1 / 34

IMPROVE Carbon Analysis

IMPROVE Carbon Analysis. John G. Watson (john.watson@dri.edu) Judith C. Chow Xiaoliang Wang Dana L. Trimble Steven D. Kohl L .-W. Antony Chen Desert Research Institute, Reno, NV Presented at the IMPROVE Steering Committee Meeting Park City, Utah October 8, 2013. Objectives.

olympe
Download Presentation

IMPROVE Carbon Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. IMPROVE Carbon Analysis John G. Watson(john.watson@dri.edu)Judith C. Chow Xiaoliang Wang Dana L. Trimble Steven D. Kohl L.-W. Antony Chen Desert Research Institute, Reno, NV Presented at the IMPROVE Steering Committee Meeting Park City, Utah October 8, 2013

  2. Objectives • Report status and improvements of IMPROVE carbon analyses • Update progress on enhancing thermal/optical analyses

  3. Carbon Laboratory Operations(July 2012 to June 2013) • Received ~1,790 samples per month (between 1,200–2,400) • Maintained 24 hours per day/6-7 days per week operation with six staff • Analyzed ~25,200 IMPROVE samples (1,400 to 2,932 per month) • Averaged ~2,500 samples per month in the queue (200 to 5,395)

  4. IMPROVE Carbon Analysis following the IMPROVE_Aa Protocol(July 2012 to June 2013) a Chow et al. (2007) JAWMA

  5. Pre-baked filters yield values within blank tolerances(Acceptance testing, July 2012 – June 2013) Acceptable OC (1.5 µgC/cm2) Acceptable EC (0.5 µgC/cm2) Average OC (0.18 µgC/cm2) Average EC (0.004 µgC/cm2)

  6. Oxygen performance tests show values within the 100 ppm tolerance

  7. Performance tests are within the ±5% tolerance(Sucrose; thrice per week between 7/1/2012 and 6/30/2013) Acceptable limit (between 17.1 and 18.9 µg C)

  8. Thermal/Optical Analysis Research Efforts(supported by NSF grant, Antony Chen sabbatical leave, Fulbright Fellowships, and DAS graduate student fellowship) • Multi-wavelength retrofit for DRI Model 2001 for radiation balance and source apportionment • Thermal analysis for carbon, hydrogen, oxygen, nitrogen and sulfur (CHONS) for non-refractory mass balance • Detailed organic compositions to optimize carbon fractions, identify source of black and brown carbon, and better understand the analytical process • Design and test Model 2014 to increase interchangeability of detectors, improve reliability, lower costs of expendables (gases), more precise control of operating parameters

  9. Extending from single to multiple wavelengths can distinguish pollution sources (EC absorption efficiency varies by source and wavelength) Smoldering Diesel Flaming Sandraweji et al., 2008, EST p. 3316-3324. Atmos. Env. p. 101-112

  10. Absorption at different wavelengths affects radiative transfer (visibility and climate) Emissions (above) and radiative transfer as a function of wavelength for funeral pyres in India Chakrabarty, R.K.; Pervez, S.; Chow, J.C.; Dewangan, S.; Robles, J.A.; Tian, G.X.; Watson, J.G. (2013). Funeral pyres in south Asia: Large-scale brown carbon emissions and associated warming. Environmental Science & Technology Letters, online.

  11. Multiple wavelengths may improve the EC char correction • BC (AAE ≈ 0.75) and char (AAE ≈ 5) Hadley, O.L.; Corrigan, C.E.; Kirchstetter, T.W. (2008). Modified thermal-optical analysis using spectral absorption selectivity to distinguish black carbon from pyrolized organic carbon. Environ. Sci. Technol., 42(22):8459-8464.

  12. A 7-wavelength optical system is practical to implement on the existing Model 2001 TOR/TOT IMPROVE_A carbon analysis system 405, 445, 532, 635, 780, 808, and 980 nm laser diodes Shorter wavelengths will become available in the future

  13. Retrofit hardware can be installed where the HeNe laser was located Fiber Optic Assembly Lasers Laser Control and Signal Conditioning PCB National Instruments DAQ (underneath PCB) Old laser (for comparison) Power Supplies

  14. Each laser diode fires sequentially with timing keyed to the detector. Fiber Optic Assembly Lasers National Instruments DAQ (underneath PCB) Laser Control and Signal Conditioning PCB Power Supplies

  15. A small microcontroller coordinates the signal creation and detection DAQ Connectivity Microcontroller Signal Conditioning and Amplification Sensor Input Laser Indicators Power supply

  16. Example 7-Wavelength Thermogram showing Laser Reflectance

  17. Example 7-Wavelength Thermogram showing Laser Transmittance

  18. Characteristic patterns are found in wavelength/analysis time (temperature) space

  19. Initial comparisons with HeNe 633 nm show comparable OC measurements 633 vs 633 nm OC replicate comparison for IMPROVE samples 635 vs 633 nm OC comparison for Fresno, dust, diesel, and fire samples

