Post-graduate theses
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Identifier |
000465799 |
Title |
Homogenization of the network of air pollution observations in Crete |
Alternative Title |
Ομογενοποίηση του δικτύου μετρήσεων ατμοσφαιρικής ρύπανσης στην Κρήτη |
Author
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Παρασκευαΐδου, Σοφία Β.
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Thesis advisor
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Κανακίδου, Μαρία
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Reviewer
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Καλυβίτης, Νικόλαος
Χρηστάκης, Νικόλαος
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Abstract |
Air quality monitoring is critical in addressing environmental and public health issues.
Low-cost microsensors offer a potential, cost-effective method for measuring particulate
matter (PM) and other hazardous atmospheric pollutants. However, their effectiveness
is heavily influenced by environmental factors such as high humidity and soil dust
occurrences, requiring precise field and laboratory evaluation of their performance.
This thesis studies the use of low-cost microsensors, specifically the commercially
available Bettair, Kunak, and PurpleAir devices, for atmospheric pollutant monitoring,
emphasizing the importance of calibration adapted to individual settings. To improve
sensor accuracy, we created computational modifications utilizing Machine Learning (ML)
methods, which turned out to be the most effective calibration method. After treating
the data and evaluating their correlations (Pearson and Spearman’s Rank Correlation
Coefficients) we started applying the ML algorithms. The models we ultimately used
were the XGBoost (EXtreme Gradient Boosting) and Random Forest (RF) regressors,
which are supervised learning algorithms. From this study, we concluded that the RF
regressor was the one giving better predictions, after training with each microsensor’s
corresponding measurements, for the concentrations values from the reference station.
However, in order to have an accurate and efficient model, large amount of data is
needed and with more features.
Our findings from both field trials and statistical analysis show that machine learning-
based calibration greatly enhances sensor performance, making low-cost microsensors a
semi-viable alternative for monitoring air quality across a wide range of environments.
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Language |
English |
Subject |
Air Quality networks |
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Calibration |
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Machine learning models |
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Microsensors |
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PM particles |
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Δίκτυα ατμοσφαιρικού ελέγχου |
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Μικροαισθητήρες |
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Μοντέλα μηχανικής μάθησης |
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Ρύθμιση/προσαρμογή |
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Σωματίδια PM |
Issue date |
2024-07-11 |
Collection
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School/Department--School of Sciences and Engineering--Department of Physics--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/4/e/b/metadata-dlib-1719838101-380806-25151.tkl
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Views |
289 |
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