Air pollution, particularly from black carbon, poses significant challenges to public health and environmental sustainability. As a key component of particulate matter resulting from incomplete combustion, black carbon has detrimental effects on both climate and air quality.
Accurate monitoring of black carbon concentrations is vital for effective environmental management. In addition to reference measurement instruments, regional air quality monitoring stations employ a variety of technologies to assess this pollutant, each utilizing different principles and exhibiting varying levels of accuracy.
The thesis aims to employ time series data sets of diverse types of black carbon sensors with varying accuracy to develop an optimal fusion strategy based on machine-learning techniques, capable of providing optimal estimates of the black carbon concentration, improving the accuracy of non-reference sensors. The thesis, in cooperation with ARPA Lombardia, will employ Regional Air Quality Monitoring station time series data.
The student will benefit from synergies with LEAP – Laboratorio Energia e Ambiente Piacenza, already active on several projects on the topic.
Area di competenza
Emissions and Air Quality
Responsabili
Fredy Ruiz (fredy.ruiz@polimi.it) – Politecnico di Milano (DEIB)
Senem Ozgen (senem.ozgen@polimi.it) – LEAP
Competenze richieste
- Capability to formulate and solve filtering and state estimation problems
- Capability to implement and validate numerical simulations of dynamic systems
- Understanding of signal processing and systems theory
Desiderable:
- Familiarity with Matlab-Simulink and/or Python
- Experience with Kalman filtering and/or Machine learning
- Familiarity with optimization tools (CVX, Casadi, …)
Data di inizio presunta
January / February 2025
Durata
9 months. Full thesis only.
Maggiori informazioni
Weekly meeting online or in presence.