Name: split point analysis and uncertainty quantification of thermal-optical organic/elemental carbon measurements
Uploaded: 2019-09-07T14:00:00.000+00:00
Duration: 10 min 22 s
Description: Watch this Scientific Journal Video about Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements at JoVE.com

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) FlareNet Strategic Network (Grant # 479641), NSERC Discovery Research Grants (Grant #06632 and 522658), and Natural Resources Canada (Project Manager, Michael Layer). The authors are grateful to those who shared raw data files for use as representative examples in this work.

<ol>
	<li>Cohen, A. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimates+and+25-year+trends+of+the+global+burden+of+disease+attributable+to+ambient+air+pollution:+an+analysis+of+data+from+the+Global+Burden+of+Diseases+Study.">Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study.</a> The Lancet. 389 (10082), 1907-1918 (2015).</li><li>Grahame, T. J., Klemm, R., Schlesinger, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Public+health+and+components+of+particulate+matter:+The+changing+assessment+of+black+carbon.">Public health and components of particulate matter: The changing assessment of black carbon.</a> Journal of the Air &amp; Waste Management Association. 64 (6), 620-660 (2014).</li><li>Kim, S. -. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+short-term+association+of+selected+components+of+fine+particulate+matter+and+mortality+in+the+Denver+Aerosol+Sources+and+Health+(DASH)+study.">The short-term association of selected components of fine particulate matter and mortality in the Denver Aerosol Sources and Health (DASH) study.</a> Environmental Health. 14 (1), (2015).</li><li>Basaga&#241;a, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short-term+effects+of+particulate+matter+constituents+on+daily+hospitalizations+and+mortality+in+five+South-European+cities:+Results+from+the+MED-PARTICLES+project.">Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project.</a> Environment International. 75, 151-158 (2015).</li><li>Jacobson, M. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Strong+radiative+heating+due+to+the+mixing+state+of+black+carbon+in+atmospheric+aerosols.">Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols.</a> Nature. 409 (6821), 695-697 (2001).</li><li>Sato, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+atmospheric+black+carbon+inferred+from+AERONET.">Global atmospheric black carbon inferred from AERONET.</a> Proceedings of the National Academy of Sciences. 100 (11), 6319-6324 (2003).</li><li>Ramanathan, V., Carmichael, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+and+regional+climate+changes+due+to+black+carbon.">Global and regional climate changes due to black carbon.</a> Nature Geoscience. 1 (4), 221-227 (2008).</li><li>Bond, T. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bounding+the+role+of+black+carbon+in+the+climate+system:+A+scientific+assessment.">Bounding the role of black carbon in the climate system: A scientific assessment.</a> Journal of Geophysical Research: Atmospheres. 118 (11), 5380-5552 (2013).</li><li>Jacobson, M. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short-term+effects+of+controlling+fossil-fuel+soot,+biofuel+soot+and+gases,+and+methane+on+climate,+Arctic+ice,+and+air+pollution+health.">Short-term effects of controlling fossil-fuel soot, biofuel soot and gases, and methane on climate, Arctic ice, and air pollution health.</a> Journal of Geophysical Research. 115, 1-24 (2010).</li><li>Doherty, S. J., Grenfell, T. C., Forsstr&#246;m, S., Hegg, D. L., Brandt, R. E., Warren, S. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observed+vertical+redistribution+of+black+carbon+and+other+insoluble+light-absorbing+particles+in+melting+snow.">Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow.</a> Journal of Geophysical Research: Atmospheres. 118 (11), 5553-5569 (2013).</li><li>Sterle, K. M., McConnell, J. R., Dozier, J., Edwards, R., Flanner, M. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retention+and+radiative+forcing+of+black+carbon+in+eastern+Sierra+Nevada+snow.">Retention and radiative forcing of black carbon in eastern Sierra Nevada snow.</a> The Cryosphere. 7 (1), 365-374 (2013).</li><li>Goelles, T., B&#248;ggild, C. E., Greve, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ice+sheet+mass+loss+caused+by+dust+and+black+carbon+accumulation.">Ice sheet mass loss caused by dust and black carbon accumulation.</a> The Cryosphere. 9 (5), 1845-1856 (2015).</li><li>Kopp, R. E., Mauzerall, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+climatic+benefits+of+black+carbon+mitigation.">Assessing the climatic benefits of black carbon mitigation.</a> Proceedings of the National Academy of Sciences of the United States of America. 107 (26), 11703-11708 (2010).</li><li>Cavalli, F., Viana, M., Yttri, K. E., Genberg, J., Putaud, J. -. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+standardised+thermal-optical+protocol+for+measuring+atmospheric+organic+and+elemental+carbon:+the+EUSAAR+protocol.">Toward a standardised thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol.</a> Atmospheric Measurement Techniques. 3 (1), 79-89 (2010).</li><li>Chen, L. -. W. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-wavelength+optical+measurement+to+enhance+thermal/optical+analysis+for+carbonaceous+aerosol.">Multi-wavelength optical measurement to enhance thermal/optical analysis for carbonaceous aerosol.</a> Atmospheric Measurement Techniques. 8 (1), 451-461 (2015).</li><li>Chow, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+Calibration+and+Equivalence+of+a+Multiwavelength+Thermal/Optical+Carbon+Analyzer.">Optical Calibration and Equivalence of a Multiwavelength Thermal/Optical Carbon Analyzer.</a> Aerosol and Air Quality Research. 15 (4), 1145-1159 (2015).</li><li>Chow, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Separation+of+brown+carbon+from+black+carbon+for+IMPROVE+and+Chemical+Speciation+Network+PM+2.5+samples.">Separation of brown carbon from black carbon for IMPROVE and Chemical Speciation Network PM 2.5 samples.</a> Journal of the Air &amp; Waste Management Association. 68 (5), 494-510 (2018).</li><li>Sunset Laboratory Inc. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Organic Carbon and Elemental Carbon Field Instrument: Model 4 User&#8217;s Manual. , (2019).</li><li>Turpin, B. J., Cary, R. A., Huntzicker, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+In+Situ,+Time-Resolved+Analyzer+for+Aerosol+Organic+and+Elemental+Carbon.">An In Situ, Time-Resolved Analyzer for Aerosol Organic and Elemental Carbon.</a> Aerosol Science and Technology. 12 (1), 161-171 (1990).</li><li>Petzold, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recommendations+for+reporting+&#8220;black+carbon&#8221;+measurements.">Recommendations for reporting &#8220;black carbon&#8221; measurements.</a> Atmospheric Chemistry and Physics. 13 (16), 8365-8379 (2013).</li><li>Nicolosi, E. M. G., Quincey, P., Font, A., Fuller, G. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light+attenuation+versus+evolved+carbon+(AVEC)+-+A+new+way+to+look+at+elemental+and+organic+carbon+analysis.">Light attenuation versus evolved carbon (AVEC) - A new way to look at elemental and organic carbon analysis.</a> Atmospheric Environment. , 145-153 (2018).</li><li>Chow, J. C., Watson, J. G., Crow, D., Lowenthal, D. H., Merrifield, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+IMPROVE+and+NIOSH+Carbon+Measurements.">Comparison of IMPROVE and NIOSH Carbon Measurements.</a> Aerosol Science and Technology. 34 (1), 23-34 (2001).</li><li>Chow, J. C., Watson, J. G., Chen, L. -. W. A., Arnott, W. P., Moosm&#252;ller, H., Fung, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Equivalence+of+Elemental+Carbon+by+Thermal/Optical+Reflectance+and+Transmittance+with+Different+Temperature+Protocols.">Equivalence of Elemental Carbon by Thermal/Optical Reflectance and Transmittance with Different Temperature Protocols.</a> Environmental Science &amp; Technology. 38 (16), 4414-4422 (2004).</li><li>Subramanian, R., Khlystov, A., Robinson, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+peak+inert-mode+temperature+on+elemental+carbon+measured+using+thermal-optical+analysis.">Effect of peak inert-mode temperature on elemental carbon measured using thermal-optical analysis.</a> Aerosol Science and Technology. 40 (10), 763-780 (2006).</li><li>Wang, Y., Chung, A., Paulson, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+metal+salts+on+quantification+of+elemental+and+organic+carbon+in+diesel+exhaust+particles+using+thermal-optical+evolved+gas+analysis.">The effect of metal salts on quantification of elemental and organic carbon in diesel exhaust particles using thermal-optical evolved gas analysis.</a> Atmospheric Chemistry and Physics. 10 (23), 11447-11457 (2010).</li><li>Peterson, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standard+Operating+Procedure+for+the+Determination+of+Organic,+Elemental,+and+Total+Carbon+in+Particulate+Matter+Using+a+Thermal/Optical-Transmittance+Carbon+Analyzer.">Standard Operating Procedure for the Determination of Organic, Elemental, and Total Carbon in Particulate Matter Using a Thermal/Optical-Transmittance Carbon Analyzer.</a> Research Triangle. , (2009).</li><li>Conrad, B. M., Johnson, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+Absorption+Cross-Section+of+Flare-Generated+Black+Carbon:+Variability,+Predictive+Model,+and+Implications.">Mass Absorption Cross-Section of Flare-Generated Black Carbon: Variability, Predictive Model, and Implications.</a> Carbon. 149, 760-771 (2019).</li><li>Akaike, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+look+at+the+statistical+model+identification.">A new look at the statistical model identification.</a> IEEE Transactions on Automatic Control. 19 (6), 716-723 (1974).</li><li>Saffaripour, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Raman+spectroscopy+and+TEM+characterization+of+solid+particulate+matter+emitted+from+soot+generators+and+aircraft+turbine+engines.">Raman spectroscopy and TEM characterization of solid particulate matter emitted from soot generators and aircraft turbine engines.</a> Aerosol Science and Technology. 51 (4), 518-531 (2017).</li><li>Lu, S., Irino, T., Igarashi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomass+burning+history+in+East+Asia+during+the+last+4+million+years+recorded+in+elemental+carbon+variability+at+IODP+site.">Biomass burning history in East Asia during the last 4 million years recorded in elemental carbon variability at IODP site.</a> U1423. Progress in Earth and Planetary Science. 5 (1), (2018).</li></ol>

