Delhi is one of the most polluted cities globally, experiencing some of the world's highest urban particulate matter concentrations. However, these studies are performed for a short period with limited temporal variation information. Here, we performed the complete long-term characterization of the ambient fine particulate matter (PM_2.5) compositions. We performed analysis on different seasons (winter, spring, summer, and post monsoon) of the years on high time-resolved PM_2.5 data, including non-refractory PM_2.5 (NR- PM_2.5), elements, and black carbon (BC), to develop a variation of the composition-based estimate PM2.5 and its sources ("C-PM_2.5" = NR-PM_2.5 + elements + BC) concentrations.

We present online aerosol chemical and elemental compositions measured at an air quality research supersite located at an urbanised arid region in the city of Doha in Qatar. Utilizing an Aerodyne Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) and Xact multi-metal analyzer, we also monitor criteria pollutants, meteorological parameters, and PMx fractions. This study represents the first continuous online aerosol chemical and elemental composition measurements in the Middle East and provides insightes in the contriubiton of natual and anthropogenic sources to PM10 and PM2.5 in different seasons.

High-mountain regions serve as valuable observatories to study atmospheric composition and dynamics, and the Italian Alpine region is of particular interest due to its sensitive environment and its proximity to the Po Valley, a major pollution hotspot in Europe. This study examined chemical composition, size distribution and optical properties of aerosols collected at three study sites located at different altitudes in the Western Italian Alps, applying a multi-proxy approach to assess sources and transport processes. Results highlighted both diurnal mesoscale circulation patterns between the Po Valley and Alpine valleys and long-range transport events, revealing different aerosol types and sources.

Since 2013, the chemical characterization on PM10 and PM2.5 samples from Milano-Pascal (UB site) are available: PAHs, elements, OC and EC, soluble inorganic salts and sugars. The chemical speciation data were also processed through the application of the PMF algorithm, statistical methods, de-seasonalization techniques, and a rolling approach over the time to evaluate long-term trends and detect potential changes in source contributions over time. High-resolution PM component analyses and size distribution measurements (SMPS) have been introduced to complement the existing long-term dataset, and to evaluate the characteristics of aerosols at different size ranges.

This study examines size-resolved particulate matter at two Moroccan sites: the rural Atlas Mohammed V observatory and urban Fez. Research conducted in September-October 2019 revealed significant urban-rural disparities. PM10 concentrations in Fez were nearly triple those at the rural site, with ultrafine particles comprising 30% of urban PM10 versus only 12% at the rural location.

Chemical analysis showed much higher carbon concentrations in urban areas. PMF identified distinct source profiles: rural areas were dominated by mineral dust and aged sea salt, while urban areas showed significant traffic emissions, biomass burning, and road dust. Secondary aerosols contributed significantly at both sites.

Aerosol chemical speciation monitors (ACSM) provide valuable data for identifying aerosol sources using techniques like positive matrix factorization (PMF). However, PMF is time-consuming, prompting the exploration of faster, machine learning-based solutions. This study investigates the use of supervised regression models trained on data from multiple European sites for aerosol source identification. Results show moderate prediction accuracy for hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), with higher error rates for less-oxidized (LO-OOA) and more-oxidized (MO-OOA) oxygenated OA.