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Browsing by Author "Ford, Bonne, committee member"

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    Estimating spatiotemporal trends in wildfire smoke concentrations in the western United States
    (Colorado State University. Libraries, 2018) O'Dell, Katelyn, author; Pierce, Jeffrey R., advisor; Fischer, Emily V., advisor; Ford, Bonne, committee member; Magzamen, Sheryl, committee member
    The United States (US) has seen significant improvements in seasonal air quality over the past several decades. However, particulate air quality in summer over the majority of the western US has seen little improvement in recent decades. Particulate matter with diameters < 2.5 microns (PM2.5) is a large component of ambient air quality that is associated with negative health effects and visibility degradation. Wildfires are a major summer source of PM2.5 in the western US. While anthropogenic-related sources of PM2.5 have decreased across the US, wildfires have increased in both frequency and burn area since the 1980s. It is currently uncertain how this increase in wildfires has impacted seasonal air quality trends and how the health effects of wildfire-emitted PM2.5 may differ from anthropogenic-sourced PM2.5. We do not directly address the latter uncertainty, but rather focus on improving smoke-exposure estimates, which are a critical, yet challenging, component to understanding the health effects of wildfire-emitted PM2.5. In this thesis, we use a combination of satellite estimates, surface observations, and chemical transport models to distinguish wildfire smoke PM2.5 from non-wildfire-smoke PM2.5 during the summer in the US. We update the record of seasonal trends in PM2.5 observed at surface monitors and provide the first estimates of trends in wildfire smoke-specific PM2.5. We find continued decreases in total-PM2.5 in most seasons and regions of the US. In the summer in heavily fire-impacted regions of the western US, we find non-decreasing total-PM2.5 while wildfire smoke-specific PM2.5 has increased and non-wildfire-smoke PM2.5 has decreased. We test the application of blended smoke exposure models, which use multiple data sources as input variables (e.g. satellite-derived aerosol optical depth, chemical transport models, etc.), across the full western US. We incorporate a novel dataset into the model, Facebook posts, which have been shown to correlate well with surface PM2.5 concentrations during the western US wildfire season. We find the blended smoke exposure model performs well across the western US (R2 = 0.66). However, the Facebook dataset is well correlated with interpolated surface monitors (another input variable) and thus does not significantly improve blended smoke-exposure estimates in the western US.
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    ItemOpen Access
    Health-relevant pollutants in US landscape fire smoke: abundance, health impacts, and influence on indoor and outdoor air quality
    (Colorado State University. Libraries, 2021) O'Dell, Katelyn, author; Pierce, Jeffrey R., advisor; Fischer, Emily V., advisor; Collett, Jeffrey L., Jr., committee member; Ford, Bonne, committee member; Magzamen, Sheryl, committee member
    Landscape (wild, prescribed, and agricultural) fires have a significant impact on air quality in the United States (US). As anthropogenic emissions decline and emissions from landscape fires increase across the coming century, the relative importance of landscape fire smoke on US air quality and health will increase. Landscape fire smoke is a complex mixture of multiple gas- and particle-phase pollutants, which are harmful to human health. The health impacts of landscape fire smoke may differ from urban pollution as the seasonal and spatial distribution, particle size distribution and composition, and relative abundance of gas-phase species in landscape fire smoke are different from urban pollution sources. Epidemiology studies of smoke events, which often rely on particulate matter (PM) concentrations as a smoke exposure tracer, show smoke negatively impacts respiratory health. The contribution of gas-phase hazardous air pollutants (HAPs) to the health impacts of smoke has yet to be directly quantified. In addition, the implications of episodic landscape fire emissions on the sub-national temporal and spatial distribution of health events are not well characterized. Finally, a majority of the work on the health and air quality impacts of landscape fire smoke has focused on outdoor air. Recent works have shown that landscape fire smoke can impact indoor air quality, but there is large heterogeneity in both smoke events and the indoor environments impacted by smoke events. To date, no study of US wildfire smoke influence on indoor air quality has analyzed indoor fine particulate matter (PM2.5) concentrations across multiple western US cities during multiple extreme smoke events. In the first chapter of this dissertation, we combine aircraft-based in-situ smoke plume observations with interpolated regulatory surface monitor observations to quantify the health risk of HAPs in US smoke. Using observations from the Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN), a 