https://www.um.edu.mt/library/oar/handle/123456789/119816 Thu, 23 Apr 2026 20:49:45 GMT 2026-04-23T20:49:45Z https://www.um.edu.mt/library/oar/handle/123456789/144069 Title: Modelling indoor household PM2.5 using positive matrix factorization and machine learning algorithms Abstract: People tend to spend most of their time indoors, yet the concentration and composition of indoor fine particulate matter (PM2.5) remain poorly understood in the Maltese Islands, with existing receptor modelling studies focusing solely on ambient air. The first objective of this study was to carry out long-term indoor air sampling of PM2.5 followed by chemical characterisation, in order to identify and quantify the main natural and anthropogenic sources of indoor PM2.5 at an urban background site in Malta using Positive Matrix Factorisation (PMF). The second objective was to explore the use of Machine Learning (ML) algorithms to model and predict indoor PM2.5 concentrations in several households in Malta and Gozo. PMF was used to identify and quantify the major sources of indoor PM2.5. Quartz and PTFE filters were collected and analysed gravimetrically and chemically using ICP-MS, IC, and an OC-EC aerosol analyser to determine concentrations of PM2.5, 18 elements, 5 ions, organic carbon (OC), and elemental carbon (EC). Eight contributing factors were identified, seven outdoor sources and one indoor source, contributing 68% and 26% to indoor PM2.5, respectively. Cooking and e-cigarette use were the main contributors to the indoor factor. Uniquely for Malta, a fireworks factor was isolated indoors, responsible for most of the measured Sb and Ba, raising concerns due to the toxicity of these elements. An RF-SHAP model was integrated with the indoor PMF model to investigate the influence of key drivers on indoor PM2.5 concentrations. An outdoor PMF analysis was also conducted, and a corresponding RF-SHAP model (CV RMSE: 2.79 µg m−3; R²: 0.80) was used to refine the outdoor source contributions. Transboundary contributions (Saharan dust and ammonium sulfate) were higher outdoors (58%) than indoors (33%) due to reduced infiltration when windows are closed. Local anthropogenic sources (Industrial, Fireworks, Traffic, Shipping) contributed more to outdoor PM2.5 (33%) than indoor (25%), with increased indoor infiltration during warmer months coinciding with peak fireworks activity. For the second objective, continuous PM measurements were taken using aerosol spectrometers at seven non-smoking residences. RF and XGBoost models were developed to predict indoor PM2.5 at six-hourly intervals. At sites with low indoor PM generation, predictions were mainly influenced by outdoor PM1 levels. At a site with high indoor emissions, indoor relative humidity was a key predictor, especially during cooking. The RF model performed best overall (RMSE: 30.65 µg m−3; IOA: 0.66). Description: Ph.D.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144069 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/135059 Title: Multicomponent reactions as a tool for a sustainable future in chemistry Abstract: This work and its abstract are both under embargo until the restriction is lifted. Description: Ph.D.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/135059 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/133787 Title: Confronting hubble tension using scalar-tensor theories Abstract: The Hubble tension, an ongoing debate in astrophysics and cosmology, pertains to different measures of the universe’s current rate of expansion, the Hubble constant (H0). This tension stems from discrepancies in the results acquired using various observational techniques and datasets. To better understand and possibly resolve these differences, it is necessary to investigate different cosmological theories. An interesting path is to modify the basic character of gravity by investigating theories such as scalar-tensor models, which include a scalar field as an extra degree of freedom. These changes seek to correlate theoretical predictions with empirical evidence, perhaps providing fresh insights into the universe’s fundamental features. In this project, cosmological models are analysed using the MCMC technique and the emcee Python toolkit, with an emphasis on the exponential and Higgs scalar field models. The MCMC approach allows for a thorough statistical study of parameter spaces by utilising current Hubble parameter measurements and other observational data, such as CC and Sn Ia. These models are then compared to the ΛCDM model, which provides the benchmark, evaluating their effectiveness in resolving the Hubble tension and establishing tighter restrictions on cosmological parameters. In comparison to the ΛCDM model, the scalar field models displayed a slightly lower value of H0 and hence a slightly larger value of Ωm,0. The inclusion of priors provided larger values of H0 for all models. Description: B.Sc. (Hons)(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/133787 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/133782 Title: Comparing quantum computing algorithms for physics simulations Abstract: We find the Boltzmann probability distribution function (PDF) of the quantum Ising model through a variational quantum algorithm (VQA) using state-vector, shots, and noisy simulations and compare their respective fidelity with the exact PDF at different temperatures and magnetic field strengths. The Rudolph and Grover quantum circuit was used alongside the classical optimizers, Powell for ‘state-vector’ and COBYLA for ‘shots’ and ‘noisy’ modes. The state vector result of a quantum circuit represents the optimal theoretical quantum state of the system. A real quantum computer can never output the optimal state vector result and instead utilises ‘shots’. ’Shots’ rely on repeatedly running a quantum circuit to obtain statistical results as a measure. Given enough shots it was shown that the results approximate state-vector results for small qubit numbers. Because real quantum computers are noisy, a noisy simulation was created using one of the most common types of noise in quantum computers, depolarizing noise. The results were then compared across the three types of simulations. One of the major findings was that even a small degree of depolarizing noise randomly implemented on the gates of a quantum circuit reduced the fidelity for different temperatures and magnetic field strengths tested. Future research analysing the fidelity of the Boltzmann distribution in noisy intermediate scale quantum (NISQ) computers would benefit from more noise-resilient qubits or more efficient noise reduction schemes. Description: B.Sc. (Hons)(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/133782 2024-01-01T00:00:00Z