Background The mechanism for spread of SARS-CoV-2 has been attributed to large particles produced by coughing and sneezing. There is controversy whether smaller airborne particles may transport SARS-CoV-2. Smaller particles, particularly fine particulate matter (≤ 2.5 µm in diameter), can remain airborne for longer periods than larger particles and after inhalation will penetrate deeply into the lungs. Little is known about the size distribution and location of airborne SARS-CoV-2 RNA. Methods As a measure of hospital-related exposure, air samples of three particle sizes (> 10.0 µm, 10.0–2.5 µm, and ≤ 2.5 µm) were collected in a Boston, Massachusetts (USA) hospital from April to May 2020 (N = 90 size-fractionated samples). Locations included outside negative-pressure COVID-19 wards, a hospital ward not directly involved in COVID-19 patient care, and the emergency department. Results SARS-CoV-2 RNA was present in 9% of samples and in all size fractions at concentrations of 5 to 51 copies m−3. Locations outside COVID-19 wards had the fewest positive samples. A non-COVID-19 ward had the highest number of positive samples, likely reflecting staff congregation. The probability of a positive sample was positively associated (r = 0.95, p < 0.01) with the number of COVID-19 patients in the hospital. The number of COVID-19 patients in the hospital was positively associated (r = 0.99, p < 0.01) with the number of new daily cases in Massachusetts. Conclusions More frequent detection of positive samples in non-COVID-19 than COVID-19 hospital areas indicates effectiveness of COVID-ward hospital controls in controlling air concentrations and suggests the potential for disease spread in areas without the strictest precautions. The positive associations regarding the probability of a positive sample, COVID-19 cases in the hospital, and cases in Massachusetts suggests that hospital air sample positivity was related to community burden. SARS-CoV-2 RNA with fine particulate matter supports the possibility of airborne transmission over distances greater than six feet. The findings support guidelines that limit exposure to airborne particles including fine particles capable of longer distance transport and greater lung penetration.

The Coronavirus Disease 2019 (COVID-19) pandemic spread rapidly despite extraordinary screening and social distancing measures. Such rapid spread was due in part to the fact that the disease transmission, particularly via airborne spread, is poorly understood. Characterizing the airborne size distribution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential to understanding the risk of airborne transmission. We collected size-fractionated (≤2.5, 2.5-10, and ≥10 μm) samples using a cascade impactor at more than 30 locations inside and outside Jaber Hospital and the nearby Temporary Quarantine Facility in Kuwait from April to July 2020. We hypothesized that airborne SARS-CoV-2 would be present in all size fractions, including fine particles, and in a size distribution that differed by sampling location. We found 6% of the samples (13 out of 210) were positive for SARS-CoV-2 RNA. Concentrations ranged from 3 to 25 copies/m 3 . The size distribution of particle-associated SARS-CoV-2 was different for each location. Large (≥10 μm) particles with the virus were found in symptomatic patient rooms. Fine (≤2.5 μm) particle-associated SARS-CoV-2 was detected in rooms with intubated patients and outside the hospital entrance gates. Coarse (2.5-10 μm) virus-laden particles were present in all locations with positive samples. This is the most comprehensive study to date on size-fractionated airborne SARS-CoV-2 RNA. Our findings support location-specific precautions that mitigate the spread of particles including fine particulate matter over distances greater than 1 meter, including in locations outside the hospital.

IMPORTANCE Aerosol-borne SARS-CoV-2 has not been linked specifically to nosocomial outbreaks. OBJECTIVE To explore the genomic concordance of SARS-CoV-2 from aerosol particles of various sizes and infected nurses and patients during a nosocomial outbreak of COVID-19. DESIGN, SETTING, AND PARTICIPANTS This cohort study included patients and nursing staff in a US Department of Veterans Affairs inpatient hospital unit and long-term-care facility during a

Diverse airborne microbes affect human health and biodiversity, and the Sahara region of West Africa is a globally important source region for atmospheric dust. We collected sizefractionated (>10, 10−2.5, 2.5−1.0, 1.0−0.5, and <0.5 μm) atmospheric particles in Mali, West Africa and conducted the first cultivation-independent study of airborne microbes in this region using 16S rRNA gene sequencing. Abundant and diverse microbes were detected in all particle size fractions at levels higher than those previously hypothesized for desert regions. Average daily abundance was 1.94 × 10 5 16S rRNA copies/m 3 . Daily patterns in abundance for particles <0.5 μm differed significantly from other size fractions likely because they form mainly in the atmosphere and have limited surface resuspension. Particles >10 μm contained the greatest fraction of daily abundance (51−62%) and had significantly greater diversity than smaller particles. Greater bacterial abundance of particles >2.5 μm that are bigger than the average bacterium suggests that most airborne bacteria are present as aggregates or attached to particles rather than as free-floating cells. Particles >10 μm have very short atmospheric lifetimes and thus tend to have more localized origins. We confirmed the presence of several potential pathogens using polymerase chain reaction that are candidates for viability and strain testing in future studies. These species were detected on all particle sizes tested, including particles <2.5 μm that are expected to undergo long-range transport. Overall, our results suggest that the composition and sources of airborne microbes can be better discriminated by collecting size-fractionated samples.

Inhaling radon and its progeny is associated with adverse health outcomes. However, previous studies of the health effects of residential exposure to radon in the United States were commonly based on a county-level temporally invariant radon model that was developed using measurements collected in the mid- to late 1980s. We developed a machine learning model to predict monthly radon concentrations for each ZIP Code Tabulation Area (ZCTA) in the Greater Boston area based on 363,783 short-term measurements by Spruce Environmental Technologies, Inc., during the period 2005–2018. A two-stage ensemble-based model was developed to predict radon concentrations for all ZCTAs and months. Stage one included 12 base statistical models that independently predicted ZCTA-level radon concentrations based on geological, architectural, socioeconomic, and meteorological factors for each ZCTA. Stage two aggregated the predictions of these 12 base models using an ensemble learning method. The results of a 10-fold cross-validation showed that the stage-two model has a good prediction accuracy with a weighted R 2 of 0.63 and root mean square error of 22.6 Bq/m3. The community-level time-varying predictions from our model have good predictive precision and accuracy and can be used in future prospective epidemiological studies in the Greater Boston area.