The authors have nothing to disclose.

Table 3 shows the contribution of the specific sources of uncertainty to the mass calibration constant for the example case described in Table 1 and Figure 2. Cumulative calibration uncertainty resulting from bias error in the NDIR detector, bias error in the concentration of sucrose, and precision and bias error in pipetted volumes are listed. Bias error in the NDIR detector (i.e., variance in the &#8220;calibration area&#8221;) tends to dominate, with bias in the pipetting procedure being second-most important (although quite small in the representative example). Proper estimation of pipetting error is thus critical to ensuring accurate quantification of overall calibration uncertainty; referring to protocol step 4.3.2, it is therefore suggested that intra-user repeatability and inter-user reproducibility be&#160;assessed for each group of users and pipette. In contrast, uncertainty due to the concentration of sucrose in the external standard is negligibly small. Furthermore, there appears to be negligible contribution from regression uncertainty, a likely consequence of the good linearity of the instrument &#8722; the coefficient of determination (R2) for a linear fit of the calibration data generally exceeds 99.95%. If calibration data are not sufficiently linear, the software automatically warns the user, who is then able to troubleshoot the dataset via the Boolean control noted in protocol step 4.2; the user can then modify their calibration dataset by acquiring replacement data as needed.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Considered Uncertainties</td>
			<td>Full (6-point) Calibration</td>
		</tr>
		<tr>
			<td>NDIR bias</td>
			<td>&#177; 2.61%</td>
		</tr>
		<tr>
			<td>+ Sucrose solution</td>
			<td>&#177; 2.61%</td>
		</tr>
		<tr>
			<td>+ Pipette</td>
			<td>&#177; 2.78%</td>
		</tr>
		<tr>
			<td>Nominal Result [&#956;gC]</td>
			<td>18.49</td>
		</tr>
	</tbody>
</table>
Table 3: Uncertainties in mass calibration constant. Representative contribution of uncertainties in the calibration of the OCEC instrument for the example six-point calibration (see Table 1). Overall calibration uncertainty is dominated by bias in the NDIR detector with error due to accuracy in the pipetting of the sucrose solution, including human-errors (inter-user reproducibility and intra-user repeatability), being second-most important, followed by uncertainty in linear regression and sucrose concentration (which are both negligible).
Calibration of the OCEC instrument is a time-consuming procedure, typically requiring 2&#8722;3 hours to complete depending upon the length of the thermal protocol employed. A more rapid calibration procedure is desirable. To this end, the efficacy of a modified, truncated calibration protocol was analyzed with the presented software tool. The developed MC procedure was executed using all possible subsets of the example calibration data listed in Table 1 &#8212; limited to cases with three or more data and a minimum of one blank measurement. All resulting mass calibration constants from this analysis are plotted in Figure 4 as a function of number of calibration data used, where calibration constants have been normalized by the full (6-point) calibration result. Unsurprisingly, uncertainty in the calibration constant increases as available calibration data are reduced. Importantly however, the means of all truncated calibrations fall within the 2&#963; CI of the full calibration result, which is a consequence of the above-noted linearity of the instrument. This consistency in the MC-average suggests that a modified, faster calibration consisting of few calibration data can be employed as a &#8220;bump test&#8221; check of the OCEC instrument&#8217;s calibration. That is, if the MC-average of a 3-point calibration data set is within the 2&#963; CI of the existing calibration, it is likely that the OCEC instrument does not require re-calibration. It is also apparent in Figure 4 that calibration uncertainty is reduced with more calibration data, but the reduction in uncertainty suffers from diminishing returns. Referring to Table 3 and its discussion above, since calibration uncertainties are dominated by NDIR bias (quantified with the standard error of the &#8220;calibration areas&#8221;), the marginal decrease in calibration uncertainty by including the nth data point can be estimated with the factor &#8730;(1&#8722;1/n). Consequently, in the representative example, the marginal reduction in uncertainty is larger when moving from a three- to a four-point calibration than when moving from a five- to a six-point calibration. The developed software tool, which can be executed following acquisition of each calibration data point (i.e., following each repetition of protocol step 3.3), allows the user to quantify calibration uncertainty throughout the acquisition of data. Critically, this ability enables the user to not only decide upon adequate convergence of the calibration in the context of its uncertainty, but also detect the presence of spurious data &#8212; that is, an incremental decrease in calibration uncertainty that is notably different than expected highlights to the user that the most recently acquired calibration data point could be flawed.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59742/59742fig4.jpg" /> 
Figure 4: Assessment of sample size on calibration uncertainty. Computed mass calibration constant for all possible combinations of calibration data listed in Table 1 (requiring a minimum of three data, including at least one blank) normalized by the result using all six data. Relative uncertainty in the calibration constant decreases with an increase in the number of calibration data. The blue shaded area in the figure corresponds to the 2&#963; CI of the calibration constant computed using all calibration data. It is apparent that all nominal results are within this CI suggesting that &#8212; although uncertain &#8212; a truncated calibration procedure of just three calibration data points might be used as a &#8220;bump test&#8221; check of the instrument&#8217;s calibration. <a href="https://www.jove.com/files/ftp_upload/59742/59742fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Calculated carbon masses and uncertainties for four example datasets are detailed in Table 4; these data are sourced from measurements of carbonaceous emissions from hydrocarbon flames27,29, gas turbines29, and fine-mode (&#60; 2 &#956;m) carbonaceous particles obtained from sub-ocean sediment samples30. Computed (MC-average) OC, EC, and TC masses are shown in Table 4 alongside the computed critical attenuation decline and the EC/TC ratio for each dataset, showing the breadth of the example data in the context of sampled carbon composition. Summarized in Table 4 are the sources and overall uncertainties in carbon mass using the presented software tool compared to those reported by the instrument. Within the software tool, the combination (product distribution) of calibration uncertainty and repeatability yields the overall uncertainty in MC-computed TC mass (quantified as &#8722;8.32/+8.40% in the present work), which is independent of uncertainty in the split point and therefore acts as a lower limit in the uncertainty of OC and EC masses. These representative calibration uncertainties are applied to each example dataset while maintaining the nominal mass calibration constant used in the original analyses.
<img alt="Table 4" src="/files/ftp_upload/59742/59742table4.jpg" /> 
