Usability of a Personal Air Pollution Monitor: Design-Feedback Iterative Cycle Study

Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models

The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.

Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.

Miniaturizing Wet Scrubbers for Aerosolized Droplet Capture

Aerosols dispersed and transmitted through the air (e.g., particulate matter pollution and bioaerosols) are ubiquitous and one of the leading causes of adverse health effects and disease transmission. A variety of sampling methods (e.g., filters, cyclones, and impactors) have been developed to assess personal exposures. However, a gap still remains in the accessibility and ease-of-use of these technologies for people without experience or training in collecting airborne samples. Additionally, wet scrubbers (large non-portable industrial systems) utilize liquid sprays to remove aerosols from the air; the goal is to "scrub" (i.e., clean) the exhaust of industrial smokestacks, not collect the aerosols for analysis. Inspired by wet scrubbers, we developed a device fundamentally different from existing portable air samplers by using aerosolized microdroplets to capture aerosols in personal spaces (e.g., homes, offices, and schools). Our aerosol-sampling device is the size of a small teapot, can be operated without specialized training, and features a winding flow path in a supersaturated relative humidity environment, enabling droplet growth. The integrated open mesofluidic channels shuttle coalesced droplets to a collection chamber for subsequent sample analysis. Here, we present the experimental demonstration of aerosol capture in water droplets. An iterative study optimized the non-linear flow manipulating baffles and enabled an 83% retention of the aerosolized microdroplets in the confined volume of our device. As a proof-of-concept for aerosol capture into a liquid medium, 0.5-3 μm model particles were used to evaluate aerosol capture efficiency. Finally, we demonstrate that the device can capture and keep a bioaerosol (bacteriophage MS2) viable for downstream analysis.

Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network

Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.

Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification

The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disease and exacerbation of respiratory diseases such as asthma and pneumonia. PM originates from natural and anthropogenic sources including combustion engines, cigarettes, agricultural burning, and forest fires. Identifying the source of PM can inform effective mitigation strategies and policies, but this is difficult to do using current techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm. We collected combustion generated PM2.5 from wood burning, diesel exhaust, and cigarettes using filters. Filters were weighted to determine mass concentration followed by extraction into cyclohexane and analysis by EEM fluorescence spectroscopy. Spectra obtained from each source served as training data for a convolutional neural network (CNN) used for source identification in mixed samples. This method can predict the presence or absence of the three laboratory sources with an overall accuracy of 89% when the threshold for classifying a source as present is 1.1 μg/m3 in air over a 24-hour sampling time. The limit of detection for cigarette, diesel and wood are 0.7, 2.6, 0.9 μg/m3, respectively, in air assuming a 24-hour sampling time at an air sampling rate of 1.8 liters per minute. We applied the CNN algorithm developed using the laboratory training data to a small set of field samples and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable.

The Washington State Twin Registry: 2019 Update

It has been over 5 years since the last special issue of Twin Research and Human Genetics on 'Twin Registries Worldwide: An Important Resource for Scientific Research' was published. Much progress has been made in the broad field of twin research since that time, and the current special issue is a follow-up to update the scientific community about twin registries around the globe. The present article builds upon our 2013 Registry description by summarizing current information on the Washington State Twin Registry (WSTR), including history and construction methods, member characteristics, available data, and major research goals. We also provide a section with brief summaries of recently completed studies and discuss the future research directions of the WSTR. The Registry has grown in terms of size and scope since 2013; highlights include recruitment of youth pairs under 18 years of age, extensive geocoding work to develop environmental exposures that can be linked to survey and administrative health data such as death records, and expansion of a biobank with specimens collected for genotyping, DNA methylation, and microbiome based-studies.