Detailed particulate and gaseous emission characterizations were conducted on six commercially available residential-scale wood pellet boilers. The objective of the study was to define emission factors for these six different appliances burning wood pellets, grass pellets, and a blend of grass pellets and corn as fuels under low and high loads. Continuous monitoring of criteria pollutants, including PM 2.5 , NO x , SO 2 , and CO, was conducted using an EPA CTM-039 dilution sampling system. The PM 10 emissions with wood as the fuel ranged between 14 and 17 mg/MJ and between 16 and 21 mg/MJ at low and high loads, respectively. The PM 10 emissions from grass were found to be higher for all of the appliances compared to wood pellets at both low and high loads (28−33 and 37−44 mg/MJ, respectively). CO emissions, an indication of combustion efficiency, were found to be higher for the grass pellets, indicating less complete combustion. NO x and SO 2 emissions were also higher for grass and grass/corn blends, attributable to the higher fuel N and S. PM samples collected on Teflon and quartz substrates were analyzed for ions and trace elements. Semi-volatile organic compounds collected on quartz and polyurethane foam (PUF) plugs were also analyzed. Levoglucosan, a molecular marker for wood combustion, was the predominant organic compound found in the grass combustion PM 2.5 and ranged between 6 and 100 μg/MJ for grass and between 9 and 130 μg/MJ for wood. Polycyclic aromatic hydrocarbon (PAH) emissions were relatively higher for grass combustion ranging from 10 to 700 μg/MJ than for wood combustion ranging from about 5 to 200 μg/MJ. Dioxin and dibenzofuran emissions were found to be substantially higher for grass pellet emissions compared to wood pellet emissions at both high and low loads for all of the appliances.
Emission factors of pollutants from combustion of five different types of grass pellets with ash content ranging from 3% to 13% were measured and compared to a premium type wood pellet with ash content 0.6% at low and high loads, respectively. The effects of fuel properties on the grass pellet combustion emissions were also studied. Criteria pollutants including PM 2.5 , NO x , SO 2 , and CO were continuously monitored using an EPA CTM-039 dilution sampling system. PM 10 emissions from grass combustion were found to be higher when compared to wood pellet emissions at both low and high loads (26−40 and 36−60 mg MJ −1 , respectively). The PM 2.5 emissions were strongly correlated to the ash content of the fuel (R 2 = 0.939). CO emissions were found to be higher for grass combustion indicating an incomplete combustion. PM 2.5 samples collected on Teflon and quartz substrates were analyzed for ions and trace elements. About 60−75% of the PM 2.5 fraction was recovered that included K of about 20−30%, sulfate about 16−25%, and chloride of about 10−15%. Semivolatile organic compounds collected on quartz and polyurethane foam (PUF) were also analyzed for molecular markers, PAHs and PCDD/Fs. PAH emissions were strongly correlated to the CO (r 2 = 0.80). The PCDD/F emissions were clearly a function of chlorine content of the fuel (r 2 = 0.98). A strong correlation exists between emitted levoglucosan and PM 2.5 indicating levoglucosan, a molecular marker for cellulose combustion (r 2 = 0.87). All of the emissions were found to be higher for grass pellets compared to wood pellets and are higher at high loads than at low loads.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.