Table 4: Uncertainties in data analysis. Contribution to uncertainty in OCEC-measured carbon masses for four example datasets from a broad range of sources and performed by different laboratories27,29,30. (a) Key numerical results from the example datasets: OC, EC, and TC masses, critical attenuation decline for the quantification of split point uncertainty, and elemental-to-total carbon ratio. (b) A summary of uncertainties in the calculated carbon masses. Contributing sources of uncertainty include that of the mass calibration constant, repeatability of the calibration procedure, and uncertainty in the split point (relative to TC mass) corresponding to the critical attenuation decline listed in (a). Uncertainties in the carbon masses using the fixed equations (Eq. (1)) employed by the instrument are also shown in (b). Red and yellow highlighting&#160;of the data correspond to under- and over-estimation of uncertainty, respectively, when using the fixed equations relative to the present method. In most cases, the instrument under-estimates carbon mass uncertainty however, if measured OC or EC mass is small, the instrument may over-estimate uncertainty as compared to the present software. <a href="https://www.jove.com/files/ftp_upload/59742/Table_4.xlsx">Please click here to download this file.</a>
For these examples, the attenuation decline technique for quantifying the split point uncertainty was employed. OCECgo-computed critical values of attenuation decline ranged from of 1.342% to 2.059% resulting in split point uncertainties from 0.10% to 4.50% of TC mass. While the employed values of attenuation decline are indeed somewhat subjective &#8722; particularly the employed factor to estimate uncertainty in the split point due to that of optical properties &#8722; these examples highlight the dependence of split point uncertainty upon the specific analysis data. For example, split point uncertainty is sensitive to the slope of the AVEC plot in the vicinity of the nominal split point. Consider dataset &#8220;A&#8221; corresponding to the example data in Figure 3 and dataset &#8220;D&#8221;; despite having similar critical attenuation declines, the relatively shallow and steep slopes of their respective AVEC plots (e.g., see Figure 3b for dataset &#8220;A&#8221;) yield the largest and smallest split point uncertainties of 4.50% and 0.10% of TC mass. Additionally, the example data show that the influence of uncertainty in the split point depends largely on its scale relative to the nominal OC and EC masses. Consider example datasets &#8220;B&#8221; and &#8220;C&#8221;, which have near-identical split point uncertainties (&#8776;1.22% of TC mass); dataset &#8220;C&#8221; contains &#8776;43% OC while dataset &#8220;B&#8221; contains &#8776;8%; the lower relative quantity of OC in the latter results in an almost doubling of OC mass uncertainty. Critically, these results highlight the requirement to directly consider split point uncertainty in the context of the analysis&#8217; AVEC data and overall carbon masses.
Manufacturer-reported uncertainties in carbon masses are also shown in Table 4. These estimates do not directly consider uncertainties in the calibration and split point, but rather are calculated using the fixed relations shown in Eq. (1)26, where mi represents the nominal mass of the specific carbon component.
&#160; &#160; &#160; &#160;&#160;<img alt="Equation 6" src="/files/ftp_upload/59742/59742eq6.jpg" /> 
(1)&#160; &#160;<img alt="Equation 7" src="/files/ftp_upload/59742/59742eq7.jpg" /> 
&#160; &#160; &#160; &#160;<img alt="Equation 8" src="/files/ftp_upload/59742/59742eq8.jpg" />&#160;&#160;
These fixed relations permit estimated uncertainties in OC and/or EC mass to be artificially less than that of TC mass &#8213; this condition occurs when OC or EC mass is less than one third of TC mass, as is the case for datasets &#8220;A&#8221;, &#8220;B&#8221;, and &#8220;D&#8221;. This result is non-physical, since relative uncertainty in OC and EC masses must be bounded below by the that of TC mass, a consequence of the propagation of split point uncertainty into the calculated OC and EC masses. Cells highlighted red and yellow in the table correspond to under- and over-estimations of carbon mass uncertainty when using Eq. (1) from the manufacturer. The fixed equations under-estimate TC mass uncertainty for all four examples, a consequence of computed TC masses being sufficiently large. In most cases, the fixed equations also under-estimate EC and OC mass uncertainty, except where OC (dataset &#8220;B&#8221;) and EC (dataset &#8220;D&#8221;) were sufficiently small to cause over-estimation via Eq. (1). This asymptotic increase in uncertainty via Eq. (1) agrees with the present software since uncertainty in OC and EC masses due to that of the split point are dependent upon their absolute magnitude; however, uncertainties via the fixed equations fail to track with those of the present software, which directly considers and propagates split point uncertainty in the context of specific analysis data.
Use of a MC framework in the presented software tool is necessary to accurately propagate component uncertainties through the non-linear algorithms of thermal-optical OC/EC analyses. It is important to note however, through their inherently random nature, that MC methods are not deterministic and tend to yield inconsistent results if the employed number of MC draws/repetitions (see protocol steps 4.3.3 and 6.5.2) is insufficient &#8213; akin to a statistically undersized sample. Therefore, there is an inherent consistency vs. computation time trade-off to be considered when processing data using OCECgo. It is thus recommended to users to perform preliminary processing and troubleshooting of the data using a small number of MC draws (e.g., 104). Once computations yield satisfactory results, the user should then increase the number of MC draws (to 106-108) to yield a result that is less-affected by the discrete and random nature of the MC method. Beyond the necessary &#8220;operational definition&#8221; of thermal-optical OC/EC analyses, there are other limitations in the processing of these data that must also be recognized when using OCECgo to compute and report OC/EC data. Firstly, NDIR-based instruments (such as that listed in the Table of Materials) suffer from drift in the NDIR signal that must be corrected. In the present protocol (see step 6.3.2 and the OCECgo documentation), a novel approach to correct drift in the NDIR detector can be optionally employed by the user. While, in the authors&#8217; experience this yields improved results over the manufacturer&#8217;s standard linear NDIR correction, it must be noted that uncertainty in this NDIR correction is challenging if not impossible to quantify and therefore remains an unaccounted component of uncertainty in the computation of carbon masses. In a similar vein, it is also challenging to quantify uncertainty in the necessary assumption that PC and EC share optical properties. If selected (see protocol step 6.4.1), the critical attenuation decline technique attempts to conservatively bound the effect of this assumption through a subjective expansion factor. Importantly however, this is necessarily only an estimate, and the user is suggested to assess the effect of this expansion factor (i.e., the critical attenuation decline) on their specific data. OCECgo is provided as an open-source tool so that it may easily be extended by the authors and other interested collaborators to not only interface with other instruments but to also include other useful, field-specific, functionalities. Overall, the developed open-source software tool coupled with the detailed calibration procedure is intended to help achieve more accurate measurements of OC, EC, and TC mass in aerosol samples while streamlining the robust calculation of measurement uncertainties.

