Aerosol Generation During Nasal Airway Instrumentation.

OBJECTIVE: Airborne aerosol transmission, an established mechanism of SARS-CoV-2 spread, has been successfully mitigated in the health care setting through the adoption of universal masking. Upper airway endoscopy, however, requires direct access to the face, thereby potentially exposing the clinic environment to infectious particles. This study quantifies aerosol production during rigid nasal endoscopy (RNE) and RNE with debridement (RNED) as compared with intubation, a posited gold standard aerosol-generating procedure. STUDY DESIGN: Prospective cross-sectional study. SETTING: Subspecialty single-center clinic and surgical study. METHOD: Three aerosol detectors (NANOSCAN-3910, OPS-3330, and APS-3321) with a particle size sensitivity of 10 to 20,000 nm were utilized to detect particulate production during the clinical care of 209 patients undergoing RNE/RNED and 25 patients undergoing intubation. RESULTS: RNE and RNED produced statistically significant particles over baseline in 29.3% and 51.0% of subjects (P = .003-.049 and .002-.047, respectively). Intubation produced statistically significant particles in 31.2% (P = .001-.015). The mean ± SD particle diameter in all tests was 69.9 ± 10.5 nm with 99.7% <300 nm. There were no statistical differences in particle production among RNE, RNED, and intubation. The presence of concomitant cough, sneeze, or prolonged speech similarly did not significantly affect particle production during any procedure. CONCLUSIONS: Instrumentation of nasal airway produces airborne aerosols to a similar degree of those seen during intubation, independent of reactive patient behaviors such as cough or sneeze. These data suggest that an improved understanding is necessary of both the definition of an aerosol-generating procedure and the functional consequences of procedural aerosol generation in clinical settings.

Airborne Aerosolized Mouse Cytomegalovirus From Common Otolaryngology Procedures: Implications for COVID-19 Infection.

OBJECTIVES: To determine whether common otolaryngology procedures generate viable aerosolized virus through a murine cytomegalovirus (mCMV) model for infection. STUDY DESIGN: mCMV model of infection. SETTING: University of Utah laboratory. METHODS: Three-day-old BALB/c mice were inoculated with mCMV or saline. Five days later, each mouse underwent drilling, microdebrider, coblation, and electrocautery procedures. Particle size distribution and PM2.5 (particulate matter <2.5 µm) concentration were determined with a scanning mobility particle sizer and an aerosol particle sizer in the range of 15 nm to 32 µm. Aerosolized samples from these procedures were collected with an Aerosol Devices BioSpot sampler for viral titer based on polymerase chain reaction and for viable virus through viral culture. RESULTS: As compared with the background aerosol concentrations, coblation and electrocautery showed statistically significant increases in airborne aerosols (Tukey-adjusted P value <.040), while microdebrider and drilling at 30,000 rpm did not (.870 < Tukey-adjusted P value < .930). We identified viral DNA in samples from coblation and drilling procedures, although we did not identify viable viruses in aerosol samples from any of the 4 procedures. CONCLUSION: Coblation and electrocautery procedures generate >100-fold increases in aerosol concentrations over background; only coblation and drilling produce aerosolized viral DNA. The high concentration of aerosols from coblation and electrocautery suggests the need for appropriate safeguards against particle exposure to health care workers. The presence of viral DNA from drilling and coblation procedures warrants the need for appropriate protection against droplet and aerosol exposure.

Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes.

Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3-7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3-21.5 µg/m3, n(normalized)RMSE: 14.9-24%), the wildfire (n: 46, RMSE: 2.6-4.0 µg/m3; nRMSE: 13.1-22.9%), and the PCAP (n: 96, RMSE: 4.9-5.7 µg/m3; nRMSE: 20.2-21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor.

Ozone (O3) is a potent oxidant associated with adverse health effects. Low-cost O3 sensors, such as metal oxide (MO) sensors, can complement regulatory O3 measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O3 although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O3 federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O3 concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O3 concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU's MO oxidizing species and temperature measurements and DAQ's solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R2 = 0.491), and the ANN exhibited good performance (R2 = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O3 for five months after the calibration period and the results showed moderate correlations (R2s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O3 levels.

Patterns of distributive environmental inequity under different PM2.5 air pollution scenarios for Salt Lake County public schools.

Previous studies have cataloged social disparities in air pollution exposure in US public schools with respect to race/ethnicity and socioeconomic status. These studies rely upon chronic, averaged measures of air pollution, which fosters a static conception of exposure disparities. This paper examines PM2.5 exposure disparities in Salt Lake County (SLC), Utah public schools under three different PM2.5 scenarios-relatively clean air, a moderate winter persistent cold air pool (PCAP), and a major winter PCAP-with respect to race/ethnicity, economic deprivation, student age, and school type. We pair demographic data for SLC schools (n = 174) with modelled PM2.5 values, obtained from a distributed network of sensors placed through a community-university partnership. Results from generalized estimating equations controlling for school district clustering and other covariates reveal that patterns of social inequality vary under different PM2.5 pollution scenarios. Charter schools and schools serving economically deprived students experienced disproportionate exposure during relatively clean air and moderate PM2.5 PCAP conditions, but those inequalities attenuated under major PCAP conditions. Schools with higher proportions of racial/ethnic minority students were unequally exposed under all PM2.5 pollution scenarios, reflecting the robustness of racial/ethnic disparities in exposure. The findings speak to the need for policy changes to protect school-aged children from environmental harm in SLC and elsewhere.