Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 μg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

Improvement of Surface PM2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission

The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM2.5. Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA's primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Low-cost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1-0.7 to 0.6-0.9 between observations and simulations of the diurnal variation of surface PM2.5. Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA's estimate of surface PM2.5.

222 nm far-UVC light markedly reduces the level of infectious airborne virus in an occupied room

An emerging intervention for control of airborne-mediated pandemics and epidemics is whole-room far-UVC (200-235 nm). Laboratory studies have shown that 222-nm light inactivates airborne pathogens, potentially without harm to exposed occupants. While encouraging results have been reported in benchtop studies and in room-sized bioaerosol chambers, there is a need for quantitative studies of airborne pathogen reduction in occupied rooms. We quantified far-UVC mediated reduction of aerosolized murine norovirus (MNV) in an occupied mouse-cage cleaning room within an animal-care facility. Benchtop studies suggest that MNV is a conservative surrogate for airborne viruses such as influenza and coronavirus. Using four 222-nm fixtures installed in the ceiling, and staying well within current recommended regulatory limits, far-UVC reduced airborne infectious MNV by 99.8% (95% CI: 98.2-99.9%). Similar to previous room-sized bioaerosol chamber studies on far-UVC efficacy, these results suggest that aerosolized virus susceptibility is significantly higher in room-scale tests than in bench-scale laboratory studies. That said, as opposed to controlled laboratory studies, uncertainties in this study related to airflow patterns, virus residence time, and dose to the collected virus introduce uncertainty into the inactivation estimates. This study is the first to directly demonstrate far-UVC anti-microbial efficacy against airborne pathogens in an occupied indoor location.

Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Indoor contribution to PM2 .5 exposure using all PurpleAir sites in Washington, Oregon, and California

Low-cost monitors have made it possible for the first time to measure indoor PM_2.5 concentrations over extended periods of time (months to years). Coupled with concurrent outdoor measurements, these indoor measurements can be divided into particles entering the building from outdoors and particles generated from indoor activities. Indoor-generated particles are not normally considered in epidemiological studies, but they can have health effects (e.g., passive smoking and high-temperature cooking). We employed The Random Component Superposition (RCS) regression model to estimate infiltration factors for up to 790 000 matched indoor and outdoor sites. The median infiltration factors for subgroups in the 3-state region ranged between 0.22 and 0.24, with an interquartile range (IQR) of 0.13-0.40. These infiltration factors allowed calculation of both the indoor-generated and outdoor-infiltrated PM_2.5 . Indoor-generated particles contributed, on average, 46%-52% of total indoor PM_2.5 concentrations. However, the site-specific fractional contribution of these indoor sources to total indoor PM_2.5 ranged from near-zero to nearly 100%. The influence of indoor-generated particles on potential exposures varied widely relative to outdoor concentrations. The greatest influence of indoor-generated particles occurred at low-to-moderate daily mean outdoor PM_2.5 levels around 6 μg/m³ and was negligible at outdoor concentrations >20 μg/m³ . Epidemiological studies incorporating only estimated exposures due to the particles of ambient origin may benefit from the newly available knowledge of long-term indoor-generated particle concentrations.