Fine particulate speciation profile and emission factor of municipal solid waste incinerator established by dilution sampling method

Abstract

UNLABELLED: In this study, fine particulate matter (PM2.5) emitted from a municipal solid waste incinerator (MSWI) was collected using dilution sampling method. Chemical compositions of the collected PM2.5 samples, including carbon content, metal elements, and water-soluble ions, were analyzed. Traditional in-stack hot sampling was simultaneously conducted to compare the influences of dilution on PM2.5 emissions and the characteristics of the bonded chemical species. The results, established by a dilution sampling method, show that PM2.5 and total particulate matter (TPM) emission factors were 61.6 ± 4.52 and 66.1 ± 5.27 g ton-waste(-1), respectively. The average ratio of PM2.5/TPM is 0.93, indicating that more than 90% of PM emission from the MSWI was fine particulate. The major chemical species in PM2.5 included organic carbon (OC), Cl(-), NH4(+), elemental carbon (EC) and Si, which account for 69.7% of PM2.5 mass. OC was from the unburned carbon in the exhaust, which adsorbed onto the particulate during the cooling process. High Cl(-) emission is primarily attributable to wastes containing plastic bags made of polyvinyl chloride, salt in kitchen refuse and waste biomass, and so on. Minor species that account for 0.01-1% of PM2.5 mass included SO4(2-), K(+), Na, K, NO3(-), Al, Ca(2+), Zn, Ca, Cu, Fe, Pb, and Mg. The mean ratio of dilution method/in-stack hot method was 0.454. The contents of water-soluble ions (Cl(-), SO4(2-), NO3(-)) were significantly enriched in PM2.5 via gas-to-particle conversion in the dilution process. Results indicate that in-stack hot sampling would underestimate levels of these species in PM2.5. IMPLICATIONS: PM2.5 samples from a municipal solid waste incinerator (MSWI) were collected simultaneously by a dilution sampling technique and a traditional in-stack method. PM2.5 emission factors and chemical speciation profiles were established. Dilution sampling provides more reliable data than in-stack hot sampling. The results can be applied to estimate the PM2.5 emission inventories of MSWI, and the source profile can be used for contribution estimate of chemical mass balance modeling.