Bioaerosol Contribution to Atmospheric Particulate Matter in Indoor University Environments

Concentration, pathogenic composition, and exposure risks of bioaerosol in large indoor public environments: A comparative study of urban and suburban areas

Biological contamination in larger indoor environments can lead to the outbreak of various infectious diseases. This study aimed to compare the pollution profiles and associated health risks of airborne microorganisms in different indoor settings between urban and suburban areas by culturing, sequencing, and toxicological evaluation. The results indicated that the average level of culturable bacteria was higher in urban areas (955 ± 259 CFU/m3) compared to suburban areas (850 ± 85 CFU/m3), with the highest concentrations found in the market (2170 ± 798 CFU/m3) and gymnasium (2010 ± 300 CFU/m3). Conversely, the total number of airborne bacteria was higher in classroom (2.09 × 105) and laboratory (1.95 × 105 copies/m3), likely due to the presence of viable but non-culturable cells. Additionally, the concentrations of 0.5-2.0 μm total particles were higher in the market and cafeteria. Dominant airborne genera included Acinetobacter and Pseudomonas for bacteria, Cladosporium and Aspergillus for fungi, as well as Geneviridae and Herpesviridae for viruses. Bacterial and viral diversity and richness were significantly higher in suburban areas compared to urban areas, with distinct viral communities observed in hospital. Cytotoxicity assays revealed lower viability of cells in response to bioaerosols from the library (52.3 %) and laboratory (54.5 %); while lower proliferation rates were found for the cells exposed to bioaerosol from gymnasium (5.4 %) and market (6.0 %), suggesting higher toxicity of these environments. Additionally, bioaerosol exposure may impair cellular innate immunity by increasing the expression of IL-6, IL-8, TNF-α, IFN-γ. Our findings provide valuable information for assessing and controlling bioaerosol-related health risks in indoor environments.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM_2.5 and PM₁₀) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %-80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2- and 2.4--hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to daytime. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North-European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

Initial experimental multi-wavelength EEM (Excitation Emission Matrix) fluorescence lidar detection and classification of atmospheric pollen with potential applications toward real-time bioaerosols monitoring

Fluorescence has the potential to identify the types of substances associated with aerosols. To demonstrate its usefulness in environmental studies, we investigated the use of Excitation-Emission-Matrix (EEM) fluorescence in lidar bioaerosol monitoring. First, the EEM fluorescence of cedar, ragweed, and apple pollens as typical bioaerosols found around our surroundings were measured using a commercial fluorescence spectrometer. We found that the patterns of fluorescence changed depending on the pollen type and excitation wavelength and it meant that studying these EEM fluorescence patterns was a good parameter for identifying pollen types. Then, we setup a simple EEM fluorescence lidar to confirm the usefulness in lidar bioaerosol monitoring. The lidar consisted of three laser diodes and one light emitting diode with output at 520 nm, 445 nm, 405 nm and 325 nm, respectively, an ultra violet camera lens as a receiver, and a fluorescence spectrum detection unit. Comparing the lidar simulation results with the EEM fluorescence dataset supported the possibility of performing bioaerosol monitoring using the EEM fluorescence lidar. Based on the results and the current technology, a feasible design of a bioaerosol detection EEM fluorescence lidar is proposed for future rel-time remote sensing and mapping of atmospheric bioaerosols.

Relationship between Indoor High Frequency Size Distribution of Ultrafine Particles and Their Metrics in a University Site

Exposure to ultrafine particles (UFPs size < 100 nm) in life and work environments can contribute to adverse health effects also in terms of health burden of related diseases over time. The choice of parameters which better characterize UFPs is challenging, due to their physical-chemical properties and their variable size. It is also strictly related to the availability of different instrumental techniques. In the present study we focus on real time high frequency (1 Hz) UFPs particle size distribution (PSD) and their relationship with total particle number concentration (TPNC) and mean particle diameter (Davg) as a contribution characterizing by size the human exposure to UFPs in an indoor site of the University of Rome “Sapienza” (Italy). Further considerations about UFPs contribution to nucleation mode (NM) and accumulation mode (AM) have been highlighted, also in order to investigate the contribution of polycyclic aromatic hydrocarbons (PAHs) surface-adsorbed on indoor air particles (pPAHs). High indoor TPNC values were registered during the rush hours (early morning and mid/late afternoon) according to the outdoor influences originated from anthropogenic activities. AM mainly contribute to the indoor TPNC during working days showing high correlation with pPAHs. These findings may provide useful indications in terms of occupational exposure to UFPs since there are many evidences that indoor exposures to such pollutants may be associated with adverse health effects also in working environments.

Seasonal Variations in the Chemical Composition of Indoor and Outdoor PM10 in University Classrooms

In the VIEPI project (Integrated evaluation of the exposure to indoor particulate matter) framework, we carried out a 1-year study of the concentration and chemical composition of particulate matter (PM) in a 5 story building in the Sapienza University of Rome (Italy). Each sampling had a duration of 1 month and was carried out indoors and outdoors in six classrooms. The chemical analyses were grouped to obtain information about the main PM sources. Micro-elements in their soluble and insoluble fractions were used to trace additional sources. Indoor PM composition was dominated by soil components and, to a lesser extent, by the organics, which substantially increased when people crowded the sites. The penetration of PM components was regulated by their chemical nature and by the dimensions of the particles in which they were contained. For the first time in crowded indoor environments, three different chemical assays aimed to determine PM redox properties complemented chemical composition measurements. These preliminary tests showed that substantially different redox properties characterised atmospheric particles in indoor and outdoor sites. The innovative characteristics of this study (time duration, number of considered environments) were essential to obtain relevant information about PM composition and sources in indoor academic environments and the occupants’ role.