The ability to accurately measure atmospheric concentrations of carbonaceous species is exceedingly important to many researchers. Carbonaceous species in ambient particulate matter (PM, the largest environmental risk factor for early death1) have been suggested to be the key component of PM responsible for adverse health effects and outcomes2,3,4. Particulate carbon in the atmosphere is a critical climate pollutant, where different carbonaceous species are known to have variable, even opposite, impacts. Black carbon is potentially the second strongest direct radiative forcer in the earth&#8217;s atmosphere5,6,7,8. When deposited on snow and ice, black carbon also reduces the reflectivity of the arctic landscape, enhancing the absorption of sunlight, and increasing the rate of melt9,10,11,12. Contrastingly, hygroscopic organic carbon particles act as cloud condensation nuclei, increasing the mean reflectivity of earth, and causing a cooling effect13. Accurate classification of sampled carbonaceous material and concurrent quantification of measurement uncertainties are thus essential aspects of particulate matter measurements.
Differentiating between organic and elemental carbon in a particulate-laden sample can be achieved using a thermal-optical analysis14. Commercial, laboratory-based systems for thermal-optical carbon analyses have been created15,16,17 including an on-line, semi-continuous analyzer18 that enables the execution of thermal-optical analyses in the field. The present work describes a detailed procedure for calibrating this latter OCEC instrument (see Table of Materials) and shares an open-source software tool for the rigorous quantification of calibration and analysis uncertainties. Although the initial release of the open-source software is designed for the output file format of the semi-continuous instrument, the software tool could readily be extended by others in future to work with outputs generated by other instruments.
The semi-continuous thermal-optical organic/elemental carbon analyzer (OCEC) quantifies organic carbon (OC) and elemental carbon (EC) in a sample volume. The analysis procedure contains four phases outlined in Figure 1. Firstly, a sample volume is pulled through the instrument, where particulate matter is deposited onto, and gas-phase organics are adsorbed by, a quartz filter. At the termination of sampling, the quartz filter is heated through a prescribed temperature protocol in an inert, helium (He) atmosphere. During this procedure, a portion of the carbonaceous material is thermally pyrolyzed from the quartz filter. Gaseous exhaust is forwarded to a fixed-temperature, Manganese Oxide (MnO2) oven that converts pyrolyzed carbonaceous species into carbon dioxide (CO2). The time-resolved concentration of released CO2 is subsequently measured by a non-dispersive infrared (NDIR) detector. Following initial heating in the He environment, the sample is heated through a similar protocol in an oxidizing (Ox) environment. In the presence of oxygen, refractory carbonaceous species remaining on the quartz filter are oxidized and then forwarded through the MnO2 oven and NDIR detector in the same manner. Once sampled carbonaceous species have been fully evolved from the quartz filter, a final in-test calibration procedure is performed. A fixed quantity (nominally 0.8 mL) of a 5% methane (CH4)-helium mixture is introduced into the instrument, oxidized in the MnO2 oven, converted to CO2, and subsequently measured by the NDIR. The integrated NDIR signal during this in-test calibration phase (termed the CH4-loop) corresponds to the known carbon mass (introduced as CH4) and therefore quantifies the sensitivity of the NDIR, which can drift in time. This measure of NDIR sensitivity is then used to infer carbon masses from the NDIR signal during the prior He- and Ox-phases of the analysis.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59742/59742fig1.jpg" /> 
Figure 1: Thermal-optical analysis procedure. Analysis procedure of the thermal-optical OCEC instrument. Following the acquisition of a sample onto the quartz filter (step 0), three main analysis steps are performed. Two thermal protocols first in a helium atmosphere (He-phase, step 1) and then in an oxidizing atmosphere (Ox-phase, step 2) are executed, where carbonaceous components are pyrolyzed/oxidized from the quartz filter, further oxidized into CO2 within a secondary catalyzing oven, and subsequently measured by an NDIR detector. A final in-test calibration procedure (CH4-loop, step 3) is performed, where the oxidation of a known mass of methane provides a measure of sensitivity of the NDIR detector. Detector sensitivity is coupled with NDIR-measured CO2 during the He- and Ox-phases to quantify carbon mass loading on the quartz filter. <a href="https://www.jove.com/files/ftp_upload/59742/59742fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The mass of injected carbon during the CH4-loop can be sensitive to operational conditions such that intermittent calibration is required. This calibration uses an aqueous sucrose solution of known concentration (approximately 0.99%m/m) as an external standard. Repeated tests are performed where different known volumes of the sucrose solution are introduced in random order into the instrument and the thermal-optical analysis is performed. Each repeated test (i.e., results from each injection and subsequent analysis) yields an integrated NDIR signal during the CH4-loop (&#8220;calibration area&#8221;) and an integrated NDIR signal for total carbon (i.e., signal during the He- and Ox-phases; referred to as &#8220;total area&#8221;), which corresponds to the known mass of carbon in sucrose. Linear regression of the known carbon mass with instrument-reported &#8220;total area&#8221; provides a measure of mean NDIR sensitivity. This sensitivity is then coupled with the mean &#8220;calibration area&#8221; to yield calibrated knowledge of the carbon mass injected during the CH4-loop.
Beyond calibration, a key challenge in the interpretation of results from the OCEC instrument is the determination of the relative fractions of OC and EC in the measured sample. As OC pyrolyzes during the He-phase of the temperature protocol, a fraction chars on the filter rather than being released, oxidized in the MnO2 oven, and detected by the NDIR. In theory, this charred refractory OC (termed pyrolyzed carbon, PC) remains on the filter until the Ox-phase, when it is oxidized alongside EC. Consequently, naively labelling all evolved carbon during the He-phase as OC and evolved carbon in the Ox-phase as EC leads to biased estimates of the true fraction of OC and EC. A common means to define the split point (i.e., the moment in time when all prior carbon evolution is considered as OC and all subsequent carbon evolution as EC) is the thermal/optical-transmittance (TOT) method19. Here, a laser is directed through the quartz filter during the thermal analysis and its power (optically downstream of the quartz filter) is detected by a photodetector. By assuming that OC is not optically active at the laser wavelength (i.e., OC negligibly absorbs light) and PC shares optical properties with EC, the split point can be estimated. The premise is to first measure the attenuated laser power at the start of the analysis. As OC evolves (partly into light-absorbing PC), the attenuation of the laser is enhanced such that the photodetector signal drops. As the Ox-phase is entered and EC/PC are co-evolved, attenuation is reduced, and the photodetector signal begins to increase. The split point is defined as the instance in time when the measured laser power returns to its initial value. While the logic of this approach is robust, the result relies upon the above-noted assumptions. As such, it is common to declare that reported OC and EC results are &#8220;operationally-defined&#8221; &#8213; i.e., they are specific to the technique employed to evaluate the split point14,20,21.
Although it is true in theory that OC is evolved in the He-phase and PC/EC are evolved in the Ox-phase, it has been observed that evolution of PC/EC can indeed occur during the He-phase due to various mechanisms22,23,24,25, such that the true split point may occur prior to the introduction of oxygen. This ambiguity in the prediction of where the split point should lie, coupled with uncertainty in the assumptions of the optical properties of OC, PC, and EC, suggests that uncertainty in the split point could be a dominant source of uncertainty in measured carbon masses. Fortunately, the methodical estimation of the split point via the TOT method enables an objective estimate of split point uncertainty. To the authors&#8217; knowledge however, there is no direct estimation (and subsequent propagation) of split point uncertainty in the manufacturer&#8217;s software; reported total uncertainties are instead computed with fixed relative and absolute components26. As part of this work, a novel technique to estimate split point uncertainty is presented &#8722; the &#8220;attenuation decline&#8221; technique. In this technique, uncertainty in the split point is quantified as the difference between evolved carbon mass at the nominal split (via the TOT method) and evolved carbon mass at a subsequent point where laser attenuation has decreased beyond some critical quantity, a prescribed fraction of the initial attenuation. A critical attenuation decline is estimated based on the uncertainty in laser attenuation relative to its initial value; in theory, this approach captures uncertainty in the key attenuation-matching principle of the TOT method. Furthermore, to consider (at least in part) split point uncertainty due to assumed optical properties of PC and EC, the suggested critical attenuation decline is expanded by a factor of two.
This article presents a detailed protocol for calibrating the OCEC instrument alongside a software tool for rigorously quantifying calibration and analysis uncertainties. Firstly, sections 1 to 3 of the protocol outline instructions for creating the aqueous sucrose solution, preparing the instrument for calibration, and acquiring calibration data. Section 4 uses the novel, open-source, software tool (see Table of Materials) to analyze calibration data via the software&#8217;s graphical user interface. Section 5 specifies considerations for acquiring a sample using the OCEC instrument and section 6 describes use of the above-noted software for calculating carbon masses and associated uncertainties, including contributions from the estimation of the split point. Novel techniques to improve the processing of OCEC data &#8213; including the &#8220;attenuation decline&#8221; technique introduced above &#8213; are described in the software&#8217;s online documentation.
Within the presented software tool, calibration constants, measured carbon masses, and associated uncertainties are computed using a Monte Carlo (MC) method. This procedure propagates errors that are not, to the authors&#8217; knowledge, currently considered in the manufacturer&#8217;s proprietary software. For calibration, these sources of error include uncertainty in the concentration of sucrose in the aqueous solution, accuracy in the volume of applied sucrose solution (instrument accuracy, inter-user reproducibility, and intra-user repeatability), and uncertainty in the linear regression. With respect to data analysis, considered sources of error include the calibration uncertainty and repeatability and, importantly, the estimated uncertainty of the split point. Ultimately, the software enables a user to accurately quantify uncertainty in the calibration of the instrument and propagate this uncertainty alongside that of split point estimation into the calculation of carbon masses. This represents a notable improvement upon the manufacturer&#8217;s protocol, by directly considering key sources of error in the measurement, rather than estimating partial uncertainties using a fixed equation.

