Browsing by Author "Carter, Ellison, advisor"

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Open Access
Evaluating personal PM2.5 and black carbon exposure variability in Beijing's rural communities
(Colorado State University. Libraries, 2025) Hirst, Kennedy, author; Carter, Ellison, advisor; L'Orange, Christian, committee member; Bareither, Christopher, committee member
Exposure to air pollution is a major public health concern, with PM2.5 and black carbon (BC) linked to adverse health outcomes. To reduce emissions of PM2.5 and BC, the Chinese government implemented the Coal-to-Clean Energy Policy (CCEP) in 2016, reducing indoor PM2.5 concentrations. However, its effect on personal exposure remains unclear. This study evaluated the role of time-activity patterns in personal exposure to PM2.5 and BC in the context of the policy. Data from the Beijing Household Energy Transition study (winters of 2018-2022) included 252 participants with concurrent indoor and personal PM2.5 measurements and GPS- based time-activity data. Geofencing classified participant locations, and generalized linear models assessed exposure determinants. Model performance was evaluated using indoor PM2.5 data with and without time-activity adjustments. Personal PM2.5 exposure averaged 52.9 μg/m3, while BC averaged 1.6 μg/m3. The best PM2.5 model used indoor PM2.5 over the full sampling period (AIC: 489.06, adjusted R2: 0.59). The top BC model used indoor PM2.5 averaged only while participants were home (AIC: 407.59, adjusted R2: 0.25). On average, participants spent 20.0 hours at home per day (95% CI: 19.4, 20.7). Despite these time-activity trends, the lack of reductions in personal exposure were not explained by time-activity patterns, indicating that other influential factors may be impacting exposure, or the available data was insufficient to fully capture exposure variability. Enhanced time-activity monitoring is necessary to improve exposure assessments and better inform air quality interventions.
Open Access
Evaluating sources of volatile organic compounds in Colorado workplaces via positive matrix factorization
(Colorado State University. Libraries, 2025) Lippmann, Jadelyn, author; Carter, Ellison, advisor; Burt, Melissa, committee member; Atadero, Rebecca, committee member
Recognition of the health risks associated with exposure to volatile organic compounds (VOCs), particularly in indoor environments, has increased the need for a stronger understanding and management of air quality. Exposure to VOCs, emitted from various sources like building materials, office equipment, and consumer products, have been linked to both acute and chronic health outcomes, including respiratory issues and carcinogenic effects. While research on residential indoor air quality is extensive, fewer studies have characterized VOC exposure, particularly in workplaces, where people may spend a significant portion of their time. The work presented in this thesis addresses this knowledge gap through analysis of a comprehensive empirical study of VOC concentrations in 50 diverse workplaces across the State of Colorado. The study presented herein, which is part of a broader initiative led by Colorado State University in partnership with the Colorado Department of Public Health and Environment (CDPHE), utilized weeklong air sampling with SUMMA canisters and analyzed 61 target VOCs via EPA Method TO-15. Positive Matrix Factorization (PMF) modeling was employed to identify and apportion the sources of VOCs, providing insights into the relative contributions of indoor and outdoor pollutants. The findings inform further understanding of patterns of indoor VOCs measured in workplaces, as well as the design and implementation of targeted interventions to improve indoor air quality in occupational settings, particularly in underserved communities. Ultimately, this work contributes to advancing exposure science and supports healthier, and more sustainable indoor environments where people work.
Open Access
Field-based approaches to characterizing long-term indoor environmental quality in homes
(Colorado State University. Libraries, 2022) Purgiel, Andrew, author; Carter, Ellison, advisor; Blotevogel, Jens, committee member; Bond, Tami, committee member
To view the abstract, please see the full text of the document.