  20. Initial comparisons with HeNe 633 nm show comparable EC measurements, except for some of the high fire values 635 vs 633 nm EC comparison for Fresno, dust, diesel, and fire samples 633 vs 633 nm EC replicate comparison for IMPROVE samples

  21. Follow-up • Specify reporting parameters • Complete software interfaces • Compare 635 and 633 nm OC and EC for IMPROVE samples (as replicates) • Replace HeNe lasers with diode lasers in routine units • Modify reporting format to include additional information

  22. C, H, N, S, and O are major PM2.5 components • Sum of C, H, N, S, and O accounts for ~>80% of PM2.5 mass for many source and ambient samples • Remaining mass is mostly associated with minerals, which may also contain CHNSO that does not decompose with moderate heating • Relative abundances indicate different particle source/properties

  23. C, H, N, S, and O can be obtained with thermal/optical techniques currently used for only for C analysis

  24. Mass spectrometer signals are linear with C, H, N, and S quantities for model compounds Sulfanilamide: C6H8N2O2S; L-Cystine: C6H12N2O4S2

  25. NDIR signal is linear with calibration chemical quantities Oxygen is converted to CO and then CO2 and detected by NDIR • H2O bound to filters and particles. Need to pre-heat sample before analysis • O2 in the carrier gas needs further reduction. Heaver gas (Ar) and higher pressure needed Benzoic acid: C7H6O2; Nonadecanol: C19H40O Pentadecanoic acid: C15H30O2

  26. Mating thermal/optical carbon analysis to more specific detectors yields specific organic compounds in thermal fractions DRI Model 2001 carbon analyzer with advanced resonance-enhanced multiphoton ionization (REMPI) single-photon ionization (SPI) detectors Grabowsky, J.et al. (2011) Hyphenation of a carbon analyzer to photo-1 ionization mass spectrometry to unravel the organic composition of particulate matter on a molecular level. Anal. Bioanal. Chem., 401(10):3153-3164.

  27. IP Sn S0 REMPI Mass spectra of thermal carbon fractions from Model 2001 with Resonance Enhanced Multi-Photon Ionization-Time-of-Flight/Mass Spectrometry (REMPI-TOF/MS) y-scale x 1 OC I y-scale x 0.25 ! OC II y-scale x 0.25 ! OC III Zimmermann, 2011

  28. Charge to mass (m/z) patterns in temperature fractions indicate origins, including secondary organic aerosol Two-dimensional time temperature REMPI/TOF-MS-spectra of PM loaded filter from engine emissions using gasoline (left) and diesel (10% biodiesel) (right). Can be extended to the study of aged emissions Grabowsky, J.et al. (2011) Hyphenation of a carbon analyzer to photo-1 ionization mass spectrometry to unravel the organic composition of particulate matter on a molecular level. Anal. Bioanal. Chem., 401(10):3153-3164.

  29. Concept for Model 2014 thermal/optical analysis system

  30. Model 2014 will change components, but will retain sample presentation and heating system to retain OC/EC consistency

  31. Outlook • Emerging technologies allow more information to be obtained from existing samples for comparable costs • Additional information for each sample will enhance data analysis and modeling opportunities for radiation balance and source apportionment • Changes must retain the OC/EC consistency of the IMPROVE long-term data base

