Top-down, data-driven models possess ample power to improve the accuracy of bottom-up carbon dioxide (CO2) emission inventories, and more work is needed to explore the merger of top-down and bottom-up estimates to better inform the metrics used to monitor global CO2 fluxes. Here we present a Bayesian inverse modeling framework over Salt Lake City, Utah, which utilizes available CO2 emission inventories to establish a synthetic data simulation aimed at exploring model uncertainties. Prescribing a high-resolution, urban-scale data product (Hestia) as the “true” emissions in the model, we combine prior emissions with an atmospheric transport model to derive modeled afternoon CO2 enhancements at six monitoring sites within the Salt Lake Valley during the month of September 2015. A global high-resolution gridded emissions data product (ODIAC) is used as the prior, and objective uncertainty structures are defined for both the a priori estimates and the transport model-data relationship which consider non-negligible spatial and temporal covariances. Optimized (posterior) emissions over the Salt Lake Valley agree closely with the assumed “true” emissions during afternoon times, while results including unconstrained times (e.g. night-time) lack such agreement. Both spatial and temporal correlations of prior errors were found to be necessary for obtaining a robust posterior estimate. Model sensitivity analyses are performed, which examine correlation length and time scales, model-data mismatch error, and measurement site network variability. Through these analyses, one measurement site is identified as being particularly prone to introducing bias into posterior emissions due to influences from a nearby point source. Increasing model-data mismatch error at this site is shown to reduce bias in the posterior without significantly compromising agreement with monthly averaged true emissions.

The growing concern of air quality and its associated health-related impacts has led to increased awareness of pollutant exposure. Most human populations spend the majority of their time indoors and the COVID-19 pandemic has likely exacerbated this behavior. While significant amounts of research have focused on outdoor air quality, to date there have been no studies that examined simultaneous long-term trends on indoor and outdoor air quality on a site using research-grade sensors. We measured fine particulate matter (PM_2.5) for a year using sensors located on the rooftop, air handling room, and indoor office space in a building and captured the impacts of three types of regularly occurring elevated pollution events: wintertime atmospheric inversions, wildfires, and fireworks. The events had different magnitudes and durations, and infiltration rates varied for each event leading to dissimilar indoor air pollution levels. The building's air handling unit and different environmental conditions (lower indoor humidity and temperature during the winter) combined to reduce indoor pollution from inversion events however, particulate matter from wildfires and fireworks infiltrated at higher rates. Together, this suggests possible intervention strategies, such as ventilation rates and filter upgrades, that could be used to mitigate contaminant intrusion during elevated pollution events. This year-long study illustrates an array of ways that elevated pollution events interact with the protective effects that buildings have against air pollution for its occupants. Furthermore, we show that outdoor air pollution is an important variable to consider when studying indoor air quality as contaminant infiltration is strongly dependent on the specific pollution source.

Fine particulate air pollution is harmful to children in myriad ways. While evidence is mounting that chronic exposures are associated with reduced academic proficiency, no research has examined the frequency of peak exposures. It is also unknown if pollution exposures influence academic proficiency to the same degree in all schools or if the level of children’s social disadvantage in schools modifies the effects, such that some schools’ academic proficiency levels are more sensitive to exposures. We address these gaps by examining the percentage of third grade students who tested below the grade level in math and English language arts (ELA) in Salt Lake County, Utah primary schools (n = 156), where fine particulate pollution is a serious health threat. More frequent peak exposures were associated with reduced math and ELA proficiency, as was greater school disadvantage. High frequency peak exposures were more strongly linked to lower math proficiency in more advantaged schools. Findings highlight the need for policies to reduce the number of days with peak air pollution.

High air pollution levels are associated with school absences. However, low level pollution impacts on individual school absences are under-studied. Understanding the variability of pollution at individual schools within an urban region could improve school recess decisions, better identify local pollution sources, and improve local economic impact assessments by providing granular information relevant to specific schools. We modelled PM_2.5 and ozone concentrations at 36 schools from July 2015 to June 2018 using data from a dense, research grade regulatory sensor network. We determined exposures and daily absences at each school. We used a generalized estimating equations model to retrospectively estimate rate ratios for association between outdoor pollutant concentrations and school absences. We estimated lost school revenue, productivity, and family economic burden. PM_2.5 and ozone concentrations and absence rates vary across the School District. Pollution exposure was associated with a rate ratio as high as 1.02 absences per μg m⁻³ and 1.01 per ppb increase for PM_2.5 and ozone, respectively. Significantly, even PM_2.5 and ozone exposure below the air quality index breakpoints for good air quality (<12.1 μg m⁻³ and <55 ppb, respectively) was associated with positive rate ratios of absences: 1.04 per μg m⁻³ and 1.01 per ppb increase, respectively. Granular local measurements enabled demonstration of air pollution impacts that varied between schools and were undetectable with averaged pollution levels. Reducing pollution by 50% would save $426 000 per year districtwide. Pollution reduction benefits would be greatest in schools located in socioeconomically disadvantaged areas. Heterogeneity in exposure, disproportionately affecting socioeconomically disadvantaged schools, points to the need for fine resolution exposure estimation. The economic cost of absences associated with air pollution is substantial even excluding indirect costs such as hospital visits and medication. These findings may help elucidate the differential burden on individual schools and inform local decisions about recess and regulatory considerations for localized pollution sources.

This study aimed to determine if short-term exposure to particulate matter (PM_2.5) and ozone (O₃) is associated with increased symptoms or lung function decline in fibrotic sarcoidosis. Sixteen patients with fibrotic sarcoidosis complicated by frequent exacerbations completed pulmonary function testing and questionnaires every three months for one year. We compared 7-, 10-, and 14-day average levels of PM_2.5 and O₃ estimated at patient residences to spirometry (forced expiratory volume in 1 s (FEV1), to forced vital capacity (FVC), episodes of FEV1 decline > 10%) and questionnaire outcomes (Leicester cough questionnaire (LCQ), Saint George Respiratory Questionnaire (SGRQ), and King’s Sarcoidosis Questionnaire (KSQ)) using generalized linear mixed effect models. PM_2.5 level averaged over 14 days was associated with lower KSQ general health status (score change −6.60 per interquartile range (IQR) PM_2.5 increase). PM_2.5 level averaged over 10 and 14 days was associated with lower KSQ lung specific health status (score change −6.93 and −6.91, respectively). PM_2.5 levels were not associated with FEV₁, FVC, episodes of FEV₁ decline > 10%, or respiratory symptoms measured by SGRQ or LCQ. Ozone exposure was not associated with any health outcomes. In this small cohort of patients with fibrotic sarcoidosis, PM_2.5 exposure was associated with increased severity of respiratory and quality of life symptoms.

Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year, state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM_2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke, as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express (“TRAX”) system were outfitted with PM_2.5 and ozone sensors to measure air quality at high spatial and temporal resolutions in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Three case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: (1) an elevated ozone event, (2) elevated particulate pollution resulting from 4th of July fireworks, and (3) elevated particle pollution during a winter time inversion event. The mobile observations were able to capture spatial gradients, as well as pollutant hotspots, during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north–south oriented rail line, where air quality was unable to be monitored previously. The TRAX Observation Project is currently being used to provide reliable pollutant data for health studies and inform urban planning efforts. Links to real-time data displays and updated information on the quality-controlled data from this study are available on the webpage for the Department of Atmospheric Sciences at the University of Utah.