Sources of variance in BC mass measurements from a small marine engine: Influence of the instruments, fuels and loads

Abstract

Knowledge of black carbon (BC) emission factors from ships is important from human health and environmental perspectives. A study of instruments measuring BC and fuels typically used in marine operation was carried out on a small marine engine. Six analytical methods measured the BC emissions in the exhaust of the marine engine operated at two load points (25% and 75%) while burning one of three fuels: a distillate marine (DMA), a low sulfur, residual marine (RMB-30) and a high-sulfur residual marine (RMG-380). The average emission factors with all instruments increased from 0.08 to 1.88 gBC/kg fuel in going from 25 to 75% load. An analysis of variance (ANOVA) tested BC emissions against instrument, load, and combined fuel properties and showed that both engine load and fuels had a statistically significant impact on BC emission factors. While BC emissions were impacted by the fuels used, none of the fuel properties investigated (sulfur content, viscosity, carbon residue and CCAI) was a primary driver for BC emissions. Of the two residual fuels, RMB-30 with the lower sulfur content, lower viscosity and lower residual carbon, had the highest BC emission factors. BC emission factors determined with the different instruments showed a good correlation with the PAS values with correlation coefficients R2 > 0.95. A key finding of this research is the relative BC measured values were mostly independent of load and fuel, except for some instruments in certain fuel and load combinations.

Comparison of black carbon measurement techniques for marine engine emissions using three marine fuel types

“…They quantified the response of AVL smoke meter, LII 300, Aethalometer and TOA relative to MSS (selected arbitrarily as the reference instrument). Table 3 compares the slope and intercept of the linear regression reported in Jiang et al (2018) with the corresponding values from the present study. As mentioned, the slopes and intercepts shown in Table 3 are based on the MSS mass concentrations being on the x-axis and the mass concentrations from other BC instruments on the y-axis (see Figure S3 in the supplementary information for more details).…”

“…The results presented in the current study are compared with the results reported in Jiang et al (2018) with similar BC instruments used to collect sample from a 2-stroke ship engine. They quantified the response of AVL smoke meter, LII 300, Aethalometer and TOA relative to MSS (selected arbitrarily as the reference instrument).…”

“…These studies suggest that the relative BC concentrations reported by different techniques as well as the correlation between them is highly source-dependent. More recently, Jiang et al (2018) employed several instruments to measure BC mass concentrations from a 2-stroke marine engine under two load conditions (i.e., 25% and 75% load) using three different fuel types including DMA, instruments are generally in better agreement for high power (i.e., 75%) engine set point. However, at 25% engine power specifically for RMG-380 with high sulfur content, the variation between mass emission factors quantified by different instruments is remarkable.…”

Development of a new correction algorithm applicable to any filter-based absorption photometer

“…Due to the numerous correction algorithms for the Aethalometer (e.g., Arnott et al, 2005;Collaud Coen et al, 2010;Kirchstetter and Novakov, 2007;Saturno et al, 2017;Schmid et al, 2006;Virkkula et al, 2007;Weingartner et al, 2003), we do not evaluate these in the present work to limit the scope. In fact, the majority of our focus is the B1999 and V2005 corrections to TAP and CLAP.…”

“…Consequently, various correction algorithms exist to minimize these biases, but they are often specific only to certain instruments. For example, some are applicable to the PSAP, CLAP, and TAP (e.g., Bond et al, 1999;Müller et al, 2014;Ogren, 2010;Virkkula, 2010;Virkkula et al, 2005), while others are applicable to the AETH (e.g., Arnott et al, 2005;Collaud Coen et al, 2010;Drinovec et al, 2017;Kirchstetter and Novakov, 2007;Schmid et al, 2006;Virkkula et al, 2007Virkkula et al, , 2015Weingartner et al, 2003).…”