  32. DRI reports and publications using the IMPROVE protocol(2012 and 2013) Bell, S.W.; Hansell, R.A.; Chow, J.C.; Tsay, S.C.; Wang, S.H.; Ji, Q.; Li, C.; Watson, J.G.; Khlystov, A. (2013). Constraining aerosol optical models using ground-based, collocated particle size and mass measurements in variable air mass regimes during the 7-SEAS/Dongsha experiment. Atmos. Environ., 78:163-173. Cao, J.J.; Wang, Q.Y.; Chow, J.C.; Watson, J.G.; Tie, X.X.; Shen, Z.X.; An, Z.S. (2012). Impacts of aerosol compositions on visibility impairment in Xi'an, China. Atmos. Environ., 59:559-566. Cao, J.J.; Huang, H.; Lee, S.C.; Chow, J.C.; Zou, C.W.; Ho, K.F.; Watson, J.G. (2012). Indoor/outdoor relationships for organic and elemental carbon in PM2.5 at residential homes in Guangzhou, China. AAQR, 12(5):902-910. http://aaqr.org/VOL12_No5_October2012/18_AAQR-12-02-OA-0026_902-910.pdf. Cao, J.J.; Shen, Z.X.; Chow, J.C.; Lee, S.C.; Watson, J.G.; Tie, X.X.; Ho, K.F.; Wang, G.H.; Han, Y.M. (2012). Winter and summer PM2.5 chemical compositions in 14 Chinese cities. J. Air Waste Manage. Assoc., 62(10):1214-1226. DOI: 10.1080/10962247.2012.701193. http://www.tandfonline.com/doi/pdf/10.1080/10962247.2012.7011933 . Chakrabarty, R.K.; Pervez, S.; Chow, J.C.; Dewangan, S.; Robles, J.A.; Tian, G.X.; Watson, J.G. (2013). Funeral pyres in south Asia: Large-scale brown carbon emissions and associated warming. Environmental Science & Technology Letters, online. http://pubs.acs.org/doi/pdf/10.1021/ez4000669. Chen, L.-W.A.; Tropp, R.J.; Li, W.-W.; Zhu, D.Z.; Chow, J.C.; Watson, J.G.; Zielinska, B. (2012). Aerosol and air toxics exposure in El Paso, Texas: A pilot study. AAQR, 12(2):169-189. http://aaqr.org/VOL12_No2_April2012/3_AAQR-11-10-OA-0169_169-179.pdf. Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; DuBois, D.W.; Herschberger, L. (2012). Chemical mass balance source apportionment for combined PM2.5 measurements from U.S. non-urban and urban long-term networks (vol 44, pg 4908, 2010). Atmos. Environ., 51:335. Chen, L.-W.A.; Chow, J.C.; Watson, J.G.; Schichtel, B.A. (2012). Consistency of long-term elemental carbon trends from thermal and optical measurements in the IMPROVE network. Atmos. Meas. Tech., 5:2329-2338. http://www.atmos-meas-tech.net/5/2329/2012/amt-5-2329-2012.pdf. Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; Green, M.C.; Inouye, D.; Dick, K. (2012). Wintertime particulate pollution episodes in an urban valley of the western U.S.: A case study. Atmos. Chem. Phys., 12(21):10051-10064. http://www.atmos-chem-phys.net/12/10051/2012/acp-12-10051-2012.pdf. Chow, J.C.; Watson, J.G. (2012). Chemical analyses of particle filter deposits. In Aerosols Handbook : Measurement, Dosimetry, and Health Effects, 2; Ruzer, L., Harley, N. H., Eds.; CRC Press/Taylor & Francis: New York, NY, 179-204. .

  33. DRI reports and publications using the IMPROVE protocol(2012 and 2013) Chow, J.C.; Lowenthal, D.H.; Watson, J.G.; Chen, L.W.A. (2013). Source apportionment of SEARCH PM2.5 measurements with organic markers. prepared by Desert Research Institute, Reno, NV, for EPRI, Palo Alto, CA. Fujita, E.M.; Campbell, D.E.; Zielinska, B.; Chow, J.C.; Lindhjem, C.E.; DenBleyker, A.; Bishop, G.A.; Schuchmann, B.G.; Stedman, D.H.; Lawson, D.R. (2012). Comparison of the MOVES2010a, MOBILE6.2 and EMFAC2007 mobile source emissions models with on-road traffic tunnel and remote sensing measurements. J. Air Waste Manage. Assoc., 62(10):1134-1149. http://www.tandfonline.com/doi/pdf/10.1080/10962247.2012.699016. Green, M.C.; Chen, L.W.A.; DuBois, D.W.; Molenar, J.V. (2012). Fine particulate matter and visibility in the Lake Tahoe Basin: Chemical characterization, trends, and source apportionment. J. Air Waste Manage. Assoc., 62(8):953-965. Green, M.C.; Chow, J.C.; Chang, M.C.O.; Chen, L.-W.A.; Kuhns, H.D.; Etyemezian, V.R.; Watson, J.G. (2013). Source apportionment of atmospheric particulate carbon in Las Vegas, Nevada, USA. Particuology , 11:110-118. Hand, J.L.; Schichtel, B.A.; Malm, W.C.; Pitchford, M.L. (2012). Particulate sulfate ion concentration and SO2 emission trends in the United States from the early 1990s through 2010. Atmos. Chem. Phys. , 12(21):10353-10365. Hand, J.L.; Schichtel, B.A.; Pitchford, M.L.; Malm, W.C.; Frank, N.H. (2012). Seasonal composition of remote and urban fine particulate matter in the United States. J. Geophys Res. - Atmospheres, 117 Ho, S.S.H.; Ho, K.F.; Liu, S.X.; Liu, W.D.; Lee, S.C.; Fung, K.K.; Cao, J.J.; Zhang, R.J.; Huang, Y.; Feng, N.S.Y.; Cheng, Y. (2012). Quantification of carbonate carbon in aerosol filter samples using a modified thermal/optical carbon analyzer (M-TOCA). Analytical Methods, 4(8):2578-2584. Kavouras, I.G.; Nikolich, G.; Etyemezian, V.; DuBois, D.W.; King, J.; Shafer, D. (2012). In situ observations of soil minerals and organic matter in the early phases of prescribed fires. J. Geophys Res. - Atmospheres, 117 McDonald, J.D.; White, R.K.; Holmes, T.; Mauderly, J.L.; Zielinska, B.; Chow, J.C. (2012). Simulated downwind coal combustion emissions for laboratory inhalation exposure atmospheres. Inhal. Toxicol., 24(5):310-319. Orasche, J.; Seidel, T.; Hartmann, H.; Schnelle-Kreis, J.; Chow, J.C.; Ruppert, H.; Zimmermann, R. (2012). Comparison of emissions from wood combustion. Part 1: Emission factors and characteristics from different small-scale residential heating appliances considering particulate matter and polycyclic aromatic hydrocarbon (PAH)-related toxicological potential of particle-bound organic species. Energy & Fuels, 26(11):6695-6704. Qadir, R.M.; Abbaszade, G.; Schnelle-Kreis, J.; Chow, J.C.; Zimmermann, R. (2013). Concentrations and source contributions of particulate organic matter before and after implementation of a low emission zone in Munich, Germany. Environ. Poll., 175:158-167.

