## 1 Introduction  
Air pollution is defined by the presence of harmful substances in the atmosphere, such as sulfur oxide (SO\(\times\)), nitrogen oxide (NOx), ground-level ozone (O\({}_{\rm 3}\)), carbon monoxide (CO), and particulate matter (PM), which are generally absent in clean air. These pollutants increase health risks and degrade the quality of life. Two major long-range sources that can contribute to the particulate matter levels sufficiently to impact air quality over vast regions are wildfires [1], which predominantly produce smoke [2], and desert areas [3], which supply mineral dust. Depending on the extent, location, duration, and intensity of the fire, as well as the movement of air masses and meteorological conditions, plumes of mineral dust or smoke can travel thousands of kilometers from their sources. Due to the Sahara desert's proximity to Europe, North Africa is a significant source of dust plumes affecting the region [3]. Locally, PM pollution sources are primarily linked to traffic, local industry, and residential heating, with their effects varying seasonally. Traffic emissions are a major source of PM, particularly in urban environments [4]. Local industries, such as concrete manufacturing, also contribute to elevated PM levels. Additionally, residential heating becomes increasingly significant during colder months, with the impact varying based on the type of fuel used, such as biomass, gas, or waste [5]. Aerosol source identification relies on various modeling approaches, which are crucial for understanding the impact of different sources on PM levels. The two main approaches are receptor-oriented and source-oriented models. Receptor-oriented methods, such as Cluster Analysis (CA), Canonical Correlation Analysis (CCA), Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and Non-Parametric Wind Regression (NWR), use pollutant data to identify source profiles and assess local influences. In contrast, source-oriented models, including Eulerian and Lagrangian types, simulate pollutant dispersion and track particles to analyze emissions and transport processes [6]. However, receptor-oriented methods often require a significant number of additional parameters [6]. Pollution by PM, particularly PM\({}_{10}\), particles less than 10 microns in aerodynamic diameter, is a critical air pollution issue in urban areas due to its significant adverse effects on public health and overall living standards [7]. PM\({}_{10}\) originates from a wide range of emission sources, including the combustion of coal, biomass, gasoline, oil, and diesel fuel. Additionally, PM\({}_{10}\) includes dust from agriculture, wildfires, industrial processes, construction activities, resuspended dust from roads and soil, and natural sources like pollen. The chemical composition of PM\({}_{10}\) is notably complex, generally comprising of carbonaceous species, soot, inorganic ions, and various elements. This composition varies significantly depending on the geographic location and specific emission sources. While PM\({}_{10}\) contains a multitude of chemical compounds, only a few are considered hazardous to human health. Among these, polycyclic aromatic hydrocarbons (PAHs) and certain heavy metals (such as Cd, Pb, Ni, As, TI, and Hg) are of particular concern due to their carcinogenic and mutagenic properties. PAHs are primarily formed during the incomplete combustion of organic materials at high temperatures, while heavy metals are emitted from a variety of sources, including coal and biomass combustion, vehicular traffic, and industrial activities [8]. In addition to these well-known sources, the burning of waste materials containing polyethyleneenephthalate (PET), such as clothing and food packaging, is a significant contributor to PM\({}_{10}\) pollution, particularly in Romania. Moreover, there is compelling evidence that the frequent burning of used furniture in major Romanian cities also contributes to elevated PM\({}_{10}\) levels [5].