Biological particulate matter or bioaerosol are a subset of atmospheric aerosol. They influence climate, air quality, and health via several mechanisms which often are poorly understood. In particular, the quantitative study of possible relationship between bioaerosol viability and air quality or meteorological conditions is an open and relevant issue. The difficulty of retrieving such possible correlations by analyses of data collected during in-field campaigns can benefit of targeted experiments conducted in well-controlled conditions inside atmospheric simulation chambers (ASCs). ChAMBRe (Chamber for Aerosol Modelling and Bio-aerosol Research) is an ASC in Genoa (Italy) designed and built to perform experimental research on bioaerosol. In this article, we focus on bacteria viability. A multi-step protocol was developed and thoroughly tested to cultivate a suitable bacteria population (E. coli), nebulize and inject in a chamber of viable cells, expose and monitor the viability variation inside ChAMBRe, hold at selected conditions, and finally incubate and count the concentration of viable bacteria. The whole procedure showed an estimated lifetime of total (T) and viable (V) E. coli of about 153 and 32 min, respectively and a V : T lifetime ratio of 40±5 min when ChAMBRe is held in a reference baseline condition. The coefficient of variation of 13 % shows how sensitive the protocol is also to changes in viability when the bacteria are exposed to other (e.g. polluted) conditions. First results showing a viability reduction observed exposing the E. coli strain to NO_x concentrations and solar irradiation are presented and discussed. The present results pave the way for systematic studies aimed at the definition of dose–effect relationship for several bacteria strains under different conditions of atmospheric pollutants.

Soot aerosol generated from the incomplete combustion of biomass and fossil fuels is a major light-absorber; however its spectral optical properties for varying black carbon (BC) and brown carbon (BrC) content remain uncertain. In this study, soot aerosols with varying maturity and composition, i.e. elemental-to-total-carbon ratio ($\mathrm{EC}/\mathrm{TC}$ ), have been studied systematically in a large simulation chamber to determine their mass absorption, scattering, and extinction cross sections (MAC, MSC, MEC); single-scattering albedo (SSA); and absorption and scattering Ångström exponents (AAE, SAE). The MAC, MEC, SSA, and AAE show a variability between the different types of soot with varying $\mathrm{EC}/\mathrm{TC}$ ratios. The MAC (MEC) at 550 nm increases for increasing $\mathrm{EC}/\mathrm{TC}$ , with values of 1.0 (1.4) m² g⁻¹ for $\mathrm{EC}/\mathrm{TC}$ = 0.0 (BrC-dominated soot) and 4.6 (5.1) m² g⁻¹ for $\mathrm{EC}/\mathrm{TC}$ = 0.79 (BC-dominated soot). The AAE and SSA (550 nm) decrease from 3.79 and 0.29 ($\mathrm{EC}/\mathrm{TC}$ = 0.0) to 1.27 and 0.10 ($\mathrm{EC}/\mathrm{TC}$ = 0.79). Combining present results for soot from propane combustion with literature data for flame soot from diverse fuels supports a generalised exponential relationship between particle $\mathrm{EC}/\mathrm{TC}$ and its MAC and AAE values $({\mathrm{MAC}}_{\mathrm{550}}=(\mathrm{1.3}\pm \mathrm{0.05})$ $e^{(\mathrm{1.8}\pm \mathrm{0.1})\left(\frac{\mathrm{EC}}{\mathrm{TC}}\right)}$ ; $\mathrm{AAE}=(\mathrm{0.73}\pm \mathrm{0.12})+(\mathrm{3.29}\pm \mathrm{0.12})$ $e^{-(\mathrm{2.32}\pm \mathrm{0.30})\left(\frac{\mathrm{EC}}{\mathrm{TC}}\right)})$ , which represents the optical continuum of spectral absorption for soot with varying maturity. From this, it is possible to extrapolate a MAC of 7.9 and 1.3 m² g⁻¹ (550 nm) and an AAE (375–870 nm) of 1.05 and 4.02 for pure EC (BC-like) and pure OC (BrC-like) soot. The established relationship can provide a useful parameterisation for models to estimate the absorption from combustion aerosols and their BC and BrC contributions.

