This study explores the toxicological effects of PM_0.3-2.5 and its organic extract (O-PM) on BEAS-2B cells, using two exposure models: classic submerged exposure and an air-liquid interface (ALI) system treated using the Vitrocell® Cloud alpha system. We report on the low deposition efficiency of airborne PM_0.3-2.5 sampled at an industrial site in northern France and the comparison of the effects of O-PM on cells at equal mass/surface concentrations in both exposure conditions. The importance of properly determining the deposition efficiency and comparing the effects by selecting the proper negative control is also highlighted.

This study investigates the oxidative potential (OP) of particulate matter (PM) emitted from a modern biomass combustion boiler with a nominal power of 15 kW (REKA HKRST-FSK-20 kW). Four biomasses for residential heating which are hardwood pellets, softwood pellets, hardwood chips, and softwood chips were tested. The sampled PM was analyzed by using two oxidative potential (OP) assays: ascorbic acid (AA) and dithiothreitol (DTT). By identifying the biomass fuels that produce PM with higher OP, this study provides critical insights for optimizing biomass selection and usage to minimize adverse health effects.

We developed an innovative technology for near real-time, autonomous detection of airborne viral particles using an optical detection system. The system leverages particle scattering spectra and machine-learning algorithms to identify and distinguish viral particles from common airborne particles. It was tested with air samples spiked with inactivated SARS-CoV-2 virus particles, showing strong correlation with digital PCR measurements. This technology enhances public health preparedness by providing quick warnings with minimal human intervention, making it suitable for indoor settings like airports, hospitals, and schools. It can increase our ability to respond to emerging viral threats.

The atmosphere transports microorganisms across ecosystems, with dust storms carrying large quantities over great distances. This study analyzed air samples from 13 dusty and 32 clear days in the Middle East, identifying facultative human and plant pathogens like Klebsiella pneumoniae and Fusarium poae. Pathogen abundance increased with dust storms and rising temperatures. Dust also carried up to 125 times more antibiotic resistance genes (ARGs) than clear air, some linked to mobile genetic elements, enabling resistance transfer. These findings highlight dust storms’ role in pathogen and ARG dispersal, stressing the need for continuous atmospheric microbiome monitoring for health and environmental risks.

Particulate matter concentration is employed in air quality guidelines. However, its oxidative potential has been suggested as an alternative. We propose a deep-learning method to estimate oxidative potential mean daily concentration from the combination of features extracted from surface reflectance images and meteorological variables. The proposed architecture comprises: a ResNet50, working on the images, and an MLP, which makes the prediction based on the concatenation of both information. We conducted preliminary experiments to predict PM10 concentration at three monitoring stations in Grenoble, and we obtained promising results to be exploited as baseline for the estimation of PM10’s OP daily average.

Monitoring CO2 levels is usually considered as a simple way to assess the renovation of indoor air. Moreover, COVID-19 pandemic generated an increased interest in enhancing the indoor air quality. To address these issues, the concentration of CO2 and PM were measured in the conference venue during the European Aerosol Conference (EAC2023) in Malaga (Spain). A balance equation is shown to fit well the CO2 measurements. Autoregressive average values for the PMs are also discussed. These CO2 and PM data are correlated to show the validity of CO2 measurements to evaluate the overall ventilation quality in a public indoor space.