Browsing by Author "Brehmer, Collin, author"

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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
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.