<table><tbody><tr><td>10% oxygen gas in helium</td><td>Local gas supplier</td><td>- - -</td><td>Primary or certified standard preferred</td></tr><tr><td>5% methane gas in helium</td><td>Local gas supplier</td><td>- - -</td><td>Primary or certified standard preferred</td></tr><tr><td>Distilled, de-ionized water</td><td>Harleco</td><td>6442-85</td><td>ASTM D1193-91 Type II or Type I (preferred)</td></tr><tr><td>Filter punch tool</td><td>Sunset Laboratories Inc.</td><td>- - -</td><td>Included with carbon analyzer</td></tr><tr><td>Filter removal tool</td><td>Sunset Laboratories Inc.</td><td>- - -</td><td>Included with carbon analyzer</td></tr><tr><td>Glass jar (4 oz.)</td><td>ULINE</td><td>S-17982P-BL</td><td>Or suitable equivalent; borosillicate glass preferred</td></tr><tr><td>Helium gas</td><td>Local gas supplier</td><td>- - -</td><td>Ultra-high purity (&gt; 99.999%) or better preferred</td></tr><tr><td>High-accuracy thermal gas mass flow meter</td><td>Bronkhorst</td><td>EL-FLOW Prestige</td><td>For accurate measurement of sample volume (see &lt;strong&gt;Protocol&lt;/strong&gt; step 5)</td></tr><tr><td>High-purity sucrose</td><td>Sigma Aldrich</td><td>S9378</td><td>Purity &amp;ge; 99%&lt;sub&gt;m/m&lt;/sub&gt; or higher preferred</td></tr><tr><td>Lint-free tissues</td><td>Kimtech</td><td>34155</td><td>Or suitable equivalent</td></tr><tr><td>MatLab Runtime (R2016a or newer)</td><td>MathWorks Inc.</td><td>mathworks.com</td><td>Search &quot;runtime compiler&quot; and install appropriate version for the operating system</td></tr><tr><td>Non-powdered, disposable, plastic gloves</td><td>VWR</td><td>89428-752</td><td>Or suitable, properly-sized equivalent</td></tr><tr><td>&lt;em&gt;OCECgo&lt;/em&gt; software</td><td>Carleton University, Energy and Emissions Research Lab.</td><td>GitHub Repository</td><td>Source and build distributions of the software are available on GitHub</td></tr><tr><td>Oxygen trap</td><td>Supelco</td><td>22449</td><td>Or suitable GC-quality equivalent</td></tr><tr><td>Pipette</td><td>Eppendorf</td><td>3120000020</td><td>Model: Research&amp;reg; Plus 0.5 - 10 &amp;mu;L - Or any single-channel, adjustable volume, manual pipette</td></tr><tr><td>Pipette tips</td><td>Eppendorf</td><td>022492012</td><td>Model: epT.I.P.S.&amp;reg; Standard, 0.1 - 20 &amp;mu;L</td></tr><tr><td>Precision scale / balance</td><td>AND</td><td>FX-3000IWP</td><td>Precision balance with capacity &gt; 1 kg</td></tr><tr><td>Quartz filters</td><td>Pall</td><td>7202</td><td>Model: Tissuquartz 2500 QAT-UP - 47 mm</td></tr><tr><td>Semi-continuous thermal-optical organic/elemental carbon analyzer</td><td>Sunset Laboratories Inc.</td><td>- - -</td><td>Model 4 semi-continuous analyzer</td></tr></tbody></table>

split point analysis and uncertainty quantification of thermal-optical organic/elemental carbon measurements

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon (EC). This is commonly accomplished using thermal-optical OC/EC analyzers (TOAs), which enable measurement via controlled thermal pyrolysis and oxidation under specific temperature protocols and within constrained atmospheres. Several commercial TOAs exist, including a semi-continuous instrument that enables on-line analyses in the field. This instrument employs an in-test calibration procedure that requires relatively frequent calibration. This article details a calibration protocol for this semi-continuous TOA and presents an open-source software tool for data analysis and rigorous Monte Carlo quantification of uncertainties. Notably, the software tool includes novel means to correct for instrument drift and identify and quantify the uncertainty in the OC/EC split point. This is a significant improvement on the uncertainty estimation in the manufacturer&#8217;s software, which ignores split point uncertainty and otherwise uses fixed equations for relative and absolute errors (generally leading to under-estimated uncertainties and often yielding non-physical results as demonstrated in several example data sets). The demonstrated calibration protocol and new software tool enabling accurate quantification of combined uncertainties from calibration, repeatability, and OC/EC split point are shared with the intent of assisting other researchers in achieving better measurements of OC, EC, and total carbon mass in aerosol samples.

