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Identifying Functional Groups and Predicting OC-EC on Cookstove Source Emissions Using FTIR
Citation:
Li, E., M. Hays, A. Dillner, S. Takahama, Jim Jetter, AND G. Shen. Identifying Functional Groups and Predicting OC-EC on Cookstove Source Emissions Using FTIR. Air Quality Measurement Methods and Technology, Durham,NC, April 02 - 04, 2019.
Impact/Purpose:
Human exposure to smoke emissions from domestic biofuel burning for cooking and heating contributes to millions of deaths and chronic illnesses annually and accounts for approximately 4% of all lost healthy life years. Residential biomass burning also contributes 30% of global emissions of black carbon, which is estimated to have the second highest global warming impact after carbon dioxide. This research uses Fourier transform infrared spectroscopy (FTIR) to characterize chemical functionality of particulate matter (PM2.5) from cookstove source emissions from five different fuel types collected on Teflon membrane air filters. This study evaluated the spectral differences between charcoal, kerosene, red oak wood, alcohol, and liquid petroleum gas fuels burned in fifteen cookstove types. We find that emissions from charcoal, kerosene, and red oak wood met the minimum detection limit for FTIR functional group analysis, and we present distinct spectra for these three fuel types. We also find that source emissions FTIR spectra and a training set of organic carbon and elemental carbon (OC-EC) measurements can be used in a data-driven machine learning model to predict OC-EC concentrations, which is supported by a high coefficient of variation, low bias, low error, and low normalized error. The results presented indicate that FTIR analysis is a fast, non-destructive method for revealing organic functional group compositions of biomass combustion source emissions, as well as for providing OC information complementary to the organic aerosols estimated from the same set of sample spectra. These results further inform efforts by the EPA in partnership with the Clean Cooking Alliance (formerly the Global Alliance for Clean Cookstoves).
Description:
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.