In today's rapidly evolving society, the sources of atmospheric particulate matter (PM) emissions are shifting significantly. Stringent regulations on vehicle tailpipe emissions, in combination with a lack of control of non-exhaust vehicular emissions, have led to an increase in the relative contribution of non-exhaust PM in Europe. This study analyzes the spatial distribution, temporal trends, and impacts of brake wear PM pollution across Europe by modeling copper (Cu) concentrations at a high spatial resolution of ∼250 m which is a key tracer of brake-wear emissions. We integrated coarse-resolution brake-wear Cu from CAMx chemical transport model and high-resolution land use data into a random forest (RF) model to predict Cu concentrations at ∼250 m over whole of continental Europe. The RF model was trained using an unprecedented dataset of over 50,000 daily Cu measurements from 152 sites. It corrected CAMx underestimation and downscaled Cu to a higher spatial resolution. In validation, the model showed robust spatial and temporal prediction with good Pearson's correlation coefficients of 0.6 and 0.7, respectively. We generated 10 years (2010-2019) of daily Cu concentrations over Europe, revealing spatial patterns aligned with urbanization and road networks, with peaks in cities and lower values in rural areas. Temporal trends reveal that Cu concentrations generally peak on weekdays and in winter. Despite a decline in PM across Europe over decades, Cu concentrations showed no decrease in many cities from 2010 to 2019. Cu levels are strongly correlated with population density with more than 12 million Europeans exposed to levels exceeding 40 ng/m³, equivalent to around 1 μg/m³ of total PM₁₀ from brake wear. Our findings highlight the need for expanded metal measurement for non-exhaust tracers for a better understanding of the health relevance of PM composition including Cu, and more effective regulations of non-exhaust PM emissions as included in EURO 7 vehicles.

The airborne transmission of bacterial pathogens poses a significant challenge to public health, especially with the emergence of antibiotic-resistant strains. This study investigated environmental factors influencing the survival of airborne bacteria, focusing on the effects of different carbon dioxide (CO₂) and dust concentrations. The experiments were conducted in an atmospheric simulation chamber using the non-resistant wild-type E. coli K12 (JM109) and a multi-resistant variant (JM109-pEC958). Different CO₂ (100 ppm, 800 ppm, 3000 ppm) and dust concentrations (250 µg m⁻³, 500 µg m⁻³, 2000 µg m⁻³) were tested to encompass a wide range of CO₂ and dust levels. The results revealed that JM109-pEC958 exhibited greater resilience to high CO₂ and dust concentrations compared to its non-resistant counterpart. At 3000 ppm CO₂, the survival rate of JM109 was significantly reduced, while the survival rate of JM109-pEC958 remained unaffected. At the dust concentration of 250 µg m⁻³, JM109 exhibited significantly reduced survival, whereas JM109-pEC958 did not. When the dust concentration was increased to 500 and 2000 µg m⁻³, even the JM109-pEC958 experienced substantially reduced survival rates, which were still significantly higher than those of its non-resistant counterpart at these concentrations. These findings suggest that multi-resistant E. coli strains possess mechanisms enabling them to endure extreme environmental conditions better than non-resistant strains, potentially involving regulatory genes or efflux pumps. The study underscores the importance of understanding bacterial adaptation strategies to develop effective mitigation approaches against antibiotic-resistant bacteria in atmospheric environments. Overall, this study provides valuable insights into the interplay between environmental stressors and bacterial survival, serving as a foundational step towards elucidating the adaptation mechanisms of multi-resistant bacteria and informing strategies for combating antibiotic resistance in the atmosphere.

Instruments measuring aerosol light absorption, such as the Aethalometer and the Multi-Wavelength Absorbance Analyzer (MWAA), have been extensively used to characterize optical absorption of atmospheric particulate matter. Data retrieved with such instruments can be analysed with mathematical models to apportion different aerosol sources (Aethalometer model) and components (MWAA model). In this work we present an upgrade to the MWAA optical apportionment model. In addition to the apportionment of the absorption coefficient b_abs in its components (black carbon and brown carbon) and sources (fossil fuels and wood burning), the extended model allows for the retrieval of the absorption Ångström exponent of each component and source, thereby avoiding initial assumptions regarding these parameters. We also present a new open-source software toolkit, the MWAA model toolkit (MWAA_MT), written in both Python and R, that performs the entire apportionment procedure.

This study investigates the optical properties and variability of the mass absorption coefficient (MAC) of carbonaceous aerosols produced by the combustion of different fuels. Emissions were also characterized in terms of particle size distribution and concentrations of elemental carbon (EC) and organic carbon (OC). Experiments were conducted in an atmospheric simulation chamber with a soot generator fueled with propane and a commercial diesel engine running on regular diesel and hydrotreated vegetable oil (HVO). Different methods of sampling and analyzing carbonaceous aerosols were evaluated, focusing on workplace environments. The EC : TC (total carbon) ratios were found to be 0.7 ± 0.1 for propane, 0.15 ± 0.05 for diesel, and 0.4 ± 0.2 for HVO, indicating a higher proportion of OC in the diesel and HVO samples. Fresh soot particles showed monomodal log-normal distributions with peaks varying based on the fuel type and combustion process, with propane particles exhibiting a peak at larger particle sizes compared to HVO and diesel. The optical properties revealed that the MAC values varied across different fuel exhausts. Diesel combustion produced more light-absorbing particles compared to propane and HVO, with MAC values measured between 870 and 635 nm ranging from 6.2 ± 0.5 to 9.4 ± 0.4 m² g⁻¹ for commercial diesel, 5.2 ± 0.5 to 7.8 ± 1.1 m² g⁻¹ for propane, and 5.8 ± 0.2 to 8.4 ± 0.6 m² g⁻¹ for HVO.