Representative results for the calibration of the OCEC instrument are presented using example calibration data acquired by the authors, shown in Table 1. Here, a six-point calibration is used, obtained using the developed software and following the manufacturer&#8217;s example in the instrument manual18. Results are shown in Figure 2. Figure 2a presents the results of linear regression on the calibration data under an MC framework. Black points correspond to the 2&#963; confidence interval of each of the six calibration points &#8212; i.e., carbon mass in the deposited sucrose is on the horizontal axis and instrument-reported total area (column 2 of Table 1) is on the vertical axis. The red shaded region represents the 2&#963; CI of the linear regression based on these six uncertain calibration data points &#8213; two each for 0, 5, and 10 &#956;L of the sucrose solution (column 1 of Table 1). The mean carbon mass injected during the CH4-loop is determined within the MC method. For each MC draw (2&#215;107 in this example), a randomized calibration area (based on the distribution of the mean of calibration areas from column 3 of Table 1) is coupled with the uncertain linear model to obtain a MC-estimate of CH4-loop carbon mass. The horizontal and vertical blue shaded regions correspond to the 2&#963; CIs of the mean calibration area and mass calibration constant from the MC analysis. The MC-estimates of these calibration data are shown in the scatterplot-histogram in Figure 2b. This example dataset yielded a calibration constant of 18.49 &#956;gC &#177; 2.78%.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Calibration Data</td>
		</tr>
		<tr>
			<td>Volume of Sucrose Solutiona [&#956;L]</td>
			<td>&#34;Total&#34; NDIR 
			Area [-]</td>
			<td>&#34;Calibration&#34; NDIR 
			Area [-] (CH4-loop)</td>
		</tr>
		<tr>
			<td>0</td>
			<td>3041</td>
			<td>31297</td>
		</tr>
		<tr>
			<td>5</td>
			<td>38229</td>
			<td>31281</td>
		</tr>
		<tr>
			<td>5</td>
			<td>37321</td>
			<td>32056</td>
		</tr>
		<tr>
			<td>10</td>
			<td>72472</td>
			<td>31435</td>
		</tr>
		<tr>
			<td>0</td>
			<td>1589</td>
			<td>31583</td>
		</tr>
		<tr>
			<td>10</td>
			<td>72914</td>
			<td>30926</td>
		</tr>
		<tr>
			<td colspan="3">a Nominal concentration of 4.148 &#177; 0.022 gC/L at 25 &#176;C</td>
		</tr>
	</tbody>
</table>
Table 1: Representative calibration data. Representative calibration data containing two blanks and two measurements each at 5 &#956;L and 10 &#956;L of sucrose solution, consistent with the manufacturer&#8217;s example in the instrument manual18.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59742/59742fig2.jpg" /> 
Figure 2: Representative calibration results. (a) Results of the MC-regression of a six-point calibration dataset obtained by the authors. Calibration data with 2&#963; uncertainties are represented by black boxes and the 2&#963; CI of the linear regression is shown in the red shaded region. The horizontal blue bar corresponds to the 2&#963; CI of the mean NDIR area during the CH4-loops (&#8220;calibration areas&#8221;), which are coupled with the uncertain linear regression to yield the 2&#963; CI of the mass calibration constant (represented by the vertical blue bar). (b) A scatterplot-histogram representing the MC-computed calibration data; mass calibration constant on the horizontal axis and mean calibration area on the vertical axis. In this example uncertainty in the mass calibration constant was approximately 2.78%. <a href="https://www.jove.com/files/ftp_upload/59742/59742fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A representative data analysis using the developed software is provided for a measurement of carbonaceous emissions from a laboratory soot generator (MiniCAST 5201 Type C) burning nitrogen-diluted propane fuel29. Summaries of the sample data in the form of an OCEC thermogram (a plot displaying laser power, NDIR, and oven temperature during an OCEC analysis) and an AVEC21 plot &#8212; created during protocol step 6.3.4 &#8212; are shown in Figure 3a and Figure 3b, respectively. In this example, uncertainty in the split point is estimated using the above-described &#8220;Attenuation decline&#8221; technique developed by the authors. The critical decline in attenuation was quantified as 1.342% yielding a split point uncertainty of 4.50% of TC mass. Key results of this analysis &#8213; carbon mass statistics and best-fitting posterior distributions &#8213; are summarized in Table 2.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59742/59742fig3.jpg" /> 
Figure 3: Representative analysis data. (a) A thermogram of OCEC-measured laser power, NDIR signal, desired (set), and measured (actual, act.) oven temperature. (b) The attenuation vs. evolved carbon (AVEC) plot corresponding to the presented histogram. The points correspond to instantaneous data reported at 1 Hz, with point colouring representing the instantaneous front oven (filter) temperature. The horizontal black line corresponds to the initial laser attenuation (following sample acquisition and prior to thermal analysis) used in the determination of the split point, while the horizontal red line corresponds to a 1.342% decline in initial laser attenuation used to estimate uncertainty in the split point. The grey shaded region represents the 2&#963; CI of the split point for this analysis, which is approximately 4.50% of TC mass. <a href="https://www.jove.com/files/ftp_upload/59742/59742fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Carbon Type</td>
			<td>Mean Mass 
			[&#956;gC]</td>
			<td>2&#963; Uncertainty 
			of MC Data</td>
			<td>Best-fitting 
			Distributiona</td>
		</tr>
		<tr>
			<td>Organic 
			Carbon (OC)</td>
			<td>26.94</td>
			<td>-21.3% 
			+22.2%</td>
			<td>N(26.94, 2.925)</td>
		</tr>
		<tr>
			<td>Elemental 
			Carbon (EC)</td>
			<td>93.11</td>
			<td>-9.98% 
			+10.4%</td>
			<td>&#915;(385.7, 0.2414)</td>
		</tr>
		<tr>
			<td>Total 
			Carbon (TC)</td>
			<td>120.05</td>
			<td>-8.32% 
			+8.40%</td>
			<td>N(120.1, 5.014)</td>
		</tr>
		<tr>
			<td colspan="4">a Normal: N(&#956;, &#963;); Gamma: &#915;(a, b[scale])</td>
		</tr>
	</tbody>
</table>
Table 2: Representative carbon mass results. MC-computed OC, EC, and TC masses for the example data plotted in Figure 3 with a 2&#963; uncertainty in the split point of 4.50%, corresponding to a 1.342% decline in initial laser attenuation. Listed are the mean and 2&#963; uncertainties of the MC data, in addition to the best-fitting posterior distributions.

