Development of a Geospatial Microplastic Pollution Analysis and Prediction System

The client faces challenges in analyzing and predicting the distribution of microplastics in water bodies to identify high-risk contamination areas. Accurate spatial analysis is crucial for developing targeted removal strategies and optimizing resource allocation, but existing methods lack comprehensive geospatial insights and predictive capabilities.

A research-focused environmental organization specializing in water quality monitoring and pollution mitigation, with capabilities in geospatial data analysis and environmental engineering.

• Create a system enabling precise identification of microplastic contamination hotspots through geospatial analysis.
• Implement a predictive model to forecast future microplastic concentration areas based on environmental and land use data.
• Develop an interactive mapping interface that layers multiple datasets, including land use, wastewater sources, and stormwater systems.
• Build a scalable, secure cloud infrastructure to manage large-scale environmental geospatial data with high performance.
• Provide actionable insights that improve resource deployment and strategy planning for pollution mitigation efforts.

• Two prototype solutions: one based on GIS platform and another utilizing open-source frameworks, with evaluation and recommendation reports.
• Data collection and management capabilities incorporating scientific research, procedural metadata, and environmental parameters.
• Development of an interactive, layered mapping system visualizing datasets such as land use, wastewater treatment plants, stormwater infrastructure, and pollution sources.
• Deployment of a cloud-based infrastructure for scalable, high-performance, and secure data processing and storage.
• In-depth analysis tools using GIS software for detailed spatial data interpretation.
• Reporting features that produce comprehensive, easy-to-understand analysis reports and visualizations.
• Future development including enhancement of visualizations, integration of additional datasets, and machine learning models for hotspot prediction.

• Environmental and water quality scientific databases
• Land use and infrastructure datasets
• Sensor data streams related to water quality and pollution levels

• System must support large geospatial datasets with high performance and responsiveness
• Cloud infrastructure must ensure data security and compliance with relevant standards
• Solution should support future scaling for increased data volume and user access
• Reliable data update and synchronization processes

The implemented platform will enable the client to accurately pinpoint microplastic hotspots, enhance predictive capabilities for future contamination trends, and optimize resource deployment. This will lead to more targeted mitigation strategies, improved monitoring efficiency, and a significant contribution toward achieving a cleaner aquatic environment. The system aims to improve hotspot detection accuracy and predictive precision, ultimately supporting more effective environmental management actions.