  34. DRI reports and publications using the IMPROVE protocol(2012 and 2013) Schichtel, B.A.; RodriguezB, M.A.; Barna, M.G.; Gebhart, K.A.; Pitchford, M.L.; Malm, W.C. (2012). A semi-empirical, receptor-oriented Lagrangian model for simulating fine particulate carbon at rural sites. Atmos. Environ., 61:361-370. Wang, X.L.; Watson, J.G.; Chow, J.C.; Kohl, S.D.; Chen, L.-W.A.; Sodeman, D.A.; Legge, A.H.; Percy, K.E. (2012). Measurement of real-world stack emissions with a dilution sampling system. In Alberta Oil Sands: Energy, Industry, and the Environment, Percy, K. E., Ed.; Elsevier Press: Amsterdam, The Netherlands, 171-192. Watson, J.G.; Chow, J.C.; Lowenthal, D.H.; Chen, L.-W.A.; Wang, X.L. (2012). Reformulation of PM2.5 mass reconstruction assumptions for the San Joaquin Valley: Literature review. prepared by Desert Research Institute, Reno, NV, for San Joaquin Valley Air Pollution Study Agency, Fresno, CA. Watson, J.G.; Chow, J.C.; Wang, X.L.; Lowenthal, D.H.; Kohl, S.D.; Gronstal, S. (2013). Characterization of real-world emissions from nonroad mining trucks in the Athabasca Oil Sands Region during October, 2010. prepared by Desert Research Institute, Reno, NV, for Ft. McMurray, AB, Canada, Wood Buffalo Environmental Association. Watson, J.G.; Chow, J.C.; Wang, X.L.; Zielinska, B.; Kohl, S.D.; Gronstal, S. (2013). Characterization of real-world emissions from nonroad mining trucks in the Athabasca Oil Sands Region during September, 2009. prepared by Desert Research Institute, Reno, NV, for Ft. McMurray, AB, Canada, Wood Buffalo Environmental Association. Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Sodeman, D.A. (2013). Measurement of real-world stack emissions in the Athabasca Oil Sands Region with a dilution sampling system during August, 2008. prepared by Desert Research Institute, Reno, NV USA. Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Gronstal, S.; Zielinska, B. (2013). Measurement of real-world stack emissions in the Athabasca Oil Sands Region with a dilution sampling system during March, 2011. prepared by Desert Research Institute, Reno, NV USA. White, W.H.; Farber, R.J.; Malm, W.C.; Nuttall, M.; Pitchford, M.L.; Schichtel, B.A. (2012). Comment on "Effect of coal-fired power generation on visibility in a nearby National Park (Terhorst and Berkman, 2010)". Atmos. Environ., 55:173-178. Xu, H.M.; Tao, J.; Ho, S.S.H.; Ho, K.F.; Cao, J.J.; Li, N.; Chow, J.C.; Wang, G.H.; Han, Y.M.; Zhang, R.J.; Watson, J.G.; Zhang, J.Q. (2013). Characteristics of fine particulate non-polar organic compounds in Guangzhou during the 16th Asian Games: Effectiveness of air pollution controls. Atmos. Environ., 76:94-101. j.atmosenv.2012.12.037. Zhou, J.M.; Cao, J.J.; Zhang, R.J.; Chow, J.C.; Watson, J.G. (2012). Carbonaceous and ionic components of atmospheric fine particles in Beijing and their impact on atmospheric visibility. AAQR, 12(4):492-502. http://aaqr.org/VOL12_No4_August2012/4_AAQR-11-11-OA-0218_492-502.pdf.

More Related