spatial associations between pollution hotspots and anthropogenic pressure. Data preprocessing and classification were carried out with Python libraries (pandas, geopandas, matplotlib), while spatial visualization and cartographic refinement were performed in QGIS. The results highlight areas of high contamination along the Durr s -Sh ngjin corridor, closely aligned with regions of high waste generation, while southern beaches such as Himar and Ksamil generally showed low to moderate pollution. The findings underscore the role of informatics in transforming raw open datasets into actionable insights for policy makers, municipalities, and tourism authorities. Beyond identifying environmental risk zones, the study demonstrates a reproducible, low-cost digital workflow that can be adopted by researchers, NGOs, and academic institutions for ongoing monitoring and education. By linking open science principles with sustainable coastal management, this work illustrates how environmental informatics can support both evidence-based policy and public awareness in rapidly developing coastal regions.
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