Remote Sensing of Urban Stream Eutrophication and Its Implications for Public Health

This paper introduces a data-driven framework for integrating environmental and public health monitoring, specifically targeting the impacts of harmful algal blooms in urban water systems—a growing concern in the context of climate change. Recognizing the documented health risks associated with algal blooms, such as digestive and respiratory issues, the authors advocate for scalable and holistic policy strategies grounded in real-world data.

The proposed approach links environmental indicators (namely the Normalized Difference Chlorophyll Index, NDCI) with health outcome data, derived from hospital records and population health sources, to enable early detection of correlations between environmental degradation and public morbidity. The framework leverages remote sensing technologies to monitor eutrophication levels and merges these insights with epidemiological data to inform timely, targeted public health interventions.

To demonstrate practical application, the study includes an interactive web-based tool developed using Python’s Dash library. This tool enables users to explore spatial and temporal patterns between bloom intensity and health incidents (based on synthetic datasets) in selected regions of Crete—specifically Gazanos and Almyros. This pilot underscores the potential of tech-enabled environmental health intelligence systems to support policy development and community risk mitigation.

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Author(s): Stratos Kokolakis,  HL7 Europe