Watch this Scientific Journal Video about Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements at JoVE.com

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon analyzer.

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon ...

split-point-analysis-uncertainty-quantification-thermal-optical

Research

JoVE Journal

Environment

8.2K Views.  Carleton University. This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon analyzer.

Video: Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids (BPCA)

Fire-derived, pyrogenic carbon (PyC), sometimes called black carbon (BC), is the carbonaceous solid residue of biomass and fossil fuel combustion, such as char and soot. PyC is ubiquitous in the environment due to its long persistence, and its abundance might even increase with the projected increase in global wildfire activity and the continued burning of fossil fuel. PyC is also increasingly produced from the industrial pyrolysis of organic wastes, which yields charred soil amendments (biochar). Moreover, the emergence of nanotechnology may also result in the release of PyC-like compounds to the environment. It is thus a high priority to reliably detect, characterize and quantify these charred materials in order to investigate their environmental properties and to understand their role in the carbon cycle.
Here, we present the benzene polycarboxylic acid (BPCA) method, which allows the simultaneous assessment of PyC&#39;s characteristics, quantity and isotopic composition (13C and 14C) on a molecular level. The method is applicable to a very wide range of environmental sample materials and detects PyC over a broad range of the combustion continuum, i.e., it is sensitive to slightly charred biomass as well as high temperature chars and soot. The BPCA protocol presented here is simple to employ, highly reproducible, as well as easily extendable and modifiable to specific requirements. It thus provides a versatile tool for the investigation of PyC in various disciplines, ranging from archeology and environmental forensics to biochar and carbon cycling research.

Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids BPCA

We present the benzene polycarboxylic acid (BPCA) method for assessing pyrogenic carbon (PyC) in the environment. The compound-specific approach uniquely provides simultaneous information about the characteristics, quantity and isotopic composition (13C and 14C) of PyC.

Fire-derived, pyrogenic carbon (PyC), sometimes called black carbon (BC), is the carbonaceous solid residue of biomass and fossil fuel combustion, such as ...

Chemistry

Emission Spectroscopic Boundary Layer Investigation during Ablative Material Testing in Plasmatron

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution for future high-speed reentry missions. But despite the advancements made since Apollo, heat flux prediction remains an imperfect science and engineers resort to safety factors to determine the TPS thickness. This goes at the expense of embarked payload, hampering, for example, sample return missions. 
Ground testing in plasma wind-tunnels is currently the only affordable possibility for both material qualification and validation of material response codes. The subsonic 1.2MW Inductively Coupled Plasmatron facility at the von Karman Institute for Fluid Dynamics is able to reproduce a wide range of reentry environments. This protocol describes a procedure for the study of the gas/surface interaction on ablative materials in high enthalpy flows and presents sample results of a non-pyrolyzing, ablating carbon fiber precursor. With this publication, the authors envisage the definition of a standard procedure, facilitating comparison with other laboratories and contributing to ongoing efforts to improve heat shield reliability and reduce design uncertainties.
The described core techniques are non-intrusive methods to track the material recession with a high-speed camera along with the chemistry in the reactive boundary layer, probed by emission spectroscopy. Although optical emission spectroscopy is limited to line-of-sight measurements and is further constrained to electronically excited atoms and molecules, its simplicity and broad applicability still make it the technique of choice for analysis of the reactive boundary layer. Recession of the ablating sample further requires that the distance of the measurement location with respect to the surface is known at all times during the experiment. Calibration of the optical system of the applied three spectrometers allowed quantitative comparison. At the fiber scale, results from a post-test microscopy analysis are presented.

Development of new ablative materials and their numerical modeling requires extensive experimental investigation. This protocol describes procedures for material response characterization in plasma flows with the core techniques being non-intrusive methods to track the material recession along with the chemistry in the reactive boundary layer by emission spectroscopy.

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution ...

Engineering

Laser-heating and Radiance Spectrometry for the Study of Nuclear Materials in Conditions Simulating a Nuclear Power Plant Accident

Major and severe accidents have occurred three times in nuclear power plants (NPPs), at Three Mile Island (USA, 1979), Chernobyl (former USSR, 1986) and Fukushima (Japan, 2011). Research on the causes, dynamics, and consequences of these mishaps has been performed in a few laboratories worldwide in the last three decades. Common goals of such research activities are: the prevention of these kinds of accidents, both in existing and potential new nuclear power plants; the minimization of their eventual consequences; and ultimately, a full understanding of the real risks connected with NPPs. At the European Commission Joint Research Centre&#39;s Institute for Transuranium Elements, a laser-heating and fast radiance spectro-pyrometry facility is used for the laboratory simulation, on a small scale, of NPP core meltdown, the most common type of severe accident (SA) that can occur in a nuclear reactor as a consequence of a failure of the cooling system. This simulation tool permits fast and effective high-temperature measurements on real nuclear materials, such as plutonium and minor actinide-containing fission fuel samples. In this respect, and in its capability to produce large amount of data concerning materials under extreme conditions, the current experimental approach is certainly unique. For current and future concepts of NPP, example results are presented on the melting behavior of some different types of nuclear fuels: uranium-plutonium oxides, carbides, and nitrides. Results on the high-temperature interaction of oxide fuels with containment materials are also briefly shown.

We present experiments in which real nuclear fuel, cladding, and containment materials are laser heated to temperatures beyond 3,000 K while their behavior is studied by radiance spectroscopy and thermal analysis. These experiments simulate, on a laboratory scale, the formation of a lava-phase following a nuclear reactor core meltdown.

Major and severe accidents have occurred three times in nuclear power plants (NPPs), at Three Mile Island (USA, 1979), Chernobyl (former USSR, 1986) and ...

Quantifying X-Ray Fluorescence Data Using MAPS

The quantification of X-ray fluorescence (XRF) microscopy maps by fitting the raw spectra to a known standard is crucial for evaluating chemical composition and elemental distribution within a material. Synchrotron-based XRF has become an integral characterization technique for a variety of research topics, particularly due to its non-destructive nature and its high sensitivity. Today, synchrotrons can acquire fluorescence data at spatial resolutions well below a micron, allowing for the evaluation of compositional variations at the nanoscale. Through proper quantification, it is then possible to obtain an in-depth, high-resolution understanding of elemental segregation, stoichiometric relationships, and clustering behavior.
This article explains how to use the MAPS fitting software developed by Argonne National Laboratory for the quantification of full 2-D XRF maps. We use as an example results from a Cu(In,Ga)Se2 solar cell, taken at the Advanced Photon Source beamline 2-ID-D at Argonne National Laboratory. We show the standard procedure for fitting raw data, demonstrate how to evaluate the quality of a fit and present the typical outputs generated by the program. In addition, we discuss in this manuscript certain software limitations and offer suggestions for how to further correct the data to be numerically accurate and representative of spatially resolved, elemental concentrations.

Here, we demonstrate the use of the X-ray fluorescence fitting software, MAPS, created by Argonne National Laboratory for the quantification of fluorescence microscopy data. The quantified data that results is useful for understanding the elemental distribution and stoichiometric ratios within a sample of interest.