2018 aircraft-based field campaign that measured HAPs and PM in western US wildfire smoke plumes, we identify the relationships be- tween HAPs and associated health risks, PM, and smoke age. We find the ratios between acute, chronic noncancer, and chronic cancer HAPs health risk and PM in smoke decrease as a function of smoke age by up to 72% from fresh (<1 day of aging) to old (>3 days of aging) smoke. We show that acrolein, formaldehyde, benzene, and hydrogen cyanide are the dominant contributors to gas-phase HAPs risk in smoke plumes. We use ratios of HAPs to PM along with annual average smoke-specific PM to estimate current and potential future smoke HAPs risks. Next, we use a health impact assessment with observation-based smoke PM2.5 to determine the sub-national distribution of mortality and the sub-national and sub-annual distribution of morbidity attributable to US smoke PM2.5 from 2006-2018. We estimate disability-adjusted life years (DALYs) for PM2.5 and 18 gas-phase HAPs in smoke using the HAPs to PM ratios developed in Chapter 2. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and morbidity (on average 75% across 2006-2018) attributable to smoke PM2.5 occurs outside the West due to a higher population density in the East. Across the US, smoke-attributable morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM2.5 are approximately three orders of magnitude higher than DALYs associated with gas-phase smoke HAPs. These results indicate that awareness and mitigation of landscape fire smoke exposure is important across the US, not just in regions in proximity to large wildfires. Finally, we use a large low-cost sensor network of indoor and outdoor PM2.5 monitors to characterize the relationship between indoor and outdoor air quality during smoke events. We identify smoke-impacted regions of the western US with a high density of co-located (distance < 1000 m) indoor and outdoor PurpleAir monitors. In these regions, we calculate indoor PM2.5/outdoor PM2.5 ratios on smoke-impacted and smoke-free days and find this ratio is < 1 (indoor PM2.5 less than outdoor PM2.5) at 98% of the monitor pairs for smoke-impacted days, compared to 54% on smoke- free days. On smoke-impacted days, indoor PM2.5 concentrations increase as outdoor PM2.5 Air Quality Index (AQI) increases by 25% per AQI bin, on average. However, the ratio of indoor PM2.5 to outdoor PM2.5 decreases by 28% per AQI bin. These results show that landscape fire smoke influences indoor air quality across many indoor environments in multiple cities, and this impact increases with smoke event intensity. In addition, this work highlights the utility of low-cost monitoring in quantifying indoor air quality during smoke events. However, we show that the present distribution of these indoor monitors suggests a bias towards census tracts of lower social vulnerability.
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    Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools
    (Colorado State University. Libraries, 2022) Cheeseman, Michael J., author; Pierce, Jeffrey R., advisor; Barnes, Elizabeth, committee member; Fischer, Emily, committee member; Ford, Bonne, committee member; Volckens, John, committee member
    Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas.
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    The influence of prescribed burning on springtime PM2.5 concentrations in eastern Kansas
    (Colorado State University. Libraries, 2023) Sablan, Olivia, author; Fischer, Emily V., advisor; Pierce, Jeffrey R., advisor; Magzamen, Sheryl, committee member; Ford, Bonne, committee member
    Annual springtime (March - May) prescribed burning is practiced in the Flint Hills of eastern Kansas to mitigate wildfire risk, improve nutritional value of vegetation for cattle grazing, limit woody encroachment, and maintain the health of the tall grass prairie ecosystem. Smoke from these prescribed fires produces fine particulate matter (PM2.5), degrading air quality. Smoke from prescribed fires is understudied due to their short duration and a lack of monitoring in the rural regions where prescribed burning occurs. To quantify the contribution of springtime prescribed burning to PM2.5 concentrations in the Flint Hills and downwind regions, we deployed 38 PurpleAir PM2.5 sensors for the 2022 burning season. We used observations from this ground-based network alongside a suite of satellite products to determine the PM2.5 attributable to smoke. In 2022, the Flint Hills were also impacted by dust and transported smoke from high winds, drought, and wildfires in New Mexico. We separated the local and transported smoke effects for our exposure estimates. Across the low-cost sensor network, 24-hour median PM2.5 increased by 5.2 µg m-3 on days impacted by smoke from fires in the eastern Kansas region versus smoke-free days. We compared our findings to two existing PM2.5 estimates derived from satellites and ground-based measurements. Satellite-based products show a similar daily smoke-driven median increase in PM2.5 concentration and a consistent increase in seasonal average PM2.5 concentrations in the Flint Hills region as our estimates based on in situ monitors.