Open Access
In-home environmental quality: indices of indoor air pollution and indoor discomfort and their patterns in Colorado homes
(Colorado State University. Libraries, 2022) Walker, Kelsey, author; Carter, Ellison, advisor; Rojas, David, committee member; Atadero, Rebecca, committee member
Understanding the indoor residential environment is important for the health and well-being of occupants. The data used for this thesis included homes from the IEQ Study, which was conducted in partnership with an energy efficiency program of the City of Fort Collins (Epic Homes). Using an index that combines indoor air pollution and indoor thermal comfort, the indoor environmental index (IEI), served as a tool to quantify indoor environmental quality (IEQ) of twenty-eight homes. Daily averages of continuous measurements of PM2.5, CO2, TVOC, T, and RH were used to estimate a daily IEI. The median IEI of homes in the study ranged from 3.8 to 6.3 out of 10 (the lower score indicating a better IEQ). This study undertook a unique approach to estimating some in-home activities by categorizing disaggregated energy data in time spent cooking, cleaning, and temperature control. The Spearman correlation coefficient was used to relate various behavior, home, and outdoor factors to IEQ. Daily time spent cooking was correlated with IEI, as well as outdoor PM2.5, year built, estimated volume, and type of cooking fuel. A multivariate linear regression model was constructed to understand the predictive factors from a combination of outdoor continuous measurements, continuous energy use data as a proxy for occupant behavior, categorical occupant behavior, and categorical home characteristics. Smoking was the only significant factor in estimating IEI. The IEI was comprised of two subindices, the indoor air pollution index (IAPI) and the indoor discomfort index (IDI), which underwent the same process of multivariate linear regression modeling, and also showed limited predictive utility.
Open Access
Novel applications of data-driven approaches for understanding the impacts of household energy interventions
(Colorado State University. Libraries, 2025) Brehmer, Collin, author; Carter, Ellison, advisor; Davenport, Frances, committee member; Keller, Kayleigh, committee member; Sharvelle, Sybil, committee member
Air pollution from household solid fuel combustion is associated with premature death, disease, and radiative climate forcing. Beginning in 2015, the Chinese government implemented the Clean Heating Policy in Northern China (CHP) with the goal to transition 70% of homes in the Beijing region from coal-based space heating to natural gas or electric-powered space heating. Studies of the impact of the CHP on air pollution and the potential mechanisms of action are limited. The continued use of a secondary solid fuel or heating device after the primary solid fuel heating stove is replaced with a cleaner alternative could weaken the impacts of the effort to replace the primary solid fuel stove. In Chapter 1, we identified heating events from biomass kang stoves as a proxy for stove use using a combination of manually labeled data and XGBoost modeling. We showed that biomass kang stove usage did not change because of the CHP and agreed with self-reported measures of heating duration. Our results demonstrated the capability of XGBoost to identify stove use events when trained on manually labeled event data and provided evidence that self-reported measures of stove use may be sufficient for understanding how secondary stove use changes as a result of a household energy intervention. Fine particulate matter air pollution (PM2.5) is of particular interest when evaluating household energy transitions since it is a product of incomplete combustion and is related to several health outcomes. We evaluated the impacts of the CHP on seasonal indoor, seasonal outdoor, and 24-hr personal PM2.5 exposure in 50 villages, 300 homes, and 500 participants during three years over a four-year period. The CHP had high uptake, with a significant decrease in coal usage in treated groups. We also observed a significant reduction in seasonal average indoor PM2.5 (22.2 [4.2, 40.3] µg/m3). Seasonal outdoor and 24-hr personal PM2.5 exposure did decrease over time but the decrease could not be attributed to the CHP due to similar decreases in treated and untreated groups. Our study suggests that the CHP yielded promising results in reducing indoor PM2.5 and provided valuable insights for household energy transitions worldwide. Given that most household energy interventions target one source of air pollution, using a mixture of sources as an outcome, like PM2.5, when only one of the sources of air pollution is targeted by the policy can make it hard to disentangle the effects of the policy if the variability in the non-targeted sources is high. Chapter 3 identified sources and their contributions to outdoor and personal PM2.5 exposure using chemical analysis and source apportionment. We used the concentration of the coal-containing source in outdoor and personal exposure measurements as the outcome in policy analysis models and compared the findings to the models where total PM2.5 was the outcome. We found a significant reduction in personal exposure to the coal containing source (-7.75 [-13.4, -2.14] µg m-3), which contrasts with our findings that the CHP had no impact on personal exposure to total PM2.5. This work demonstrates how additional granularity in the air pollution outcome can serve as a better outcome than a mixture of sources.
