Inteligentny system identyfikacji zanieczyszczenia powietrza = Intelligent air pollution identification system - Szwed, Mirosław. Autor (IGiPZ PAN, 2025)
The aim of the work is to develop an air pollution identification system using neural networks. The use of artificial intelligence (AI), which uses the analysis of surface images of selected air pollution indicators to build a machine learning algorithm, has enabled the development of a cheap and effective method for identifying hazardous substances. Scanning electron microscopy photos of two-year-old needles of Scots pine Pinus sylvestris L. from representative research catchments of the national network of Integrated Environmental Monitoring were used to build the model. Scanning electron microscopy photos were processed in a graphics program so that the particles classified based on size, shape and chemical composition had the same attribute. The layers made were an element necessary to develop a machine learning algorithm identifying pollutants divided into previously defined categories. The use of neural networks to build a self-learning algorithm allowed us to optimize the analysis of deposited contaminants imaged on the surface of pine needles. The developed system for identifying natural and anthropogenic particles in the form of categorized layers provides high level of prediction efficiency. Thanks to the use of multiple convolutional layers, the neural network captured the most important features from the image during training and then used them to predict segmentation masks of interesting objects. Based on the association of input pixels and the features extracted from them and the pixels of the real segmentation mask, algorithm adjusted its parameters to later recreate masks for completely new input data. The network response was at the level of 80%, which is the optimal result of the developed system.
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Keywords: Geography