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<dc:title xml:lang="en"><![CDATA[Przegląd Geograficzny T. 97 z. 1 (2025)]]></dc:title>
<dc:title xml:lang="en"><![CDATA[Inteligentny system identyfikacji zanieczyszczenia powietrza = Intelligent air pollution identification system]]></dc:title>
<dc:title xml:lang="pl"><![CDATA[Przegląd Geograficzny T. 97 z. 1 (2025)]]></dc:title>
<dc:title xml:lang="pl"><![CDATA[Inteligentny system identyfikacji zanieczyszczenia powietrza = Intelligent air pollution identification system]]></dc:title>
<dc:creator><![CDATA[Szwed, Mirosław. Autor]]></dc:creator>
<dc:subject xml:lang="en"><![CDATA[air pollution identification]]></dc:subject>
<dc:subject xml:lang="en"><![CDATA[artificial intelligence]]></dc:subject>
<dc:subject xml:lang="en"><![CDATA[machine learning]]></dc:subject>
<dc:subject xml:lang="en"><![CDATA[electron microscopy]]></dc:subject>
<dc:subject xml:lang="en"><![CDATA[neural networks]]></dc:subject>
<dc:subject xml:lang="en"><![CDATA[image analysis]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[identyfikacja zanieczyszczeń powietrza]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[sztuczna inteligencja]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[uczenie maszynowe]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[mikroskopia elektronowa]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[sieci neuronowe]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[analiza obrazów]]></dc:subject>
<dc:description xml:lang="en"><![CDATA[24 cm]]></dc:description>
<dc:description xml:lang="en"><![CDATA[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.]]></dc:description>
<dc:description xml:lang="en"/>
<dc:description xml:lang="pl"><![CDATA[24 cm]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Celem opisanego w pracy działania jest opracowanie inteligentnego systemu identyfikacji zanieczyszczeń powietrza. Zastosowanie sztucznej inteligencji, wykorzystującej analizę obrazów powierzchni wybranych indykatorów zanieczyszczenia powietrza do budowy algorytmu uczenia maszynowego umożliwiło opracowanie taniej i skutecznej metody identyfikacji niebezpiecznych substancji. Do budowy modelu zostały wykorzystane zdjęcia skaningowej mikroskopii elektronowej dwuletnich igieł sosny zwyczajnej Pinus sylvestris L., z reprezentatywnych zlewni badawczych krajowej sieci Zintegrowanego Monitoringu Środowiska Przyrodniczego. Zdjęcia mikroskopowe zostały przetworzone w programie graficznym, tak aby zaklasyfikowane na podstawie wielkości, kształtu i składu chemicznego cząstki posiadały jednakowy atrybut (barwę). Wykonane warstwy (maski) stanowiły element właściwy do opracowania algorytmu uczenia maszynowego identyfikującego zanieczyszczenia z podziałem na zdefiniowane wcześniej kategorie. Zastosowanie sieci neuronowych do budowy samouczącego się algorytmu pozwoliło zoptymalizować analizę zdeponowanych zanieczyszczeń zobrazowanych na powierzchni igieł sosny. Opracowany system identyfikacji naturalnych i antropogenicznych cząstek w postaci skategoryzowanych warstw daje skuteczność predykcji na wysokim poziomie.]]></dc:description>
<dc:description xml:lang="pl"/>
<dc:publisher><![CDATA[IGiPZ PAN]]></dc:publisher>
<dc:date><![CDATA[2025]]></dc:date>
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<dc:identifier><![CDATA[0033-2143 (print)]]></dc:identifier>
<dc:identifier><![CDATA[2300-8466 (on-line)]]></dc:identifier>
<dc:identifier><![CDATA[10.7163/PrzG.2025.1.3]]></dc:identifier>
<dc:identifier><![CDATA[https://rcin.org.pl/igipz/dlibra/publication/281496/edition/244837/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:rcin.org.pl:244837]]></dc:identifier>
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<dc:relation><![CDATA[oai:rcin.org.pl:publication:281496]]></dc:relation>
<dc:rights xml:lang="en"><![CDATA[Creative Commons Attribution BY 4.0 license]]></dc:rights>
<dc:rights xml:lang="pl"><![CDATA[Licencja Creative Commons Uznanie autorstwa 4.0]]></dc:rights>
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