ARCHITECTURE OF THE GEO-INFORMATION SYSTEM FOR MONITORING AND MODELING THE SPREAD OF MICROPLASTIC MASSES IN THE AIR

Authors

DOI:

https://doi.org/10.31891/2307-5732-2025-347-70

Keywords:

air pollution by microplastics, geoinformation system, GIS, real-time monitoring, modeling, data visualization, Raspberry Pi

Abstract

The article explores the problem of uncontrolled microplastic spread in the air and its disintegration. It reviews existing studies and publications focused on detecting, monitoring, and visualizing the distribution of microplastic particles.

A universal geoinformation system architecture is proposed for comprehensive monitoring and modeling of microplastic spread in the air. The article describes both the software and hardware components of the system, including an air sampling unit, a modeling system for microplastic dispersion, as well as the mathematical component for pollution spread modeling.

The system is designed around a portable station equipped with sensors that measure airborne particles, alongside a web interface for data visualization and analysis. The station continuously gathers data on microplastic concentrations in the air and transmits it to cloud storage for further processing and pollution modeling. The web interface allows users to visualize spatial pollution patterns and configure system parameters. Article also details the architecture of the portable station used for air sampling, including the integration of its hardware components and software. The system enables real-time monitoring, prediction of microplastic dispersion, and provides geographic data on pollution zones. The proposed architecture leverages cloud technologies, ensuring scalability, flexibility, and seamless integration with other hardware and software solutions.

Published

2025-01-30

How to Cite

MANZHULA, V., & FAIFURA, V. (2025). ARCHITECTURE OF THE GEO-INFORMATION SYSTEM FOR MONITORING AND MODELING THE SPREAD OF MICROPLASTIC MASSES IN THE AIR. Herald of Khmelnytskyi National University. Technical Sciences, 347(1), 512-522. https://doi.org/10.31891/2307-5732-2025-347-70