Master Thesis Defence by Lilja Dahl
Title: Source-Apportionment of Non-Methane Volatile Organic Compounds, Halogenated Species and Non-CO2 Greenhouse Gases at Mt. Cimone (Italy) by applying Positive Matrix Factorization with a Lifetime Correction Method
Abstract:
Receptor models are applied to atmospheric measurements to discover hidden patterns of variability and identifying relevant sources of pollutants. A source-apportionment investigation was carried out on non-methane volatile organic compounds (NMVOCs), halogenated species and non-CO2 greenhouse gases (GHGs) at Mt. Cimone in the period 2015-2018, using positive matrix factorization (PMF). NMVOCs are important precursors of tropospheric ozone formation and many halogens contribute to the stratospheric ozone depletion and the greenhouse effect.
In particular, the impact of photochemistry on NMVOCs seasonal cycle is considered in this thesis, and the added value of this work is the application and evaluation of the PMF tool by coupling a lifetime correction method with the PMF model. The lifetime correction proved to be a valid method to apply on data, in order to scale the reactive NMVOCs as a function of their rate constant. Moreover, a consolidated framework of the data pretreatment and analysis process was established, which is essential for performing a sound source apportionment study.
Eight factors were identified during Summer season and seven factors were identified during Winter season. The eight factors characteristics are determined to be:
(1) vehicle exhaust;
(2) gasoline evaporation; (3) Liquified petroleum gas; (4) solvent evaporation; (5) industrial solvents; (6) chlorinated solvents; (7) octane gasoline; and (8) halogenated species and non-CO2 GHGs. The last factor (8) is not related to emission sources, but is rather explaining the variability of long-lived halogenated species and non-CO2 GHGs on a continental scale.
Explorative analysis using cluster analysis and principal component analysis (PCA) gave a better understanding of species variability and correlation among them which was useful for the validation and interpretation of PMF results. Results from PMF and cluster analysis demonstrated similar classification of species. However, PCA was unsuccessful to explain meaningful data variance and was sensitive towards "problematic species".
Overall, the PMF results indicate seven potential emission sources, although further analysis and comparison of PMF results with sophisticated models are needed, in order to evaluate if the obtained factors are robust.
Supervisors:
Anders Svensson
Paolo Cristofanelli
Michela Maione
Censor: Claus Nordstrøm
Participating via Zoom https://ucph-ku.zoom.us/j/61334559257