Quantifying functional group compositions of household fuel-burning emissions

Abstract

Globally, billions of people burn fuels indoors for cooking and heating, which contributes to millions of chronic illnesses and premature deaths annually. Additionally, residential burning contributes significantly to black carbon emissions, which have the highest global warming impacts after carbon dioxide and methane. In this study, we use Fourier transform infrared spectroscopy (FTIR) to analyze fine-particulate emissions collected on Teflon membrane filters from 15 cookstove types and 5 fuel types. Emissions from three fuel types (charcoal, kerosene, and red oak wood) were found to have enough FTIR spectral response for functional group (FG) analysis. We present distinct spectral profiles for particulate emissions of these three fuel types. We highlight the influential FGs constituting organic carbon (OC) using a multivariate statistical method and show that OC estimates by collocated FTIR and thermal-optical transmittance (TOT) are highly correlated, with a coefficient determination of 82.5 %. As FTIR analysis is fast and non-destructive and provides complementary FG information, the analysis method demonstrated herein can substantially reduce the need for thermal-optical measurements for source emissions.

Figure 1.

Flow chart from sample collection to measurements, post-processing, and results.

Figure 2.

Photo showing 15 stoves used in this work. Charcoal stoves: (a) EcoZoom Jet, (b) Prakti Leo, (c) Envirofit CH4400, (d) Jikokoa (inset), and (e) Éclair (inset). Kerosene stoves: (f) Butterfly Wick (model 2668), and (g) Butterfly Pressure (model 2412). Red oak wood stoves: (h) Jiko Poa, (i) Envirofit G3300, (j) Philips HD4012, (k) Home stove (Biolite), (l) Eco Chula XXL (inset), and (m) three-stone fire (inset). Alcohol stove: (n) Cleancook (inset). LPG stove: (o) Sol gas.

Figure 3.

Emission factors (mg MJ⁻¹) of gravimetric PM_2.5 and artifact-corrected TOT OC and EC, separated by fuel type and test phase (CS – cold start, HS – hot start, and SIM – simmering). Blue crosses show the median for each category.

Figure 4.

Average mid-infrared spectra of unburned fuels and their particulate emissions separated by source and test phase. The emission spectra are shown in terms of emission factors (absorbance divided by MJ energy delivered to the pot). Shaded bands show the mean spectrum ±0.5 standard deviation. CS – cold start, HS – hot start, and SIM – simmering. Features in the region between 1000–1300 cm⁻¹, which have interferences from PTFE filters, have been omitted. The spectra from unburned fuels (blue lines), presented with arbitrary scaling, are taken from the literature: Guo and Bustin (1998) for charcoal, Xia et al. (2017) for kerosene, and Pandey (1999) for red oak.

Figure 5.

(a–c) OM emission factors calculated from mid-infrared spectroscopy, separated by functional group contribution, and averaged over each phase. (d–f) OM / OC ratios calculated from mid-infrared spectroscopy and separated by functional group contribution. CS – start, HS – hot start, and SIM – simmering.

Figure 6.

Scatter plot of aromatic CH concentration estimated using the peak at 750 cm⁻¹ and GC–MS total PAH concentration.

Figure 7.

VIP scores of GC–MS sum of PAHs regressed against the FTIR spectra.