Marian-Emanuel IONASCU, Public Dissertation of PhD Thesis

Thesis Title: Improvement of Calibration Techniques and Data Quality for Personal Space Air Monitoring using a Platform of Fixed and Mobile Low-Cost Devices

Habilitation Board: 

Chair: Professor Univ. Dr. Eng. Radu-Emil PRECUP (Politehnica University of Timișoara)

Scientific leader: Professor Univ. Dr. Eng. Marius George MARCU (Politehnica University of Timișoara)

Members: Senior Scientist dr. Nuria Castell  NILU (The Climate and Environment Institute) 

                  Professor Univ. Dr. Eng. Ciprian Mihai DOBRE (National University of Science and Technology Politehnica Bucharest)

                  Professor Univ. Dr. Eng.  Adrian FLOREA ( Lucian Blaga University Sibiu)

Air quality monitoring (AQM) is essential for mitigating environmental pollution by measuring and analyzing atmospheric pollutants. Traditional monitoring systems, while highly accurate, are costly and require frequent maintenance, limiting their large-scale deployment [1]. In contrast, low-cost air quality sensors provide a scalable and accessible alternative, offering improved spatial and temporal resolution. However, these sensors face challenges related to measurement accuracy, calibration complexity, and environmental influences, which requires further research to enhance their reliability [2]. Real-time air quality data are crucial for public health, especially vulnerable groups such as children, the elderly, and individuals with respiratory diseases [3]. Air quality indices simplify complex pollutant measurements, enabling communities to make informed decisions, such as reducing outdoor activities during periods of high pollution [4]. Despite their advantages, low-cost sensors exhibit key limitations, including measurement errors, environmental interference, sensor drift, and cross-sensitivity to multiple pollutants [5]. To address these issues, advanced calibration techniques, machine learning models, and novel sensor materials are being explored to improve precision and stability over time [2]. Currently, AQM in Europe is based on high-precision reference stations, which despite their precision, suffer from sparse spatial coverage, high installation and maintenance costs, and data availability delays [1]. Low-cost sensors can complement these systems by providing greater coverage and real-time data availability. This research focuses on improving the performance of low-cost air quality sensors by addressing key sources of error and developing efficient calibration methodologies.