Open Access
Systems-based approaches for evaluating residential-based hazards to inform environmental exposure intervention design
(Colorado State University. Libraries, 2022) Oke, Oluwatobi Olamiposi, author; Carter, Ellison, advisor; Magzamen, Sheryl, committee member; Carlson, Kenneth, committee member; Sharvelle, Sybil, committee member
Housing is an essential aspect of the physical built environment, where people spend most of their time, and a key determinant of health. Sub-standard and poor physical housing conditions (e.g., disrepair, deferred maintenance, and deteriorated physical environment) and exposures of inhabitants to lead, pests, air pollutants, and other indoor contaminants are associated with a wide range of health conditions, including respiratory infections, asthma, lead poisoning, injuries, chronic disease outcomes (e.g., cardiopulmonary conditions) and mental health illness. The environmental justice (EJ) research community that has focused on residential exposures has documented that adverse environmental exposures associated with residential settings and built environments are unevenly distributed and often disproportionately affect low-income and socioeconomically disadvantaged populations in the United States. Hence, work remains to be done if we intend to develop and maintain healthy residential environments for vulnerable population groups. However, a central challenge to this effort is the complex system of sources and source activities that contribute to and drive residential exposures, which makes it difficult to identify and isolate dominant sources. This dissertation sought to holistically investigate sources of three prevalent home-based exposures in the United States – i.e., flooding, lead, and indoor chemical mixtures. Through a combination of empirically-based and modeling approaches, this work brought together information on physical dwellings, their conditions and surroundings, the overlying sociodemographic characteristics of the people living in them, and the behaviors and activities that people undertake in their homes. The central hypothesis of this work has been that this approach would improve the identification of sources and their relative contributions to exposures in the residential environment. Significant findings from this work include: (i) socioeconomically disadvantaged populations in all three studies tended to have higher environmental exposures, regardless of exposure type, including natural hazards (i.e., floods), legacy pollution (i.e., lead), and pollutants driven by daily human activity (indoor air pollutants of outdoor origin); (ii) sources of environmental exposures varied within the same study and, at times, were more subtle than initially hypothesized in the literature, suggesting that the more holistic approaches taken in this work have practical value; and (iii) residential interventions to reduce adverse exposures could provide some measurable benefits to residents if customized to local occupants' needs in solving more building-related problems and providing higher residential satisfaction. In two of the three studies, we have worked closely or directly with the communities from which the data are collected. Thus, we expect outputs from this work to improve the design and delivery of home-based interventions for adverse environmental exposures through direct engagement with local decision-makers and more traditional scholarly communication channels.
Open Access
Understanding meteorological impacts on ambient PM2.5 concentrations using random forest models in Beijing
(Colorado State University. Libraries, 2022) Brehmer, Collin, author; Carter, Ellison, advisor; Bond, Tami, committee member; Carlson, Kenneth, committee member; Pierce, Jeffrey, committee member
Policymakers and non-governmental organizations have been implementing policies and interventions designed to reduce exposure to hazardous air pollution. Having knowledge of how non-policy related factors (i.e., meteorology) impact air pollution concentrations in a given study area can better inform longitudinal studies of the effects of the policy on air pollution and health. In this study, we apply a random forest machine learning approach to evaluate how meteorological factors including temperature, relative humidity, wind speed, wind direction, and boundary layer height influence daily PM2.5 concentrations in rural Beijing villages during heating months (January and February of 2019 and 2020). Ten-fold cross validation indicated good model performance with an overall r2 of 0.85 for season 1, and 0.93 for season 2. The models were able to identify variables that were the most important for predicting PM2.5 concentrations both field seasons (relative humidity) and variables that had changes in relative importance between seasons (temperature and boundary layer height). Additionally, examination of one and two-way partial dependence plots as well as interactions through Friedman's H-statistic granted insight into how meteorology variables influence PM2.5 concentrations. Findings from this work provide a basis for adjusting for meteorological variability in important indicators of air quality like PM2.5 concentrations in an ongoing real-world policy evaluation of a province-wide ban on household use of coal for space heating in Beijing, which is critical for isolating (to the extent possible) changes in measured pollutant concentrations attributable to the policy.