AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Identifying Functional Groups and Predicting OC-EC in Cookstove Source Emissions
EMILY LI, Michael Hays, James Jetter, Guofeng Shen, Satoshi Takahama, Ann Dillner, U.S. EPA
Abstract Number: 459 Working Group: Biomass Combustion: Emissions, Chemistry, Air Quality, Climate, and Human Health
Abstract Globally, billions of people burn fuels indoors for cooking and heating, which contributes to millions of premature deaths and chronic illnesses annually. Additionally, residential burning contributes significantly to black carbon emissions, which are estimated to have the highest global warming impact second to carbon dioxide. In this study, we use Fourier transform infrared spectroscopy (FTIR) to analyze PM2.5 emissions collected on Teflon membrane filters from fifteen cookstove types and five fuel types. Emissions from three fuel types (charcoal, kerosene, and red oak wood) were above the minimum detection limit for functional group analysis. We present distinct spectra for these three fuel types. We also show that FTIR spectra can be used in multivariate linear regression analysis in a data-driven machine learning model to predict organic carbon and elemental carbon (OC/EC) ratios, which are traditionally measured using destructive, time-consuming thermogravimetric methods. Since FTIR measurement is non-destructive and only takes three minutes per sample, this ability to predict OC/EC from FTIR spectra can potentially significantly reduce the need for thermogravimetric OC/EC measurements.