The quantification of X-ray fluorescence (XRF) microscopy maps by fitting the raw spectra to a known standard is crucial for evaluating chemical ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in lamellae exsolved from host mineral phases. Thus, the analysis of the orientation, size, morphology, composition and spacing of exsolution lamellae is an area of active research in the geosciences. The conventional study of these lamellae has been conducted by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and more recently with focused ion beam (FIB)-based nanotomography, yet with limited chemical information. Here, we explore the use of atom probe tomography (APT) for the nanoscale analysis of ilmenite exsolution lamellae in igneous titanomagnetite from ash deposits erupted from the active Soufri&#232;re Hills Volcano (Montserrat, British West Indies). APT allows the precise calculation of interlamellar spacings (14&#8211;29 &#177; 2 nm) and reveals smooth diffusion profiles with no sharp phase boundaries during the exchange of Fe and Ti/O between the exsolved lamellae and the host crystal. Our results suggest that this novel approach permits nanoscale measurements of lamellae composition and interlamellar spacing that may provide a means to estimate the lava dome temperatures necessary to model extrusion rates and lava dome failure, both of which play a key role in volcanic hazard mitigation efforts.

Analysis of the morphology, composition, and spacing of exsolution lamellae can provide essential information to understand geological processes related to volcanism and metamorphism. We present a novel application of APT for the characterization of such lamellae and compare this approach to the conventional use of electron microscopy and FIB-based nanotomography.

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in ...

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

Vacuum induction melting is a popular method for refining high purity metal and alloys. Traditionally, standard process control in metallurgy involves several steps, include drawing samples, cooling, cutting, transport to the laboratory, and analysis. The whole analysis process requires more than 30 minutes, which hinders on-line process control. Laser-induced breakdown spectroscopy is an excellent on-line analysis method that can satisfy the requirements of vacuum induction melting because it is fast and noncontact and does not require sample preparation. The experimental facility uses a lamp-pumped Q-switched laser to ablate melted liquid steel with an output energy of 80 mJ, a frequency of 5 Hz, a FWHM pulse width of 20 ns, and a working wavelength of 1,064 nm. A multi-channel linear charge coupled device (CCD) spectrometer is used to measure the emission spectrum in real time, with a spectral range from 190 to 600 nm and a resolution of 0.06 nm at a wavelength of 200 nm. The protocol includes several steps: standard alloy sample preparation and an ingredient test, smelting of standard samples and determination of the laser breakdown spectrum, and construction of the elements concentration quantitative analysis curve of each element. To realize the concentration analysis of unknown samples, the spectrum of a sample also needs to be measured and disposed with the same process. The composition of all main elements in the melted alloy can be quantitatively analyzed with an internal standard method. The calibration curve shows that the limit of detection of most metal elements ranges from 20-250 ppm. The concentration of elements, such as Ti, Mo, Nb, V, and Cu, can be lower than 100 ppm, and the concentrations of Cr, Al, Co, Fe, Mn, C, and Si range from 100-200 ppm. The R2 of some calibration curves can exceed 0.94.

During vacuum induction melting, laser-induced breakdown spectroscopy is used to perform real-time quantitative analysis of the main-ingredient elements of a molten alloy.

Vacuum induction melting is a popular method for refining high purity metal and alloys. Traditionally, standard process control in metallurgy involves ...

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases

During energy conversion, material production, and metallurgy processes, reactions often have the features of unsteadiness, multistep, and multi-intermediates. Thermogravimetry-mass spectrum (TG-MS) is seen as a powerful tool to study reaction features. However, reaction details and reaction mechanics have not been effectively obtained directly from the ion current of TG-MS. Here, we provide a method of an equivalent characteristic spectrum analysis (ECSA) for analyzing the mass spectrum and giving the mass flow rate of reaction gases as precise as possible. The ECSA can effectively separate overlapping ion peaks and then eliminate the mass discrimination and temperature-dependent effect. Two example experiments are presented: (1) the decomposition of CaCO3 with evolved gas of CO2 and the decomposition of hydromagnesite with evolved gas of CO2 and H2O, to evaluate the ECSA on single-component system measurement and (2) the thermal pyrolysis of Zhundong coal with evolved gases of inorganic gases CO, H2, and CO2, and organic gases C2H4, C2H6, C3H8, C6H14, etc., to evaluate the ECSA on multi-component system measurement. Based on the successful calibration of the characteristic spectrum and relative sensitivity of specific gas and the ECSA on mass spectrum, we demonstrate that the ECSA accurately gives the mass flow rates of each evolved gas, including organic or inorganic gases, for not only single but multi-component reactions, which cannot be implemented by the traditional measurements.

Precise determination of the evolved gases&#39; flow rate is key to study the details of reactions. We provide a novel quantitative analysis method of equivalent characteristic spectrum analysis for thermogravimetry-mass spectrum analysis by establishing the calibration system of the characteristic spectrum and relative sensitivity, for obtaining the flow rate.

During energy conversion, material production, and metallurgy processes, reactions often have the features of unsteadiness, multistep, and ...

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

A novel elemental and chemical analysis scheme based on electron-channeling phenomena in crystalline materials is introduced, where the incident high-energy electron beam is rocked with the submicrometric pivot point fixed on a specimen. This method enables us to quantitatively derive the site occupancies and site-dependent chemical information of impurities or intentionally doped functional elements in a specimen, using energy-dispersive X-ray spectroscopy and electron energy-loss spectroscopy attached to a scanning transmission electron microscope, which is of significant interest to current materials science, particularly related to nanotechnologies. This scheme is applicable to any combination of elements even when the conventional Rietveld analysis by X-ray or neutron diffraction occasionally fails to provide the desired results because of limited sample sizes and close scattering factors of neighboring elements in the periodic table. In this methodological article, we demonstrate the basic experimental procedure and analysis method of the present beam-rocking microanalysis.

We provide a general outline of quantitative microanalysis methods for estimating the site occupancies of impurities and their chemical states by taking advantage of electron-channeling phenomena under incident electron beam-rocking conditions, which reliably extract information from minority species, light elements, oxygen vacancies, and other point/line/planar defects.

A novel elemental and chemical analysis scheme based on electron-channeling phenomena in crystalline materials is introduced, where the incident ...

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

The risks and possible accidents related to the normal use of lithium-ion batteries remain a serious concern. In order to get a better understanding of thermal runaway (TR), the exothermic decomposition reactions in anode and cathode were studied, using a Simultaneous Thermal Analysis (STA)/Gas Chromatography-Mass Spectrometry (GC-MS)/Fourier Transform Infrared (FTIR) spectrometer system. These techniques allowed the identification of the reaction mechanisms in each electrode, owing to the analysis of evolved gaseous species, the amount of heat released and mass loss. These results provided insight into the thermal events happening within a broader temperature range than covered in previously published models. This allowed the formulation of an improved thermal model to depict TR. The heat of reaction, activation energy, and frequency factor (thermal triplets) for each major exothermic process at material level were investigated in a Lithium Nickel-Manganese-Cobalt-Oxide (NMC (111))-Graphite battery cell. The results were analyzed, and their kinetics were derived. These data can be used to successfully simulate the experimental heat flow.

This work aims at determining the reaction kinetics of Li-ion battery cathode and anode materials undergoing thermal runaway (TR). Simultaneous Thermal Analysis (STA)/Fourier Transform Infrared (FTIR) spectrometer/Gas Chromatography Mass Spectrometry (GC-MS) were used to reveal thermal events and to detect evolved gases.

The risks and possible accidents related to the normal use of lithium-ion batteries remain a serious concern. In order to get a better understanding of ...

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Podsumowanie

Przeglądaj więcej filmów

PLAYLIST